rafmacalaba/datause-encoder-data huggingface dataset:
Chinese imports, the value of this measure is 0, while if all the products face competition from Chinese imports and China’s import share in all these products is close to 1, this measure is close to 1.

_NTMExposurei_ 0 _,t_ : The measure of NTM exposure of the firm _i_ selling product _g_ in month _t_ . The measure creates an exposure variable by combining the initial weights of different HS-10 products in a firm’s import basket with product-level changes in NTM exposure over time at the HS-10 level. We combine the trade data with monthly data on NTMs applied by Indonesia on imports at HS-10 digit level. The NTM exposure for exporter _i_ is weighted using an invariant weight to reduce the potential endogeneity bias. It is computed as the share in value of HS-10 products that are subject to the NTM in question as a share of total imports on the first year of trading. Thus, the exposure is computed as follows: _j_ is the HS product, _NTMjt_ = 1 if an NTM on HS-10 product _j_ was in effect in month _t_ and 0 otherwise. _IMPij_ =<sup>�</sup><sup>_t_</sup> _t_<sup>=</sup> =<sup>_m_</sup> _m_<sup>12</sup> 1<sup>_Importsijt_andistheinvariantweightofproduct</sup><sup>_j_in</sup> exporter _i_ imports. _m_ 1 is the first month of the year that firm _i_ joined the export market and _m_ 12 is the last month of that year. By definition, this weight will only have values for exporting firms that also import directly. For the aggregate NTM measure in Section 5.1, the NTM dummy ( _NTMjt_ ) is 1 any of the 60+ NTMs in our data was in effect. For the specific, trade distorting NTMs discussed in Section 5.2, _NTMjt_ = 1 if that specific measure was in _TariffExposurei_ 0 _,t_ : Similar to the _NTMExposurei_ 0 _,t_ , this is the measure of tariff exposure of the firm _i_ selling product _g_ in month _t_ , where the weights of different HS-10 goods in the firm’s import bundle are determined in the initial time period. We combine the trade data with monthly data on tariffs applied by Indonesia on imports. The exposure is 15
18 <!-- Start of picture text --> Figure 7: The Brazillan Gold Market Structure<br>Mines and Garimpos<br>Field Gold Dealers<br>Gold Firms in Large Cities<br>Deposit Banks (Certificates)<br>Commodity Exchange ~ Trade<br><!-- End of picture text --> 61. Domestic gold prices are primarily determined by three factors: The international gold price from the commodities exchange in New York (COMEX), domestic supply and demand, and the dollar exchange rate at the domestic parallel market. The Amazon gold rush of the 1980's is thus closely correlated with these parameters.

62. Smuggling is wide spread in the gold market, but background sources indicate that it has diminished2 . It takes place primarily due to low prices paid by the federal government and high taxes; however, recent changes in these variables may have actually decreased the disparity between the official and the estimated production thereby increasing government revenues. To a smaller extent, gold is also used to launder drug money and for the commerce of narcotics; therefore, some level of contraband will always exist (estimated 10 to 20% of total gold contraband). Additionally, guarding borders in a region the size of the Amazon and with its lack of infrastructure is a difficult and costly task. As gold is smuggled out, it may also be illegally brought in as suggested in Ourinvest (1990) 22 Curiously, for example, in 1988 Uruguay is said to have exported significant amounts of gold (up to 15 tons) without having a single mine operating.

23 The report indicates for instance that due to high domestic prices in the middle of 1989, as much as five tons of gold may have entered the state of Rondonia illegally that year.
1.3 joint trade-fiscal reform: a case study

13 capita income and the share of agriculture have the expected signs in selected regressions.

There are caveats to this analysis. In addition to the methodological complexity in asserting a precise relationship between lost trade taxes and domestic taxes, all indirect effects through which control variables like GDP or openness may generate tax revenue over the long run are not analyzed. Indeed, this section of the paper should not be seen as a definitive analysis of the impact of trade liberalization on revenue, but rather as shedding light on what has happened to the share of domestic taxes in GDP across an imperfect sample of poor countries when – for whatever reason – import duties change relative to GDP.

