rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Arrival to gate arrrival to lodging lodging to payment payment to gate<br><!-- End of picture text -->

**Graph 3 – Boxplot analysis of cargo dwell time sequential components (container imports in Douala, 2009)**<sup>**13**</sup> Souce: CAMEROONIAN CUSTOMS, 2009 – Values in days

For all these steps however the fact that median values are 40 to 65% lower than means demonstrates the substantial impact of a minority of very long or abnormal delays. A specific look into the determinants of these very long delays is then necessary, and we recommend using the median rather than the mean (traditionally used in Douala and other ports) as the benchmark for long delays.

# **Shipment level analysis and the need for segmentation**

## **_Main conclusions from shipment level analysis_**

Since the dwell time issue came to the forefront in 1997<sup>14</sup> local stakeholders in the Port of Douala have implemented multiple monitoring tools that provide extensive shipment level data. Preliminary analysis of these data lead to conclusions that are consistent with previous research findings (notably Arvis _et al.,_ 2010):

##  **Broad-tailed multimodal distribution**

The first observation is that there is a large dispersion of values in the distribution of dwell times), with a broad-tailed shape quite specific to developing regions. This is symptomatic of the long storage patterns and opportunistic behaviors mentioned here before. This also reflects that the

> 13 For a better visibility whiskers have been limited to 1.5 times the Inter-quartile range.

> 14 Mostly because of a very serious deterioration of import conditions in Douala, leading to quasi paralysis and overwhelming complains from the business community, mainly targeted towards the Port Authority and to a lesser extent Customs.

10
only a handful of residents in ring 12, it may instead be proper to view the distance decline for the edge resident as only 2 km. Welfare cost, which equals the resulting saving in the edge resident's commuting cost, is computed for both distance values.

While the 3 km distance reduction applies to the 1990 city, it is easier to estimate the resulting reduction in commuting cost using data from nearer the year 2000, which are more readily available. The magnitude of the commuting-cost saving is ultimately judged, however, by expressing it as a percentage of income. Under the assumption that incomes and commuting costs in Bangalore have grown in step with one another since 1990, this percentage value can be then used to measure the welfare cost for the 1990 city.

The key requirement for the welfare-cost calculations is an estimate of the commuting-cost parameter _t_ based on Indian data. Commuting cost has two components, money cost and time cost, and as explained by Calfee and Winston (1998), traditional studies have assumed that commuters value the time spent in commuting at approximately 60 percent of the wage rate. Using a 1999 Indian wage estimate of 12.7 Rs. per hour, the implied time cost of commuting is then 7.6 Rs. per hour.<sup>8</sup> Converting this figure into a cost per kilometer requires an estimate of traffic speed. Urban roadways in India are highly congested, and a common speed estimate is 20 to 30 km per hour. Using the lower value, the time cost of commuting is then 7.6/20 = .38 Rs. per km.

The money cost of commuting depends on the transport mode chosen. Because estimates of operating costs for private vehicles are not readily available for India, a money cost estimate for bus commuters is used instead. Since money cost for this mode is presumably cheaper than for any type of private vehicle, the estimate serves as lower bound on the average money cost of commuting, thus biasing the welfare-cost computation in a conservative direction.

20
Demand charges are widespread for non-residential customers, but are almost twice as frequent for industrial customers as for commercial customers (figure 4b). This suggests that most countries find it important to reflect load considerations in designing tariffs for nonresidential customers (table 3). Peak demand is a critical cost driver in the power sector, because it defines the amount of installed capacity needed to provide a given volume of electricity.

<!-- Start of picture text --> Figure 4. Prevalence of specific tariff schemes: Commercial and industrial users<br>a. Tariff type b. Fixed charges<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>FR TOU IBT DBT None Only fixed Only Demand<br>demand and fixed<br>commercial industrial commercial industrial<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Figure 5. Characterization of nonresidential block pricing<br>a. Numbers of pricing blocks b. Nonlinear tariff price differential between first and last<br>block (%)<br>20 6<br>5<br>15<br>4<br>10 3<br>2<br>5<br>1<br>0 0<br>One Two Three 4 or more negative no change 0-100% greater<br>than 100%<br>Commercial Industrial Commercial Industrial<br>number of countries number of countries<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Volume charges for nonresidential customers are typically linear for at least 15 of the countries in the sample. In the cases where block tariff structures are used, they are more frequently decreasing blocks (DBT) rather than increasing blocks (IBT) and these are relatively simple with not more than three blocks (figure 5b). The reason for preferring decreasing blocks is to capture the strong scale economies associated with power generation and transmission. The volumetric charge tends to be higher for commercial than for industrial customers. Most commercial tariffs start at over 12 cents/kWh, while most industrial tariffs start at around 8–10 cents/kWh. On average, African commercial tariffs are about 40 percent higher than industrial ones. Only the Democratic Republic of Congo, the Republic of Congo, and Chad have commercial tariffs set at a lower level than residential ones (table 5).

