rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3:** Sovereign bond yields during Zambia’s hidden debt saga

<!-- Start of picture text --> 10,000<br>Protracted default, data reconciliation<br>9,000 and<br>restructuring process<br>Creditors reject<br>8,000<br>restructuring over<br>transparency concerns<br>7,000<br>Zambia asks banks<br>6,000<br>for restructuring<br>advice on $11.2bn<br>5,000 of external debt<br>4,000<br>First rumors<br>about hidden debt<br>3,000<br>emerge<br>2,000<br>1,000<br>0<br>Jan 2018May 2018Aug 2018Dec 2018Apr 2019Aug 2019Dec 2019Apr 2020Aug 2020Dec 2020Apr 2021Aug 2021Dec 2021Apr 2022Aug 2022Dec 2022Apr 2023Aug 2023Dec 2023Mar 2024<br>(in basis points)<br>Sovereign bond yield spread<br><!-- End of picture text --> _Sources:_ Financial Times (2020, April 1) _“Zambia’s bonds drop on expected restructuring”_ , Financial Times (2020, September 30) _“Bondholders balk at Zambia’s plan to delay debt payments”_ , Financial Times (2021, November 10) _“Zambia’s new president tackles debt mountain and empty treasury”_ and J.P. Morgan (2024). _Notes:_ The figure plots the EMBI+ spread for Zambia from January 2018 until April 2024.

Statistics (IDS) and its predecessor publications. This subsection explains why we focus on the World Bank debt statistics and how we compile our new database.

**Why focus on the World Bank IDS?** The World Bank’s International Debt Statistics is uniquely suited for the purpose of our measurement approach. First, it is the most widely used source for developing and emerging market country external debt data and frequently cited by researchers, investors, rating agencies and market commentators. Reporting is mandatory for all countries with outstanding liabilities to the World Bank and debtor countries that violate their reporting obligations risk losing eligibility for financial support (World Bank, 2017). These conditions ensure high coverage across countries and time.

Second, the IDS has numerous desirable features that facilitate the interpretation of debt data revisions as a measure of hidden debt:

- The International Debt Statistics and its predecessor publications are based on direct debtor reporting. While the World Bank compiles the annual statistical report and performs consistency checks, all underlying data on debt instruments are reported by national debt management offices. Omissions and revisions of debt data can therefore be traced back to the reporting decisions of sovereign debtor countries.<sup>13</sup> An upward

> 13As explained in greater detail in Appendix Section A.2, the World Bank occasionally provides debt estimates if a reporting country does not fulfill its reporting obligations, but we exclude these estimates from our analysis.

8
# **Infrastructure Policy**

## **_Basic Design Options_**

_Michael Klein_<sup>_1_</sup> JEL classification: L9

### Sector Board: Urban Development (URBAN)

This paper was originally written as a background paper for the Urbanization Review Flagship report, which provides a policy framework to help Mayors and other policymakers anticipate and implement strategies to improve how cities can enhance their contribution to green and inclusive growth. The views reflected in this paper are of the authors, and does not necessarily reflect the views of the World Bank and the countries it represents.

> 1 The author can be contacted at muklein@gmail.com
for 14 “countries” (13 non-EU countries, plus the composite EU region) and 14 industries, which are listed in Table 1.<sup>36</sup>

# **3.2 Bilateral**

We construct bilateral, industry-level tariffs on final goods for four benchmark years: 1995, 2000, 2005, and 2009. We briefly describe the data sources and procedure here; see Appendix B for details.

We start with national government, product-level tariff schedules collected by UNCTAD (TRAINS) and the WTO, which we obtain via the World Bank’s WITS website [ `http: //wits.worldbank.org` ]. Multilateral MFN applied tariffs are typically available in the WTO data, while bilateral applied tariffs are from TRAINS. Combining these sources and aggregating product lines yields a data set of bilateral tariffs at the Harmonized System (HS) 6-digit level.

