rafmacalaba/datause-encoder-data huggingface dataset:
Figure A.3: Correlation between the share of refugees and the population growth prior to their arrival (a) Share of refugees in 1928 <!-- Start of picture text -->
-100 -50 0 50 100<br>Population growth between 1907/13 and 1920 (%)<br>coef = .069, (robust) se = .047, t = 1.47, N = 1405, R-squared = .58<br>(b) Share of refugees in 1923<br>-100 -50 0 50 100<br>Population growth between 1907/13 and 1920 (%)<br>coef = .002, (robust) se = .015, t = .14, N = 1405, R-squared = .51<br>100<br>50<br>0<br>Share of refugees in 1928 -50<br>-100<br>100<br>50<br>0<br>Share of refugees in 1923 -50<br>-100<br><!-- End of picture text -->

**Notes.** This figure presents the correlation between the share of refugees (in 1928 in panel (a) and in 1923 (in panel (b)) and the pre-treatment population growth, i.e., population growth between 1907/1913 and 1920, before the arrival of refugees, controlling for province fixed effects and a set of municipal-level covariates, which comprises: log population density in 1907/1913; dummy variables indicating whether the municipality was a city in 1907/1913 and whether it is an island; distances to railway network, to shoreline, and to national borders; altitude, mean annual precipitation and temperature, and crop suitability of the municipality. The sample excludes outliers, defined as the top and bottom 2.5% of municipalities in terms of population growth between 1907/1913 and 1920.. Data sources: The 1928 population census of the Kingdom of Greece.

43
16 bargaining model should allow for the possibility that the wife and husband might value some "goods* differently, such **as** their own 1Ieisures." Hence, a test of the fauily demand model is the strong restriction that the incomecompensated effect of the husband's **wage** on **the** wife s nonmarket time is equal to the income-compensated effect of the wife's wage on the husband's nonmarket tim. Heckun (1971) tested this statistieally and rejected it with U.S. data, although this finding wa not emphasized in a subsequent paper (Ashenfelter and Heckman, 1974).8 But the test is conditional on many other aspects of the demand uodel, including functional-form approximations. To *valuate the partial effect of the husband's or wife's nonearned incom on household expenditure patterns, the wage rates of both partners must be hel1 contant. If expenditure shares are analyzed, then an instrumental variable estimate of **the** family's total expenditures might **be** included **as** a proxy for the effect of permanent income. It **is** expected that increases in the wife's nonearned income share of total expenditures will have the partial effect of increasing expenditure shares on children's nutrition, health, and ed,ocation as well as on her leisure. This assumos, however, that the wife's nonearned income has no effect on fertility, and the above variables representirg Lnvestments in children are defined on a per child basis. Unfortunately, most expenditure **surveys** do not distinguish these child investment **expenditures,** e.g. child and adult health care **are** coubined in a single coded expenditure category (Schultz and Thomas, 1988), and tests of this form **are** not yet reported.

The neoclassical family demand model, nonetheless, **has** the appeal of simplicity and widespread applicability, and some successful empirical application (Smith, 1980). How much realism should **be** sacrificed by a theoretical
<!-- Start of picture text --> E. F=E*(1-B) G. H. 1=D*H<br>Line of business PML gross PML net retained | Non-prop XL RI Non-prop XLRI XL RI premium<br>after prop. RI 2012 Rate on Premium 2012<br>Motor vehicle liability insurance 10,000 3,000 4,750 xs 250 2.5% 253<br>Other motor insurance 10,000 3,000 4,750 xs 250 5.4% 136<br>Marine, aviation and transport insurance 50,000 25,000 24,750 xs 250 18.1% 452<br>Fire and other damage to property insurance<br>Fire non natural catastrophe per risk 30,000 15,000 14,750 xs 250 20.8% 1,559<br>Fire natural catastrophe per event (EQ) 100,000 50,000 49,750 xs 250 8.9% 670<br>General liability insurance 5,000 2,500 2,250 xs 250 4.9% 122<br>Ptoral t1<br><!-- End of picture text -->

