rafmacalaba/datause-encoder-data huggingface dataset:
These observations align with results derived from land use data. Specifically, after the Rohingya arrived, areas closer to the camps experienced a decrease in dense-open forests and an increase in land covered by grass and crops, compared to the period before 2017. The consistency of the raw data trends supports the validity of the parallel trends assumption.<sup>2</sup> Furthermore, the findings remain robust when we control for differential linear pre-trends between the treatment and control groups. These effects are also visually evident in map illustrations depicting changes in the areas around the camps.

We also analyzed the impacts of proximity of host-community individuals to the refugee camps on several groups of outcomes including adult and household welfare (wealth, income, expenditures, food consumption, and assistance); labor markets; public services (health and education); safety perceptions; and host attitudes toward the Rohingya. These estimates include multiple hypotheses-testing corrections considering the large number of outcomes evaluated.

The findings suggest that the welfare effects of camp proximity on adults and households in host communities are limited and mostly related to humanitarian aid. Particularly, when the distance between the individuals and the camps decreases by 30 km, the likelihood of receiving humanitarian food aid increases by 8.2 pp, the likelihood of receiving food in-kind increases by 5.5 pp, and the likelihood of receiving assistance in the form of household items increases by 10.6 pp. The results do not allow us to distinguish statistically significant effects on wealth, income, or expenditures.

The findings related to labor market outcomes suggest that when the weighted distance from an individual to the refugee camps drops by approximately 30 km, the likelihood of having a written contract increases by 4.6 pp. These are large effects, representing deviations from the mean of 33 percent. Unsurprisingly, the results also suggest a higher likelihood of employment by nongovernmental organizations (NGOs, likely represented by humanitarian organizations) and reductions in employment by all other employers.

> 2The parallel trends assumption posits that the treatment and control groups must exhibit similar trends in the absence of the treatment under study. This assumption is crucial for accurate estimation in differencein-differences analysis. The parallel trends assumption is verified graphically by plotting the outcome variable for both groups over time, both before and after the treatment.

4
<!-- Start of picture text --> Cost of increasing the RAI by 1% for NIC Cost of increasing the RAI by 1% for LSO<br>om<br>e e<br>e<br>175 100<br>150<br>3z<br>¢100 = e<br>=<br>%e_<br>S75 e S e<br>eevee? 40 e~,?<br>50 °<br>a° ° eo?<br>316 20 °<br>32.5 35.0 375 400 425 45.0 235 30 35 40<br>RAI (%) RAI (%)<br>Cost of increasing the RAI by 1% for TGO Cost of increasing the RAI by 1% for GIN<br>e e<br>200 400 °<br>@<br>e<br>~ 150 e<br>2 e |2 300 e °<br>= % fay =<br>a a :<br>= 100 . e e 2 °<br>8 e & § 200 e<br>50 ee ° e°**<br>ea,°e? e °<br>e 100 A<br>0 ® ° °<br>40 50 6 70 30 35 40 45<br>RAI (%) RAI (%)<br><!-- End of picture text -->
documentation or only a tourist visa. We would expect to find an estimated rate close to one for this group, but only get rates of around 50%, with the 95 confidence intervals going up to 79 percent for Morocco and 88 percent for Ethiopia. While one can think of reasons for this discrepancy (for example, migrants may have entered without documents but have subsequently obtained them), this row does suggest there is likely some noise amongst the signal provided by this list randomization question.

# **5. Conclusions and Discussion**

By adding questions to surveys conducted in four countries, we were able to trial the use of list randomization as a means of eliciting illegal migration rates. We find a variety of evidence that taken together suggests that the method is providing useful information. In particular, we find higher rates of illegal migration in surveys which were designed to oversample migrants; that households who report having a migrant are also more likely to have an illegal migrant; that the rankings of illegal migration rates conditional on migration are consistent with other estimates of illegal migration; and that in two countries where it was not considered too sensitive to also ask directly about illegal migration we obtain rates which are reasonably close to our list randomized estimates.

