rafmacalaba/data-use-mentions-extended huggingface dataset:
cost to the provides of transport services.<sup>3</sup> Collection of high-quality survey data requires well-established sampling and stratification procedures on the universe of shippers or carriers in a country. Conducting it in a comparable manner over regular intervals would help inform changes over time. Such capabilities typically fall within the purview of national statistical agencies. Examples from developed countries showcase the variation in the information that is available depending on survey design. In Canada, the Trucking Commodity Origin and Destination Survey collects information on for-hire trucking industry since 1994, targeting trucking companies with at least one establishment and a minimum annual revenue of about 1 million USD. It excludes foreign-based trucking establishments operating in Canada and private fleets of non-trucking establishments. While it contains data on the revenue of carriers on a shipment basis (including information about the commodity being shipped, weight, distance and destination), it does not report the value of goods shipped.<sup>4</sup> In contrast, the Commodity Flow Survey conducted by the U.S. Census Bureau since 1993 reports the commodity, mode (truck, rail or other modes), weight and travel distance of shipments together with the value of the goods, but does not report freight charges. In Europe, Eurostat’s Road Freight Transport Survey is conducted in EU member countries and Norway since 2012. For around 3 million shipments, it contains information on origin and destination regions, industry of the shipper, the weight of the shipment and the distance covered. While separate vehicle-related, journey-related and goods-related variables inform users about the fleet, itineraries and commodities being shipped, there is no data about freight charges or the value of the goods. For rail shipments, the Carload Waybill Sample is a stratified sample of carload waybills for all U.S. rail traffic collected by the Surface Transportation Board. It includes information about the commodity, origin and destination, weight and freight charges from the sampled shipments. Evidently, each lacking some critical variables, whether any of these
and the National Nigerian Petroleum Company (NNPC)—and their inability to effectively share data and information.

- The use of suitable and secure computer-based information technology systems to provide consistent data to various users and to analyze this data is limited or often nonexistent.

- The NNPC monthly returns on crude oil sales do not indicate the volume of crude, the applicable price, the exchange rate used, and the method used for computing the proceeds.

- In the determination of the profits tax liability, all the joint venture companies fail to apply the 2000 MOU regarding the use of realizable price. Misapplication of the MOU provisions revealed significant differences in tax liability leading to additional assessments.

- Some companies claimed capital allowances on construction in progress. The argument was that paragraph 15 of the second schedule to the Petroleum Profits Tax Act allows for the claiming of capital allowances from the moment the expenditure is incurred, rather than when the asset is first put to use.

- The NEITI audit indicated potentially significant petroleum profits tax under assessments in relation to operating expenses, intangible drilling and development costs, investment tax allowance, and capital allowance on non-associated gas. In several areas, the legislation is unclear as to how the liabilities and allowances should be computed.

# **Spending Resource Wealth**

88. Nigeria‘s fiscal system, in particular the arrangements for sharing federally collected revenue among the three tiers of government, has undergone several changes since independence (for details, see World Bank 2007). Fiscal federalism was revived in 1999 at the start of the Obasanjo administration. In principle, all oil revenues collected at the federal level are paid into the CRF and then shared among the three tiers of government. Under the budget reform program of 2003, however, the government began using an oil-price-based fiscal rule. A reference price for oil is set annually and oil revenues accrue to the CRF in line with this oil price. Revenue derived from oil prices that are higher than the reference price is put into the excess crude account (
due to weak monitoring systems discussed further in the section. Only about a quarter of regulators with the authority to take action against violations of consumer protection provisions provided statistics on the number of times they took these actions. Among these, only half issued more than 5 warnings and about a half imposed fines in 7 or more instances. Only Italy, Japan, Lithuania, Nicaragua, Slovenia and the United Kingdom report more than one case of issuing a public notice of violation. “Naming and shaming” is potentially the most powerful tool of a regulator. A recent study by the Office of Fair Trading (2009) of the U.K., assessing consumer credit interventions, suggests that the more public the actions against violating institutions, the higher the deterrent effect will be. As many financial consumer protection regulators reform their approach, it may be useful to evaluate the scope of enforcement powers that the regulators have under the legislation.

