rafmacalaba/datause-encoder-data huggingface dataset:
# 7. References

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Chubrik, A. (2018). Return of the domestic demand: Too early to panic, too soon to be delighted, IPM Research Center _Discussion Paper_ DP/18/02.

Bornukova, K., Cuesta J., Shymanovich G., and Valetka U. (2019). “Commitment to equity for children: redistributive effects and efficiency of social assistance to households with children in Belarus.” Mimeo. UNICEF.

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26
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26
# **4. Validation results**

# _4.1. Directional income mobility estimates_

I start by comparing point estimates for the actual and simulated poverty headcount in round 1.<sup>5</sup> Results are shown in Table 1. Actual poverty estimates (and 95 percent confidence interval) in column 1 arise from using the panel dataset, while the predicted ones in columns 2, 3, and 4 result from applying the synthetic panel method. In general, the method performs well and actual point estimates are close to synthetic panel results.<sup>6</sup> Models’ predictive power increases substantially when moving from the first to the third specification (the _R_<sup>_2_</sup> doubles). However, unlike Cruces et al. (2015), point estimates are stable and they do not change drastically when increasing the sophistication level of the underlying income model.

I proceed to calculate the joint distribution of poverty transitions. Tables 2 and 3 show the point estimate and 95 percent confidence interval for the actual panel mobility together with the point estimates from using synthetic panels in the two periods. Results are encouraging; with few exceptions, most of the point estimates are close to the actual estimates of poverty mobility and lie within the 95 percent confidence intervals. For instance, actual panel data in Table 2 shows that about 17 to 25 percent remained in poverty in Peru between 2007 and 2011 (based on the 95 percent confidence interval), while according to the synthetic panel technique with interaction terms (specification 3) about 23 percent of the population were poor in both years. The two tables also suggest that the method performs well irrespective of the length of the panel.

Again, even when predictive power increases with the level of sophistication, results are stable to the selection of time invariant variables included in the underlying models. This is an important result for empirical applications at the regional level, as it suggests that the methodology can be implemented in a large set of countries simultaneously in contexts where there is a reduced set of comparable regressors available for all countries.

> 5 This is a useful excersice to perform in the absence of actual panel data since it allows us to check the validity of estimates from synthetic panels by comparing them with the actual poverty headcount that results from using cross sectional data in round 1.

> 6 Underlying income model estimations for Specification 1 are shown in the appendix.

9
<!-- Start of picture text --> Distribution of net arrivals in settlements<br>2 over 2018-2020<br>i=}<br>So<br>2S<br>©<br>i=}<br>=}oS<br>S<br>223S<br>N<br>i=}SoOS<br>oO<br>0 50000 100000 150000 200000 250000<br>Number of arriving displaced individuals<br>kernel = epanechnikov, bandwidth = 301.9669<br><!-- End of picture text -->
# **Design of Partial Population Experiments**

**with an Application to Spillovers in Tax Compliance**<sup>*</sup>

Guillermo Cruces, _U. of Nottingham & CONICET-CEDLAS-UNLP_ Dario Tortarolo, _World Bank DECRG_ Gonzalo Vazquez-Bare, _UC Santa Barbara_ JEL CODES: C01, C93, H71 , H26 , H21 , O23.

KEYWORDS: partial population experiments, spillovers, randomized controlled trials, cluster experiments, two-stage designs, property tax, tax compliance.

> *We thank Yuehao Bai, Youssef Benzarti, Augustin Bergeron, Javier Birchenall, Matias Cattaneo, Max Farrell, Kelsey Jack, Heather Royer, Doug Steigerwald and Alisa Tazhitdinova for valuable discussions and suggestions, and seminar participants at the 2021 National Tax Association conference, IFS, CEDLAS-UNLP, and the 2022 Advances with Field Experiments conference. We thank Julian Amendolaggine and Juan Luis Schiavoni for their invaluable support throughout the project. We thank Bruno Cr ́epon and Roland Rathelot for their help in obtaining their data. The views expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Corresponding author: Dario Tortarolo, E-mail: dtortarolo@worldbank.org. This project was reviewed and approved in advance by the Institutional Review Board at the University of Nottingham. The design for this experiment was preregistered in the AEA RCT Registry (RCT ID: **AEARCTR-0006569** ). All remaining errors are our own.
we find no distinguishable difference in whether respondents have a savings account, which is not surprising given that the majority of those induced to open an account already had one prior to the screenings. More concerning, however, we find that males in the **Movie/MFB** group report having been _less_ likely to save some money in the month prior to the follow up survey and show no differences in saving amounts relative to their income. While it is not clear what may be driving this result, it is possible that the event, while successfully motivating business owners to act in the moment and put money in a new savings account, only served to displace future savings, with no net gain.