To identify final goods tariffs in the data, we use the Broad Economic Categories (BEC) classification. We retain HS 6-digit categories classified as consumption and capital goods, discarding both mixed use and intermediate input categories.<sup>37</sup> We then concord these HS 6-digit final goods categories to WIOD industries using a cross-walk from HS categories to ISIC Revision 3 industries to the WIOD industry codes. We take simple averages across HS categories within each industry to measure industry-level applied bilateral and MFN tariffs.

# **3.3 Temporary Trade Barriers**

We obtain data on temporary trade barriers (TTBs) — antidumping, safeguards, and coun— tervailing duties from the World Bank’s Temporary Trade Barriers Database [Bown (2014)]. These data identify the importing country imposing the TTB, the countries and product lines on which the TTB is imposed, and the timing of when TTBs are imposed and removed.<sup>38</sup> Following Trefler (1993) and Goldberg and Maggi (1999), among others, we construct import coverage ratios to track TTB use over time. These coverage ratios measure the stock of accumulated bilateral TTBs imposed by each importer against individual exporters

> 36We exclude two industries from the raw WIOD data: (1) Mining and Quarrying, which contains no final end use products, and (2) Coke, Refined Petroleum and Nuclear Fuel, which contains only one final end use HS 6-digit category.

> 37Roughly 40 percent of the HS 6-digit codes in the raw data are classified as final goods, which corresponds to the value share of final goods in world trade.

> 38The data cover all countries in Table 1, except for Russia. In our analysis of TTBs, we exclude China and Taiwan because nearly all of their TTBs are imposed on intermediate inputs.

22
reduction in the production of essential public goods or in any other commodities that generate positive externalities, such as health care. Additional investment may also imply an increase in taxes or public debt. All of these actions have a social cost that must be compared with the social benefit of the additional investment. Conversely, when the transfer is positive (i.e., taxes on profits), it helps to reduce distortionary taxation or to finance investment. The assumption of costly public funds is a way to capture the general equilibrium effects of sectoral intervention. To avoid introducing bias into the integration decision, we assume that both countries have the same cost of public funds, _λ_ .

In the following, we express the results in terms of Λ, which increases with _λ ∈_ [0 _,_ + _∞_ ): We first briefly describe the case of a closed economy, marked _C_ . Each regulator maximizes the expected national welfare (6) subject to the autarky production condition _Qi_ = _qi_ . The optimal autarky quantity is When Λ = 0, public funds are costless, and the price is equal to the marginal cost _P_ ( _qi_<sup>_C_) =</sup> _θi_ + _γqi_<sup>_C_.WhenΛ</sup><sup>_>_0,thepriceisraisedabovethemarginalcostwitharulethatisinversely</sup> _<u>i</u>_<sup><u>)</u></sup> proportional to the elasticity of demand (Ramsey pricing): _P_ ( _qi_<sup>_C_)=</sup><sup>_θi_+</sup><sup>_γ q_</sup> _i_<sup>_C_+ Λ</sup><sup>_P_</sup><sup><u>(</u></sup> _ε_<sup>_<u>qC</u>_</sup> . The optimal pricing rule diverges from marginal cost pricing in proportion to the opportunity cost of public fund Λ because the revenue of the regulated firm allows the level of other transfers in the economy (and thus distortive taxation) to be decreased.

The closed economy case corresponds to a pure autarky model in which the electricity is mated at approximately 0.3 (Snow and Warren 1996). In developing countries, low income levels and difficulty implementing effective taxation imply higher values for _λ_ . The World Bank 1998 suggests an opportunity cost of 0.9 as a benchmark, but it may be much higher in heavily indebted countries.

11
(or control) the revenue from the sales. In the Guatemala and Vietnam surveys, questions are asked about the value of animal products sold, but not about which household member keeps the revenue. Adding in a question about the control over animal products and the revenue generated would provide some information about the ownership and control over livestock assets. In addition, the right to decide about whether to slaughter the animal should be included.

