To Work or to Wait for Alms: The Behavioral Effects of Unconditional Cash Transfers in Indonesia - IEA World Congress
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To Work or to Wait for Alms:
The Behavioral Effects of Unconditional Cash Transfers in Indonesia
Ridho Al Izzati, Daniel Suryadarma‡, Asep Suryahadi
The SMERU Research Institute
November 2019
Abstract
Dependence on cash transfer programs, either universal basic income, targeted conditional, or
unconditional programs, could have undesirable behavioral response among the recipients. Potential
adverse outcomes include reduced labor market participation, reduced economic activity, lack of
insurance or savings, or increased risky health behavior such as smoking. In this paper, we estimate
the effect of receiving unconditional cash transfer on individual behavior. The unconditional cash
transfer program targeting poor households in Indonesia began in 2005. With 15.5 million beneficiary
households, the program remains one of the largest cash transfer programs in the world. We utilize
three waves of the Indonesia Family Life Survey, a nationally representative household-level
longitudinal dataset, to empirically examine this issue. First, we implement coarsened exact matching
to achieve balance in the characteristics of recipients and non-recipients in 2000, before the cash transfer
program was implemented. We then estimate a first-difference specification using 2007 and 2014 data
to remove time-invariant unobserved heterogeneity. We find no evidence that receiving the
unconditional cash transfer program changes employment status or risk-taking related behavior such
as risk or time preferences, smoking behavior, or health-related behavior. Therefore, overall we do not
find significant undesirable or risky behavior as a result of receiving the program benefit. On the other
hand, we find a positive effect of the program on the behavior related to community participation.
Keywords: cash transfer; behavioral effects; labor market outcomes; poverty; Indonesia
JEL Codes: D10,H53, I12, I31, I38
Corresponding author. Email: rizzati@smeru.or.id
‡ Email: dsuryadarma@smeru.or.id
Email: suryahadi@smeru.or.id
11. Introduction
Governments provide social protection to mitigate the adverse impacts of shocks, especially
to those who are at risk or have fallen into poverty. In general, social protection programs
assume that once a shock is over and the recipients recover, they would no longer receive
government assistance. An exception to this is the permanent assistance provided to
chronically poor individuals, for example, disabled or elderly.
However, receiving government transfers could alter the behavior of the recipients.
Specifically, they expose themselves to unnecessary risks and do not sufficiently insure
themselves. Furthermore, they participate less in the labor market because they assume that
the government would provide transfers indefinitely.
Evidence from the United States shows that farmers reduce insurance purchases as
expectations of government disaster payments increase (Deryugina and Kirwan, 2016).
Ashenfelter and Plant (1990) find that households reduce labor supply as subsidies get
higher. Kousky, Kerjan, and Raschky (2014) use a panel dataset from Florida and show that
increases in federal post-disaster assistance grants significantly decrease individual
insurance coverage.
At the country level, increases in the level of foreign aid result in higher death tolls from
natural disasters (Raschky and Schwindt, 2009). That result implies that foreign aid in
previous periods provide perverse incentives in terms of a country’s effort to provide
protective action for their citizens.
In contrast to these findings, however, Banerjee et al (2017) find no statistically significant
effects of receiving transfers on employment. They summarized RCT study conducted in six
countries, including Indonesia, and use the working status and working hours as outcome
variables. They test the effect of conditional cash transfer program of PKH (Program
Keluarga Harapan) on working status only but not on working hours for Indonesia case.
Marinescu (2018) also find no statistically significant effects of receiving transfers on
employment. The latter study also finds positive effects of transfers on health and education
outcomes, while decreasing criminality and drug and alcohol use. Looking specifically at
Native American children, Akee et al (2018) find positive effects of unconditional cash
transfers on behavioral and personality traits.
Meanwhile, Handa et al (2018) evaluate large-scale government unconditional cash transfer
in Sub-Saharan Africa. Findings from their investigation reject the perception regarding
negative effects of the transfer: 1) induce higher spending on alcohol or tobacco; 2) are fully
consumed (rather than invested); 3) create dependency (reduce participation in productive
work); 4) increase fertility; 5) lead to negative community-level economic impacts (including
price distortion and inflation); and 6) are fiscally unsustainable. In summary, the literature
provides mixed results on the behavioral effects of government transfer programs.