## **_Borrowing Behavior_**

For borrowing behavior, we rely only on self‐reported responses in the follow up survey. The movie message centered on “responsible borrowing”, highlighting the problems with relying on moneylenders, and we reflect on this through two particular indicators: (i) borrowing rate in last 4 months and (ii) the source of borrowing. In particular, we were interested in identifying whether business owners used formal or informal sources for financing. We find firstly that borrowing rates are substantial – about half of all business owners reported taking out a loan in the past four months, and half of those that took a loan did so from an informal source. The interventions have no effect on borrowing rates (although there is a reduction in all treatment groups, this is not significant). Similarly, we find (see results in Table 14) little to no evidence on changes in the form of lending, although females in the **Movie/MFB** group reduce informal lending by 14 percentage points, which is borderline significant. Interestingly there seems to be more congruency between intentions to borrow and actual borrowing than for savings intentions and behavior. While 54% of people mentioned that they were planning to take out a loan in the next 6 months immediately after the screening, we find 4 months later that 51% of people did so. This contrasts sharply with the intended savings (90%) and actual savings rates (60%) which seems to confirm that, in terms of saving behavior, there are several additional barriers at play in addition to those that the interventions address directly.

# **7. Robustness Checks**

Our results show a significant effect of **Movie/MFB** on motivating business owners to open a savings account, but with little to no evidence of longer term impact on a broad range of savings and borrowing perceptions and behavior. A null effect could be a result of (i) limited power, driven by sample sizes too small to detect true impacts; (ii) spillovers improving outcomes for the control group; or (iii) selection bias 18
**Table 8. Average Value of Risk Reduction Based on Projected WTP**

||**Normal**<br>**demand**|**Extremely**<br>**high demand**|**Normal+high**<br>**demand**|**Whole**<br>**sample**|
|---|---|---|---|---|
|**Mean WTP**|439.4|546.0|460.2|411.7|
|**Projected (Yuan)**|||||
|**Mean cancer**<br>**morbidity (1/10000)**|15.5|14.3|15.3|15.7|
|**Mean cancer**<br>**mortality (1/10000)**|9.3|8.2|9.1|9.5|
|**Average Value of**<br>**morbidity (Yuan)**|283,273|382,548|301,424|261,446|
|**Average value of**<br>**mortality (Yuan)**|471,442|664,767|505,530|431,596|

**Table 9-1: WTP Function of Cancer Morbidity Risk Reduction**

|**Model**<br>**(Dependent**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**<br>**Log(WTP)**|
|---|---|---|---|---|---|---|
|<br>**Variable)**|**(WTP)**|**(WTP)**|**(WTP)**|**(WTP)**|**(WTP)**||
|Risk Reduction|1.705|19.808|13.939|74.795|5.990|0.023|
||(0.18)|(0.90)|(1.18)|(2.21)**|(0.99)|(3.17)***|
|Risk Reduction<sup>2</sup>||-0.328||-0.782|-0.065|-0.0002|
|||(1.06)||(2.00)**|(1.47)|(3.18)***|
|risk_worker|1.319|-5.571|-0.186|-6.850|||
||(0.23)|(0.53)|(0.03)|(0.43)|||
|risk_smoking|-3.868|-11.533|-4.281|-15.086|||
||(0.69)|(1.31)|(0.69)|(1.10)|||
|risk_fincome|-0.000|-0.001|-0.000|-0.001|||
||(1.12)|(2.34)**|(0.60)|(1.02)|||
|risk_education|12.669|14.230|-4.153|-36.287|||
||(2.11)**|(0.79)|(0.61)|(1.50)|||
|risk_Rcancer|8.794|19.533|-10.375|-28.900|||
||(0.97)|(1.05)|(0.90)|(0.91)|||
|risk_uncertainty|-3.638|-8.494|4.346|18.219|||
||(0.39)|(0.60)|(0.42)|(0.72)|||
|risk_health check|-9.162|-7.938|-4.964|2.853|||
||(1.80)*|(0.86)|(0.91)|(0.20)|||
|risk_religion|21.435|17.468|20.942|24.437|||
||(1.54)|(0.70)|(1.26)|(0.63)|||
|risk_trust|7.553|20.635|-2.006|-6.548|||
||(1.85)*|(2.52)**|(0.42)|(0.48)|||
|risk2_worker||-0.061||0.031|||
|||(0.36)||(0.16)|||
|risk2_smoking||0.057||0.096|||
|||(0.52)||(0.71)|||
|risk2_fincome||0.000||0.000|||
|||(2.41)**||(1.00)|||
|risk2_education||-0.185||0.336|||
|||(0.83)||(1.32)|||
|risk2Rcancer||-0.230||0.358|||
|_||(1.09)||(1.21)|||
|risk2_uncertainty||0.221||-0.250|||
|||(1.05)||(0.82)|||
|risk2_health check||0.088||-0.078|||
|||(0.65)||(0.45)|||

