rafmacalaba/datause-encoder-data huggingface dataset:
- [6] Chalendard, C., Fernandes, A., Raballand, G., and Rijkers, B. (2020). “Technology (Ab)use and Corruption in Customs,”World Bank mimeo.

- [7] Demir, B., and Javorcik, B. (2020). “Trade policy changes, tax evasion and Benford’s law,”Journal of Development Economics 144, 102456.

- [8] Ederington, J. and Ruta, M. (2016). "Non-Tari¤ Measures and the World Trading System". World Bank Policy Research Working Paper 7661.

- [9] Ferrantino, M., and Wang, Z. (2008). “Accounting for Discrepancies in Bilateral Trade: The Case of China, Hong Kong and the United States,” China Economic Review 19: 502-520.

- [10] Ferrantino, M., Liu, X., and Wang, Z. (2012). “Evasion behaviors of exporters and importers: Evidence from the U.S.–China trade data discrepancy,”Journal of International Economics 86: 141–157.

- [11] Fisman, R. and Wei, S. (2004). “Tax Rates and Tax Evasion: Evidence from "Missing" Imports in China,”Journal of Political Economy, vol. 112, no. 2.

- [12] Goldberg, P., and Pavcnik, N. (2016). “The E¤ects of Trade Policy,”Handbook of Commercial Policy, edited by Kyle Bagwell and Robert Staiger.

- [13] Grigoriou, C. (2019). “Mirror analysis as a support for risk management and valuation: a practical study.”World Customs Journal 13(2): 91-113.

- [14] Grigoriou C., Kalizinje, F., and Raballand, G. (2019). “How helpful are mirror statistics for Customs reform? Lessons from a decade of operational use.”World Customs Journal 13(2): 105-114.

35
Additional robustness checks are as follows.<sup>23</sup> First, we estimate our model for imports from all countries, including those from other FTA member countries. We obtain significant results for the FTA dummy and the tariff rate when introducing the variables separately. Only the coefficient for the FTA dummy is estimated to be significantly positive when introducing both variables simultaneously.

Second, we use transaction-level data rather than aggregated data according to year. Although the coefficient for the FTA dummy is negative when introducing both variables, the results are similar to those in the preliminary robustness check when introducing the two variables separately. Third, to examine differences in the RoO effect across RoO, we introduce interaction terms for the FTA dummy variable with various dummy variables indicating RoO. The results reveal that only the interaction term incorporating the regional value content rule is significantly negative. This sign is not consistent with our expectations. Furthermore, the absolute magnitude of the coefficient is abnormally large.<sup>24</sup> 23 The results are shown in tables S2.3–S2.5 in the supplemental appendix. In table S2.6, we also report estimation results for the equations with an interaction term for the tariff variable with demand elasticity.

24 The other checks are as follows. First, to circumvent the effects of the 2007/2008 financial crisis, we exclude the years 2007 and 2008 from the estimation. Second, we include import establishment-year fixed effects instead of import firm-year fixed effects. In these cases, only the coefficient for the tariff rate is estimated to be significantly negative, as in the baseline results. Last, we also examine the role of certain elements (such as invoicing currency) that are not explicitly considered in Section 2. The results are shown in table S2.7 in the supplemental appendix. For example, we found that importing firms with higher total import values are found to limit import-price rises. The effect of FTA use on import prices is also found to be larger for Thai baht (THB) invoiced transactions. These findings probably reflect the difference in bargaining power between importers and exporters.

34
50 the cease-fire in the Iran-Iraq war, some refugees will undoubtedly start to go home, but a large residual will remain behind for quite some time.

According to the latest World Refugee Survey (U.S. Committee for Refugees, 1988), in 1987 there were 3,541,400 refugees from Afghanistan in Pakistan and an additional 2,200,000 such refugees in Iran. Iran also has about 400,000 additional refugees, mostly of Iraqi origin. if even 5-10 percent of these refugees stayed on permanently, it would have a major effect on net international migration estimates for Pakistan, Iran, and Afghanistan. These considerations will affect primarily the 1980-1985 period although at least 68,500 new refugees arrived in Pakistan in 1987 and there was a new influx of 41,000 Afghan refugees in Iran in the last three months of 1986 alone (U.S. Committee for Refugees, 1988).