11
source and the destination economies, the geographical distance between these two economies, differences in time zones, whether these economies share a common language, whether they have a common legal origin, whether they share a border, and whether there is a colonial relationship between them.<sup>5</sup> In some regression specifications, we also include bilateral trade flows between economies, which cover the 1990-2014 period for 176 source and recipient economies. The source of the trade data is the IMF’s Direction of Trade Statistics (DOTS).

We conduct the analyses in the paper for three distinct groups of economies: EAP, North, and South. The EAP region includes the following economies: American Samoa, Australia, Brunei Darussalam, Cambodia, China, Democratic People’s Republic of Korea, Federated States of Micronesia, Fiji, French Polynesia, Guam, Hong Kong SAR (China), Indonesia, Japan, Kiribati, Lao People’s Democratic Republic, Malaysia, Marshall Islands, Mongolia, Myanmar, Nauru, New Caledonia, New Zealand, Northern Mariana Islands, Papua New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Timor-Leste, Tonga, Tuvalu, and Vietnam. The North comprises the following G-7 and Western European economies: Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, United Kingdom, and the United States. The South includes all other economies not defined in the other two regions. We exclude from the analysis offshore financial centers as defined by the IMF.<sup>6</sup>

> 5 The source of all control variables at the bilateral level is CEPII’s GeoDist database. The only exception is the variable on economies’ common legal origin, which comes from La Porta et al. (1998). The GDP data come from the World Bank’s WDI database.

> 6 The following offshore financial centers were excluded from the analysis: Andorra, Anguilla, Aruba, Bahamas, Belize, Bermuda, British Virgin Islands, Cayman Islands, Cook Islands, Cyprus, Gibraltar, Guernsey, Isle of Man, Jersey, Liechtenstein, Macao, Monaco, Montserrat, Netherlands Antilles, Palau, Panama, Samoa, Seychelles, Turks and Caicos Islands, and Vanuatu. The list comes from https://www.imf.org/external/NP/ofca/OFCA.aspx.

7
Board’s _Key Attributes_ for effective resolution frameworks (Box 7), and all offer relevant measures for EMDEs in this context, but also pose implementation challenges in light of institutional capacity constraints and level of economic and financial market development.

This paper is organized as follows. Section 2 describes the conceptual framework of the sovereign‐bank nexus. Section 3 discusses whether the nexus is rising based on data from 140 EMDEs. Section 4 offers six policy implications. Box 5 offers a selected, high‐level overview of relevant developments across regions.

# 2. What is the nexus? A conceptual framework

Conceptually, the sovereign‐bank nexus links the health of the domestic sovereign and the banking system together through two direct and two indirect channels (Figure 1).<sup>1</sup> Depending on national circumstances, the sovereign – bank nexus can act as a shock absorber or amplifier and foreign investors may play a role as well (Box 3). There are two _direct channels_ :

1. _Banks’ direct exposures to the sovereign._ Direct exposures to the sovereign make banks’ balance sheets vulnerable to losses on government assets holdings (e.g., through lending to the public sector and holdings of securities issued by the public sector). Conversely, if banks expand their sovereign exposures, they could help the sovereign to overcome temporary fiscal stress.

2. _Government support to failing banks._ The sovereign is often implicitly expected to support failing banks, particularly those that are systemically important, because it cannot credibly commit ex ante to refrain from doing so during a crisis. This “time inconsistency” bestows funding and other stability benefits on systemic banks. However, it may also lead to excessive risk taking by banks (i.e., moral hazard) and expose taxpayers to losses.

> 1 Sovereign debt is understood to mean the sum of money borrowed by a country’s central government.  However, this section refers to “sovereign” more broadly as a country’s public sector (including key state‐owned enterprises) due to its implications for the domestic financial system.