|Line ofbusiness|E.<br>PMLgross|F=E*(1-B)<br>PMLnet retained |<br>after prop. RI|G.<br> Non-prop XL RI<br>2012|H.<br>Non-prop XLRI<br>Rate on Premium|1=D*H<br>XL RI premium<br>2012|
|---|---|---|---|---|---|
|Motorvehicle liability insurance|10,000|3,000|4,750 xs 250|2.5%|253|
|Other motor insurance|10,000|3,000|4,750 xs 250|5.4%|136|
|Marine, aviation and transport insurance|50,000|25,000|24,750 xs 250|18.1%|452|
|Fire and other damage to property insurance||||||
|Fire non natural catastrophe per risk|30,000|15,000|14,750 xs 250|20.8%|1,559|
|Fire natural catastrophe perevent (EQ)|100,000|50,000|49,750 xs 250|8.9%|670|
|General liability insurance<br>|5,000|2,500<br>|2,250 xs 250<br>|4.9%<br>|122<br>|
|Ptoral|||t|||

|e<br>A.|e<br>>|
|---|---|
|NL underwriting risk|6,968|
|B. Deafult||
|C. Diversification effect||
|E=C-D:TotalSCR|7,794|
<!-- Start of picture text --> Dhaka - 2007 Dhaka - 2013<br>160<br>160<br>140<br>140<br>120<br>120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Storm induced outages Storm-induced outages<br>Annual average (all causes) Annual average (all causes)<br>Number of outages per month<br>Number of outages per month<br>January February March April May June July August September October November December January February March April May June July August September October November December<br><!-- End of picture text --> _Source: authors based on BREB data and ES data_ Overall these results provide two key insights. Firstly, the absolute frequency of outages is significantly larger than in developed countries studied earlier.<sup>18</sup> In particular, this is true for the number of outages caused by storms, providing evidence for relatively low resilience of Bangladeshi electricity supply. Secondly, the data indicates that the _share_ of storm-induced power outages in Bangladesh is significantly _lower_ than in the US or Europe – while increasing over time. This suggests that even for increasingly wellmanaged power systems natural shocks continue to pose a threat which grid operators struggle to deal with.

Spatial distribution of storm-induced power outages The number of storm-induced power outages varies significantly across divisions. Figure 3.8 shows the disaggregation of power outage reports by the seven Bangladeshi divisions<sup>19</sup> . The majority of storms that induced outage during the period 2000-2017 were recorded in Dhaka, Chittagong and Rajshahi, while none are attributable to Barisal. The reason for missing data for Barisal is unclear. While we had to exclude several PBS Zones in Barisal due to reporting and date issues, it is unlikely that Barisal did indeed not experience any storm-related outages in the included zones. Anecdotal evidence – e.g. reported by local newspapers – suggests that Barisal did indeed experience storm-related outages in the period covered by the data.<sup>20</sup> Thus, this division is excluded in the rest of this study.

On average, he longest storm-induced outages are experienced by Rangpur’s population while in Dhaka and Sylhet such disruptions are shorter but more frequent power disruptions: In other words, a storm in

> 18 The World Bank Doing Business measure for instance estimates that firms in the USA experience 0.5 power outages per year on average. In Bangladesh, no such number is reported –the Doing Business methodology does not report a number if outage frequency exceeds 100 times per year, as no accurate estimate is possible.<sup>18</sup> In other words, disruptions in the electricity network in Bangladesh are far more frequent than serious storms.

> 19 The analysis that follows will be conducted at the division level, to accommodate the climate data.

> 20 https://www.thedailystar.net/backpage/hailstorm-rain-take-country-unawares-660337

