rafmacalaba/datause-encoder-data huggingface dataset:
|Vocational TrainingProject||
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|El Dikheila Steel Project||
|El Dikheila Steel Project||
|El Dikheila Steel Project||
|El Dikheila Steel Project||
|Road Maintenance Project||
|Road Maintenance Project||
|Road Maintenance Project||
|Road Maintenance Project||
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|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Construction IndustryProject|M. Nabil Shehadeh|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Export Industries Development Project|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Small and Medium Scale IndustryProject|Yermal T. Shetty|
|Water Supplyand Sewerage Engineeringin Dagaliya Damietta and|B|
|Water Supplyand Sewerage Engineeringin Dagaliya Damietta and|B|
|Water Supplyand Sewerage Engineeringin Dagaliya Damietta and|B|
- **Davis, Lucas W.** 2008. “The Effect of Driving Restrictions on Air Quality in Mexico City.” _Journal of Political Economy_ , 116(1): 38–81.

- **Duflo, Esther, Michael Greenstone, Rohini Pande, and Nicholas Ryan.** 2013. “Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India.” _The Quarterly Journal of Economics_ , 128(4): 1499–1545.

- **Dunn, Daniel C, Sara M Maxwell, Andre M Boustany, and Patrick N Halpin.** 2016. “Dynamic ocean management increases the efficiency and efficacy of fisheries management.” _Proceedings of the National Academy of Sciences_ , 113(3): 668–673.

- **Englander, Gabriel.** 2019. “Property rights and the protection of global marine resources.” _Nature Sustainability_ , 2(10): 981–987.

- **Englander, Gabriel.** 2022. “englander/replication ~~c~~ losures: Replication files for ‘Information and Spillovers from Targeting Policy in Peru’s Anchoveta Fishery’.” v1.0.4, Zenodo, `https://doi.org/10.5281/zenodo.7048152` .

- **Erdem, T ̈ulin, Susumu Imai, and Michael P Keane.** 2003. “Brand and Quantity Choice Dynamics Under Price Uncertainty.” _Quantitative Marketing and Economics_ , 1(1): 5–64.

- **FAO.** 2018. “The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals.” Food and Agricultural Organization of the United Nations (FAO).

- **Fr ́eon, Pierre, Juan Carlos Sueiro, Federico Iriarte, Oscar F Miro Evar, Yuri Landa, Jean-Fran ̧cois Mittaine, and Marilu Bouchon.** 2014. “Harvesting for food versus feed: a review of Peruvian fisheries in a global context.” _Reviews in Fish Biology and Fisheries_ , 24(1): 381–398.

- **Getu, Addis, Kidanie Misganaw, and Meseret Bazezew.** 2015. “Post-harvesting and Major Related Problems of Fish Production.” _Fisheries and Aquaculture Journal_ , 6(4).

- **Gibson, Matthew, and Maria Carnovale.** 2015. “The effects of road pricing on driver behavior and air pollution.” _Journal of Urban Economics_ , 89: 62–73.

- **Gillingham, Kenneth, David Rapson, and Gernot Wagner.** 2016. “The Rebound Effect and Energy Efficiency Policy.” _Review of Environmental Economics and Policy_ , 10(1): 68–88.

- **Gordon, H. Scott.** 1954. “The Economic Theory of a Common-Property Resource: The Fishery.” _Journal of Political Economy_ , 62(2): 124–142.

- **Grainger, Corbett A, and Dominic P Parker.** 2013. “The Political Economy of Fishery Reform.” _Annual Review of Resource Economics_ , 5(1): 369–386.

- **Gray, Wayne B, and Jay P Shimshack.** 2011. “The Effectiveness of Environmental Monitoring and Enforcement: A Review of the Empirical Evidence.” _Review of Environmental Economics and Policy_ , 5(1): 3–24.

48
DMS. One explanation could be that public DMS are not necessarily payment systems and do not need to generate direct instructions to the central bank or lenders.

Regarding report production (See Figure 6), users seems to be especially satisfied with the ability of their systems to generate reports across all solutions. The survey only queried the ability of the DMS to produce consolidated and individual reports without dwelling deeper into any specific reports. Security features, audit trails and getting technical support captured in other (back office functions) also received high marks across all solutions. On the other hand, connection with the FMIS scored low across all systems. In some areas, results were mixed. Public DMS scored especially low when it came to the connectivity with the treasury system.

