rafmacalaba/datause-encoder-data huggingface dataset:
country-pair by 3.2 percent, or alternatively a change in port efficiency from the 75% percentile to the 25% percentile led to a 5 percent increase in trade.

Further progress came from studies digging deeper into cartel behavior, long known to be prevalent among “shipping conferences”. Inspired by the observation that Caribbean and Central American countries trade far less than would be predicted by the gravity model (Guatemala’s exports of manufactures to Caribbean partners are far less than 1%, yet they are close and have easy access to each other by sea), Wilmeister and Hoffman (2008) analyze freight rates charged by one major liner shipping company on 189 routes in the region. Their estimates show that distance is trumped by the number of liner shipping companies providing services between pairs of countries, a result that would likely also carry over to Sub-Saharan Africa where transshipments are frequent.<sup>11</sup> Again focusing on US ports and maritime traffic to Latin America, Hummels, Luggovsky and Skiba (2009) estimate the market power of shipping companies by using the cross-product variation of tariffs to identify unobserved market power.<sup>12</sup> They estimate that eliminating market power in shipping would boost trade volumes by 5.9% for the US and 15.2% for Latin America. Furthermore, high tariffs on trade give market power to shippers: a 1% increase in tariffs leads to a 1-2% increase in shipping prices per kilo.

Turning to road transport, Teravaninthorn and Raballand (2008) showed that trucking deregulation in Rwanda after the civil war had effects similar to those of shipping deregulation in Chile: nominal rates dropped by 30% and the domestic trucking fleet expanded instead of shrinking. By contrast,

> 11 Their model of liner shipping freight rates controls for transshipment vs. direct services; the number of competing carriers; UNCTAD’s liner shipping connectivity index; transit time; port infrastructure endowment in the importing and exporting countries. The model accounts for three fifth of the variance of liner shipping freight rates across the Caribbean.

> 12 When tariffs are high, the share of freight costs in consumer prices is lower, and so is the price elasticity of demand perceived by the shipping lines, which will, if they have market power, induce them to raise freight rates. Thus, the comovement of tariffs and freight rates identifies market power.

14
15 subsidized foods - baladi wheat flour, edible oil and sugar - are not inferior goods in urban Egypt.

While the results of Table 3 are suggestive, they do not "prove" that the Egyptian food subsidy system is self-targeted to the urban poor. Since the rate of subsidy usually varies by type of food, it is important to consider the actual income transfer going to different income groups from each subsidized food. The amount of income transfer from each subsidized fooid can be defined as the difference betiween what a household pays for that subsidized food, and what they would have to pay for that food in the "free market" in the absence of food subsidies.

Unfortunately, iin Egypt it is not easy to establish the amount of income transfer going to consurners from different subsidized foods. The principal ministry in Egypt - the Ministry of Tracle and Supply - keeps no detailed records on the rate of subsidy for subsidized foocds. In the absence of any established "free market" prices for subsidized foods in Egypt, it is therefore necessary to calculate equivalent border prices for each subsidized item. This is a sensible approach, because much of the subsidized food in Egypt is imported. 27 To calculate equivalent border prices, it is necessary to start with the CIF import prices for each subsidized food, and then add in estimated costs of handling, transportation, retailing and - in the case of baladi bread - milling and baking expenses. Unfortunately, this procedure does not include any of the costs for administering food subsidies in Egypt, because these administrative costs a,re unknown. However, since it can be assumed that administrative costs would be roughly equal for each of the four subsidized foods, omitting these costs probably just gives a slight downward bias to the
innovation ecosystems that support sustainable AI adoption rather than merely seeking to close the divide.

countries facing similar structural challenges can exchange best practices and tailor their AI readiness strategies. By promoting knowledge-sharing and collaboration, the framework proposed here helps transcend income-based categorizations and instead fosters a more comprehensive AI development landscape. This peer-learning approach also supports policy experimentation, enabling nations to test scalable, cost-effective solutions before committing to large-scale deployments. By spotlighting overperformers across income groups, the methodology provides actionable insights into the pillars—whether regulatory, educational, or infrastructural—that drive AI overperformance.