15
Table 6: Location Effect and R<sup>2</sup> estimated on the basis of 41 pseudo surveys

|||With|out local|Controls|: Naïve|With|local Co|ntrols: P|referred|
|---|---|---|---|---|---|---|---|---|---|
|Pseudo-Sample||R<sup>2</sup>|σu|σ<sup>∗</sup><br>c|σ<sup>∗2</sup><br>c <sup>/ σ2</sup><br>u|R<sup>2</sup>|σu|σ<sup>∗</sup><br>c|σ<sup>∗2</sup><br>c <sup>/ σ2</sup><br>u|
|'PNAD' obs: 11,721|1|0.538|0.740|0.179|0.0584|0.602|0.686|0.101|0.0215|
|'PNAD’|2|0.536|0.742|0.181|0.0597|0.590|0.696|0.106|0.0233|
|'PNAD’|3|0.583|0.707|0.150|0.0450|0.596|0.696|0.098|0.0199|
|'PNAD’|4|0.568|0.704|0.175|0.0619|0.593|0.684|0.094|0.0189|
|'PNAD’|5|0.580|0.691|0.167|0.0587|0.602|0.674|0.101|0.0224|
|'PNAD’|6|0.531|0.740|0.204|0.0762|0.568|0.710|0.107|0.0227|
|'PNAD’|7|0.529|0.732|0.199|0.0736|0.567|0.703|0.097|0.0192|
|'PNAD’|8|0.540|0.726|0.190|0.0686|0.573|0.699|0.102|0.0212|
|'PNAD’|9|0.551|0.715|0.186|0.0678|0.578|0.693|0.114|0.0273|
|'PNAD’|10|0.534|0.727|0.187|0.0659|0.560|0.708|0.119|0.0284|
|'PNAD’|11|0.447|0.800|0.231|0.0837|0.506|0.756|0.107|0.0200|
|'PNAD’|12|0.525|0.744|0.190|0.0652|0.554|0.722|0.115|0.0253|
|'PNAD’|13|0.563|0.708|0.188|0.0706|0.586|0.690|0.129|0.0348|
|'PNAD’|14|0.531|0.733|0.190|0.0670|0.556|0.713|0.112|0.0249|
|'PNAD’|15|0.522|0.736|0.201|0.0748|0.555|0.710|0.110|0.0240|
|'PNAD’|16|0.554|0.715|0.175|0.0601|0.575|0.698|0.114|0.0268|
|'PNAD’|17|0.554|0.715|0.180|0.0630|0.576|0.698|0.126|0.0326|
|'PNAD’|18|0.529|0.734|0.205|0.0778|0.559|0.710|0.130|0.0333|
|'PNAD’|19|0.555|0.716|0.175|0.0598|0.572|0.702|0.127|0.0324|
|'PNAD’|20|0.529|0.737|0.192|0.0677|0.553|0.716|0.116|0.0263|
|'POF' obs: 2,800|1|0.571|0.725|0.144|0.0393|0.579|0.718|0.123|0.0293|
|'POF'|2|0.582|0.699|0.152|0.0471|0.590|0.693|0.131|0.0355|
|'POF'|3|0.568|0.710|0.150|0.0445|0.581|0.700|0.112|0.0257|
|'POF'|4|0.578|0.696|0.153|0.0483|0.587|0.689|0.126|0.0335|
|'POF'|5|0.579|0.706|0.136|0.0371|0.584|0.702|0.125|0.0317|
|'POF'|6|0.591|0.700|0.160|0.0525|0.599|0.693|0.135|0.0381|
|'POF'|7|0.590|0.698|0.145|0.0429|0.606|0.688|0.116|0.0285|
|'POF'|8|0.576|0.707|0.161|0.0518|0.587|0.698|0.133|0.0364|
|'POF'|9|0.579|0.702|0.156|0.0491|0.589|0.693|0.128|0.0341|
|'POF'|10|0.603|0.691|0.137|0.0394|0.612|0.684|0.103|0.0228|
|'POF'|11|0.583|0.712|0.143|0.0406|0.593|0.704|0.117|0.0278|
|'POF'|12|0.582|0.697|0.149|0.0454|0.595|0.687|0.110|0.0255|
|'POF'|13|0.595|0.689|0.154|0.0502|0.608|0.677|0.113|0.0279|
|'POF'|14|0.598|0.694|0.133|0.0365|0.607|0.687|0.104|0.0229|
|'POF'|15|0.585|0.695|0.155|0.0495|0.594|0.687|0.127|0.0339|
|'POF'|16|0.599|0.686|0.164|0.0569|0.614|0.674|0.114|0.0284|
|'POF'|17|0.598|0.692|0.135|0.0378|0.604|0.687|0.110|0.0256|
|'POF'|18|0.623|0.664|0.141|0.0451|0.631|0.657|0.118|0.0323|
|'POF'|19|0.616|0.684|0.136|0.0395|0.625|0.676|0.102|0.0227|
|'POF'|20|0.625|0.668|0.148|0.0490|0.625|0.659|0.111|0.0282|
|'POF'|21|0.541|0.744|0.168|0.0508|0.558|0.732|0.108|0.0219|

Source: Authors’ Calculation.