Recommendations for Afghanistan. I would recommend a much higher level of net emigration than the previous World Bank estimates for 1980-1985, perhaps on the order of 200,000 to 250,000. About 60 percent of the outflow would be to Pakistan and 40 percent to Iran. The sex ratio of the emigrants might be slightly higher than 100, but it should not be much higher because most of the emigrants from Afghanistan move as entire families or even entire villages. For 1985-2000, I would suggest a modest net outflow of 20,000-25,000.

Recommendations for Pakistan. I would recommend reducing the World Bank's previous figures by the change in the amount of inflow from Afghanistan suggested above, i.e. to a net outflow of 198,000-228,000 for 1980-1985 and 255,000-258,000 in 1985-1990. The previous estimates of -150,000 for 1990-1995 and -50,000 for 1995-2000 may be a bit too low. The sex ratio of emigrants seems too high in all years, but particularly in the last two periods since relatively more women should emigrate as this migration stream matures.

Recommendations for Iran. The previous estimates for Iran should be reduced by the recommended level of permanent migration from Afghanistan in the 1980s. This would reduce emigration to around zero for 1980-85 or a small amount of net immigration (5,000-25,000). The level of net emigration for 1985-1990 would be reduced to 40,000-42,000. For 1995-2000, I would recomrmend a slight increase in the previous net emigration estimate to 20,000 persons.

# Bangdladesh

Net international'mfgration for Bangladesh should be held constant at its 1980-1985 level for the rest of the century. Emigration from Bangladesh to the United States has doubled in the last three years from about 800 annually in FY1984 to over 1600 in FY1986 and FY1987. Given the family reunification provisions of U.S. immigration law, immigration from Bangladesh will probably continue to increase for the next several years.
Table E5: School Test Scores, Controls

|||No co|ntrols|||Additiona|l controls||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Math|(2)<br>Eng|(3)<br>Urdu|(4)<br>Avg|(5)<br>Math|(6)<br>Eng|(7)<br>Urdu|(8)<br>Avg|
|High|0.155|0.181*|0.115|0.150|0.157*|0.185*|0.108|0.151*|
||(0.105)|(0.102)|(0.092)|(0.096)|(0.093)|(0.094)|(0.088)|(0.088)|
|Low Treated|-0.066|0.108|-0.059|-0.006|-0.0832|0.069|-0.087|-0.038|
||(0.122)|(0.114)|(0.114)|(0.111)|(0.106)|(0.104)|(0.102)|(0.0981)|
|Low Untreated|0.021|0.055|0.007|0.028|0.005|0.046|-0.024|0.007|
||(0.091)|(0.091)|(0.081)|(0.083)|(0.078)|(0.082)|(0.077)|(0.074)|
|Baseline|||||0.373***|0.457***|0.312***|0.433***|
||||||(0.077)|(0.064)|(0.01)|(0.086)|
|R-Squared|0.08|0.06|0.08|0.08|0.27|0.20|0.21|0.24|
|Observations|732|732|732|732|722|722|722|722|
|# Schools (Rounds)|732 (1)|732 (1)|732 (1)|732 (1)|722 (1)|722 (1)|722 (1)|722 (1)|
|Mean Depvar|-0.21|-0.18|-0.24|-0.21|-0.21|-0.18|-0.24|-0.21|
|Test pval (H=0)|0.14|0.08|0.21|0.12|0.09|0.05|0.22|0.08|
|Test pval (_L_<sup>_t_</sup>= 0)|0.59|0.34|0.60|0.96|0.43|0.51|0.40|0.70|
|Test pval (_L_<sup>_t_</sup>=H)|0.07|0.52|0.13|0.16|0.02|0.27|0.05|0.05|

Notes: * p<0.10, ** p<0.05, *** p<0.001 a) This table conducts robustness checks on our school test score results. School test scores are generated by averaging child average (across all subjects) test scores for a given school. Columns 1-4 are the same regressions as Table 4, Columns 1-4, but without any baseline controls. Columns 5-8 repeat these regressions with additional controls, which include the baseline score, percentage of students in specific grades and percentage female. Test scores are averaged across all children in a given school separately for each round, and child composition is hence different across rounds. b) Regressions are weighted to adjust for sampling and include strata fixed effects, with standard errors clustered at village level. We include a dummy variable for the untested sample at baseline across all columns and replace the baseline score with a constant. Since the testing sample was chosen randomly at baseline, this procedure allows us to control for baseline test scores wherever available. The number of observations and the unique number of schools are the same since test scores are only collected once after treatment. The number of schools is lower than the full sample due to attrition and zero enrollment in some schools in the tested grades. The mean of the dependent variable is the test score for those schools tested at random at baseline.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