In this study, the methodological decision to use the recent AIPI from the IMF instead of the AI Readiness Index developed by the private UK-based consulting firm Oxford Insights is based on several considerations. The IMF, as a globally recognized multilateral institution, offers a higher level of accountability, scrutiny, and transparency in the development of its products. Conversely, while the contributions of private entities like Oxford Insights are valuable, they do not operate under the same oversight or accountability. Moreover, the AI Readiness Index specifically assesses the preparedness of governments to deploy AI in public service delivery, whereas the AIPI provides a more comprehensive evaluation of how countries can effectively harness AI capabilities. That said, the choice of index does not significantly impact the broader contribution of this study, as the proposed methodology remains adaptable and replicable. The focus is on providing a transparent, data-driven approach that allows policy makers to assess their country’s AI preparedness relative to their economic complexity. By prioritizing a clear and simple framework, this study enables comparisons across different datasets and indices, reinforcing the robustness and utility of its findings regardless of the specific AI readiness index used.

Future research could also explore ways to improve the AIPI or similar indexes. For instance, our findings suggest that a significant number of East Asian countries—particularly those influenced by Confucian traditions—tend to overperform in AI preparedness regardless of their income levels, from Viet Nam to Singapore and China. This trend warrants further investigation to determine whether it reflects specific cultural norms and values that prioritize “social harmony" and collective design (Kung and Ma, 2014) that are appropriate for AI readiness, and/or if it is influenced by higher average cognitive abilities in these countries (Lynn and Meisenberg, 2010; Jones, 2011). Notably, collective cognitive abilities have been shown to positively impact economic performance (Madsen, 2016) and could be better captured in future iterations of the AIPI if they prove relevant for readiness in cuttingedge technologies like AI.

22
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10 as the country, other things equal, is more interested in taxing foreign citizens than its own citizens.5 Knowing this, firms have an incentive to indigenize their operations by borrowing in the country of location as well as by selling equity shares to local investors. Bradley (1977) mentions divestment to local investors as a strategy for multinationals against expropriation. Host countries generally have an incentive to facilitate this process as it forces foreign firms to divest partially. Regulation of this kind is desirable if the foreign investors indi-vi-ually are too insignificant to influence the rate of taxation of foreign investments. Ownership restrictions of this variety allow a host country to precommit to a low rate of taxation on foreign investment ex post. Such restrictions potentially benefit all parties, as they can simultaneously increase host country tax receipts, foreign capital investments and after-tax value of the home country's foreign investments. Ownership restrictions are a policy prevalent in many countries.

Ownership restrictions alter the interaction between the affiliate and the parent, as well as between the affiliate and the host country tax authority. The former aspect of ownership restrictions is examined by Katrak (1983). In this regard, ownership restrictions are undesirable if they induce the multinational firms to transfer profitable activities back to the parent or to subsidiaries in other locations.

# 3. The Evidence

# _The Data_

The U.S. Commerce Department collects data separately for majority-owned and minority-owned U.S. affiliates abroad. Data collection for majority-owned affiliates tends to be more detailed. Data is further collected separately for bpuik and non-bank affiliates. Reflecting data availability and the interest of this study, all data in this paper are for majority-owned non-bank U. S. affiliates abroad.

The U.S. Commeice Department has covered virtually all affiliates in its benchmark surveys of 1977, 1982 and 1989. The 1977 and 1982 survey data at this point are available in final revised form, while the 1989 data are currently available only in preliminary form. As a
Policy Research Working Paper 8069

# **Abstract**

Should public investment be targeted to big cities or to small towns, if the objective is to minimize national poverty? To answer this policy question, this paper extends the basic Todaro-type model of rural-urban migration to the case of migration from rural areas to two potential destinations, secondary town and big city. The analysis first derives the labor income, migration cost and poverty line conditions under which a poverty gradient from rural to town to city will exist as an equilibrium phenomenon. Then sufficient statistics are developed for the policy decisions based on these parameters. The empirical remit of the model is illustrated with long-running panel data from Kagera, Tanzania. Further, the paper shows that the structure of the sufficient statistics is maintained in the case where the model is generalized to introduce heterogeneous workers and jobs.

This paper is a product of the Jobs Cross Cutting Solution Area. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lchristiaensen@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The FEI was originally expected to include the internal rate of return (IRR) and/or economic rate of return (ERR) of projects, depending on data availability. However, given that many of these investments were proposed for projects with no direct monetary benefits and largely indirect economic effects (i.e. mainly projects with a large public good component<sup>18</sup> ), the calculation of economic IRR would have produced unrealistic or incalculable results. The alternative would have been to account for all indirect positive effects and estimate benefits. This would have required data on monetized benefits for such effects, which was unavailable. For these reasons, benefit‐cost ratios (BCRs) were used. The BCRs also allowed for the analysts to control for project size and avoid penalizing projects with higher costs but potentially higher benefits.<sup>19</sup> The results from the transport project SEI and FEI rankings are presented in Figure 7.