In this paper, we estimate the behavioral effects of unconditional cash transfer in Indonesia.
The unconditional cash transfer program targeting poor households in Indonesia began in
2005. With 15.5 million beneficiary households, the program remains one of the largest cash
transfer programs in the world. We use a wide range of outcomes, from risk aversion and
time preferences to insurance purchase and labor market participation. Our estimation
2results show little evidence that receiving unconditional cash transfer changes the behavior
of the recipients. While we find some effect heterogeneity by sex, initial household welfare,
or education level, most of these effects are relatively small. Therefore, together with the
positive welfare benefits of the program (Bazzi, Sumarto, and Suryahadi, 2015), we find that
the unconditional cash transfer program has a positive welfare effects with little evidence of
unintended behavioral consequences. Besides that, we also test the effect of the program to
community participation behavior. Instead of involved in such a risky behavior, the
individuals who received unconditional cash transfer are more engaged with the community
through religious and village program activity.
We organize the rest of the paper as follows. The next section provides more information on
the unconditional cash transfer program. Section 3 presents the estimation strategy, while
Section 4 discusses the findings. Section 5 concludes.
2. Indonesia’s Unconditional Cash Transfer
Introduced in the last quarter of 2005, the unconditional cash transfer was intended to
reduce the impact of a fuel subsidy reduction, hence an increase in domestic fuel prices, by
reallocating the budgetary savings as direct benefit given to vulnerable households which
were most at risk from induced general price increases (World Bank, 2017). The fuel subsidy
reduction was necessitated by a steep increase in international oil prices that, given the fixed
domestic fuel prices, caused ballooning fuel subsidy in the government budget. At the
beginning, the program which called BLT (Bantuan Langsung Tunai/cash direct assistance)
reached the poorest 30% households. The initial program ran for one year, until the third
quarter of 2006. It was later re-implemented several times intermittently whenever the
government reduced fuel subsidies and consequently increased domestic fuel prices.
Figure 1 shows the years the unconditional cash transfer program was implemented and the
number of beneficiaries and budget allocated. In 2005/2006 (BLT I), there were initially 15.4
million beneficiary households, before an increase to 18 million households. They received a
transfer of Rp 1.2 million for one year, provided on a quarterly basis (Rp 300,000 per three
month). That benefit is accounted for 15% of quarterly expenditures for the average recipient
(Bazzi, Sumarto, and Suryahadi, 2015). In 2008/2009 (BLT II), another transfer was made by
the government to respond to the global financial crisis. This time, the transfer targeted 19
million households. The amount of benefit was the same at Rp 100,000 per month, but only
provided to cover nine months (Rp 900,000 per household). The transfer was again made in
2013 under a different name (BLSM) for a few months. In 2014/2015, the government
eliminated fuel subsidy to relieve national budget and as the consequences another transfer
was made to 15.5 million people for several months. However, the BLSM in 2014/2015 had a
total benefit less than of the total benefit of BLT in 2005/2006.
In a short time after the shock in late 2005, BLT was implemented in mitigating the reform’s
impacts. And one study from Yusuf and Resosudarmo (2008) found that BLT as a large scale
but temporary unconditional cash transfer could compensate the poor especially for the
rural poor. However in another study, Bazzi, Sumarto, and Suryahadi (2015) evaluated the
implementation of BLT and showed that timely receipt of transfer is important for
consumption smoothing behavior. They found that there is no difference on per capita
3expenditure growth between recipient and non-recipient if transfer received timely.
However, delayed receipt reduces expenditures of recipient by 7.5 percentage points.
Figure 1. Target number of beneficiaries and Government allocation budget for unconditional cash
transfer
20000 19 19 20
18
18000 18
16
15.4 15.5 15.5
16000 16
14000 18619 14
13966
12000 12
Rp billion
million
10000 10
9300 9470
8000 8
6000 6
6200
4000 4
4487
3733
2000 2
0 0
2005 2006 2008 2009 2013 2014 2015
Government budget (LHS) Target number of beneficiaries (RHS)
Source: World Bank (2017)
3. Empirical Estimation Strategy
Data
We use three rounds of Indonesian Family Life Survey (IFLS)1: 2000, 2007, and 2014. IFLS is
a large on-going longitudinal household survey in Indonesia, representative of 83% of
Indonesian population (Strauss et al., 2016). The first wave, conducted in 1993, consisted of
over 30,000 individuals in about 7,000 households in 13 of 27 provinces.