42
— Table 5. Share of harvest consumed and sold

|Shar<br><br>|e ofWarrantage cro<br>|ps ...<br>|
|---|---|---|
|pb<br>Consumed|Sold|Givenaway|
|PanelA: Householdrandomization<br>(1)|(2)|<br>(3)|
|Access towarrantage<br>-0.044***|0.036**|0.002|
|(0.016)|(0.016)|(0.005)|
|Observations<br>516|516|516|
|Control mean<br>0.753|0.214|0.0262|
|PanelB: Village randomization (Sample: Non-lotterypartic|ipants andControl|lottery participants)|
|High treatment village<br>0.038|0.009|0.038*|
|(0.056)|(0.055)|(0.019)|
|Observations<br>340|340|340|
|Lowtreatmentvillagemean<br>0.703|0.257|0.0223|

Notes: In Panel A, the sample is restricted to lottery participants. In Panel B, the sample is restricted to all control households and non-lottery participants. Standard errors are clustered by village and presented in parentheses. *, **, *** indicate significance at the 1, 5, and 10 percent respectively.
for the lowest 10 lines in the RDV data set, giving the line of $32.74 per month or $1.08 per day, though using they showed that using the predicted line in the poorest country gave virtually the same value ($1.05 per day).

From our study, the most comparable estimate to these is the predicted line for the poorest country. In Table 1, the regression of log poverty line on a quadratic function of consumption per capita is the same specification used by Chen and Ravallion (2001) and this gives $33.76 per month ($1.11 per day) in 2005 prices on our new data set. However, as also noted above, a more robust estimate is that based on the non-parametric regression, which gives $37.16 per month or $1.22 per day. As also argued above, a good case can be made for using instead the average of the poorest 15 countries, giving $1.25 per day.

It is clear that our proposed new international poverty line of $1.25 has a lower value in the US than our old lines. The $US value in 1993 of our new international poverty line of $1.25 a day is $0.92 a day—15% lower than the Chen and Ravallion (2001, 2004) poverty line of $1.08 a day at 1993 PPP. The $1.25 line in 2005 is equivalent to exactly $1.00 a day in the US in 1996. To put the point another way, if we simply updated the old 1993 line for inflation in the US then we would get a line of $1.45 a day in 2005; the 2005 value of the predicted poverty line in the poorest country—estimated as $1.05 per day by Chen and Ravallion (2001)—is $1.42 per day.<sup>31</sup> These lines are significantly higher than our $1.25 line (t=2.08; prob.=4%).

However, these calculations are deceptive, given that the PPP’s from different ICP rounds are not strictly comparable. There were substantial revisions to the PPP’s in the 2005 ICP round relative to 1993. Probably the most important difference for the present purposes is that it is believed that the 1993 ICP for developing countries used less rigorous standards of specifying the quality of commodities than the 2005 ICP, such that a lower quality of goods were priced than would have been found in the US market.<sup>32</sup> The goods priced by the 1993 ICP tended to be more typical of the items available in local markets.

Some large changes in the PPPs are evident if we convert a given national poverty line into $’s at both 1993 and 2005, and then compare the results, as given in Figure 4 for the 72

> 31 The ratio of the 2005 CPI for the US to the 1993 CPI is 1.352.

> 32 Heston and Summers (2008) describe the ways in which the 2005 ICP was an improvement over past ICP rounds, and point specifically to the “...quantum improvement in the way regional comparisons were linked together from the standpoint of reviewing price collection” (p.3). Also see World Bank (2008b).

16
# _4.2. Analysis on Development_

As discussed in Section 2, recent studies found an inverted ‘U’ curve relationship between the overall disaster impact and income level of a country. While damages to assets can be reduced with the installation of countermeasures against natural hazards as income level increases and thus sufficient financial resources can be used, losses can increase as economy becomes developed and complex. Then, the overall impact, adding damages and losses, becomes the inverted ‘U’ shape curve. However, the use of overall impact as the sum of damages and losses can be considered as double-counting of economic impacts, according to Rose (2004). Damages and losses are measurements on different variables—stock and flow, implying two sides of the same phenomenon. On the other hand, total impacts of a disaster include losses and the ripple effect of initial losses. This total impact should be used to investigate the inverted ‘U’ curve relationship between economic impact and income level.