# 5.4. Nonfarm Business Assets

_Minimum questions._ The multi-topic surveys are generally not designed to obtain individual level data on business assets. Instead, the business is the unit of analysis, so the surveys focus on the flow of business income. The design of the questionnaires makes it less straight forward to obtain individual level asset data, but it can be done within the context of the LSMS module. The minimal information that is needed is who owns the business and the value of the business assets.

Typically, the household is asked to list up to three businesses (four, in the Vietnam survey). The Guatemala survey asks which household members worked in the business and how many hours per day they worked (regardless of whether they were paid or unpaid.) In addition, it asks who the boss is and whether or not that person is the informant for this section of the questionnaire. (It is interesting to note that the English translation of the questionnaire asks “who is the boss,” while the Spanish version asks about the “dueño” or the owner.) The Vietnam survey asks the respondent whether he or she or the members of the household own the business, and if so, what percentage the household owns. The Ghana questionnaire does not ask about the owner. At a minimum, we recommend adding one question in each of these questionnaires to identify which household member(s) owns the business (allowing for individual and joint ownership).

The questionnaires usually ask about the value of business assets. In all of the LSMS surveys, it is implicitly assumed that “the business” owns all of the assets, when in fact, especially for small businesses, the assets may be owned by an individual or a set of individuals.

The Guatemala questionnaire asks about the capital and inventory of each business. This includes finished goods that have not been sold, raw materials, vehicles, furniture, machines/equipment/tools, facilities and land, office equipment, other durable goods, and other. For each category, the respondent is asked whether the assets were owned or leased, the value if these items were sold today, and whether the good is also used for other purposes or shared with other household businesses. Thus, the valuation data is appropriate for our purposes.

In the Vietnam survey, the business assets section asks whether any assets are owned by any of the four most important businesses. If yes, it asks which businesses own the assets and the value of the assets in today’s prices. Again, it would be useful to specify that the value of the assets is the price at which they could be sold.

35
| While accounting for a relatively minor share of electricity trade in most countries, power<br>exchanges offer an instrument for price discovery. Exchanges can also provide helpful hedging<br>instruments for generators and consumers to lock in electricity prices for future time periods<br>providing some protection from price volatility.|
|---|
|**Policy recommendations for implementing competitive retail markets**<br>Based on the global experience with this structure, several key insights can be discerned:<br> Product differentiation is key for retail supply companies to capture higher margins associated<br>with differentiated and a unique product in a segment that traditionally has slim margins.<br> The contestability of retail markets is necessary for the cost associated with setting up these<br>complex market structures to deliver expected efficiency results. In some emerging markets,<br>this constraint may justify long durations before a transition to full retail competition.<br> Transaction costs are typically lower for sales to larger industrial and commercial consumers.<br>In some markets, a phased approach to opening up the retail market first to these customer<br>groups and then extending it over time to households may be preferable.<br> The market power of incumbent energy companies can threaten effective competition and<br>market entry of new supply companies. Strong sector regulation is needed to limit and eliminate<br>uncompetitive behavior in the market.<br> Advents in disruptive digital technology hold promise to lower the cost barriers to independent<br>supply companies that do not have these legacy advantages. Retail companies can deploy<br>digital technologies to improve their competitiveness. For example, by utilizing advanced<br>analytics on pooled customer data on customers’ usage patterns, segmentation, and preferences,<br>they can identify target customer segments and potential opportunities to deploy new products<br>and services.<br> Technological advances are placing sophisticated competitive market structures within reach<br>of emerging markets by providing solutions to the traditional constraints that make this<br>structure difficult to manage under weak regulations, a lower income customer base, and high<br>transaction costs.|