We merge the later three waves of IFLS to construct a longitudinal dataset at the individual
level. To achieve our research objective, we matched the individuals who are observed in
each round of the survey. After matching across survey rounds, our sample consists of a
panel observation from 2000 to 2014 consisting data of 13,694 individuals aged 15 and above
in 2000.
IFLS has a particular module related to social assistance programs in Indonesia called Book
KSR. The module has similar questions in the three waves we analyze. We use the question
on the BLT program, which is one of the largest social assistance programs in Indonesia.
Coincidentally, the last two survey rounds (2007 and 2014) are also the years that the
1 IFLS is publicly available at: https://www.rand.org/labor/FLS/IFLS.html
4unconditional cash transfer program was implemented. Since the BLT program was not
implemented yet in 2000, so the 2000 round is called as pre-exposure year while 2007 and
2014 round are two post exposure year of the BLT program.
Meanwhile, we utilize the individual related questionnaire to measure risk indicators.
Mainly we use Book 3A and 3B from IFLS. Those modules are answered by household
member who are 15 years old and above.
Matching process
We aim to estimate the effect of receiving BLT on risky attitude of individual. However, our
causal relationship in this study is based on non-experimental data. The BLT program was
not randomly assigned as it targeted the poorest households. Hence, we need to take into
account the potential selection bias. That means, the recipient and non-recipient household
are different in terms of characteristics. We first do a matching process between cash transfer
beneficiary households with non-recipient households with similar characteristics. To do
this, we keep households that has never received social assistance from any source in 2000.
Then, these observations are differentiated based on cash transfer recipient status in 2007. As
a rich dataset, IFLS allows us to control for a set of socio demographic characteristics in
initial year.
We adopt the variables for matching from Bazzi, Suryahadi, and Sumarto (2015). We use the
variables that significantly affect the likelihood of household to receive the BLT program as
shown in Table 2 in Bazzi, Suryahadi, and Sumarto (2015). The characteristics we match
individuals on are: gender, years of schooling, HH hold card for the poor, housing status,
drinking water sources, sanitation, land ownership, and urban status in 2000. Those
variables also capture the aspects of poverty. We also control for regional heterogeneity by
including provincial variables. Note that since behavioral variables are measured at the
individual level, while cash transfer receipt is measured at the household level, we include
all adults in the households. So that, we assume that all household member are exposed to
the UCT program.
Afterwards, we performed summary statistics on a set of household and individual
characteristics for these two groups separately in Table 1. Table 1 shows the pre-matching
characteristics in 2000 separated by recipient status of UCT in 2007.
Table 1. Pre-matching characteristics in initial year based on recipient status of transfer in 2007
No Received transfer (A) Received transfer (B) Mean difference (A)-(B)
L1 Obs. Mean Std.Dev Obs. Mean Std.Dev (tstats)
Pre-match multivariate L1 distance: 0.5603
Female 0.02 10475 0.55 0.50 3219 0.57 0.50 -0.02 (-1.55)
Years of schooling 0.28 10475 8.22 4.10 3219 5.40 3.59 2.81** (34.99)
Urban 0.16 10475 0.49 0.50 3219 0.33 0.47 0.16** (16.17)
Hold card for the poor (yes=1) 0.05 10475 0.04 0.21 3219 0.09 0.29 -0.05** (-10.81)
House ownership (no=1) 0.03 10475 0.20 0.40 3219 0.17 0.37 0.03** (4.10)
Own toilet with septic tank (no=1) 0.28 10475 0.47 0.50 3219 0.75 0.43 -0.28** (-28.99)
Safety drinking water (no=1) 0.06 10475 0.10 0.31 3219 0.17 0.37 -0.06** (-9.44)
5Farm land status (no=1) 0.00 10475 0.62 0.49 3219 0.62 0.49 0.00 (0.03) * p
Farm land status (no=1) 0.00 6862 0.62 0.48 2750 0.62 0.49 0.00 * p
Working status During the past week, did you work to get paid? (Yes=1)
Working hours last week What was the total number of hours you worked during the past week (on your job)?