**Figure 6: Functions captured in the Debt Management System**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>In‐house<br>0.6<br>Public DMS<br>0.5<br>Commercial<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Borrowing Risk Analysis Debt Recording, Reporting Other Functions<br>Operations Operations Repayment and<br>(Front‐Office) (Middle‐Office) Reporting<br>Operations<br>(Back‐Office)<br><!-- End of picture text -->

Source: World Bank Survey on Debt Management Systems However, the inability of the system to handle some of these functions is most likely not essential to the operations of these institutions **.** Figure 7 represent the satisfaction of respondents with their overall DMS system, technical support and hardware. The users seem to be generally satisfied with the current DMS, but the graphs also show that public DMS users indicated the highest level of satisfaction among all despite the relatively lower scores regarding the results on the functionality of the systems.<sup>16</sup> This observation leads us to conclude that the current capabilities of the public systems are currently enough for the needs of the surveyed countries. These users are also more satisfied with the technical support and hardware they receive for their DMS. On the other hand, hardware results fare better than the technical support potentially indicating that the latter is not as up to par with the system hardware.

Typically, more people are needed to perform back office functions. On average, more people are required to perform back office compared to middle and front office operations. One exception are the higher income countries where the number of back and middle office staff is approximately the same. The reason for this could be because higher income countries may afford to have more staff on market risk and scenario projections and spend more time on analytics rather than back office

> 16 For the satisfaction part of the survey, there were five possible answers ranging from very satisfied to very unsatisfied. For analysis purposes, scores were assigned to each possible answer where very satisfied equaled 1, satisfied equaled 0.75, neutral (indifferent) equaled 0.5, unsatisfied 0.25 and very unsatisfied a score of zero.

25
winning) in different years differ significantly. Our sample sizes are too small to examine yearto-year variation in household characteristics, but, as a simple check, we compare the characteristics of ballot winner households with members abroad for less than the median duration to those abroad for more than the median duration. The results suggest that there is little selection into when individuals entered the SQ ballot over the years considered here.

# **3.5. Verifying Randomization**

We first test whether the lottery correctly randomises households into a treatment and a control group by examining whether the households containing ballot losers are statistically different from the households containing ballot winners (both the emigrant-sending households and the non-compliers). As discussed above, attention is restricted to households where some members would have stayed according to the age and relationship rules on which Secondary Applicants may accompany the Principal Applicant. Table 1 compares the ex-ante pre-migration characteristics available from the survey for ballot winners to those who were unsuccessful, restricting the sample to individuals who had applied to the SQ in the previous two years. Unfortunately, we did not collect information on whether each household member at the time of the survey resided in the household when the SQ winners emigrated in the past. Thus, differences between ballot winners and losers from further in the past may just reflect changes in household composition that occurred after the SQ migrants emigrated and were potentially caused by this subsequent change in household composition.

Of the nine characteristics we examine in this table, the only significant difference at the 15% level is found for the mean age of stayer adults, with stayer adults in successful households four years older than those in unsuccessful households. Given the number of variables we are testing, this is consistent with a joint test indicating that the characteristics of successful and unsuccessful households are globally insignificantly different from each other.

# **4. Experimental Estimates of the Impacts on Income and Poverty**

In this section, we present experimental estimates of the impact on income and poverty of sending household members to New Zealand under the Samoan Quota. As discussed above, here the age and relationship rules governing which Secondary Applicants can move with the 15
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# **3.3 The Trade, Production, and Protection Database and construction of explanatory variables**

The construction of the explanatory variables needed for the econometric investigation requires disaggregated industry-level data. We obtain this data from the World Bank _Trade, Production, and Protection Database_ (TPP) as described in Nicita and Olarreaga (2006). The TPP has extensive data for many production-related variables for 28 3-digit ISIC manufacturing industries across a number of countries for 1976-2004. Nevertheless, we set up our model as a cross section because the time-series dimension of the data is incomplete for the developing countries, time period, and variables required for our particular investigation.<sup>16</sup>

# **3.3.1  The WTO Antidumping Agreement evidentiary criteria**

As we described in section 2.1, the WTO rules for antidumping require that the petitioning industry provide government policymakers with technical evidence that it has been injured by dumped imports. In this section we construct a number of variables to proxy for economic conditions that are consistent with the required evidence.

Our first variables are designed to capture the likelihood that the industry is facing dumping, or prices of competing exports that are below “normal value.” Our first measure is the industry ratio of gross fixed capital formation to value added in 1994. We expect that industries with a greater ratio of capital expenditure and thus higher fixed costs are more likely, ceteris paribus, to face cyclical dumping than industries with less expenditure on capital. As a second measure, we compute the average percent change in the price of competing imports in the industry over the post-1995 period.<sup>17</sup> We expect that industries 16 We do not attempt to exploit the time-series dimension of the production data, as this would force us to lose too many observations. For Argentina and Brazil we also use 1993 data to construct the industry characteristics variables as the 1994 data is not available for these countries. Finally, the need for production data requires us to focus on manufacturing industries only; thus we lose a handful of observations relating to these countries’ use of antidumping against agricultural imports.