Furthermore, to accurately assess and forecast the likely impact of reforms, there is greater need for nuanced country-specific case studies. The case for the use of in-depth country-specific case studies to understand policy regimes is best articulated by Bhagwati & Srinivasan (1999). They find several problems with crosscountry regressions as a method of policy evaluation. Even if the theoretical, data and methodological weaknesses inherent in most cross-country regressions were ignored, the cross-country results, after all, only indicate _average_ effects. In view of these shortcomings, I focus next on a detailed country case, of Nepal, where tariffs still constitute more than one-fifth of total tax revenue, and the vast majority of its 30 million people are employed in the largely untaxed agricultural and informal sectors.

# 1.3 joint trade-fiscal reform: a case study

My contribution in this section is to simulate the revenue consequences of joint trade-fiscal reforms with actual data on import, tariffs, excise duty, value-added tax and para-tariffs from Nepal. I also assess how these reforms change the price and production of domestic manufactures. Because it is often the perceived loss of
et al., 2011; Krueger, 1999), as well as large-scale expansions in childcare that also covered infants (Baker et al., 2008; Baker et al., 2015; Bernal, 2019; Bernal and Ramirez, 2019).<sup>4</sup> For our purposes, experimental (randomized controlled trials or RCTs) or quasiexperimental designs had to isolate the causal impact of preprimary programs or policies on children’s outcomes. Children’s outcomes had to include either a measure of school participation or progression, or a measure of children’s skills or development. This measurement could take place either when children were still in preprimary education or after they had progressed to higher levels of education. We also extracted outcomes observed when the beneficiaries of preprimary programs were adults, although as we will discuss in later sections, the number of outcomes and studies reporting wellbeing in adulthood was too small to estimate average effects with precision.

Included studies also had to be published in peer-reviewed journal articles or in a formal working paper series, such as the working papers of the National Bureau of Economic Research or the Policy Research Working Paper series of the World Bank. We included technical reports only if they included a suggested formal institutional citation.<sup>5</sup>

# **3.1.2 Screening**

We then screened these studies to verify that they met our inclusion criteria, focusing on the credible estimation of causal impacts of preprimary interventions and the inclusion of outcomes that captured preprimary coverage and/or children’s skills or wellbeing.

Figure 3 describes the screening process. We first eliminated studies based solely on a review of their citations, abstracts, and introductions. Of the 270 studies identified in the initial search, we excluded 111 studies during this stage because (i) they were not published in peerreviewed journals or did not appear as part of a working paper series; (ii) they were not experimental or quasi-experimental studies; (iii) they assessed early childhood interventions that targeted children outside of the 3 to 6 year age range, (iv) they measured outcomes unrelated to

> 4 We did include a subset of effects from Baker et al., (2008) – specifically those restricted to 4-year-olds in the post-period survey – who were unlikely to have access to subsidized care before age 3 since the childcare expansion started with older children.

> 5 One implication of not including unpublished studies is the potential for publication bias—that is, a bias toward positive and significant results. On the other hand, Brodeur et al. (2020) find that randomized control trials exhibit less of this bias than methods like instrumental variables and differences-indifferences, and more than 70 percent of the studies we eventually included were randomized control trials.