30
26

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- 20 -

**<u>Table 8. Multinomial logit estimation</u>**

||Coef.|Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|PRICE|-0.2401|(0.1294)|*|-0.2780|(0.1531)|*|
|DIST|0.0013|(0.0003)|***|0.0011|(0.0004)|***|
|SPEED|0.0008|(0.0281)||-0.0278|(0.0392)||
|VALU|0.0009|(0.0017)||0.0009|(0.0017)||
|AGE|0.0256|(0.0148)|*|0.0246|(0.0158)||
|EDU|-0.2059|(0.1907)||-0.2682|(0.1951)||
|FORN|-0.0018|(0.0073)||-0.0048|(0.0077)||
|LOCL|-1.3416|(0.4322)|***|-1.4015|(0.4466)|***|
|NATN|2.3681|(1.0838)|**|2.4656|(1.0563)|**|
|EXPT||||1.0120|(0.7368)||
|IMPT||||1.1558|(0.5510)|**|
|Constant|-5.0436|(1.1762)|***|-4.9387|(1.1806)|***|
|Obs.|900|||900|||
|Pseudo R squared|0.2407|||0.2587|||
|Wald chi|53.31|||61.87|||

The dependent variable is one if rail is selected. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

There are two unexpected results in **Table 8** . First, the speed variable has a statistically insignificant coefficient. Although our speed variable may have a measurement flaw as discussed above, it is expected that the better quality of rail services (i.e., faster operating speed) would increase the probability of firms choosing rail transportation. To examine sensitivity to functional forms, the quadratic form is assumed: The result is found to be unchanged ( **Table 9** ). Thus, the impact of operating speed of rail transport is inconclusive. Firms may or may not be motivated to use railways if their operating speed is increased. This does not mean that efficient rail operations would not be important. Given the current situation that many firms shifted from rail to road transportation in recent years, it should be interpreted to mean that our data do not support firms’ particular preferences to shipment speed when they use rail transport.

Second, the shipment value variable is not significant in **Table 8** . Our expectation was that firms would use rail transport for large-volume shipments. Again, our variable is not ideal to measure this effect, because the value of shipment may not necessarily be the same as the tonnage of cargo. However, there must be a broad correlation between them. When the
Table A.12: Determinants of refugee settlement at the province level

|Outcome:|Share|of refugees in|1928|
|---|---|---|---|
||(1)|(2)|(3)|
|Share of employment in non-agriculture in 1920|-0.198**<br>(0.091)|||
|Share of employment in manufacture in 1920||-0.468***<br>(0.163)||
|Literacy rate in 1920 (15-65 year olds)|||-0.368**<br>(0.153)|
|N|176|176|176|
|R2|0.73|0.74|0.74|
|Region FE|Y|Y|Y|
|Controls|Y|Y|Y|
|Average outcome|12.99|12.99|12.99|