The distribution of 184 cases, seen in Figure 4-4, displays the tendency of inverted ‘U’ curve relationship between the natural log of GDP per capita and the share of total impacts over GDP (hereafter GDP impact). While both tails, lower and higher GDP per capita countries, indicate relatively low GDP impact, many middle level GDP per capita countries have higher GDP impact values. Two outliers, with extremely large GDP impact rate, are 1963 storm in Haiti and 1988 storm in St. Lucia, small island nations hit by hurricanes. This observation appears to prove that the theory of inverted ‘U’ curve relationship can be found with total impact of disasters. It should be noted, however, that the 184 cases used in this study are screened and have some certain intensity in terms of damages. Therefore, further research might be necessary to include all the disaster cases reported in order to test this tendency.

22
Table 2: Effect of Extended Provider Training on Learning: Provider Post-Training and Clinic Endline Surveys

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Beliefs A|bout RDTs||Be|liefs About M|icroscopy Te|sts|
||RDT<br>Beliefs<br>Index|Share RDT<br>Positive<br>Simple<br>Malaria|Share RDT<br>Positive<br>Severe<br>Malaria|Share RDT<br>Negative<br>No Malaria|Microscopy<br>Beliefs<br>Index|Share<br>Microscopy<br>Positive:<br>Simple<br>Malaria|Share<br>Microscopy<br>Positive:<br>Severe<br>Malaria|Share<br>Microscopy<br>Negative:<br>No Malaria|
|_A. Data from Post-Train_|_ing Survey_||||||||
|Extended Training|0.312<sup>_∗∗∗_</sup>|0.200<sup>_∗∗∗_</sup>|0.071<sup>_∗∗∗_</sup>|0.046|0.027|-0.002|-0.005|0.037|
||(0.071)|(0.038)|(0.019)|(0.041)|(0.116)|(0.035)|(0.028)|(0.048)|
|Mean (Basic Training)|0.000|0.599|0.888|0.863|0.000|0.846|0.921|0.829|
|N|205|198|196|176|201|197|195|173|
|28|||||||||
|_B. Data from Provider F_|_ollow-Up S_|_urvey_|||||||
|Extended Training|0.406<sup>_∗∗∗_</sup>|0.158<sup>_∗∗_</sup>|0.072<sup>_∗∗_</sup>|0.038|0.277<sup>_∗_</sup>|0.055|0.024|0.065|
||(0.129)|(0.071)|(0.028)|(0.025)|(0.159)|(0.050)|(0.016)|(0.047)|
|Mean (Basic Training)|0.000|0.560|0.889|0.938|0.000|0.840|0.954|0.868|
|N|85|85|85|85|85|85|85|85|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata fixed effects. Additional covariates are selected using double lasso, from the set of all characteristics listed in Table 1 as well as their pairwise interactions. Missing variables for all characteristics are dummied out and recoded to the mean prior to forming interactions. We include these missing dummies in the potential covariate set and the set of pairwise interactions. Sample in both panels limited to providers who attended the training. Index components are standardized relative to the basic provider training mean. In cases where some (but not all) index components for a respondent are missing, missing components are imputed to the treatment group mean prior to standardization. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.
# Appendix A. Creating Demand and Streamflow Futures

We statistically generated 300 demand and streamflow futures, in addition to a future representing no change from today (855 Mm3 of demand and historical streamflows). A future is defined by a specific combination of values, one for each of the uncertainties. These futures are not predictions, and we do not assign any likelihood to their occurrence. Rather, we use them to stress test and better understand the behavior of the Master Plan. Using WEAP, we determined the 90th percentile of monthly met demand of each individual project and of the portfolios in each of these 300 futures.

In this analysis, to create the 300 futures we use Latin Hypercube Sampling (LHS). LHS is a randomized experimental design based on the higher dimensional generalization of a Latin Square. Our experience suggests LHC is more useful for multi-scenario analyses than other standard sampling methods, such Monte Carlo or full-factorial, because it provides the most complete exploration of the model's behavior over the input space for the fewest number of points in sample (Saltelli et al, 2000; Groves et al., 2014). To generate the futures, LHS

1. Takes as input the minimum and maximum values for each of the _u_ uncertainties and the number _n_ of futures desired,

2. Divides each uncertainty range into _n_ values at equally spaced intervals, and 3. Randomly generates _n_ futures such that each of the _n_ values for an uncertainty is included in exactly one future, and no future contains more than one value from the _n_ th interval.