Monitoring compliance with financial consumer protection regulations is an essential element of effective implementation of the legislation. The survey responses indicate that no more than half of financial regulators monitor compliance through proactive means such as monitoring 12
**Figure 3.6 Share of Resource Exports in Merchandise Exports and Share of Research and Development Expenditure in GDP in 2015**

<!-- Start of picture text --> 4.5<br>ISR KOR<br>4.0<br>3.5<br>JPN SWE<br>3.0 AUT DNK<br>DEU FINUSA<br>2.5 SVN BEL<br>FRA ISL<br>2.0 CHN CZE NOR<br>GBR<br>1.5 EST<br>SVK LUX ESP MYS LTU RUS<br>1.0 POL BGR GRC<br>MEX HRV<br>EGY<br>0.5 MAC KHM PAK IND CYP OMN IRQ<br>SLV BIH KGZ<br>0.0<br>0 10 20 30 40 50 60 70 80 90 100<br>Source: World Bank, World Development Indicators.<br>Note: Share of resource exports in merchandise exports in horizontal axis; and share of research and<br>development expenditure in GDP in vertical axis.<br><!-- End of picture text -->

**Figure 3.7 Share of Resource Exports in Merchandise Exports and Patent Counts Per Capita in 2015**

<!-- Start of picture text --> 500<br>450<br>ISR USA<br>JPN<br>400<br>350 KOR<br>300<br>SWE<br>250 FIN<br>200 DEU<br>DNK<br>150 ISL<br>AUT<br>NOR<br>100 GBR BEL<br>HKG<br>50 CHN MEX LUXTUNFRA KGZ EGY CYP GRC OMN RUS IRQ<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Source: World Bank, World Development Indicators ; IP5 Offices; and World Bank staff
landholdings. The average eligible household owns 0.8 hectares corresponding to the 28<sup>th</sup> percentile while the average non-eligible household owns 1.3 hectares which corresponds to the 69<sup>th</sup> percentile of the distribution of landholdings.

Figure 3 reports the targeting by landholdings. In particular, it reports the percentage of beneficiaries in each landholding quintile from the villager survey, for the start of the program in 2008/09 as well as the year of the intervention 2010/11. At the start of the NAIVS program, households with more land were also more likely to become beneficiaries. Participation in NAIVS among farmers in the lowest landholding quintile was only 5 percent and it increased to 10 percent among farmers in the highest quintile. This finding is consistent with results from Zambia and Malawi (Jayne et al., 2016; Goyal and Nash, 2017). By the year of the intervention, however, the probability of participation was roughly constant at around 15 to 20 percent across landholding quintiles.

Table 2 complements Figure 3 as it also reports improvements in the targeting of NAIVS between the start of the program in 2008/09 and the year of the intervention 2010/11. Panel A of Table 2 reports data from all villages, Panel B from study villages in Arusha, and Panel C and Panel D from non-study villages in Kilimanjaro and Morogoro, respectively. We focus on three of the eligibility criteria that can proxy for liquidity constraints, namely, whether the beneficiary had used hybrid seeds or chemical fertilizer prior to the start of the NAIVS program and whether they cultivated less than 1 hectare. Recall that eligible individuals should not have used improved inputs prior to becoming a beneficiary since purchasing inputs at market prices would signal that they are not credit constrained and thus may not have the highest marginal returns. Based on these three eligibility criteria, Table 2 reports the percentage of beneficiaries that meet all three criteria. In all panels, the targeting of NAIVS improves over time
*|**0**|**0**|**0**|**0**|**0**|**0**|**0**|||

# **_Local governments_**

Public spending flows also need to be collected for local governments so as to get a full picture of overall public spending flows. But depending on the degree of decentralization and the level of central monitoring of local government expenditures, it might be necessary to provide information for a few—not all—local authorities.

47
10 commodities, and calibrated in line with a recent set of price and income elasticities from a cross-country study (Seale, Regmi and Bernstein 2003).<sup>7</sup> In the simulations that follow, we use the standard GTAP closure. This assumes that the levels of each region’s employment of each of the productive factors is fixed in aggregate, and that the regional balance of trade is determined by the relationship of regional investment and savings, where international capital mobility is determined by equalizing rates of return across regions.