Total weeks worked last
Approximately what is the total number of weeks you work last year?
year
Working in farm business Working in farm business (Yes=1)
Working in non-farm
Working in non-farm business (Yes=1)
business
Hold private insurance Private insurance or benefits ownership (Yes=1)
We include arisan membership as a behavioral indicator. Arisan is a rotating savings group,
an informal community gathering that involves money saving activity across members.
Value of one means that the individual has joined an arisan in the past year and 0 means
otherwise. With this variable, we would like to see whether receiving a government cash
transfer reduces membership in informal insurance (Brunette et al, 2013).
We define smoking behavior as an individual who is currently not smoking or totally
stopped chewing tobacco, smoking a pipe or self-rolled cigarettes, or smoking
cigarettes/cigars (1=yes, 0=otherwise). We construct the variable this way to ensure that in
all our dependent variables, a positive answer is a good outcome.
The medical check variable means that an individual has ever checked his/her health to
medical facility in the last five years. Ownership of private insurance is defined as an
individual who holds a private insurance or saving-related insurance. We exclude social
insurance ownership, such as National Health Coverage (Jaminan Kesehatan Nasional/JKN)
that is fully subsidized by the government.
Working status is defined as individual that worked at least one hour in last week or the
individual actually has a job but temporary not working in the past week. Meanwhile,
working hour variable is defined as the total number of hours worked in last week. Since in
Indonesia workers (especially the poor) mostly worked in agriculture or informal related
sector and high likelihood to have several jobs at a time, so that we decide to sum all the
working hours of the workers for both the main job and also for additional jobs. Total weeks
worked is measured as the approximation of total number of weeks that an individual has
spent on paid work in the past year.
Meanwhile, the variable working in farm business is defined as a dummy variable taking
the value of one if the individual is working in a farm business, and zero otherwise.
Similarly, the variable working in non-farm business indicator is also a dummy variable that
is equal to one for individuals who is working in a non-farm business. We define the farm
and non-farm business when an individual is either self-employed, or self-employed with
unpaid family worker/temporary worker, or self-employed with permanent worker. For the
last four indicators (working hours, number of weeks worked, farm and non-farm business
activities), we estimate the effect exclusively for individual who are working only during the
survey round.
Overall, we have 11 different outcome variables. To avoid overemphasize and cherry
picking the result, we Follow Kling, Liebman, and Katz (2007) to estimate the average effect
8of treatment to outcomes. For this purpose, we create an index of risk behavior that averages
together the eleven measures of risky behavior. The index is called as a summary index and
defined so that more beneficial outcomes have higher scores. The index is defined as a z-
scores that are calculated by subtracting each outcome with non-recipient group mean and
dividing by the non-recipient group standard deviation. After that, we average all of the z-
score and then standardize the average relative to the non-recipient group. So that, the value
of index has average 0 with standard deviation 1 for the non-recipient while vary for the
recipient group. We present findings from this summary index that aggregate information
over all treatment effect to draw a general conclusion.
In addition to measure the effect of the unconditional cash transfer program to risky
behavior, we also test the effect of the program to behavior related to participation in the
community. This estimation is an alternative way to see the effect of the program toward
time allocation of the recipient. Ideally, the estimation is conducted using a time use survey
that include the working and leisure time of an individual. Since the IFLS does not has a
time use survey, we utilize the IFLS module called Book 3A in section PM that asks
respondents their participation in the village or community activities in the past year.
The activities include community meeting, cooperatives, voluntary labor, program to
improve villages/neighborhood, youth group activity, religious activity, village library,
village savings and loans, and health fund. Additionally, we create one variable that sum all
activities for the reason that an individual might be involved in more than one activity. That
variable will explain the intensity of the activity of an individual. We also include the
number of arisan groups joined in the last 12 month as a continuous variable to check the
robustness instead of dummy arisan as showed in the Table 4.
Some of the indicators we examine are related with risky behavior. However, they may
affect time allocation for economic activities. For example, an individual may face a trade-off
between being involved in a community meeting or voluntary labor with working.
