Electronic Food Vouchers: Banerjee, Hanna, Olken, Satriawan & Sumarto (2023)

What this is. Core results, the administrative-fidelity bargaining model, and the estimating equation from Banerjee, Hanna, Olken, Satriawan, and Sumarto (2023): a distilled skeleton for quick orientation. To replicate or extend, read the original at https://doi.org/10.1257/aer.20210461 and use the replication package at https://doi.org/10.3886/E167262V1.

TL;DR

Indonesia’s government randomized 105 districts across the transition from its in-kind rice subsidy program (Rastra: 10 kg free rice per month) to an electronic voucher program (BPNT: a debit card worth approximately the same value, redeemable for rice and eggs at private agents). Forty-two districts converted in 2018; 63 were randomized to convert in 2019. The voucher program improved fidelity to program design substantially: nearly all voucher recipients received the full entitlement amount, versus broad distribution of small amounts in the in-kind program. As a result, targeted (poor) households received 46 percent more subsidy value on net, poverty rates fell 20 percent for the bottom 15 percent of the distribution, and rice quality improved substantially. Price-theoretic channels (price effects, consumption substitution, self-targeting) explain far less of the difference than the administrative-fidelity mechanism: local village officials who previously controlled rice distribution could no longer divert benefits once distribution moved to private bank agents with individually named debit cards.

Core results

Magnitudes are as reported; locators point into the source PDF. \* = 10%, \*\* = 5%, \*\*\* = 1% (randomization inference p-values, Young 2019).

#	Result	Locator	Magnitude
R1	Targeted households (PMT ≤ 30) received 46% more subsidy per month in voucher districts than in in-kind districts	Table 1, col. 2, p. 529	Rp 13,496 more/month (SE = Rp 1,909; p < 0.001); in-kind mean = Rp 29,219
R2	Among recipients, voucher households received 85% more per month (conditional on receiving any assistance)	Table 1, col. 7, p. 529	Rp 31,333 more/month (SE = Rp 3,190; p < 0.001); in-kind recipient mean = Rp 36,931
R3	Non-targeted households (PMT > 30) received 28% less subsidy in voucher areas	Table 1, col. 3, p. 529	-Rp 2,532/month (SE = Rp 564; p = 0.002); in-kind mean = Rp 9,162
R4	Probability of receiving any subsidy fell in voucher areas: 16% decline for targeted, 49% decline for non-targeted	Table 1, cols. 5-6, p. 529	-10.5pp for PMT ≤ 30 (p < 0.001); -14.5pp for PMT > 30 (p < 0.001)
R5	Poverty rate fell 20% for the bottom 15% in voucher areas	Table 2, col. 5, p. 537	-4.3pp from a baseline of 21.0% for PMT ≤ 15 (p = 0.028)
R6	Rice quality rated 32% higher in voucher areas (recipient households)	Table 1, col. 8, p. 529	Coefficient = 0.203 on a 0-1 Likert scale (p < 0.001); in-kind mean = 0.630
R7	Total egg protein consumption rose ~4.3% for targeted households; no change in total rice consumption (consistent with Hastings and Shapiro 2018 earmarking evidence)	Table 3, Panel B, cols. 4-5, p. 539	+9.3 g/month (p = 0.10) for PMT ≤ 30; rice coefficient = -0.411 kg (p = 0.492)
R8	No overall price effect on rice; modest 3.5% increase in the most remote areas only	Table 4, cols. 1, 7, p. 541	Overall: Rp 129 (p = 0.309); above 75th pct travel time to district capital: Rp 334 (p = 0.027)

Overall (paper’s conclusion). Switching from an in-kind food program to electronic vouchers substantially increased the concentration of benefits to the poor, primarily by removing local officials from the distribution chain and replacing them with private bank agents who issued individually named debit cards (Banerjee et al. 2018 context). Price-theoretic mechanisms (consumption flexibility, supply-side price effects, self-targeting) are present but small relative to the administrative-fidelity mechanism. The result parallels the administrative gains from biometric smartcards documented in India by Muralidharan et al. (2016), here at larger scale and with electronic vouchers rather than smartcard identification. The voucher program also costs about half as much to administer (2.1 vs. 4.1 percent of benefits disbursed).