To keep the sizes of the table of results reasonable, we aggregate the GTAP 6 database to 27 regions and 29 sectors, bearing in mind the need to provide some detail in the agri-food sectors. These regions and sectors are listed in Appendix Tables A1 and A2, respectively.

# **Effects of removing distortive goods and services policies globally**

The estimated effects of full global trade liberalization as of 2001 on each of the four sectors’ value added are summarized for the key developing country regions in Table 3 and are shown with more country detail for just agricultural value added in Table 4. Beginning with the top right-hand numbers of Table 3, these results suggest real farm incomes in developing countries are still harmed by the existing pattern of global trade distortions, and more so than any of the other goods sectors (non-

> 7 While one of the benefits of using a global general equilibrium framework is the modeling of all economic agents’ behaviors, and in this case also the depiction of some specifics of agricultural markets in the GTAP-AGR model, the results necessarily depend on the parameters chosen. A natural validation of the model is to see how well the model is able to replicate historical records. Support for the use of the GTAP-AGR model is provided by Valenzuela (2006) who shows that the model replicates reasonably well historical wheat price variability in world markets.
Where: _E0_ corresponds to the expenditure of the country corresponding to the omitted fixed effect (typically an importer fixed effect) in the empirical model, and the normalization of the corresponding price terms in the structural model.

For the above approach to work in a consistent way, it is important for the dependent variable, bilateral trade, to include all directions of trade. That is, it also has to include self-trade, production that is both produced and consumed in a given country, so that the estimated fixed effects in fact relate to the output and expenditure terms implied by theory. As a result, we use services trade data sourced from the OECD-WTO Trade in Value Added (TiVA) data set. To be clear, we do not use that data set’s estimates of value added components of trade, but instead simply take data on exports and production in gross shipments terms. The advantage of this source is that it contains harmonized trade and production data, so it is a simple matter to calculate self-trade: it is production less total world exports. Using the most recent version of the TiVA data set, we start with services trade and production data for 63 exporters and importers annually for the period 2005-2015.

In order to identify the impact of potential TFiS measures, it is important to control for the up-front policy restrictiveness in the sense of the set of measures that govern market access and national treatment in services markets. We use the OECD’s Services Trade Restrictiveness Index (STRI) for the importer to do this. Ideally, we would split out the sections of the STRI that deal with transparency and those that deal with market access in a pure sense; however, the two components of the index are strongly correlated, and it is not possible to obtain meaningful estimates when they are included separately. We therefore use the overall STRI. For the regressions using aggregate services trade data, we use PCA to produce a weighted average of STRIs across all sectors. We then also use selected sectoral STRIs where
|YES|
|R-squared|0.068|0.190|0.068|0.043|0.190|0.115|
|# of EAs|433|433|433|433|433|433|
|N|12937|13522|12937|12937|13522|13522|

_Notes:_ This table provides estimates of the impacts of logarithm of (the first two columns) or inverse hyperbolic sine (IHS) of (the last four columns) refugee intensity on IHS of total household consumption expenditure per capita (columns (1) and (3)), a hybrid hyperbolic sine and its inverse transformation of the wealth score (columns (2) and (5)), a binary measure of household consumption poverty status (column (4)), and a binary measure of household wealth poverty status (column (6)). Household sample weights from ESS2 applied. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

55
# **E Construction of the Road Quality Measure**

Each discretized road segment that links two neighboring locations in our model results from aggregating a subset of actual segments. To perform this aggregation, we first use the DNIT traffic data to construct a congestion index in direction _l ∈{a_ , _d}_ , _Q_<sup>_l_</sup> ( _s_ ), for each actual road segment _s_ as where the ranking _x_ ( _s_ ) ranges between A (low congestion) and F (high congestion) depending on the capacity of the road. Then, we define the congestion index in an actual segment _s_ as _Q_ ( _s_ ) = 0.5 _×_ [ _Q_<sup>_a_</sup> ( _s_ ) + _Q_<sup>_d_</sup> ( _s_ )]. The idea is that higher congestion of traffic increases effective distance.