Figure 7. SEI and FEI, Transport Projects, Panama Pilot 2015 <!-- Start of picture text --> Social and Environmental Indicator (SEI) Financial and Economic Indicator (FEI )<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Project 3 Project 6 Project 2 Project 8 Project 9 Project 7 Project 5 Project 17 Project 19 Project 10 Project 13 Project 11 Project 18 Project 16 Project 4 Project 14 Project 1 Project 15 Project 12 Project 4 Project 17 Project 5 Project 6 Project 18 Project 1 Project 7 Project 8 Project 2 Project 19 Project 13 Project 9 Project 16 Project 11 Project 3 Project 12 Project 14 Project 10 Project 15<br><!-- End of picture text --> Figure 8 shows the results of plotting each project’s SEI and FEI scores.

Figure 8. SEI and FEI plot for selected transport projects, Panama 2015 <!-- Start of picture text --> 100<br>P12<br>90<br>80<br>P15<br>70 P1 P14<br>60<br>P4<br>50 P16<br>P18 P11<br>40<br>P13<br>30 P19<br>P10<br>20 P17<br>P7 P9<br>P8<br>10 P5<br>P2<br>P6 P3<br>0<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0<br>FEI<br>SEI<br><!-- End of picture text --> 18. An example of this sort of project would be investments in wastewater treatment facilities, where tariffs are often insufficient to cover the full costs of the assets and its sustained operation and maintenance.

19. More details on the calculation of composite indices and results of the pilot in Panama are available in Marcelo, Mandri‐Perrott, & House, 2015.

21
2 attention to all three aspects of adjustment - stabilization policies, structural adjustment policies and sectoral reforms. Another important question that needs to be answered is whether the PFPs have been fully interaied by the policy makers in the borrowing country.

A major issue in evaluating PFPs has been the relative weight attached to the IMW's macroeconomic concems versus the more *developmental" concerns of the Bank. The fist part of a PPP sets forth quantitative macroeconomic objectives for GDP growth, inflation and the balance of payments. This survey of African PFPs indicate - geater Bank influence in later PFPs than in earlier ones evident from more exposition of sectoral issues and more emphasis on poverty alleviation objectives in later PFPs than in earlier ones - evident from more exposition of sectoral issues and more emphasis on poverty alleviation objectives in later PFPs compared to the earlier ones.

The second part of a PFP describes a sere of policy measures deipned to address strucural adjustment issues. Across tie entire range of policies that a PFP can contain, an analysis of the content of first year PFPs revealed that all contain the standard macroeconomic policy prescriptions (monetary control, deficit reduction, exchange rate policy and debt management). These macroeconomic poliies form an identifiable 'core program' that has retained this preeminence also in later PFPs. However, sectoral policies have grown in importance again indicating tie increased role of the Bank in later PFPs.
<!-- Start of picture text --> sct —————————————————<br>Fuel subsidy —EEEEEE——EEEEEs<br>Electricity subsidy Es<br>FISP —{&;<~—<< liTZ_ZOEE_oON_<br>All subsidies ——————)<br>Clinic care —EEEE——————————————————————————)<br>Hospital care —_—_——E>E~————————————_—————SS—s)<br>Primary education ~~—__—E>E>E>E>Ec~xL——————————_—_———SSs]<br>Secondary education ~__—_—_SSS)<br>Tertiary education ==<br>All in-kind Eee<br><!-- End of picture text --> <!-- Start of picture text --> All transfers, aubsidies —EEEEEE——_—_—_———————EEanpEEE<br>Excise |<br>VAT (indirect) =<br>VAT =<br>PAYE Lee)<br>All indirect taxes |<br>All taxes _—_—Eas)<br>-40 -20 oO 20 40 60 80 100<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->

<!-- Start of picture text --> 60<br>[Visit to a public hospital?<br>50 BBBVisit to a public clinic?<br>3= a|<br>5 0 | I iT<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market Income + Pensions decile<br>Source: Authors’ estimates based on LCMS 2015.<br><!-- End of picture text -->

<!-- Start of picture text --> 16<br>14 Sl<br>@ Clinic-level expenditures @Hospital-evel expenditures 7<br>212 |<br>g> el | oBe<br>g 2 |<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>OHS Wealth Index decile<br>Source: Authors’ calculations based on DHS 2013-2014,<br><!-- End of picture text -->
commodity markets, this phenomenon arguably poses significant future challenges. Government interventions including food price subsidies and controls, along with political insecurity in the region, may have contributed to a sluggish market response to downward movements in world food prices. However, this paper does not identify systematically the causes of the asymmetric food price transmission process. They are complex and should be an area for future research.

