rafmacalaba/datause-encoder-data huggingface dataset:
_5_ payments accounts (G), and the difference between reported net official capital and the change in external debt acLording to World Bank deta (H-H').<sup>6</sup> The World Bank, Morgan, and Cline methods use variations of the residual method.

World Bank: A + B + F + H' Morgan: A + B + E + F + H' Cline: A - (Al +A2+A3+A4) + B + E + F + H' The Morgan mnetho **d** iffers slightly from the World Bank's. It includes E, the change in banking system's foreign assets (which has a negative sign in BoPY and is thus subtracted). The Cline method is similar to Morgan's but substracts travel (credit), reinvested FDI Income (abroad and domestically), and other investmeni income (credit).

# The Doolgy Method.

This seeks to measure the stock of privately held foreign assets that do not generate income reported to the domestic authorities. It does so by cumulating the identified capital outflows in the balance-of-payments accounts arid making three adjustments to capture unreported capital flows. The first is to add errors and oixiissions (G). The second is based on a comparison of the World Bank data on the stock of external debt and the external borrowing flows reported in the balance-of-payments accounts. Dooley adds the difference between each year's change in external debt (according to the World Bank) and the lows as officially recorded (that is, H' - H) to his estimate of the increase in private-sector foreign assets. Dooley assumes thus that the entire difference is private-sector acquisition of foreign assets.

The third adjustment is to calculate the stock of external assets needed to give the (balance-of-payments) investment income, by using an international market interest rate (for exaimple, the one-year US Treasury Bill rate). If investment income is underreported, then the imputed stock of external assets will less than external assets using balance-of-payments figures (and after making the previous two adjustments). The difference between the two is the stock of flight capital; and difference from year-to year is the measure of capital flight.<sup>7</sup> 61f both the balance of payments and the World 3ank report net official capital accurately, then the change in the stock of debt reported by the World Bank will match the net borrowing flows reported in the balance-of-payments accounts. This is often not the case. Exchange rate revaluation effects, debt reclassification, and "discoveries" of existing debt may cause estimates to diverge. If, however, a discrepancy remains after corrections for these effects are made (see section 4), then the un:ecordel increase in external liabilities must be due to an urnderestimation of balancing transactions, such as an unrecorded increase in external assets by the private sector (that is, capital flight).

7As the Dooley method obtains sorne stock figures for external assets a. ' lainis by cumulating balance of payments flows figures, flight ca.pital depends on the stock assumed **ii** the ini al year (i.ormally zero).
Our evidence is also supportive of this indirect mechanism, but only in countries with certain types of commodity exports. Isham, Woolcock, Pritchett and Busby (2005) argue rent-seeking associated with the resource course is greater in fuel exporting economies because fuel extraction is point-based, meaning revenues typically transit directly through government coffers, in contrast with economies exporting commodities with a more diffuse production base. Consistent with this, we find that control of corruption measured by expert surveys is weaker in economies dependent on fuel exports compared to economies specializing in exports of ores and metals or food commodities. In our sample, the association between tariffs and import market concentration is stronger for economies that are dependent on fuel exports than for those that are dependent on ores and metals or food. Using a measure of tariff evasion based on the underinvoicing of imports relative to exports reported by partner countries, we find a positive relationship between the tariff rate and tariff evasion in economies dependent on fuel exports, but not in economies dependent on other commodity exports. This evidence suggests that tariff evasion explains import market concentration especially in fuel exporting economies.

Our paper is the first to systematically explore differences across countries in import market structure, contributing to several literatures beyond that about the resource curse. While the export sector has been the traditional focus of the trade and development literature, in developing countries the value of imports is about as large as that of exports, and many exported goods are produced using imported inputs (UNCTAD, 2021). We identify patterns in import market structure that contrast starkly with those in studies examining export market structure. Fernandes, Freund and Pierola (2016) use the same customs transactions data on the export side to document that higher-income economies have more exporting firms, but also more concentrated export markets dominated by “superstars,” or firms with especially large market shares, whose characteristics are described by Freund and Pierola (2015, 2016).<sup>4</sup> The pattern in import markets is the opposite. Higher-income economies

> 4Freund and Pierola (2015) show national revealed comparative advantage is shaped by the presence of

5
**Table 2. EIAs & bilateral distance: Controlling for depth of agreement** _5-year data, 1965-2010_

|**Variables**|(1a)|(1b)|(1c)|
|---|---|---|---|
||**ln_Xijt**|**ln_Intijt**|**ln_Othijt**|
|**EIAijt*lndistij**|-0.066*** <br>(0.020)|-0.110*** <br>(0.022)|0.068***<br>(0.022)|
|**Depth_0ijt**|0.776***|1.124***|-0.401**|
||(0.176)|(0.190)|(0.184)|
|**Depth_1ijt**|0.724***|1.119***|-0.385**|
||(0.172)|(0.186)|(0.181)|
|**Depth_2ijt**|0.739***|1.156***|-0.307*|
||(0.164)|(0.177)|(0.173)|
|**Depth_3ijt**|0.705***|1.086***|-0.440**|
||(0.171)|(0.184)|(0.179)|
|**Depth_4ijt**|1.188***|1.592***|-0.071|
||(0.164)|(0.178)|(0.173)|
|**Depth_5ijt**|1.031***|1.426***|-0.150|
||(0.164)|(0.178)|(0.173)|
|**Depth_6ijt**|0.664***<br>(0.192)|1.122***<br>(0.209)|-0.611***<br>(0.198)|
|**Depth_7ijt**|0.614***<br>(0.192)|0.972***<br>(0.206)|-0.575***<br>(0.201)|
|Fixed Effects|Yes|Yes|Yes|
|_(i, t-(t-5)); (j, t-(t-5))_||||
|**No. obs.**|99,861|90,174|86,513|
|**R**<sup>**2**</sup>|0.887|0.877|0.893|