Meanwhile, other indicators such as cooperatives involvement, village savings and loans,
and health fund can have similar function with arisan. These groups act more as an informal
gathering with the possibility of getting assistance in a hardship.
4. Estimation Results
Behavioral effect of receiving unconditional cash transfer
Tables 5 and 6 show the main results on the effects of receiving unconditional cash transfer
on individual behavior. To visually show the effects, Figure 2 the mean outcomes for non-
recipients (dark bars) and recipients (light bars) of UCT. We see almost identical outcomes
between recipients and non-recipients and the confidence intervals indeed confirm that
there are no significant differences between the two. Hence, we find that there is no evidence
that receiving unconditional cash transfer affects behavior.
The coefficients on risk aversion index, time preference index, participation in arisan,
medical check-up, smoking behavior, working status, and ownership of private insurance
are all statistically insignificant. Similarly, among those who are working, receiving
unconditional cash transfer has no statistically significant effect on working hours in the past
9week, total weeks worked last year, and activity on farm or non-farm business. The last
columns of Tables 5 and 6 shows the standardized average effect of receiving unconditional
cash transfer, which both are not statistically significant. This confirms that receiving
unconditional cash transfer has no effect on individual risk behavior.
One possible reason of this finding is that the amount of the cash transfer received by
households is too small, around 15% of total expenditure (Bazzi, Sumarto, and Suryahadi,
2015). To test this possibility, instead of using recipient status of the cash transfer program,
we estimate model (1) using the cash transfer received as a proportion of total household
expenditure annually. The IFLS questionnaire also include the amount of benefit that was
received by the recipient households. The results are shown in Appendix A. We find similar
findings that there is no effect of receiving unconditional cash transfer on behavior, except
for one outcome: ownership of private insurance. We find that one standard deviation
increase of share of benefit of the program increases ownership of private insurance by 0.2
percentage point. That is considered a small effect and, furthermore, the estimation of
standardized average effect is not significant.
Our main finding is in line with the finding of Banerjee et al (2017) in six developing
countries, including Indonesia, that unconditional cash transfer has limited effect on
behavior. It is also similar with the finding of Marinescu (2018) in developed countries
context.
10Table 5. The estimation results of the effect of unconditional cash transfer on behavior
Standardized
Risk Time Join Quit/never Medical Working Private
average
Aversion preference arisan smoking check status insurance
effect
Received the transfer 0.008 0.044 0.010 -0.002 -0.020 0.007 -0.001 0.002
(0.346) (0.056) (0.013) (0.009) (0.013) (0.016) (0.001) (0.028)
Average of control
group
14.81 1.61 0.37 0.70 0.12 0.78 0.02 0.04
Number of observations 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612
Notes: Robust standard error (provincial clustered) in parenthesis, * pFigure 2. The effect of unconditional cash transfer on behavior outcomes
Note: The dark (left) bar is the mean of the outcome variable in the non-recipient group, the light (right) bar is the mean of the outcome
variable in the non-recipient plus the treatment effect from in Table 4. The bars represent 95% confidence intervals.
12Heterogeneity analysis
Table 7 and 8 show the results of effect heterogeneity analyses. Table 7 shows that among
females, unconditional cash transfer has no significant effect on risky behavior. Meanwhile,
among males, unconditional cash transfer has significant effects on arisan activity by 2
percentage point higher compare to average outcome of non-recipient group. Tables 6 also
show the results among individuals above and below the median per-capita expenditure. To
avoid endogeneity concerns, we use the 2000 level of per-capita household expenditure.
Among individuals living in households with per capita expenditure above the median, we
find there is no effect of the cash transfer to the behavior as well as among individuals living
in households below the median. Similarly, there is no effect of the program on the behavior
among individuals with either higher or lower six years of schooling.
Tables 8 show the estimation results conditional for working sample only. We find no
significance effect of UCT on behavior almost for all sub-sample categories, except among
individuals living in households with per capita expenditure above the median, where
unconditional cash transfer has a strong negative effect on working hours, by as much as 4.2
hours. However, it relatively small compared to average of the control group (44 hours a
week).