Nevertheless, there are several limitations of this approach. A first limitation is that the confidence intervals for estimates using this method are much larger than those obtained when asking migrants directly, so in situations where illegal migration is widespread and not considered sensitive, it may be better to just ask directly. Secondly, the accuracy of the method relies on individuals understanding that the question protects their privacy and in answering the question accurately. If individuals who do not understand the question just give a random answer between 0 and the number of statements, this will inflate the estimate of the illegal migration rate. Adding a third treatment group which is given the same three statements as the control plus a fourth statement that it is expected that almost zero will respond yes to (e.g. “a member of my family is a millionaire”) would be a good way to test for such an effect, and enable an adjustment to be made for any such noise effect.

In practice, asking about illegal migration status is likely to be a far more sensitive issue when asked of the migrants themselves in migration destinations than when asking sending households 12
# **Table A29: OLS; Effects of refugee presence on household welfare disaggregated by time-varying main source of income, with analysis at cluster level and the distance weighted refugee index at -** **<u>50km from the clusters (Panel 2009 2012)</u>**

||Log of welfare|(consumption aggr<br>equivalent)|egate per adult|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|0.0219*|0.0219*|0.0263*|
||(0.0114)|(0.0114)|(0.0136)|
|Log of refugees(50km)#Commercial farming|0.0515*|0.0515*|0.0770***|
||(0.0280)|(0.0279)|(0.0276)|
|Log of refugees(50km)#Wage employment|0.0143|0.0141|0.0032|
||(0.0121)|(0.0121)|(0.0129)|
|Log of refugees(50km)#Non-Ag. self-employment|-0.0136|-0.0136|-0.0010|
||(0.0127)|(0.0127)|(0.0115)|
|HH head age||0.0015|0.0123**|
|||(0.0050)|(0.0056)|
|Age squared||-0.0000|-0.0001|
|||(0.0000)|(0.0001)|
|HH head sex (male)||-0.0000|0.0213|
|||(0.0348)|(0.0379)|
|Household size|||-0.0259***|
||||(0.0054)|
|**_HH head marital status_**||||
|Married monogamously|||-0.3770**|
||||(0.1466)|
|Married polygamously|||-0.3238**|
||||(0.1542)|
|Separated (divorced/widowed)|||-0.3659**|
||||(0.1562)|
|**_HH head education level_**||||
|Completed primary sch|||0.2182***|
||||(0.0370)|
|**_HH main source of income_**||||
|Commercial farming|0.1835*|0.1829*|0.1777*|
||(0.0962)|(0.0961)|(0.0951)|
|Wage employment|-0.0646|-0.0661|-0.1020**|
||(0.0452)|(0.0452)|(0.0476)|
|Non-agricultural self-employment|0.1485***|0.1459***|0.0766*|
||(0.0410)|(0.0410)|(0.0429)|
|**_Other source of HH income_**||||
|Property income and investments|||0.2156**|
||||(0.0842)|
|Transfers and other benefits|||0.1191***|
||||(0.0408)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.1913***|
||||(0.0319)|
|Observations|5,453|5,450|4,105|
|R-squared|0.3966|0.3967|0.3911|

49
- 22 -

increased risk of accidents and pollution levels at toll plazas. Other problems have been encountered, such as the lack of financial control of toll box revenues. Scandals periodically arise in which toll operators embezzle cash cages. The need to physically transfer large amounts of coins is another problem.

24. Besides the manual operation of tollbooths, automatic coin machines and hoppers for exact change are also used to save on personnel costs; automatic coin machines marginally improve the throughpu **t** o about 400 vehicles per hour for a toll lane. The disadvantage of these machines is that they still require vehicles to come to a stop. Note that six approach toll lanes are required to serve one expressway lane in order to ensure that the capacity of the road itself is not constricted by the capacity of the tollbooths.'2 This suggests that tollbooths could be designed in a way that does not disrupt traffic, but manual charging would involve unacceptably high set-up and operating costs relative to other mechanisms. One minor disadvantage of using automatic coin machines is that charging for one type of vehicle is only applicable on a per lane basis, unless different vehicle types are designated to use specific toll lanes, which would decrease the capacity of the toll booths and increase the complexity of the charging instrument.