4
_4.10 Protection ofproperty_ We end this section with a short description of how surveyed traders protect their property. In the year preceding the survey, 16% of Benin traders and 33% of Malawian traders were victims of theft. This is higher than the incidence of theft reported in Fafchamps and Minten (2001b) for Madagascar. The value of stolen property was fairly low, however: $22 per year on average. Some traders, however, incurred much higher losses-of the order of $2500 in each country.

Few respondents directly blame employees for the thefts, but only 62% of Benin traders are confident that employees were not involved (72% in Malawi). Contrary to what Fafchamps and Minten (2001b) report for Madagascar, fear of pilferage does not discourage traders from hiring employees: only 3% of Benin respondents and 11% of Malawian respondents state they refrain from hiring additional employees for fear of theft.

The methods most commonly used to protect one's property are to lock it at night (75% of those who store in Benin, 87% in Malawi), hire a guard (40% and 28% respectively) and sleep on the premises (19% and 48% respectively). For those who transport, some protection is occasionally sought as well: some traders travel in convoy (10% in Benin, 19% in Malawi); some avoid particular routes (8% in Benin, 17% in Malawi); and some hire guards during transport (4% and 17%). Protection during transport thus appears slightly more problematic in Malawi, possibly because the country is less densely populated and ambush is easier to organize.

36
_Table 2. Trigger events_ _<u>identified in the countries of focus</u>_

|Country|Time of<br>event|Event type|Name of<br>discovery|Location of<br>discovery|Operating<br>company|
|---|---|---|---|---|---|
|Ghana|2007|First giant oil<br>discovery|Jubilee|Deepwater|Kosmos<br>Energy|
|Guinea Bissau|2004|First oil<br>discovery|Sinapa|Shallow water|Premier Oil|
|Kenya|2012|First oil<br>discovery|Ngamia|Onshore|Tullow Oil|
|Liberia|2012|First oil<br>discovery|Narina|Deepwater|African<br>Petroleum|
|Mauritania|2001|First oil<br>discovery|Chinguetti|Deepwater|Woodside<br>Petroleum|
|Mozambique|2010|First giant gas<br>discovery|Windjammer|Deepwater|Anadarko<br>Petroleum|
|Niger|2005|First<br>commercial oil<br>discovery|<br>Jaouro|Onshore|Petronas|
|São Tomé and<br>Principe (incl.<br>Nigeria JDZ)|2006|First oil<br>discovery|Obo|Deepwater|Chevron|
|Senegal|2014|First<br>commercial oil<br>discovery|<br>SNE|Deepwater|Cairn Energy|
|Sierra Leone|2009-<br>2010|Sequence of oil<br>discoveries|<br>Venus,<br>Mercury|Deepwater|Anadarko<br>Petroleum|
|Tanzania|2010|First giant gas<br>discovery|Pweza|Deepwater|Ophir Energy,<br>BG|
|Uganda|2006-<br>2008|Sequence of oil<br>discoveries|<br>Mputa,<br>Waraga,<br>Kingfisher|Offshore (in<br>Lake Albert)|Tullow Oil,<br>Hardman,<br>Heritage|

_Source: Cust, Mihalyi, Rivera-Ballesteros (2020); authors’ desk research of company announcements_ In six of the countries, the trigger event was the first discovery that put them on the map. For example, in Kenya, after intermittent exploration activity since 1954, oil was finally discovered by Tullow in March 2012. This first find was large enough for it to highlight the country’s potential. At the time, Tullow described as an “excellent start” (Tullow, 2012) and the Kenyan president called it a “major breakthrough” (Kibaki 2012, cited in BBC, 2012).

Uganda’s first oil discovery in June 2006 was quickly followed by two more discoveries that year and a string of discoveries in 2007 and 2008. The key event was the discovery of fields with better quality reservoirs in the north of Lake Albert in 2008. The following year, Tullow described Uganda as having a “world-class basin” and declared that the commercial threshold had been exceeded (Tullow, 2009, p. 1).

In four of the countries, oil and gas had been discovered much earlier than the identified trigger 4
APPENDIX <u>Sample: Our original historical database contains 329,637 monthly bond price quotes of 1,712 bonds</u> traded in London and New York and listed in the original sources under categories such as “Foreign Government”. Upon closer inspection, we drop roughly one third of these observations, so as to get to a homogenous sample of foreign-currency sovereign bonds issued by independent states in US dollars or British pounds.