12
_-_ **_Model of an IPP The Indiantown Cogeneration Project_** To illustrate the application of _INFRISK_ to a real-life project, we draw on the Indiantown Cogeneration Project in this section. This project provides an excellent test case due to the extensive amount of detailed public information that is available on the project's financing mix, regulatory environment, and projected operating results which are contained in the prospectus for the 1994 bond issues. This information is readily obtainable through the U.S. Securities and Exchange Comrnission and the project company. A detailed examination of the Indiantown project is also available from Finnerty (1996).

||**IndiantownCogenerationProject**|
|---|---|
|**C**<br>*|**haracteristics of the project**<br>|
||**LIai-**<br>**I.diantaon.Flolda,**USA|
|.|**Capacidt**<br>330**MW**|
|.|**P.,**<br>,P-rhas<br>Sa leof bothc p<br>odly<br>ndeneto<br>**AreemeMT**<br>**FfondtPoer**<br>**and**Liyh<br>**p(19962025)**|
||**g.**<br> <br> <br>**.**<br>**(2)**catiiable**mondhly enypayrgl**|
|.|FinandngTenns:<br>**(I)**SiO5nFi.tMoriageBon.ds,tentamnch.<br>**(2)**<br>**ih2iae**<br>**Taex e**<br>**7mpt**<br>**BondS**<br>**in8**<br>**t5ao**|

The Indiantown Cogeneration Project is a coal-fired facility with an electric generating capacity of 330 megawatts (MW) and a steam export capability of 175,000 pounds per hour. Construction of the facility, located in Martin County, Florida, began on October 21, 1992 and was completed in 1996. Construction of the project was financed with a $140 million equity contribution from the partners, $505 million in First Mortgage Bonds (1994), and $125 million in tax-exempt bonds (1994) arranged through the Martin County Industrial Development Authority.

The project company entered into a 30-year Power Purchase Agreement (PPA) with the Florida Power & Light Company (FPL), a utility under the regulatory authority of the Florida Public Service Commission. FPL's service area covers 35 counties in Florida with a population of 6 million or approximately half the population of Florida. The PPA features a two-tiered pricing arrangement consisting of: (i) a fixed capacity charge covering

# **Indiantown Cogeneration Project**

<!-- Start of picture text --> pcmpnY:<br>P-o ePu.e.unAgrmt:<br>FRndnPomedtUgog idie.tD C<br>of .M.)<br>R -<br><!-- End of picture text --> fixed operational costs, and other financial commitments; and (ii) a variable energy charge covering costs of fuel and variable operations and maintenance expenses. Additionally, the project company contracted to provide its cogenerated steam to the Caulkins Indiantown Citrus Company for a period of 15 years.

The following section presents a simplified financial model of such an IPP. The purpose is to highlight the implications for project viability of the credit risk of the utility off-taker. The model focuses on features such as pricing and long-term contracting, which are common in IPPs.<sup>8</sup> 8 See Dailami (1999) for a more detailed discussion.

8
**Figure 6:** Average years of schooling in MENA among women and men 15-24 years old.

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>1940 1960 1980 2000 2020<br>Women Men<br>Average years of schooling<br><!-- End of picture text --> Source: Own calculations using the Barro and Lee (2013) dataset. We use population weights to obtain the regional average.

**Figure 7:** Gender Employment Gaps by Skill Level

# Employment-to-Population Ratio (ER) by Skill and Gender

<!-- Start of picture text --> 0.96 0.97 0.96<br>0.91<br>0.80<br>0.75<br>0.73<br>0.66 0.64 0.68 0.66<br>0.62 0.60 0.62<br>0.56 0.58 0.58<br>0.53 0.53<br>0.48<br>0.45<br>0.43<br>0.39<br>0.32 0.29 0.33<br>0.19<br>0.14<br>0.10 0.11<br>0.06 0.04<br>S U S U S U S U S U S U S U S U<br>ARE DZA EGY IRN PSE QAT TUN YEM<br>Female Male<br>Note: U=Unskilled (not college educated) and S=Skilled (college educated).<br>Plots the employment-to-population ratio for four groups, skilled-women, skilled-men, unskilled-women,<br>unskilled men (all of working age). For example ER(s,i)=skilled employed(i)/skilled pop(i); where i=M,F<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

**Model Description.** We replace the standard Cobb-Douglas of Equation 2 with a constant elasticity of substitution production function, where _S_ and _U_ denote the number of skilled and unskilled workers, respectively, with _L_ = _S_ + _U_ :