33
-28-

There is, moreover, a vicious circle aspect to<sup>this policy</sup> experiment. Cutting public sector investment reduces<sup>output growth, which in</sup> turn will lead to less of a private sector's<sup>savings surplus.</sup> As a consequence, fiscal deficits and hence public sector<sup>investment need to be cut</sup> further to maintain external balance, growth slows down<sup>more and so on. As a</sup> result, a five percentage points cutback in real<sup>interest rates requires a cut</sup> in the fiscal deficit of 2.1 percentage points of GNP<sup>if external balance is</sup> to be maintained through reduced government consumption.<sup>However, with 60</sup> percent of the cuts coming from public sector investment,<sup>deficits need to be</sup> reduced by 2.8 percentage points of GNP, a full<sup>0.7 percentage point of GNP</sup> more.

The analysis also suggests that there is some<sup>threshold at which the</sup> fall in public sector investment would just offset<sup>the increase in private</sup> investment triggered by lower real rates. If only<sup>28 percent of the cut in</sup> government expenditure falls on public sector investment,<sup>the model results</sup> suggest that output growth will not be affected.<sup>A caveat concerns the</sup> medium-term nature of this model; a short run<sup>recession triggered by such a</sup> fiscal retrenchment could still lower output.

The arguments presented here do not imply a blanket<sup>endorsement of</sup> ever increasing public sector investment; public sector<sup>investment of course</sup> does come at a cost.<sup>9</sup> They do highlight, however, that public sector investment has played an important role in Turkey's<sup>strong growth performance</sup> over the last few years. Moreover, they show<sup>that stabilization programs</sup>

> %/ Other expenditure components need to be<sup>cut or alternative means of</sup> financing need to be found; each carries its own<sup>cost.</sup>

ChhibberD isk/chhibber-Turkey Paper!ac :07-28 -88: 1c
41 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a.

Gascoigne, Bamber. (2001) “History of Latin America” HistoryWorld. From 2001, ongoing.

http://www.historyworld.net/wrldhis/PlainTextHistories.asp?groupid=850&HistoryID=aa87& gtrack=pthc (retrieved 2017-06-12).

Gencer, E. A. (2013). Natural Disasters, Urban Vulnerability, and Risk Management: A Theoretical Overview. In The Interplay between Urban Development, Vulnerability, and Risk Management. Volume 7 of the series SpringerBriefs in Environment, Security, Development and Peace (pp. 7-43). New York, NY: Springer Berlin Heidelberg.

Glaeser EL (1998) Are cities dying? _Journal of Economic Perspectives_ 12: 139–160 Hasse, J. E. and Lathrop, R. G. (2003) Land resource impact indicators of urban sprawl, Applied Geography, _23_ , 159–175.

- Hsu, F. C., Baugh, K. E., Ghosh, T., Zhizhin, M., & Elvidge, C. D. (2015). DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. _Remote Sensing_ , _7_ (2), 1855–1876. https://doi.org/10.3390/rs70201855

Huang, J., Lu, X. X., & Sellers, J. M. (2007). A global comparative analysis of urban form:

- Applying spatial metrics and remote sensing. _Landscape and Urban Planning_ , _82_ (4), 184–197. https://doi.org/10.1016/j.landurbplan.2007.02.010

- Inostroza, L; Baur, R; Csaplovics, E (2013). Urban sprawl and fragmentation in Latin America: A dynamic quantification and characterization of spatial patterns. Journal of Environmental Management, 115: 87-97

- Jaffe A, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. _Quarterly Journal of Economics_ 108: 577–598
# **Figure 13a Criteria used by banks to make business loans, by firm size**