Specifically, we drop:

- 388 bonds that were issued by a state under colonial status. These are colonial bonds by Antigua and Barbuda, Barbados, Fiji, Ghana, Grenada, Guyana, Hawaii, Hong Kong, Iceland, India, Indonesia, Iran, Jamaica, Kenya, Liberia, Mauritius, Morocco, Newfoundland, Nigeria, Palestine, Philippines, Saint Luca, Sierra Leone, Singapore, Southern Rhodesia, Sri Lanka, Straits Settlement, Sudan, Tanganyika, Trinidad and Tobago, and Zanzibar. This reduces our dataset to 292,000 monthly bond prices.

- 256 sub-national bonds, issued by provinces and municipalities but not the central government, reducing the sample to 233,968 monthly bond prices.

- 80 local currency bonds as well as 7 bonds denominated in other foreign currencies (e.g., German Mark or French Franc).

- 12 bonds that were explicitly guaranteed by other governments, via a respective clause in the bond contract. These include the Greek 1833 bonds whose interest and principal payments were fully guaranteed by Great Britain, France and Russia, or the Austrian 6% guaranteed bond of 1923 which had been issued under the auspices of the League of Nations with guarantees by several European governments. Investor reports of the time regard these bonds not as debt owed by the issuing sovereign but as a liability of the guarantor sovereign. For example, the IMM lists guaranteed bonds in the section of the guaranteeing nation, for example, Great Britain.

- Finally, we exclude three sets of bonds: 10 bonds with erratic and infrequent price quotes (this includes a bond issued by the Confederate States of America in London)<sup>1</sup> ; 11 exotic bonds such as land warrant bonds or bonds issued by the Khedive of Egypt, but not by Egypt itself; and 37 bonds for which we did not find basic details on coupon, amount issued, or currency.

Our cleaned, <u>final historical dataset after these adjustments covers 209,890 monthly prices of 911</u> sovereign external bonds issued in US dollar and British pound.

# **Modern bond period, 1990-2016**

For the modern period, we use pricing data provided by JP Morgan, which is available from 1989 onwards, albeit initially for less than five countries that issued bonds in their Brady debt restructurings. More details are provided in the draft. In total the EMBI data cover 56,244 monthly price quotations of 6411 sovereign external bonds issued in US dollar (none is issued in British pound).

> 1 The so-called Confederate cotton bond was a 20-year security with a 7% coupon payable in British sterling and issued in London. The bonds were illiquid and appear only in our pricing database for a few months.

A6
# **APPENDIX B - The Effect of Trade Liberalization on Prices and Wages, Additional Results.**

This appendix utilized the pass-through elasticites and the price-wage elasticities in a simulation exercise to assess-- the actual effect of trade liberalization upon prices and wages.

# **B.1 The effect of trade liberalization on prices** .

To obtain the difference between the actual prices observed and the hypothetical prices in the case of no trade liberalization, I utilize the results from equation 3.13.

Equation 3.13 measures the effect on domestic prices in various regions as a consequence of a change in the border prices. Therefore the difference in prices due exclusively to the trade liberalization between 1989 and 2000 can be written as: where _dp_ represents the percentage change in the prices due to trade liberalization, and _Pobserved_ represents the prices as observed from the year 2000 household survey. The subscripts for region and goods are omitted for the sake of simplicity.

Table B.1 reports the percentage change in prices of the twelve product aggregates due to trade liberalization. For explanatory purposes, results are aggregated at the national level and for five regions (United States border, North, Center, Mexico City, South).

Results indicate that trade liberalization has had the effect of decreasing the relative prices of 9 out of the 12 product aggregates, while the prices of the other 3 product aggregates remained unaffected by the change in tariffs. In other words, without the price effect of trade liberalization, we would have observed higher prices for 9 product aggregates. Of the 12 product aggregates under scrutiny, I will illustrate the effect of 40
- [23] Kee, Hiau Looi, Nicita, Alessandro, & Olarreaga, Marcelo (2008). “Import demand elasticities and trade distortions.”Review of Economics and Statistics, 90(4), 666-682.