13
countries in 1995 and 140 in 2006. Descriptive statistics for our sample are shown for our indices in Table 1 .

||<br>Descrip<br>|Table 1<br>tive sta<br>|tistics<br>|||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. dev.|Min|Max|
|_k_<br>_n_|54030|144|53|12|253|
|All exporters<br>_OECD_<br>_k_<sup>_n_</sup>|54030|57|35|1|223|
|Herfindahl|54030|0.19|0.13|0.03|1|
|Theil|54030|2.68|0.48|0.86|4.87|
|Extra-OECD expo<br>_OECD_<br>_k_<br>_n_|rts only<br>53769|35|31|1|194|
|Herfindahl|53769|0.35|0.22|0.03|1|
|Theil|53769|3.05|0.60|0.13|5.30|

Note: All variables are defined at the product (SITC4) level. That is, the “number of countries with nonzero exports 2 consecutive years in the sample period” means the number of countries that exported a given product 2 consecutive years to somewhere in the world (i.e. the number of potential exporters of that product).

# **3.2   Intensive and extensive margins: Prima-facie evidence**

Figure 3 shows the evolution of simple averages over all products of our two concentration indices (Herfindahl and Theil ) expressed as indices relative to the sample’s initial year. That is, for Herfindahl, Figure 3 shows _H t_  100 _H t_ / _H_ 0 (18) where _H t_   _k H kt nt_ is the simple average for year _t_ of the Herfindahl indices calculated for all goods _k_ imported at _t_ . The calculation is the same for the Theil index.

Panel a) shows concentration indices calculated using all OECD imports (i.e. imports from all partners, including intra-OECD ones). A strong diversification trend is shown by both indices until 1999 (Herfindahl) and 2002 (Theil), after which both rise until 2006, the sample’s last year (by 8.6% for Herfindahl and 1.5% for Theil). Panel b) shows concentration indices calculated using only extra-OECD partners (i.e. developing countries). Both Herfindahl and Theil indices decrease until 1990 (modestly for Theil, which goes down by about 10% 17
eeee ee 0 [no] [woaramnoos ws BOSeer enna] a OR **O** a A a CR) OO OO OD Naa a aPB a OR a Ec a a OB | a UO [evaresacom © sermnere wavremmr [sent] enna] asia UP) OS **a** ES OO aesREE]SPORTING AND ATHLETICmae GOODS a af af

# §.3.3 Air Pollution Indicators

The major air pollution intensities compiled in this paper can be grouped into 5 distinctly different categories. The first group, consisting of S02, NO2, CO and total Particulates, 52
of higher salary. The result is still present once we add a rich set of worker controls. Note that a positive effect on salary is not surprising, as in addition to rent sharing motivations, any compensating differentials from the higher exposure to pollution should also be reflected into higher wages.

Finally, in column 7 we study whether the productivity benefits from being located on a large road, hence in a high pollution area, come at the expenses of higher land prices. Note that it is not obvious that this would be the case as there could be negative effects of pollution on land prices if pollution is perceived as a dis-amenity. To shed light on this, we exploit our survey questions about the rental value and the size of the business premises.<sup>36</sup> The result in column 7 shows a precisely estimated and large positive coefficient. Prime locations, that give access to customers, are more expensive. It is relevant to notice that, despite the higher rental cost, the overall effect on profits (shown in columns 3 and 4) is positive. This result is expected, given the magnitude of the effect on revenues and rents and the fact that rents are only a small share of the overall costs.

Finally, it is worthwhile to highlight that all the results just discussed are estimated exploiting variation across grid cells _within_ sub-county. Recall that a grid cell is a square of 500 _m ×_ 500 _m_ , and that a typical sub-county only includes approximately 72 grid cells. Therefore, our results are capturing the benefits from location choice within cities and neighborhoods rather than between them.