Following next is section 2 which documents the sources for the MENA food time-series price data and presents statistics that explore MENA‟s potential exposure to food price shocks, including dependency ratios by food commodity. Section 3 reviews the empirical literature on food price pass-through effects, with the aim of establishing benchmarks and informing the empirical methodology. Section 4 details the econometric approach, while section 5 presents pass-through coefficients for 18 MENA countries. It shows also food price transmission dynamics and decompositions of pass-through estimates into approximate sources. Section 6 concludes with a summary.

# **2. What Do the Data Tell Us?**

Historic and current price data for MENA are scarce and for most countries not readily available. For the 18 individual countries, monthly food consumer price data are compiled from various sources. The primary data sources are the national statistical offices, either directly, or collected over time by World Bank country economists. The food consumer price data are complemented with historical information from the International Labor Organization (ILO) and several updates provided by national statistical offices themselves.

Efforts were made to ensure data accuracy. Specifically, we compared trend and annual growth consistency of different time-series from 1998–2011. The data were further corroborated with general market information from various press releases, field documentation from USDA, and country updates by World Bank economists. In some cases, such as Djibouti, Jordan, Lebanon, and Tunisia, the level data in different series show small divergences from original series due to rebasing, yet the effects on annual growth rates are negligible. A small number of missing monthly observations were interpolated for the Gulf Cooperation Council (GCC) countries and Algeria. Preference was always given to the original data provided by the national statistical offices. The two exceptions are Lebanon, where the main data

-6-
Annex 5: <u>Guinea - Detailed results for impact of food price increase on headcount index in sample of West and Central African Countries</u>

|**Food item**|**Share in**<br>**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**25% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**50% increase**<br>**Upper**<br>**bound**<br>**Impact**<br>**(Cons. only)**|**25% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|**50% increase**<br>**Lower**<br>**bound**<br>**Impact**<br>**(Cons.& Prod.) **|
|---|---|---|---|---|---|---|---|
||<br>||**G**<br>|<br>**uinea National**<br>|<br>|||
|Rice|13.0|90.7|**49.1**|50.7|52.1|50.0|50.7|
||||<br>|**Guinea Urban**<br>||||
|Rice|9.4|89.9|**23.5**|26.6|29.0|26.6|29.0|
|||||**Guinea Rural**<br>||||
|Rice|16.1|91.0|**59.9**|60.8|61.7|59.8|59.8|

Source: Authors’ estimation using country household survey data 24
WPS4266

# The Economic Community of West African States

Fiscal Revenue Implications of the Prospective Economic Partnership Agreement with the European Union<sup>**1**</sup> Simplice G. Zouhon-Bi

## Lynge Nielsen

### Abstract

This paper applies a partial equilibrium model to analyze the fiscal revenue implications of the prospective economic partnership agreement between the Economic Community of West African States (ECOWAS) and the European Union. The authors find that, under standard import price and substitution elasticity assumptions, eliminating tariffs on all imports from the European Union would increase ECOWAS’ imports from the European Union by 10.5–11.5 percent for selected ECOWAS countries, namely Cape Verde, Ghana, Nigeria and Senegal. This increase in imports from the EU would be accompanied by a 2.4–5.6 percent decrease in total government revenues, owing mainly to lower fiscal revenues. Tariff revenue losses should represent 1 percent of GDP in **Nigeria** , 1.7 percent in **Ghana** , 2 percent in **Senegal** and 3.6 percent in **Cape Verde** . However, the revenue losses may be manageable because of several mitigating factors, in particular the likelihood of product exclusions, the length of the agreement’s implementation period and the scope for reform of exemptions regime. The large country-by-country differences in fiscal revenue loss suggest that domestic tax reforms and fiscal transfers within ECOWAS could be important complements to the agreement’s implementation.

JEL Classification Numbers: C82, F10, F13, F15, F17, F41 Keywords: Revenue Effects of Trade Liberalization, Regional Trade Agreement, Africa

## World Bank Policy Research Working Paper 4266, June 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1This paper was written in connection with the IMF Trade Policy Division’s and the World Bank Africa region’s work programs. The authors wish to thank Hans Peter Lankes and Philip English for their insightful comments and suggestions. Comments on an earlier draft by Robert Blake, Nancy Benjamin, Paul Brenton, Manuela Francisco, Mombert Hoppe, Boileau Loko, Emmanuel Pinto Moreira and discussions with other colleagues are gratefully acknowledged. Dustin Smith provided able research assistance. Paula White provided logistical support.