17 First note that for the production-level variables required from the TPP database, most of the data ends in 2000 if not a year or two earlier, which forces us to use data from 1995-2000 only to construct the changing market conditions variables. Second, with respect to the changing import price variable, we rely on the mirrored import unit values data in the TPP database to construct this measure. These are prices constructed from statistics reported by exporters rather than importers. We expect that estimated coefficients for this variable may be biased toward zero. This is because some antidumping investigations result in price undertakings, which provide an inducement for exporters to raise their prices to avoid facing duties.

11
**Figure 5: Prices and Exports of the Nearest Neighbor.** Green markers indicate economies in continental Africa. The neighboring country is identified using the minimum of the bilateral distances between country centroids reported by Head and Mayer (2014). Data from 2011 and 2017 are shown.

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of electricity (both generated and purchased), petrol, diesel, oil, and coal consumed. The value of self-generated electricity is calculated from the average price that a firm pays to purchase electricity. Material cost is the total aggregate purchase value for domestic and foreign intermediate inputs. We define the price of capital as the ratio of total rent to the net fixed capital. The price of labor is calculated by dividing total wage by the number of employees. Energy and material prices are defined as weighted expenditure per unit output. Output value is weighted by factor cost shares.

Data quality has been examined by cross referencing with standard growth accounting principles as well as by reviewing comments from other researchers who have used these data. The geographic attributes allow us to identify each firm at the district level.

# _Data Imputation:_

The 1999 ASI data used for estimation have a significant number of incomplete cases. Many firms did not report their capital, output, depreciation, and other related input price information. Even when there are reported values, some of them are not consistent (e.g., 0 capital when capital depreciation is reported positive). Missing or inconsistent data can be a serious problem when such data points are not completely random.

To take into account the limitations arising from the less than perfect ASI data, we first adopted the following set of rules to clean the data, and then imputed missing values in the cleaned data using SAS MI procedure. First, cases that are missing too much vital information (e.g., input, output, capital, and employment) are deleted (only 78 cases were deleted from this step). Second, when the value for plant and machinery depreciation is positive and the size of employment is greater than 10, but the closing value of capital is reported 0, capital is converted 20
cases, where the economy is more industrialized, wages and employment are the predominant sources of income, which can be affected by commodity prices. Since prices can affect both consumption and income, the simple framework opens the door to a discussion of how household welfare is affected by changes in commodity prices, both in the short- and long-run. The literature provides numerous empirical findings that can bring together various pieces of the puzzle illustrated by the framework.

First, we review the literature on the first-order impacts of price changes. These are the impacts that would take place if households adjust neither the quantities of commodities that they consume nor change the sources of income they receive in response to the price change. The article derives the commonplace net-consumer-versus-net-producer result, which establishes that a price increase hurts net-consumers and benefits net-producers.

Second, we discuss spillovers from commodity prices to other sectors. This refers to, for instance, whether households adjust all consumption choices in response to higher food prices, or whether only food expenditures are affected. Also, we report evidence on spillovers in employment and wages; that is, changes in food prices can affect wages and employment not only in the food sectors (primary, agro-manufactures) but also in non-food sectors.

Third, the paper reviews how prices are transmitted to the local economy. Because commodities are traded in international markets, most of the shocks associated with commodity prices are global in nature. Consequently, a relevant question is the extent to which changes in international prices pass-through to domestic markets.

Fourth, the paper explores available evidence on consumption and production responses. Here, we review how consumers adjust the quantity and quality of the consumption basket, as well as other behavioral responses associated with consumption. We also explore some of the patterns of adjustment in production, particularly supply responses in agriculture.

It is important to mention at the outset that there are at least five related literature surveys. These are Winters, McCulloch and McKay (2004) and Goldberg and Pavcnik (2004), which cover trade liberalization and poverty; Goldberg and Pavcnik (2007) and Harrison, McLaren and McMillan (2011), which cover trade and the distribution of income; and Lederman (2013), which covers trade and inclusive growth. Our review complements 2
costs have a significant effect on the geographic concentration of economic activity, no matter whether we consider plants, employment, or sales to measure that concentration.