25. Another form of charging is with the use of coupon books. They have similar advantages to automatic coin machines but the printing cost is not insignificant. Simila4y, problems of fraud occur. (The magnetic card method of running magnetically-striped cards, such as credit cards, through a mechanical reader may help control operator fraud, but would be the slowest and least practical method of all.) Based on a sample of five ticket system toll roads, the cost per transaction ranges from US18.4 cents to US42.4 cents in 1986 (Wustefeld, 1988, Table 6).

26. Perhaps the irony of using the tollgate approach to tackling congestion can be expressed as follows: if an urban area is congested, then the planner ought not add to the congestion extemality the non-negligible cost of transaction itself. At the other extreme, if the facility is 22 I **am** indebted to **Uchard Scrfield** for **caufymg thee points.**
this case it takes the form of cheap grain received from the government and meant to be sold to poor households at a special low price instead being sold illegally at the higher market price). A little calculation shows that for a fully honest ration shop owner, the profit margin is so low that it is not worthwhile for a person to run a ration shop unless he or she is prepared to be corrupt (Khera, 2011). It is this idea that drives our theoretical model.

The model may give the impression that corruption is inevitable in competitive industries. That is, however, not true; our model was developed for an environment in which bribery is worthwhile for amoral individuals. The way to root out corruption is to make sure that this is not the case in society. If, for instance, we choose a police force by selecting innately honest people or by having punishment rules which make it not worthwhile for an officer to take a bribe, then even in competitive industries there will be negligible corruption. Further it is possible for societies to develop self-enforcement norms whereby bad behavior is collectively punished by all and where people punish bad behavior because not to punish bad behavior is itself bad behavior (Akerlof, 1976; Basu, 1986).

Finally, by moving away from the patently unrealistic textbook assumption that all human beings are amoral and by allowing for the fact that people incur psychological cost for being corrupt, the paper draws attention to the role of the distribution of morality in a society.

# **References**

Acemoglu, D. and Verdier, T. (2000). “The Choice Between Market Failures and Corruption.” _American Economic Review:_ 90, 194-211.

Ades, A. and Di Tella, R. (1999). “Rents, Competition, and Corruption.” _American Economic Review:_ 89, 982-993.

Akerlof, G. (1976). “The Economics of Caste and of the Rat Race and Other Woeful Tales.” _Quarterly Journal of Economics_ 90(4), 599-617.

7
Croke et al. (2016) 2016; Bleakley, 2007; Croke, 2014; Ozier, 2015).<sup>1</sup> Many organizations that have examined the evidence have judged deworming to be highly cost effective, including the Copenhagen Consensus (Hall and Horton, 2008), the Disease Control Priorities Project (Hotez et al., 2006)<sup>2</sup> , Givewell (Givewell, 2013; Givewell, 2014), the Abdul Latif Jameel Poverty Action Lab (J-PAL Policy Bulletin, 2012) and the World Bank (World Bank, 1993).

However, a recent Cochrane Review (Taylor-Robinson et al., 2015; hereafter “TMSDG”) argues against this long-standing consensus. TMSDG conduct a meta-analysis of randomized studies focusing on the short-term impacts of deworming. The authors express support for treating those known to be infected (p. 30), and estimate that single dose treatment for those known to be infected (and to have moderate intensity infections on average) increases weight by 0.75 kg (95% CI: 0.24,1.26; p=0.0038). However, they take a strong stand on mass drug administration in areas where worms are endemic, arguing that there is “substantial evidence” that mass treatment has no impact on child weight or other outcomes (p. 2). This has led a subset of the TMSDG authors to conclude in other work that the belief in long-term educational and economic impacts discussed above are “delusional'' (Garner, Taylor-Robinson and Sachdev, 2015).

Of course, failure to reject the null hypothesis of no effect does not constitute evidence of no effect. Particularly when treatment is inexpensive and side-effects are mild, studies and meta-analyses must have adequate power in order to rule out possibly modest effect sizes that would still render treatment cost-effective. Unfortunately, underpowered

> 1 Givewell calculates the cost of deworming for soil-transmitted helminthes in India at $0.30 per child per treatment, which includes both drug and delivery costs, including the value of staff time (Givewell (2016)). 2 We note that while analysts at Givewell identified errors in the calculations of deworming's cost effectiveness presented in the Disease Control Priorities Project, their revised calculations also find it to be a cost effective intervention at $82.51 -- $138.28 per DALY (Givewell, 2011).