- [24] Koopman, Robert, Zhi Wang, and Shang-Jin Wei (2012). “Estimating Domestic Content in Exports When Processing Trade Is Pervasive,” Journal of Development Economics, 99:1, pp.178-89.

- [25] Koopman, Robert, Zhi Wang, and Shang-Jin Wei (2014). “Tracing Value-Added and Double Counting in Gross Exports.”American Economic Review, 104(2): 459-94.

- [26] Liu, Xuepeng (2013). “Tax avoidance through re-imports: The case of redundant trade,” Journal of Development Economics, 104: 152-164.

- [27] Ma, Yue, Heiwai Tang, and Yifan Zhang (2014). “Factor Intensity, Product Switching, and Productivity: Evidence from Chinese Exporters,” Journal of International Economics, 92 (2), 349-362.

- [28] Ministry of Commerce of China Regulations Concerning Customs Supervision and Control over the Inward Processing and Assembling Operation URL: english.mofcom.gov.cn/aarticle/lawsdata/chineselaw/200211/20021100053665.html

- [29] National Bureau of Statistics of the People’s Republic of China (2007). National InputOutput Survey Methods of China.

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|3|Equatorial Guinea|23|39|109|98|9|7|11|29|14|32|98|68|26.9|45.6|33.7|8.1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|3|Ethiopia|319|207|310|562|62|108|100|125|257|98|210|437|49.1|11.6|21.9|21.3|
|3|Kiribati|3|1|1|1|3|2|5|8|0|-1|-3|-7|0.0|-1.3|-9.1|-12.3|
|3|Madagascar|304|311|312|297|29|41|38|63|275|271|273|234|61.3|54.9|31.9|18.8|
|3|Sierra Leone|56|20|7|16|22|17|27|36|34|3|-19|-20|14.3|1.9|-5.9|-4.1|
|3|Somalia|139|86|90|105|65|26|53|57|74|60|38|48|19.9|33.5|13.4|13.9|
|4|Central African Rep.|66|53|58|64|2|7|3|5|64|46|55|58|90.1|34.0|68.8|48.2|
|4|Congo, Dem. Rep.|275|227|59|101|63|95|69|154|212|132|-10|-53|21.7|12.5|-1.8|-4.2|
|4|Lao PDR|9|47|143|127|9|5|23|32|1|42|120|95|0.7|34.3|17.9|9.6|
|4|Mali|87|170|164|258|13|21|32|47|74|149|132|211|27.4|41.8|16.1|17.5|
|4|Sao Tome & Principe|19|4|4|6|2|2|2|4|17|1|1|2|61.5|3.5|3.4|3.1|
|4|Senegal|22|61|43|78|128|175|307|422|-107|-115|-264|-344|-12.4|-10.9|-14.3|-13.0|
|4|Solomon Islands|43|48|64|152|4|8|5|5|40|40|59|148|69.0|42.4|64.1|114.8|
|4|Tuvalu|0|0|0|0|1|1|1|2|0|0|-1|-2|-11.2|-5.1|-11.0|-7.9|
|4|Vanuatu|18|14|21|18|4|2|3|5|15|12|18|13|38.7|6.4|13.6|6.0|
||Memo Items:|||||||||||||||||
|1-4|All LDCs (50)|4,484|4,222|5,069|7,135|1,701|2,255|3,472|5,549|2,783|1,967|1,597|1,586|13.7|7.8|3.6|2.2|
|2|Oil exporters (4)|589|398|400|623|400|478|752|1,223|188|-81|-352|-600|4.6|-2.1|-5.1|-3.8|
|3|Civil conflict (8)|948|747|940|1,226|267|265|453|554|681|482|487|673|12.2|7.2|6.4|6.1|
|4|Small Islanders (9)|539|624|556|804|225|316|445|677|314|308|110|127|87.2|36.1|8.8|7.1|
|1|Other LDCs(29)|2,408|2,454|3,174|4,481|808|1,196|1,822|3,095|1,600|1,258|1,352|1,386|15.4|9.0|4.8|3.2|

Note: Food and agricultural materials are defined as raw food plus agric raw materials, excluding processed food products and seafood (see details in Appendix Table 1). 1 = All other LDCs; 2 = Oil exporters; 3 = Civil conflict countries; 4 = Small Islanders (see detailed classifications in Appendix table 3).

Source: Based on partner data from UN COMTRADE Statistics.

51