13Table 7. The estimation results of the effect of unconditional cash transfer (heterogeneity)
Risk Time Quit Medical Working Private Standardized
Join arisan
Aversion preference smoking check status insurance average effect
Female
Received the transfer -0.172 0.069 0.004 0.003 -0.025 0.020 0.000 0.011
(0.307) (0.057) (0.019) (0.009) (0.015) (0.019) (0.001) (0.030)
Average of control group 15.53 1.61 0.49 0.97 0.11 0.65 0.01 0.26
Number of observations 5,465 5,465 5,465 5,465 5,465 5,465 5,465 5,465
Male
Received the transfer 0.274 0.007 0.019* -0.010 -0.012 -0.011 -0.003 -0.011
(0.586) (0.061) (0.009) (0.011) (0.014) (0.022) (0.002) (0.042)
Average of control group 13.88 1.62 0.21 0.34 0.13 0.94 0.02 -0.25
Number of observations 4,147 4,147 4,147 4,147 4,147 4,147 4,147 4,147
Above median
Received the transfer 0.158 0.049 0.011 0.005 -0.013 0.008 -0.003 0.021
(0.474) (0.071) (0.022) (0.017) (0.019) (0.015) (0.003) (0.041)
Average of control group 14.60 1.62 0.40 0.70 0.14 0.75 0.02 0.10
Number of observations 4,409 4,409 4,409 4,409 4,409 4,409 4,409 4,409
Below median
Received the transfer -0.052 0.041 0.010 -0.005 -0.023 0.007 -0.000 -0.007
(0.430) (0.056) (0.015) (0.008) (0.015) (0.019) (0.001) (0.033)
Average of control group 15.05 1.60 0.32 0.69 0.09 0.80 0.01 -0.04
Number of observations 5,203 5,203 5,203 5,203 5,203 5,203 5,203 5,203
More than six years of schooling
Received the transfer 0.160 0.030 0.042** 0.014 -0.019 0.034 -0.004 0.057
(0.330) (0.052) (0.012) (0.009) (0.015) (0.022) (0.004) (0.041)
Average of control group 14.22 1.66 0.42 0.68 0.15 0.76 0.03 0.12
Number of observations 4,186 4,186 4,186 4,186 4,186 4,186 4,186 4,186
Six years of schooling and less
Received the transfer -0.046 0.046 0.002 -0.007 -0.019 0.003 0.001 -0.008
(0.506) (0.071) (0.015) (0.011) (0.014) (0.024) (0.001) (0.043)
Average of control group 15.42 1.57 0.31 0.71 0.08 0.79 0.00 -0.05
Number of observations 5,426 5,426 5,426 5,426 5,426 5,426 5,426 5,426
Notes: Robust standard error (provincial clustered) in parenthesis, * pTable 8. The estimation results of the effect of unconditional cash transfer on behavior conditional for working
individual sample only
Working Total weeks
Farm Non-farm Standardized
hours last worked last
business business average effect
week year
Female
Received the transfer -0.808 -0.700 -0.008 -0.003 -0.056
(1.684) (1.579) (0.015) (0.017) (0.077)
Average of control group 40.13 42.28 0.08 0.37 -0.06
Number of observations 2,789 2,758 2,778 2,778 2,789
Male
Received the transfer -0.474 -0.822 0.022 0.008 0.018
(1.021) (1.164) (0.019) (0.015) (0.048)
Average of control group 45.53 43.27 0.26 0.23 0.12
Number of observations 3,698 3,673 3,691 3,691 3,698
Above median
Received the transfer -4.222** -1.804 0.001 -0.028 -0.155
(1.077) (1.481) (0.023) (0.018) (0.079)
Average of control group 44.63 43.75 0.14 0.33 0.10
Number of observations 2,864 2,838 2,856 2,856 2,864
Below median
Received the transfer 0.899 -0.321 0.010 0.015 0.042
(0.934) (1.247) (0.014) (0.019) (0.052)
Average of control group 41.20 41.76 0.22 0.25 -0.03
Number of observations 3,623 3,593 3,613 3,613 3,623
More than six years of schooling
Received the transfer -1.798 -0.494 -0.002 0.049* -0.023
(1.605) (1.226) (0.013) (0.024) (0.068)
Average of control group 44.67 43.57 0.09 0.30 -0.01
Number of observations 2,778 2,758 2,770 2,770 2,778
Six years of schooling and less
Received the transfer -0.036 -0.776 0.011 -0.011 -0.007
(0.995) (1.295) (0.016) (0.019) (0.054)
Average of control group 41.33 42.03 0.26 0.29 0.08
Number of observations 3,709 3,673 3,699 3,699 3,709
Notes: Robust standard error (provincial clustered) in parenthesis, * pEffect of unconditional cash transfer on participation in community activity
In this study, we also test the effect of unconditional cash transfer on participation in
community activities. Appendix B shows the estimation results. Overall, the effect of the
program on participation in community activities is mostly positive. We find that the UCT
program has a strong and positive effect on religious related activity by 4.1 percentage point.