Theory / model

The paper has no structural model. It posits a simple Nash bargaining framework (Section II.C, pp. 532-534) to explain why the voucher program produced a point mass at the full entitlement amount while in-kind transfers produced a diffuse distribution.

Setup. A beneficiary is entitled to transfer $b$ from the program. A village head can impose a penalty $X_i$ on beneficiary $i$ (e.g., exclusion from community activities). The village head and beneficiary split the surplus with bargaining weight $\alpha$ for the village head. The beneficiary’s net transfer and the village head’s rent are:

$$\text{Transfer}_i = b - (1-\alpha)\,X_i, \qquad \text{village head rent} = \alpha\,X_i.$$

There is a fixed cost $F$ for the village head to initiate bargaining with beneficiary $i$.

In-kind program. The village head must distribute rice regardless, so $F$ is sunk. The village head always bargains, and the distribution of $X_i$ across beneficiaries produces a spread of realized transfer amounts (matching the broad histogram in Figure 1, Panel A, p. 526).

Voucher program. Distribution moves to private bank agents with individually named debit cards; the village head no longer has a role in the transfer unless he actively seeks one. Now $F$ is not sunk. The village head bargains only if $\alpha X_i > F$:

$$\text{Transfer}_i = \begin{cases} b & \text{if } \alpha X_i \leq F \\ b - (1-\alpha)\,X_i & \text{if } \alpha X_i > F. \end{cases}$$

This generates: (i) a point mass at the full entitlement $b$ for beneficiaries where $\alpha X_i \leq F$, (ii) a gap just below $b$, and (iii) a left tail for those with large $X_i$. The predicted distribution matches Figure 1, Panel A: in voucher districts, 81 percent of monthly deliveries are exactly the nominal Rp 110,000 entitlement, versus 24 percent in in-kind districts (p. 525).

Identification. The paper exploits budget-constrained random assignment: 105 districts were deemed potentially ready to convert, but the budget allowed converting only about 42. The government randomized which 42 were treated in 2018 and which 63 were treated in 2019, stratifying by geography. Balance checks across 11 baseline variables show no significant imbalance (joint F-test p = 0.384; online appendix Table 1, p. 523). The paper estimates intent-to-treat effects since only 3 of the 63 control districts converted early (p. 522).

Method

The main estimator is OLS on the randomized intent-to-treat design with double-LASSO-selected controls (Belloni, Chernozhukov, and Hansen 2014). The method builds on panel-regression for the regression structure and lasso for variable selection.

Control variables $\mathbf{X}_{hvds}$ are selected from a large candidate set (UDB household characteristics, village-census covariates, and district $\times$ urban/rural baseline averages from SUSENAS) using a double LASSO procedure. The LASSO simultaneously selects variables predictive of (i) the outcome and (ii) treatment assignment. Including the double-LASSO-selected controls raises precision without affecting consistency (Belloni, Chernozhukov, and Hansen 2014).

Standard errors are clustered at the district (kabupaten) level, which is the unit of randomization (d). Permutation-based (randomization inference) p-values are computed using 1,000 permutations of the treatment vector (Young 2019).

Empirical specifications

Main estimating equation. All outcomes are estimated via a single equation (equation 1, p. 523):

$$y_{hvds} = \beta_0 + \beta_1\,\text{Voucher}_{ds} + \mathbf{X}_{hvds}'\,\gamma + \alpha_s + \varepsilon_{hvds}, \tag{1}$$

where $y_{hvds}$ is the relevant outcome for household $h$ in village $v$, district $d$, stratum $s$; $\text{Voucher}_{ds}$ is an indicator equal to 1 if district $d$ was randomly assigned to receive the voucher program in 2018; $\mathbf{X}_{hvds}$ is the vector of double-LASSO-selected control variables; $\alpha_s$ is a stratum fixed effect; and $\varepsilon_{hvds}$ is the error term. Standard errors are clustered at the district level; randomization-inference p-values from 1,000 permutations (Young 2019) are reported in brackets in all tables.