**Notes.** This table examines the determinants of the distribution of refugees in 1928 across provinces. All specifications control for region fixed effects (for the following 10 regions of Greece: Crete, Peloponnese, Central Greece and Euboea, Cyclades Islands, Aegean Islands, Ionian Islands, Epirus, Thessaly, Macedonia and Western Thrace) and a set of province-level covariates, which comprises: log population density in 1920; dummy variables indicating whether the observation unit was a city in 1920 and whether it is an island; the distances to railway network in 1920, to shoreline, and to national borders; altitude, mean annual precipitation and temperature, and crop suitability of the province. Heteroskedasticity-robust standard errors are reported in parentheses. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

42
|**Table 4: Summary statistics of lab**|**or days and**|**labor force**|**participatio**|**n rate by res**|**ervation sta**|**tus**|||
|---|---|---|---|---|---|---|---|---|
||**Total**|**Reservat**<br>**Ever**|**ion status**<br>**Never**|**Test**|**Total**|**Reserva**<br>**Ever**|**tion status**<br>**Never**|**Test**|
|||**M**|**ales**|||**Fem**|**ales**||
|**Panel A: Labor supply**|||||||||
|Participated in labor market|0.86|0.87|0.86|0.01|0.62|0.67|0.58|0.09***|
|... self-empl. in agric.|0.39|0.41|0.37|0.04***|0.32|0.35|0.29|0.06***|
|... self-empl. in non-agric.|0.06|0.05|0.06|-0.01***|0.01|0.01|0.01|0.00*|
|... casual labor in agric.|0.33|0.35|0.32|0.03**|0.29|0.31|0.28|0.03***|
|... casual labor in non-agri.|0.49|0.49|0.49|0.00|0.1|0.1|0.09|0.01***|
|<br>... in NREGA|0.23|0.25|0.21|0.04***|0.27|0.31|0.22|0.09***|
|... regular salaried work|0.07|0.07|0.07|0.00|0.01|0.01|0.01|0.00|
|No of days worked|184.8|182.3|187.6|-5.30***|64.1|67.2|60.8|6.40***|
|... self-empl. in agric.|19.9|21.8|17.8|4.00***|12.2|14.2|10.0|4.20***|
|... self-empl. in non-agric.|13.8|12.5|15.2|-2.70***|2.3|2.3|2.4|-0.10|
|... casual labor in agric.|33.2|32.1|34.3|-2.20***|23.3|21.9|24.7|-2.80***|
|... casual labor in non-agri.|90.6|88.5|92.8|-4.30***|12.1|12.1|12.2|-0.10|
|... in NREGA|6.6|7.5|5.7|1.80***|10.7|13.2|8.0|5.20***|
|... regular salaried work|20.8|19.9|21.8|-1.90**|3.5|3.6|3.4|0.20|
|Individual income (Rs.)|65,000|63,724|66,000|-2276*|20,986|22,490|19,804|2686**|
|Would like to work more|0.274|0.296|0.251|0.05|0.316|0.359|0.268|0.09|
|If participated in NREGS work|||||||||
|Got dated receipt|0.734|0.744|0.721|0.02*|0.660|0.684|0.615|0.07***|
|Paid directly to bank account|0.889|0.905|0.865|0.04***|0.866|0.905|0.795|0.11***|
|<br>Was paid less than was due|0.388|0.412|0.350|0.06***|0.454|0.453|0.463|-0.01|
|<br>If less, did complain|0.557|0.649|0.390|0.26***|0.590|0.658|0.464|0.19|
|No. of obs.|34,427|17,990|16,437||31,935|16,717|15,218||
|**Panel B: Intra-household decisio**|**n making**||||||||
|Participates in decisions on ....|||||||||
|<br>... food|0.655|0.669|0.638|0.03***|0.839|0.851|0.824|0.03***|
|... nonfood|0.828|0.835|0.819|0.02**|0.761|0.769|0.751|0.02**|
|... health|0.798|0.807|0.786|0.02***|0.866|0.875|0.855|0.02***|
|... education|0.854|0.862|0.844|0.02***|0.737|0.763|0.704|0.06***|
|No. of obs.|12,284|5,390|6,894||11,395|4,978|6,417||

_Note:_ Author’s own calculation from 2014/15 REDS follow-up survey. As discussed in the text, funding constraints required to limit collection of information on intra-household decision to 5 states (Gujarat, Uttar Pradesh, Maharashtra, Orissa, and West Bengal). To test difference in means, p values from regressions with district fixed effects and standard errors clustered by village panchayat are reported in the last column.

19