Once we have _Q_ ( _s_ ), we create the following network weight, _W_<sup>�</sup> ( _s_ ): where _d_ ( _s_ ) is the length of the road segment; _χP_ and _χK_ and road user-cost mark-ups for gravel relative to paved roads and secondary roads relative to other national roads, respectively; _P_ ( _s_ ) is the road pavement status; _K_ ( _s_ ) is the road category (available only for federal roads). Following Fajgelbaum and Schaal (2020) and Combes et al. (2005), we set _χP_ = 1.35, _χK_ = 1.07, and _P_ ( _s_ ) = 2 for paved roads and zero otherwise.

Following the DNIT classification, we distinguish between the following categories for federal roads: double-lane paved road, single-lane paved road in the process of conversion to double-lane road, single-lane paved road, single-lane unpaved road in the process of conversion to paved road, unpaved gravel road, and unpaved dirt road. We assume that improving road coverage by each level reduces the cost of passing through this road by 10%
scores for all the subsectors and modes available in the new STPD (analyzed in the previous subsection) and the 2016 scores for the subset of sectors and modes for which data were collected in 2008-11 (i.e. those described in this subsection). In this part, we present the economies in the income groups to which they belonged in 2008, to show how these economies' regulatory frameworks have evolved in the past decade.<sup>18</sup> Looking at changes over time in the level of restrictiveness (Figure 7) reveals a secular and broad pattern of liberalization in services trade policies of the 55 economies covered in our analysis. The median STRI decreased by 4 points, from 49 in 2008-11 to 45 in 2016. The STRI ranged from 34 to 73 in 2008-11, and between 25 to 72 in 2016. It appears that the decrease of the STRI is greater for many high-income economies (mainly European Union members). However, some dynamic developing economies such as Bangladesh, India or China have also liberalized significantly in the past decade. Other lower- and middle-income economies that have liberalized their services trade

> 17 See in particular section 2 (Comparing policy information over time), for a detailed comparison and a full decomposition of changes in STRI scores relative to the World Bank 2008-11 STRI.

> 18 Groups as of 2008:

> <u>High income economies (H): AUS AUT BEL CAN CZE DEU DNK ESP FIN FRA GBR GRC HUN IRL ITA JPN KOR NLD</u> NZL OMN PRT SWE USA; Upper-middle income economies (UM): ARG BRA CHL COL CRI DOM KAZ LTU MEX MYS PAN PER POL RUS TUR URY ZAF; Lower-middle income economies (LM): CHN ECU EGY IDN IND LKA NGA PAK PHL THA TUN UKR; Low income economies (L): BGD KEN VNM.

16
mobility of refugees. These in turn affect key drivers of growth: public and private investment, and the labor force. We also make different assumptions for human capital growth and TFP growth across the three scenarios. Depending on the scenarios, our simulation results suggest that it would take between 10 and 20 years for Syria to reach its pre‐conflict GDP level and between 10 and 30 years to reach its GDP per capita level (both at 2010 constant prices). If there were to be an unsanctioned and misguided “forced” repatriation of refugees, this would result in significantly lower GDP per capita compared to the voluntary mobility case. Under voluntary return, labor would adjust gradually to capital reconstruction, thus keeping labor productivity from falling.

# **2. Syria Pre‐Conflict Developments and Projections**

This section focuses on building a calibration using the LTGM‐PC for a no‐conflict scenario based on developments and projections prior to the conflict. Recent data indicate that average real GDP growth in Syria was around 4.7% over 2001‐2010 (Graph 1(a)). World Bank’s World Development Indicators (WDI)<sup>1</sup> data suggest an average growth of 4.9% over 2001‐2010, while IMF World Economic Outlook (WEO), points to 4.5%, over the same period.<sup>2</sup> Just prior to the conflict, projections for near‐ term growth were still robust: an average of 5.6% over 2011‐2012 in the World Bank Global Economic Prospects (GEP), January 2011<sup>3</sup> ; and an average of 5.0% over 2011‐2016 in the IMF WEO, April 2011 edition<sup>4</sup> ‐ see Graph 1(b). In the following subsection we look at pre‐conflict trends and projections

> 1 WDI data for Syria are publicly available only up to 2007.
that includes the boundaries of Kebeles for all regions in Ethiopia except Somali Region (15,670 Kebeles).<sup>6</sup> For Somali Region, we rely on boundaries of administrative units one higher than Kebeles—Woredas (N=44). However, for simplicity, we refer to the units of the combined dataset as ”Kebeles” throughout the paper. Kebeles are small enough to capture granular changes in the road network; 15,714 Kebeles cover Ethiopia, and the average size of a Kebele is 70km<sup>2</sup> (the median size is 24km<sup>2</sup> ). Appendix S5 shows a map of Kebeles. When aggregating data to Kebeles, we take the average values of nighttime lights and the total number of urban and cropland pixels.