3
**TABLE 3—DISTRIBUTION OF COMPOSITIONAL DEVIATIONS**

||**Net Resource**<br>**Deviation < -5%**|**Net Resources**<br>**Close to Budget**|**Net Resource**<br>**Deviation > 5%**|**Totals**|
|---|---|---|---|---|
|CountryObservations|49|54|56|159|
|Heads Under-spent < -5%|||||
|Number of Heads|568|313|221|1,102|
|Average Deviation, _eu_|-13.1%|-4.4%|-3.0%|-6.6%|
|Heads within ±5%|||||
|Number of Heads|257|428|261|946|
|Average Deviation, _eb_|-0.2%|0.0%|0.2%|0.0%|
|Heads Over-spent > 5%|||||
|Number of Heads|162|302|557|1,021|
|Average Deviation, _eo_|2.5%|4.5%|17.4%|8.4%|
|Total|||||
|Number of Heads|987|1,043|1,112|3,142|
|Average Deviation, _e_|-10.7%|0.1%|14.6%|1.8%|

# _D. Compositional Deviations and Net Resource Deviations_

Figures 7a through 7c illustrate how over- and under-spending varies with deviations in total net resources ( _e_ = _tnr_ ). Figure 7a is focused on over-spent heads _eo_ while Figure 7b is focused on under-spent heads _e_ u. Both figures should and do display positive slopes: the positive deviations in over-spent heads should increase as resource deviations become more positive while the negative deviations in under-spent heads should become less negative as resource deviations become more positive. Figure 7c shows there is no obvious relationship between resource deviations and the depth of over- or under-spending for the well-spent share of budget heads.

## **PERCENT DEVIATIONS AS A FUNCTION OF RESOURCE DEVIATIONS**

<!-- Start of picture text --> Figure 7a. Over-spent Groups, eo Figure 7b. Under-spent Groups, eu Figure 7c. Accurately Spent Groups, eb<br>50 0 2<br>40 -10 1<br>30 -20 0<br>20 -30 -1<br>10 -40 -2<br>0 -50 -3<br>-60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60<br>Resource Deviation, e Resource Deviation, e Resource Deviation, e<br><!-- End of picture text --> The correlations in Figures 7a through 7c pose three puzzles. First, why is there over- and underspending when _e_ is close to zero? Second, a glance at Figure 7a shows over-spending despite unanticipated net resource shortfalls, while Figure 7b shows under-spending despite gains in net resources. Inspection of the data shows that some of the under-spending is paired with overspending that occurred in excess of gains in net resources. Similarly, some of the over-spending is accompanied by under-spending that occurred in excess of losses in net resources. Yet to be explained, however, is why this sort of thing happens at all. Common pool and agency problems, explored in Section IV below may provide part of the answer.

13
||Proportion<br>of children<br>|Schooling ra<br>t<br>|te of children by di<br>heirparents (%)<br>|sability of|
|---|---|---|---|---|
|Groups|with parents<br>with<br>disability (%)|Parents<br>without<br>disability|Parents<br>with<br>disability|Total|
|_Consumption quintiles_|||||
|Poorest|7.12|78.15|63.94|77.13|
|Near poorest|5.66|84.45|74.25|83.88|
|Middle|4.65|87.58|71.48|86.83|
|Near richest|4.91|89.35|78.88|88.84|
|Richest|5.34|93.23|88.18|92.96|
|Number of observations|8,790|507|8,283|8,790|

_Source: Estimation from the 2006 VHLSS_ Table 2. Proportion of schooled children by their educational performance