Outcome variables and samples. The paper estimates equation (1) on several outcomes: total subsidy received (Rp/month, the sum of Rastra and BPNT values), an indicator for receiving any subsidy, total food consumption of rice and eggs (from the separate SUSENAS consumption module), rice quality (a 0-1 Likert scale), rice price (for non-eligible households to avoid compositional effects), and the poverty indicator. Results are presented for (i) the full sample, (ii) households with PMT score ≤ 30 at baseline (the approximate target population, PMT ≤ 30 being the program eligibility threshold), and (iii) PMT > 30 (those not targeted). For the poverty analysis (Table 2, p. 537), the sample is further restricted to PMT ≤ 25, ≤ 20, ≤ 15, ≤ 10, and ≤ 5 to document heterogeneous poverty effects at different points of the distribution.

Price specification. To isolate the general-equilibrium price effect, equation (1) is estimated with rice price as the outcome for households not in the UDB (i.e., those ineligible for the programs, whose reported prices are not affected by selection into which program they receive). Heterogeneity by supply shock size and geographic isolation is assessed through interaction terms $\text{Voucher}_{ds} \times \text{Variable}_{d}$ (Table 4, cols. 2-7, p. 541).

Subsidy-fidelity specification. To examine overall leakage at the district level, the unit of observation becomes the district: the fraction of intended subsidy actually received (subsidy received from SUSENAS divided by intended subsidy, computed from the official number of beneficiaries times the entitlement amount) is regressed on $\text{Voucher}_{ds}$ with district-level strata fixed effects (Table 5, p. 544; N = 105 districts).

Datasets used

Dataset	Role in paper	Wiki page
SUSENAS (Survei Sosial Ekonomi Nasional)	Primary outcome data: subsidy receipt, food consumption, prices, poverty; March 2018 (baseline) and March 2019 (endline) waves	No page yet
Unified Targeting Database (UDB)	Household-level PMT scores and baseline characteristics for control selection and heterogeneity analysis; 2015 data merged by the government using national IDs; deidentified version in replication package	No page yet
PODES (Potensi Desa) village census	Village-level baseline control variables (roads, infrastructure, remoteness measures); 2018 wave	No page yet
Program administrative data	District-level intended subsidy disbursements (number of official beneficiaries times entitlement) for leakage calculations	No page yet

Sample scope: 105 districts across Indonesia; approximately one-fifth of Indonesia’s population (53 million individuals); 3.4 million targeted beneficiary households. Primary analysis uses household-level March 2019 SUSENAS (endline), approximately 66,000 households. Merged deidentified replication data available at https://doi.org/10.3886/E167262V1.

When to read the full paper

Use the original if you are: studying the design and analysis of large-scale RCTs in the presence of general-equilibrium effects (Muralidharan and Niehaus 2017); evaluating the relative merits of in-kind vs. voucher / cash transfer programs in settings with limited administrative capacity; replicating (the ICPSR replication package at https://doi.org/10.3886/E167262V1 contains all code and data); or reading for the price-effects analysis of the transition (Section III.B; Cunha, De Giorgi, and Jayachandran 2019 predictions tested at scale). Table 1 (p. 529) gives the full delivery and targeting results; Table 2 (p. 537) the poverty heterogeneity; Table 3 (p. 539) the consumption results.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(2). This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. The AEA copyright applies; no CC licence is recorded in Crossref. Extract-only; the verbatim PDF is not hosted here.

Banerjee, Abhijit, Rema Hanna, Benjamin A. Olken, Elan Satriawan, and Sudarno Sumarto. “Electronic Food Vouchers: Evidence from an At-Scale Experiment in Indonesia.” American Economic Review 113, no. 2 (February 2023): 514-547. DOI: 10.1257/aer.20210461. Copyright 2023 American Economic Association. Replication data: DOI 10.3886/E167262V1. This page is an extract by the Institute for Automated Research: core results and equations summarized.