To test the sensitivity of results to the unit of analysis, when relevant, we also estimate models using 1x1km pixels—the original resolution of DMSP-OLS data (1.1 million 1x1km pixels cover Ethiopia). As GlobCover landcover data are captured at a 300m resolution, we aggregate cropland and urban to the 1x1km pixels by creating binary variables indicating whether any portion of the 1x1km pixel contains urban or cropland area. However, we use Kebeles as the primary unit of analysis because they represent a more

> 6The Kebele shapefile is available at `https://ethiopia.africageoportal.com/datasets/africageoportal::kebeles-level-4/ about`

10
appropriate trade weights. We work with 47 sectors based on the Global Trade Analysis Project (GTAP) categorization and excluding services. Since each of those sectors contains many HS2 or HS4 categories, the composition of trade flows within each GTAP sector differs across country pairs. Hence, we compute the “value of time” for each pair of countries and GTAP sector to reflect the actual composition of each sector within each origin‐destination flow. These country pair‐sector values of time can be further aggregated to quantify changes in trade costs by country.

The results combining our GIS network analysis and the ad‐valorem conversion suggest that implementing all BRI transport infrastructure projects will result in a large decrease in trade costs for many countries. For the world, the reduction of aggregate trade costs ranges between 1.1 percent in the lower bound scenario and 2.2 percent in the upper bound scenario. For the BRI economies, the change in trade costs will range between 1.5 and 2.8 percent, under the lower and upper bound scenario respectively. As for shipping times, the gains in trade costs exhibit significant heterogeneity across pairs of countries, with East Asia and Pacific as well as South Asia being the regions with the larges average reductions. Finally, trade costs have the sharpest drops along corridors, ranging between 2.4 percent for the China‐Mongolia‐ Russia Economic Corridor (CMREC) and 10.2 percent for the China‐Central Asia‐ West Asia Economic Corridor (CCWAEC).

The focus of this paper is the impact of BRI‐related transport infrastructure projects on shipment times and trade costs. But the Belt and Road Initiative includes efforts to improve the efficiency of customs, reduce border delays or to improve management of economic corridors. As an extension of our main database, we present two scenarios where those elements are explicitly taken into account. We find that the implementation of complementary policy reforms magnifies the impact on shipment times and trade costs, especially along the corridors. For instance, if
product to the same country the year before. As indicated in Table A-1 in the Appendix, the proportion is 69.8% when considering foreign exports of the same product to other countries and 88.6% when considering foreign exports of other products to the same country.

The geographic and sectoral distributions of the new trade linkages established by Chinese domestic firms over the period are described in Table 3. The export starts are quite diversified in terms of destinations. The US is the main destination for new trade linkages over the period, but it only represents 1.8% of overall export starts, followed by Hong Kong, South Korea, and Japan, with between 1.6% and 1.7% of all export starts. However, new transactions are more concentrated in terms of the province of origin, the most dynamic exporters being, not surprisingly, Guangdong (8.5%) and Zhejiang (7.5%). The export starts are also more concentrated from a sectoral viewpoint: “Nuclear reactors, machinery etc.” account for 10.5% of new transactions over the period in comparison with 6.6% for “Electrical machinery etc.” and 4.4% for “Articles of iron and steel”.

Table 3: Summary Statistics on Domestic Starts: Share in Total Export Starts Over the Period

|Destinations||
|---|---|
|USA|1.8%|
|Hong-Kong|1.7%|
|South-Korea|1.7%|
|Japan|1.6%|
|Malaysia|1.5%|
|Provinces||
|Guangdong|8.5%|
|Zhejiang|7.5%|
|Shanghai|7.0%|
|Jiangsu|7.0%|
|Beijing|6.7%|
|Sectors (HS2)||
|Nuclear reactors, machinery et al.|10.5%|
|Elect machinery et al.|