Meanwhile, the effect on health fund activity is positive at 2.9%. Not surprisingly, the effect
of the program has positive effect to number of all activities involved in the village. Overall,
the standardized average effect indicator also shows a positive and significant effect of the
program.
Meanhile, Appendix C is the estimation result for the effect of unconditional cash transfer on
participation in community activity using share of cash transfer from total household
expenditure as independent variable for robustness test. The variable of the number of arisan
now is insignificant. On the other hand, we find positive and significant effect of the
program on voluntary labor. The effect is even stronger for the involvement in the program
to improve the village. The effect of the program on religious activity is still positive and
significant. However, surprisingly, as share of benefit goes up, the involvement of recipients
on village savings and loans related activity decreases. Meanwhile, the effect is still strong
and positive on the number of all activity involved as well as the standardized average
effect.
5. Conclusion
Cash transfer is now a popular poverty reduction program in many developing countries all
over the world. This was spurred by evidence that shows cash transfer is effective in
reducing poverty, increasing educational attainment, and improving health status of the
poor. However, due to behavioral effects among the recipients, cash transfer can have
potential adverse outcomes, such as reduced labor market participation, reduced economic
activity, lack of insurance or savings, or increased risky health behavior such as smoking. In
this paper, we empirically examine the behavioral effects of a large-scale unconditional cash
transfer program in Indonesia.
The unconditional cash transfer program targeting poor households in Indonesia began in
2005 to mitigate the impact of increasing fuel prices. Over the course of one decade, the
program has been implemented intermittently whenever the government raised fuel prices.
Covering between 15 up to 19 million households, Indonesia’s unconditional cash transfer
program is one of the largest of such programs in the world.
To examine the behavioral effects of this program, we use a wide range of outcomes of risk
indicators, from risk aversion and time preferences to insurance purchase and labor market
participation. Our estimation results show little evidence that receiving unconditional cash
transfer changes the behavior of the recipients. While we find some effect heterogeneity by
sex, initial household welfare, and education level, most of these effects are relatively small.
Therefore, we find little evidence that the unconditional cash transfer program has
significant adverse behavioral effects.
16On the other hand, we find some positive effects of unconditional cash transfer on
participation in community activities. In particular, it has a strong and positive effect on
religious related activity. In addition, its effect on health fund activity is also positive and
significant.
References
Akee, R., W. Copeland, E.J. Costello, and E. Simeonova (2018). How Does Household
Income Affect Child Personality Traits and Behaviors. American Economic Review, 108(3),
775-827.
Ashenfelter, O. and M.W. Plant (1990). Nonparametric Estimates of the Labor-Supply Effects
of Negative Income Tax Programs. Journal of Labor Economics 8(1), S396-S415.
Banerjee, Abhijit V., Rema Hanna, Gabriel E. Kreindler, and Benjamin A. Olken (2017).
Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer
Programs. The World Bank Research Observer. doi:10.1093/wbro/lkx002
Bazzi, S., S. Sumarto, and A. Suryahadi (2015). It’s All in the Timing: Cash Transfers and
Consumption Smoothing in a Developing Country. Journal of Economic Behavior and
Organization, 119, 267-288.
Blackwell, Matthew, Stefano Iacus, Gary King, and Giuseppe Porro (2009). cem: Coarsened
exact matching in Stata. The Stata Journal, 9, Number 4, pp. 524–546.
Brunette, M., Cabantous, L., Couture, S. et al. Theory Decision (2013) 75: 153.
https://doi.org/10.1007/s11238-012-9321-8
Deryugina, T and Kirwan, B (2016). Does The Samaritan's Dilemma Matter? Evidence from
U.S. Agriculture. NBER Working Paper.