To create these futures, we first need to identify appropriate ranges for the demand and streamflow uncertainties considered. We discuss these in turn.

## Generating Demand Futures

We used four sources of data of population and water use projections for the Lima Metropolitan Region: two from SEDAPAL (SEDAPAL, 2014),<sup>11</sup> one from Peru’s National Institute of Statistics and Informatics (INEA<sup>12</sup> ), and one from a study by the German Federal Enterprise for International Cooperation for SEDAPAL. Each source projected (i) the total population in Lima in 2040 and (ii) demand per capita, which includes system losses. We multiplied each demand per capita projection by each population projection, as illustrated in Table A-1. We defined the ranges by multiplying the minimum and maximum population projections with the minimum and maximum demand per capita, respectively. From this method, the estimated minimum and maximum are 730 Mm3 and 1790 Mm3, respectively (red values in Table A-1). For each of the 300 futures, we assume that the ratio of regional demand to total demand remained

> 11 Personal communication with SEDAPAL, January 2014.

> 12 http://www.inei.gob.pe/

41
# **Figure 4– Bank Lending and Interest Rates to Firms Given State Public Debt**

The figures plot the evolution of bank lending (Panel A) and interest rates on bank loans (Panel B) to private firms relative to January 2014. In both panels, states are divided into two groups depending on whether their indebtedness in 2016Q1 was above <u>or below the median indebtedness. The vertical lines in both panels mark the introduction of the FD Law.</u> <!-- Start of picture text --> Loan Volume Interest Rate of Loans to Private Firms<br>2015m1 2016m1 2017m1 2018m1 2015m1 2016m1 2017m1 2018m1<br>Time Time<br> States Low Public Debt States High Public Debt States Low Public Debt States High Public Debt<br>1.4 .5<br>0<br>1.2<br>-.5<br>1<br>Index<br>-1<br>.8 -1.5<br>Percentage Points Relative to April 2016<br>.6 -2<br><!-- End of picture text --> 41
The focus of the paper will be addressing the second type of selection so as to be able to compare households with migrant mothers to households with migrant fathers. On the other hand, the results of proposition 1 will provide a guide for interpreting the direction of the bias that may arise from conditioning on _selection of households into migration_ . I thus build on the intuition of the theoretical model introduced in section I to derive a set of instrumental variables that may influence the decision of migrant households about which of the spouses to send out for migration but will not have any direct effect on the outcome variables of equation (14). The model of section I is translated to the data by first assigning “counterfactual” gender specific destinations to each household. To this purpose I identify, for each household, the year in which the migration decision has been taken as that in which the migrant has departed; I then consider the destinations chosen by previous migrants from the same village and take the destination that was most common among female migrants as counterfactual destination for women and the one that was most common among male migrants as counterfactual destination for men. Counterfactual destinations will vary by gender, time of departure, and village. The choice of assigning the most common destination of migrants from the same village to perspective migrants is supported by the evidence reported in table 5, which shows that over 60% of migrants from the same village had chosen the same destinations. This is in line with the literature that has documented the formation of _networks of migrants_ (Bartel 1989; Altonji and Card 1991; Lafortune and Tessada 2012; Patel and Vella 2013) but can also, in this particular context, be explained by the widespread use in South Asia of recruiting agencies that are connected to other agencies in a foreign country and, therefore, typically send all of the people of the village they visit to the same destination (Parrenas 2001; Suradji 2004). Table 6 shows the gender specific destinations assigned to each household for the whole IFLS sample and for the restricted sample of households with a migrant parent. The only significant difference between the two samples is that migrant mothers are much more likely to be assigned Saudi Arabia as counterfactual destinations than the full sample of women, this is in line with the evidence in table 2 that women who leave children behind are much more likely to migrate internationally than any other type of migrant.

**Table 5.** Migrants per Village. IFLS 2007.

||Men|Women|
|---|---|---|
|Adults pervillage|55.31<br>(40.99)|58.70<br>(42.29)|
|Migrantspervillage|15.64|9.39|
||(14.002)|(9.185)|
|%Migrantsat samedestination|.617|.622|
||(0.231)|(0.234)|

**~~Notes:~~** ~~Standard deviations reported~~ <u>in</u> ~~parenthesis.~~

15