22
Mirroring the view that Bharat is drowning, absolute achievement, as measured by the percent correct score, is low compared to curricular standards. A significant fraction of children have not mastered the content categories expected for their grade (Table 1). By ninth grade, only 11 percent of children in Rajasthan and 17 percent in Orissa can correctly choose the smallest value from the set 0.625, 0.25, 0.375, 0.5, and 0.125 (Table 1, Q2). The question "A runner ran 3000m in exactly 8 minutes. What was his average speed in meters per second?" (Q7) stumps all but 23 percent of children in Rajasthan and 32 percent of children in Orissa. A simple test of division and fractions—"if 4 times a number is 48 what is 1/3 of the number?" (Q10)—is too difficult for 65 percent of children in Rajasthan and 64 percent of children in Orissa. While the item-by-item comparison suggests that Indian children are performing significantly below the international average, interpreting the magnitude of this effect is difficult because it depends on a test-specific metric. As discussed, the percentage correct score is a function of latent achievement differences—our true parameter of interest—and the discriminating power of the test, and thus inseparable from the specific test design.

Figure 5 uses the linking methodology proposed previously to present cross-country comparisons on the TIMSS achievement scale.<sup>8</sup> Based on the average score, Rajasthan and Orissa rank below 46 (42) of the 51 countries tested with a score of 382 and 404. This ranking straddles Bahrain, Chile and Morocco and is boxed in by Egypt above and the Philippines below. Averaging across the entire tested sample, India scores 392—below 43 of 51 countries. This compares to the international average of 487 in 1999 and 467 in 2003. Seen in standard deviations of all children tested, the two Indian states are 0.7 student standard-deviations or 1 country standarddeviation below the TIMSS mean. Relative to the OECD mean, the tested Indian children are 3.1 (OECD) standard deviations below. That is, if we rank all the OECD countries, India would lie below the 1st percentile in the distribution of OECD country scores.

The true picture may be worse. Since the tests included only enrolled children, the comparisons favor India to the extent that enrollment is lower relative to other countries. In both

> 8We follow the TIMSS methodology as closely as possible and compute sample averages using the EAP scores, which is, in this case, simply more efficient than using plausible values. The MLE scores, which are estimated using BILOG-MG rather than our custom MCMC routines, yield somewhat lower estimates of the average: 374 and 386. The discrepancy between the EAP and MLE averages is likely due to students scoring in an area where the likelihood function is virtually flat or undefined. In this situation, regularity and stability become a major concern with MLE.