<!-- Start of picture text --> 100%<br>Small Enterprises Medium Enterprises Large Enterprises<br>80%<br>64%<br>60% (47)<br>(51)<br>60%<br>49%<br>(43)<br>40%<br>20% 14% 12% 12%<br>8% (12) (10) (9) 8% 8%10% 7% 9%10%<br>(7) 4%(3) 3%(2) 5%(4) 5%(4) 1%(1) (7) (7) (7) 2%(2) 2%(2) 1%(1) (6) (8) (7)<br>0%<br>Collateral Financial Firm's credit Firm's credit Firm's owner Size of the loan Purpose of the<br>assessment of history with the history from a characteristics loan<br>the business bank credit registry (age, sex, etc.)<br>Percentage of banks<br><!-- End of picture text --> 36
**Labor Markets in Rural and Urban Haiti Based on the First Household Survey for Haiti**

By

**Dorte Verner** World Bank<sup>1</sup>

> 1 I am very grateful to Nadim Khouri for inviting me to join the Haiti Rural Development Team, Michael Justesen for excellent research assistance, and the Haiti Rural Development Team for answering many questions. Moreover I am especially grateful to Willy Egset for data information and other support with the household data set. Without the help of all these people this paper would not have existed. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author.
for example, a similar exercise recently undertaken by the SF estimates the size of this market at COP 22 trillion as of end-2006<sup>25</sup> . It is interesting to note that, while interviewed credit institutions reported different SME portfolio volumes depending on whether the legal or business definition of SMEs was used, the combined total SME exposure across all interviewed credit institutions was fairly similar for both definitions. Finally, it is worth highlighting the significant growth of leasing in recent years, which represents an important source of longer-term SME funding (see section 5 below).

Figure 3.1: Evolution of SME Loan and Lease Portfolio by Credit Institutions (2003-2006) <!-- Start of picture text --> 16,000 30%<br>14,000<br>25%<br>12,000<br>20%<br>10,000<br>8,000 15%<br>6,000<br>10%<br>4,000<br>5%<br>2,000<br>0 0%<br>2003 2004 2005 2006*<br>Year<br>Loans Leases % of Commercial Loans<br>% of Commercial Loan Portfolio<br>Outstanding Volume (COP Billion)<br><!-- End of picture text --> _Source: Asobancaria. * Figures for 2006 are for November. Note: The legal (as opposed to the business) definition of SMEs is used in the above Figure._ A substantial proportion of all SME credits is supported by government programs. In particular, as can be seen in Figure 3.2, it is estimated that FNG guarantees and Bancóldex rediscounting had ‘mobilized’ SME loans and leases amounting to COP 4.5 trillion as of end-2006, which represented around one-fourth of outstanding SME exposures based on Asobancaria’s figures. Both institutions have significantly expanded their operations in recent years (see section 4.3), although their contribution has remained relatively unchanged since 2004 given the growth in this market. Although data on nonperforming loan rates is not available on a disaggregated basis (e.g. by program type or loan size) for these institutions, existing evidence suggests that these remain relatively

> SMEs (based on reported asset size, per the legal definition) were included in the total market size. This excludes the possibility that other borrowers that are not captured in the database (e.g. because their tax identification numbers could not be matched) are, in fact, SMEs.

> 25 This figure excludes personal loans to SME owners that are actually used to fund their business activities.

15
0.8-~ Figure 13 USA The Movement of MCY from 1929 to 1991 <!-- Start of picture text --> 0 .68<br>. .. , ...................... . . . ............... . . , . .......<br>............ .................<br>0.6~~~~~~~~~~~~~~~~~ ___. ... ... .....<br>.<br>........... .. .... . .............................<br>. ......<br>. ..... ............<br>.... ... ..................... ........................................ . ..<br>00 .5 . .....<br>0.4-<br>.* ..........<br>................-.....................................<br>'/ -..-.-.<br>'B<br>111<br>W<br>;'<br>;><br>0.3-<br>.......................................<br>. .<br>..........................<br>..... ........<br>1929 .........<br>1935<br>1932 1941<br>1938 1947<br>1944 1953<br>1950 1959<br>1956 1965<br>1962 1971<br>196 1977<br>1974 1983<br>year 1980 1989<br>1986<br><!-- End of picture text -->