||Childre|n with paren|ts without di|sability||Parents wi|th disability||
|---|---|---|---|---|---|---|---|---|
|Groups|Excellent|Good|Middle,<br>Bad|Total|Excellent|Good|Middle,<br>Bad|Total|
|Total|14.01|37.29|48.7|100|11.02|39.82|49.16|100|
|_Gender_|||||||||
|Girls|17.03|40.51|42.46|100|13.27|49.22|37.51|100|
|Boys|11.04|34.11|54.85|100|8.38|28.86|62.76|100|
|_Age_|||||||||
|Age 6-10|20.85|35.71|43.44|100|12.05|41.47|46.48|100|
|Age 11-14|12.27|39.31|48.42|100|11.13|38.61|50.26|100|
|Age 15-17|8.53|36.03|55.44|100|10.27|40.56|49.17|100|
|_Ethnic minorities_|||||||||
|Kinh|15.89|40.15|43.96|100|13.31|46.62|40.07|100|
|Ethnic minorities|3.5|21.27|75.23|100|2.77|15.45|81.78|100|
|_Rural/Urban_|||||||||
|Rural|10.88|35.75|53.37|100|6.09|38.63|55.28|100|
|Urban|25.15|42.76|32.09|100|27.53|43.81|28.66|100|
|_Regions_|||||||||
|Red River Delta|15.41|46.35|38.24|100|6.76|50.3|42.94|100|
|North East|8.98|37.85|53.17|100|7.8|18.01|74.18|100|
|North West|5.92|19.53|74.55|100|0|21.27|78.73|100|
|North Central Coast|7.83|37.42|54.75|100|6.64|51.06|42.3|100|
|South Central Coast|19.98|30.85|49.17|100|6.01|31.86|62.13|100|
|Central Highlands|10.62|28.29|61.09|100|7.91|43.68|48.41|100|
|South East|22.21|38.59|39.2|100|24.03|37.17|38.8|100|
|Mekong River Delta|15.41|36.69|47.9|100|14.74|47.32|37.94|100|
|_Education of_<br>_household head_|||||||||
|No degree|4.95|27.52|67.53|100|4.29|30.78|64.93|100|
|Primary school<br>degree|11.58|34.02|54.41|100|10.86|41.11|48.02|100|
|Lower-secondary<br>school degree|11.59|40.39|48.03|100|9.26|43.75|46.99|100|

8
# **Price Adjustments and Poverty Measurement**

Nicola Amendola nicola.amendola@uniroma2.it Giulia Mancini giulia.mancini@uniroma2.it Silvia Redaelli sredaelli@worldbank.org Giovanni Vecchi giovanni.vecchi@uniroma2.it

**JEL codes:** C43, E31, I32

**Keywords:** poverty, inequality, prices, price adjustments, price indexes, cost-of-living differences,

inflation

**Acknowledgments.** We are grateful to Giovanni D’Alessio, Carolina Diaz Bonilla, Benoît Decerf,

Dean Jolliffe, Cristoph Lakner, Laura Liliana Moreno Herrera, Zurab Sajaia, Nobuo Yoshida, for useful comments and suggestions. All remaining errors are our own.
an indicator of whether a country is a military regime, which Belkin and Schofer also show to be a good predictor of coup d’état. While political instability and anocracy indicators have been used extensively in prior work, our use of the coup d’état risk and history, and the history of riots are novel operationalizations of state weakness (also used in Author 2007). By using the five years lagging structure for the history of coups and riots, and the ten year for the risk of coup, we mitigate concerns that our state weakness operationalizations are endogenous to civil war onset.

The literature uses a large number of measures for natural resource endowments, and we use all available data for oil and diamond production. Our theoretical account is better suited by the use of quantitative measures (rents or quantities produced), however several of these measures miss data before 1960 (Humphreys 2005, Ross 2008), some measures miss data on the production of major producers (Fearon and Laitin 2003, Humphreys 2005) and production volumes and rents themselves may be endogenous to the risk of civil war. On the other hand, dummy indicators for oil or diamond production have very good coverage, are less likely to be endogenous to conflict, yet they aggregate countries with different production profiles.

Consequently, we employ all of the most recent measures<sup>8</sup> and seek to ascertain robustness across different specifications: Fearon and Laitin (2003) dichotomous indicator variable for oil exporters, equal to 1 for country-years in which fuel exports are greater than one-third of export revenues;<sup>9</sup> Humphreys (2005) variable recording quantities of diamonds mined and variable 8 Collier and Hoeffler’s measure for primary commodity exports / GDP is not used because it has been criticized extensively in the literature (Ross 2004, Lujala et al. 2005, Ross 2006) and Fearon (2005) shows that the results are driven mainly by oil exports.

9 We consider this indicator still a quantitative measure, because a value of 1 implies that relatively large quantities of oil are exported.

17