14
**Table 18: Comparison of Center and Teacher Characteristics by Villages**

||||P|laygroups||||Kin|dergarten|s||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-|roject|||Diff. Between Project|Non-|Project|||Diff. Be|tween|
||<br>Vil<br>Playg|<br>lage<br>roups|Projec<br>Play|t Village<br>groups|<br>Villages & Non-<br>Project Villages|<br>Vil<br>Kinde|<br>lage<br>rgartens|Project<br>Kinder|Village<br>gartens|<br>Project Vil<br>Non-Projec|<br>lages &<br>t Villages|
|**Center Characteristics**|Mean|S.D.|Mean|S.D.||Mean|S.D.|Mean|S.D.|||
|_Center Quality (ECERS-R)_||||||||||||
|Space and Furnishing|2.565|1.071|3.168|1.257|0.603<br>**|3.004|1.230|2.777|1.155|-0.227||
|Personal Care Routines|2.491|1.113|2.587|1.063|0.096|2.552|1.146|2.422|1.021|-0.130||
|Language-Reasoning|3.189|1.521|3.495|1.207|0.306|3.794|1.570|3.522|1.522|-0.272||
|Activities|2.122|0.950|2.700|0.907|0.578<br>***|2.403|1.012|2.293|0.907|-0.110||
|Interactions|3.755|1.667|4.151|1.422|0.396|4.062|1.557|4.040|1.522|-0.022||
|Program Structure|2.565|1.537|2.842|1.305|0.277|2.686|1.425|2.610|1.436|-0.076||
|Parents and Staff|2.515|1.096|2.457|0.878|-0.058|2.889|0.945|2.601|0.936|-0.288|*|
|Total (Mean) ECERS-R Score|2.743|1.080|3.057|0.906|0.314<br>*|3.056|1.075|2.895|0.966|-0.161||
|_Center Schedule_||||||||||||
|Days of operation/week|5.082|0.997|4.448|1.289|-0.634<br>**|5.981|0.138|5.988|0.154|0.007||
|Hours of operation/day|2.214|0.470|2.195|0.395|-0.019|2.524|0.348|2.527|0.411|0.003||
|**Teacher Characteristics**||||||||||||
|_Education_||||||||||||
|Elementary at most|0.009|0.096|0.007|0.086|-0.002|0.000|0.000|0.000|0.000|0.000||
|Junior high|0.018|0.135|0.024|0.153|0.006|0.005|0.070|0.008|0.089|0.003||
|<br>Senior high|0.734|0.444|0.708|0.455|-0.026|0.420|0.495|0.373|0.484|-0.047||
|Higher education|0.239|0.428|0.261|0.440|0.022|0.576|0.495|0.619|0.486|0.043||
|_Type of ECED Training_||||||||||||
|None|0.142|0.350|0.140|0.347|-0.002|0.140|0.348|0.265|0.442|0.125|***|
|Some Project (100 hrs)|0.000|0.000|0.044|0.206|0.044<br>*|0.000|0.000|0.000|0.000|0.000||
|<br>Full Project (200 hrs)|0.000|0.000|0.246|0.431|0.246<br>***|0.000|0.000|0.000|0.000|0.000||
|Some Project (100 hrs) & Other|0.000|0.000|0.034|0.181|0.034|0.000|0.000|0.000|0.000|0.000||
|<br>Full Project (200 hrs) & Other|0.000|0.000|0.186|0.390|0.186<br>***|0.000|0.000|0.003|0.051|0.003||
|Other only|0.858|0.350|0.350|0.477|-0.508<br>***|0.860|0.348|0.733|0.443|-0.127|***|
|_Experience in ECED_||||||||||||
|ECED|0.377|0.487|0.245|0.431|-0.132<br>**|0.405|0.492|0.389|0.488|-0.016||
|ECED & Cadre|0.113|0.318|0.140|0.348|0.027|0.135|0.343|0.138|0.345|0.003||
|Cadre|0.160|0.369|0.298|0.458|0.138<br>**|0.125|0.332|0.185|0.389|0.060||
|None|0.349|0.479|0.317|0.466|-0.032|0.335|0.473|0.288|0.454|-0.047||
|_Years of teaching_|6.183|5.561|5.287|3.393|-0.896<br>*|9.376|7.115|9.076|7.017|-0.300||

Note: Sample consists of Playgroups (N=306) and Kindergartens (N=272) for which center quality measures were collected. * p<0.05, ** p<0.01, *** p<0.001 54
firms experience an increase in their tax liability after adopting a POS, as we show in monthly event studies. We also find no evidence that firm POS adoption responds to subsidies for POS usage, to a reduction in tax withholding rates applied by card processing companies or to a reduction in the commissions charged on card transactions. This suggests that accelerating firms’ adoption of POS would require much larger financial incentives or a mandate obliging firms to offer card payment facilities.

The second explanation for our results is the fact that, even among retail and wholesale firms with a POS, card sales constitute on average less than 30 percent of total reported sales, and less than 20 percent in the majority of firms. This means that firms already report a large share of their cash sales. Thus, even if cross-checks between the card sales and firms’ self-reported sales, combined with audits on misreporters, create a lower bound on what firms report for tax purposes, the relatively high compliance level means that firms have room to increase card sales without increasing their total reported sales.

This study connects to several sets of literature. First, financial inclusion and the use of financial technology have been shown to have far-reaching development benefits (Jack and Suri 2014, Dupas and Robinson 2013, Burgess and Pande 2005). Technologies for electronic identification and transaction processing have been shown to enhance governments’ capacity to manage expenditure and prevent leakages (Muralidharan et al. 2016, Banerjee et al. 2020). It is thus a natural extension to investigate the contribution of electronic payment technology to enhancing also other aspects of state capacity, namely tax capacity (Okunogbe and Santoro 2021).