Taken together, the results in Table 3 confirm that there are direct and tangible benefits to firms from being located on busy roads with high traffic (and high pollution). In Table A2, we show that we reach similar conclusions if we replace Median Road in the grid cell, with average residual pollution in the cell. This confirms that there are tangible benefits of being located in more polluted areas.<sup>37</sup>

**Road traffic provides access to customers.** Our final step is to provide support for interpreting the positive effect of being located in polluted areas as being generated by better access to customers, hence higher demand. To shed light on this, we leverage our firm survey, where we collected detailed information on how firms access customers. Panel A of Table 4 shows that marketing activities are extremely limited: only 7% of managers spend any money on marketing, and when asked about strategies they adopt to communicate the quality of their products to customers, about 60% say that they just talk to the customers directly, and 22% report engaging in no strategies at all to market their products. In addition, sales are

> 36This information is available for those firm owners who rent the business premises (rather than owning or using them free of charge), which is about 2/3 of the sample.

> 37The number of observations is lower in Table A2 than Table 3 because, as described in Section 4.2, information on pollution is available in 32 of our 52 sampled sub-counties, while road size is available in all sub-counties.

23
distance to the pick-up point now averaging 250 meters. Richer horticulturists have their own pick-ups or even trucks. Those with smaller orchards usually band together in groups to make up a truckload for long-haul trips. In an interesting development, the villagers have started to diversify into pears, kiwis and cherries, all more delicate fruits requiring good packing and fair roads if spoilage is to be kept low. The road has given this development a strong impulse, and without the need to grub up apple trees to make way for the new stands, some spare land being available.

Applying (1) with the maintenance costs _m_ = 0 _._ 0095 _, m_<sup>_′_</sup> = 0 _._ 345 and salvage parameter _κ_ = 0 _._ 25, we obtain _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 055) = Rs 34.32 million and _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10) = Rs 30.71 million. Turning to the benefits so generated, an average household saves about Rs 6500 (= 500 _×_ (20 _−_ 7)) a year in transport costs for the marketing of its main crop. Summing over the 315 households who own orchards yields an aggregate of Rs 2.05 million a year, whose present value (allowing for the period of construction) at _ρ_ = 0 _._ 055 is Rs 29.38 million. This is certainly smaller than the lower bound on _M_<sup>_c_</sup> , first, because other goods besides apples are traded and, second, because diversification into other crops is occurring. Hence, even if one restricts the benefits to those in the commercial domain, the road is almost surely socially profitable at the discount rate of 5.5 percent.

This finding prompts the question, how big must the benefits not included in the above calculation be if (bare) profitability is to be maintained at the stiffer test rate of 10 per cent? Since _B_ 1<sup>+(</sup><sup>_ρ_= 0</sup><sup>_._10</sup><sup>_,_0) = 0</sup><sup>_._10 (1</sup><sup>_−_1</sup><sup>_._10</sup><sup>_−_29)</sup><sup>_−_1</sup><sup>_·_30</sup><sup>_._71 = 3</sup><sup>_._28million,the</sup> answer is, at least Rs 1.23 (= 3 _._ 28 _−_ 2 _._ 05) million annually, or just over one half of the savings in transporting the current main crop. This sum is not beyond the realms of the possible; but in the light of the modest volume of trade in other commodities and the fact that the villagers already enjoyed good access to education and health facilities before the road was constructed, it seems rather doubtful that help from this quarter would ensure profitability at the test rate of 10 percent. No allowance is made, however, for the possibility that the value of the total benefits will grow over the life of the road, a possibility taken up in Section 5.

To illustrate the results for the alternative, in which changes in leisure and the opportunity cost of labor are ignored, I draw once more on Bell (2009a), this time in connection with Orissa’s impoverished uplands. The following calculations are a little rough, but arguably convey a fair idea of what more refined methods and data might well yield. This field-trip covered six PMGSY roads, all recently constructed and typically costing Rs 20 - 30 million. The populations so served – some directly, others 13