Handa, Sudhanshu, Silvio Daidone, Amber Peterman, Benjamin Davis, Audrey Pereira, Tia
Palermo, and Jennifer Yablonski. (2018). Myth-Busting? Confronting Six Common
Perceptions about Unconditional Cash Transfers as a Poverty Reduction Strategy in Africa.
TheWorld Bank Research Observer. Oxford University Press.
Iacus, S., King, G., & Porro, G. (2012). Causal Inference without Balance Checking:
Coarsened Exact Matching. Political Analysis, 20(1), 1-24. doi:10.1093/pan/mpr013
Kousky, C., E. O. Michel-Kerjan, and P. A. Raschky (2014). Does federal disaster assistance
crowd out private insurance? Working Paper.
Kling, Jeffrey R., Jeffrey B. Liebman, and Lawrence F. Katz. (2007). Experimental Analysis of
Neighborhood Effects. Econometrica, 75(1): 83-119.
Marinescu, I. (2018). No Strings Attached: The Behavioral Effects of U.S. Unconditional Cash
Transfer Programs. NBER Working Paper 24337. Cambridge, MA: National Bureau of
Economic Research.
Raschky, P. and M. Schwindt (2009). Aid, natural disasters and the Samaritan’s dilemma.
World Bank Policy Research Working Paper No. 4952.
17Sanjaya, Muhammad R. (2013). On the Source of Risk Aversion in Indonesia Using Micro
Data 2007. Economics Discussion Papers, No 2013-33, Kiel Institute for the World Economy.
http://www.economics-ejournal.org/ economics/discussionpapers/2013-33.
18Appendix A. The estimation results of the effect of unconditional cash transfer on behavior using share of cash
transfer from total household expenditure as independent variable
Risk Time Quit/never Medical Working Private Standardized
Panel A Join arisan
Aversion preference smoking check status insurance average effect
Share of transfer to HH expenditure 0.072 0.013 0.028 0.049 -0.066 0.098 0.021* 0.146
(3.571) (0.415) (0.133) (0.083) (0.080) (0.140) (0.010) (0.211)
Average of control group 14.81 1.61 0.37 0.70 0.12 0.78 0.02 0.04
Number of observations 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612
Notes: Robust standard error (provincial clustered) in parenthesis, * pAppendix B. The estimation results of the effect of unconditional cash transfer on participation in community activity
Program to Youth Village Number of
Number of Community Cooperatives Voluntary Religious Village Health fund Standardized
improve the groups savings and all activity
arisan joined meeting activity labor activities library activity average effect
village activity loans joined
Received the transfer 0.034 0.006 0.009 0.011 0.025 0.009 0.041** -0.000 -0.003 0.029* 0.126* 0.090*
(0.016) (0.015) (0.010) (0.012) (0.013) (0.005) (0.014) (0.002) (0.004) (0.011) (0.047) (0.037)
Average of control group 0.56 0.25 0.04 0.28 0.24 0.04 0.61 0.01 0.03 0.07 1.57 0.02
Number of observations 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612 9,612
Notes: Robust standard error (provincial clustered) in parenthesis, * pAppendix D. Summary statistics of first-difference 2014-2007
Variabels Obs. Mean Std.Dev Min Max
Absolute risk aversion index 9612 -1.71 11.87 -24.5 24.5
Time preference index 9612 0.13 1.44 -4 4
darisan 9612 0.08 0.50 -1 1
Never/quit smoking 9612 0.01 0.25 -1 1
Medical check-up 9612 -0.05 0.40 -1 1
Working status 9612 0.01 0.45 -1 1
Ownership private insurance 9612 0.01 0.14 -1 1
Standardized average effect 9612 0.03 1.15 -7.46 6.16
Recipient status 9612 -0.13 0.51 -1 1
Working hours last week 6487 -3.26 33.15 -167 154
Total weeks worked last year 6431 -0.22 18.67 -52 51
Farm business 6469 0.02 0.38 -1 1
Non-farm business 6469 0.02 0.43 -1 1
Standardized average effect 6487 0.03 1.17 -10.56 4.53
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