Table 6: Estimation results for specification (5), robustness checks.

|Variables|Dependent<br>**(Model 8)**|variable is the<br>**(Model 9)**|cdfat50km<br>**(Model 10)**|
|---|---|---|---|
|Ad valorem trucking costs (residual)|-0.245<sup>_a_</sup><br>(0.082)|-0.198<sup>_b_</sup><br>(0.083)|-0.192<sup>_b_</sup><br>(0.086)|
|Asian share of imports|-1.119<sup>_a_</sup>|-1.035<sup>_a_</sup>|-1.041<sup>_a_</sup>|
||(0.387)|(0.398)|(0.401)|
|Input distance|-0.337<sup>_a_</sup>|-0.238<sup>_a_</sup>|-0.232<sup>_a_</sup>|
||(0.056)|(0.055)|(0.056)|
|Output distance|-0.324<sup>_a_</sup>|-0.292<sup>_a_</sup>|-0.298<sup>_a_</sup>|
||(0.041)|(0.039)|(0.038)|
|Average minimum distance|-0.264<sup>_a_</sup>|-0.290<sup>_a_</sup>|-0.272<sup>_a_</sup>|
||(0.040)|(0.037)|(0.037)|
|Other trade shares included|Yes|Yes|Yes|
|Industrycontrols included|Yes|Yes|Yes|
|_Additional controls:_||||
|Distance to major container ports|-0.235<sup>_b_</sup>||-0.140|
||(0.098)||(0.104)|
|Eastern share of plants||0.758<sup>_a_</sup>|0.715<sup>_a_</sup>|
|||(0.159)|(0.159)|
|Number ofnaicsindustries|257|257|257|
|Number of years|17|17|17|
|Year dummies|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|
|Observations (naics_×_ years)|4,369|4,369|4,369|
|_R_<sup>2</sup>|0.525|0.551|0.553|

_Notes:_ The dependent variable is the unweighted (count based) DurantonOverman _K_ -density cdf at 50 kilometers distance. _a_ , _b_ and _c_ denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. All industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

Second, one may worry that import entry points are geographically localized, so that increasing imports could lead to the geographic concentration of industries around those entry points. This may, in turn, bias the transport cost coefficient. To address this potential concern, we include as a control the average minimum distance of plants in each industry from one of the five major Canadian container ports.<sup>23</sup> As can be seen from **Model 8** in Table 6, although 23Those ports are Halifax (NS), Saint John (NB), Montreal (QC), Vancouver (BC), and Prince Rupert (BC).

22
|2014-12|Jiangsu Changjiang|STATS<br>ChipPAC|Singapore|Technology|1660|
|---|---|---|---|---|---|

Source: China Global Investment Tracker.

Table 2: China’s top 10 construction projects in BRI countries since 2014

|Year-<br>Month|Constructor|Transaction<br>Partner|Country|Sector|Subsector|Value<br>(million<br>USD)|
|---|---|---|---|---|---|---|
|2017-04|China Railway<br>Engineering||Indonesia|Transport|Rail|3190|
|2016-08|China Railway<br>Engineering||Bangladesh|Transport|Rail|3140|
|2015-12|State Construction<br>Engineering||Pakistan|Transport|Autos|2890|
|2015-08|China Railway<br>Construction and<br>China Railway<br>Engineering||Thailand|Transport|Rail|2840|
|2016-01|Shanghai Electric||Egypt, Arab<br>Rep.|Energy|Coal|2640|
|2017-04|CNPC||Russian<br>Federation|Energy|Gas|2520|
|2016-01|Three Gorges||Pakistan|Energy|Hydro|2400|
|2016-06|Harbin Electric|DEWA|United Arab<br>Emirates|Energy|Coal|2150|
|2016-11|China Railway<br>Contruction, China<br>Railway Engineering,<br>and China<br>Communications<br>Construction||Malaysia|Transport|Rail|2120|
|2016-03|Sinomach|Electricite<br>du Laos|Lao PDR|Energy||2100|

Source: China Global Investment Tracker.

# 3.2 Transportation Cost

To measure transportation costs, we construct detailed transportation cost data by the mode of transportation. First, we measure air distance using the population-weighted great-circle distance from the CEPII GeoDist data set. Second, we measure sea distance using data from Feyrer (2009).<sup>10</sup> Lastly, we construct road distance and driving time data using Google Maps Distance Matrix API.<sup>11</sup>

> 10 We thank Jim Feyrer for kindly sharing the data.

> 11 In a related project, Baniya et al. (2018) examine the role of BRI in international trade by estimating the effects of driving time reductions. We thank Michele Ruta for the valuable suggestion of looking at driving time as a measure of transportation cost.

16