The mechanism through which electronic payment technology can impact tax capacity – the – generation of third-party reports on taxable transactions has been prominently discussed in the public finance literature (Kleven et al., 2011; Jensen, 2019). Pomeranz (2015) and Naritomi (2019) show that third-party reporting improves VAT compliance in Chile and Brazil respectively.<sup>1</sup> Closely related to our study is Das et al. (2022), who show that India’s demonetization campaign led firms to significantly increase reported taxable sales, and likely also tax liabilities. Demonetization lead to a much larger increase in electronic sales than Uruguay’s reforms, but demonetization also had large economic costs, and is hence at best a debatable strategy for policy makers wishing to promote electronic payment technology with a view on improving tax compliance.

Our work also relates to a set of studies evaluating government policies to generate third-

> 1Carrillo et al. (2017) and Slemrod et al. (2017) show that third-party reporting is not a panacea, since firms might offset increased third-party reporting (and hence tax compliance) on the sales margin by increasing reported costs.

4
these may or may not be reflected on the service fees charged by professional accountants depending upon the market conditions.<sup>24</sup> Finally, we account for several firm characteristics such as size (turnover and employment), location (region or city), industry, main taxes paid as well as some perception variables when possible. All details about these variables and other data issues are provided in the summary statistics tables for each country.

The main data sets used in this analysis are surveys conducted by the World Bank from three countries: South Africa (USAID, (2008)), Nepal (IFC (2012a)), and Ukraine (IFC (2009)).

# **3.1.1 South Africa**

We take three different approaches to identify the relevant TCC for e-filing analysis in South Africa. The TCC Survey allows us to identify not only the subcomponents of TCC but also total time spent on each tax. After making use of all the detailed data, we are able to measure TCC as:

- 1- Total tax compliance cost of firms (in Rands) that do all TC tasks in-house,

- 2- Sub-group of TCC under the task: “filling and submitting tax forms”,

- 3- Total time spent for TCC of VAT.

Estimating the salaries of tax staff is another issue in the TCC Surveys, especially when it comes to estimating owners’ imputed wage rates. All salary estimates are taken from Coolidge, Ilic and Kisunko (2009). These estimates depend on firm turnover and associated average salaries (regardless of skill level) and do not consider other sources of variation that may prevail such as the province, sector, or occupation of the tax staff at the work place etc. This approach may result in an underestimate of TCC. To mitigate this issue, we estimate the model for three different groups of firms according to their TCC: firms with only one tax staff, firms with one or at most two tax staff and finally, firms with any number of tax staff. Lastly, the information provided in the survey on the total time (hours) spent on

> 24 If the market is fairly competitive, one may expect to see professional accountants reflecting all the cost savings in the service fees. Yet, this is not necessarily the case in especially developing countries with relatively thin markets.

29
- 21 -

goods in national customs schedules upon which tariffs are applied). Table 4 shows 1986 nominal freight rates derived from equation (1) for each Caribbean country's major manufactures, metal ore, or petroleum exports to the U.S. and also provides similar transport coat information for all other exporters of the product. 13/ Differences between these figures have been computed with a negative value for the result indicating a favorable freight rate differential for Caribbean exporters. To assist in evaluating these results the 1986 value and share of each product in total exports of the country are shown along with the "applied" United States tariff on the good.141 The latter information allows comparisons to be made between post-Tokyo tariffs and ad valorem transport costs.

The main point that emerges from Table 4 is that the Caribbean countries as a group generally encounter total (i.e., the average of air and vessel charges) nominal freight rates that are below the average of their competitors, at least for shipments to the United States. For the 81 products listed, 65 (eighty per cent) have lower than average freight rates for Caribbean exports and in 3 instances (about four per cent of the total) the Caribbean and competitor's rates are equal. In addition, the table suggests that for most products the Caribbean transports cost for manufactured goods are relatively low as 63 items (about 78 per cent of the total) have nominal

- 13/ The "other exporters" transport rate shown in Table 4 is the weighted average ad valorem freight cost for all other developed and developing country suppliers of the tariff line product. Mexico and Canada have been excluded from these computations since their goods often pass directly over the common border with the U.S. and do not bear international transport costs.

- 14/ The tariff rate shown in Table 4 has been computed by taking the ratio of all import duties collected on each Caribbean export to the f.o.b. value of these shipments. As such, it represents a trade weighted average of the most-favored-nation or preferential rates actually applied. In addition, the tariff charges for offshore assembly (OAP) items are net charge which exclude imported components from the United States. As such, the figures in Table 4 reflect the actual net tariffs paid on OAP items.