Notes: This table shows the estimation results for equation (8). Robust standard errors are in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively, in two-tailed t-tests.

As highlighted in Figures (1) and (2), economic similarity as proxied by the absolute difference in the log of trading partners’ GDP per capita increases with trade agreement depth. Additionally, GDP per capita is more similar among nearby countries in general. This motivates a further extension, whereby we control specifically for economic similarity among trading partners.

To this end, Table 3 presents the results of specifications (9) and (10). In columns (1a) – (1c) we control only for economic similarity. While this variable is negative and statistically significant for all three goods categories, it does not change our main result that the negative interaction between geographic distance and EIAs is driven by intermediate goods. Furthermore, the effect of the 11
23 articles by a notary is not required, although such requirement is now being debated.90 Companies with foreign participation must then submit the joint venture agreement (or similar document) to the Ministry of Foreign Affairs, whose approval is deemed granted if no response is received within 30 days. All companies must then send all relevant documents to the re,ular court at the seat of registration, which is supposed to issue its decision within 30 days (Company Law, Articles 13-187). The company is a legal entity upon approval by this court. In practice approvals by both the Ministry of Foreign Affairs and the courts have been relatively quick. The final step is entry in the court register (at which time it is binding against third parties) and publication in the Officiai Gazette (Article 186).

# Foreign Investment

Foreign investment was first allowed in Yugoslavia in the 1978, with the passage of the Law on Investment of Foreign Persons into Domestic Organizations of Associated Labor.91 This law was, however, relatively restrictive, with high requirements for invested capital and strict limits on profit repatriation. Furthermore, foreign investment had to accommodate the Yugoslav self-management rights of workers, which in practice meant an often intolerable sacrifice of managerial control for the foreign partner. Amendments in 1984 and 1986 did little to change this restrictive regime. As a result, the flow of foreign investment was small, amounting to less than 1 percent of domestic investment over this period (but sti'l more significant than that in any other CEE country in that period).

The Foreign Investment Law of 1988,92 introduced simultaneously with the Enterprises Law, represented a radical departure from the previous regime. Pursuant to this law, which is still the law in force in Slovenia today,'3 90 Predlog zakona c notariatu (Proposal of Law on Notary), Internal Material, Government of the Republic of Slovenia, January 1992.

The requirement of notarial approval is proving to be an expensive and timeconsuming process in some other reforming socialist countries, and Slovenia should consider its merits carefully before introducing it. Part of the problem in these other countries arises from the shortage of notaries, in part due to long-standing government monopolies over the profession. See Gray et.al., Poland Legal Framework and Romanian Legal Framework.

91 Za'kon o vlaganju sredstev tujih oseb v domabe organizacije zdrutenega dela (Law on Foreign Investment into Domestic Organizations of Associated Labor], Yugoslav Officiai Gazette, No 18 (1978), most recent amendment Yugoslav Official Gazette, No 38 (1986).

- 92 Supra note 81.

93 As with many of the other Yugoslav laws discussed in this paper, Slovenia implicitly recognized this law as applicable in late 1990, with the only change being the approving ministry (which is now the Slovenian Ministry of Foreign Affairs). A new foreign investment law is now under preparation in
15

# **6.  Policy priorities for East Asia**

# **6.1. Strengthening the implementation and enforcement of corporate governance**

The empirical results suggest that corporate governance—as proxied by director liability and shareholder suits—is a significant determinant of stock market efficiency. In particular, it is an important determinant of the extent to which individual stock prices move in response to firm specific information.

As Table 4 shows, the EA region now fares quite well in terms of the coverage of securities laws with respect to these aspects, although individual countries, notably the Philippines, Thailand and Indonesia score relatively low in one or more aspects of the laws.

Table 4. Ratings of different aspects of securities laws

|**Economy**|**Disclosure**<br>**requirements index**|**Liability standards**<br>**index**|**Anti self dealing**<br>**standards index**|
|---|---|---|---|
|China|10.0|1.0|4.0|
|Indonesia|8.0|5.0|3.0|
|Korea|7.0|2.0|7.0|
|Malaysia|10.0|9.0|7.0|
|Philippines|1.0|2.0|7.0|
|Thailand|10.0|2.0|6.0|
|Hong Kong|10.0|8.0|9.0|
|Singapore|10.0|9.0|9.0|
|_Memorandum:_||||
|Japan|7.0|6.0|8.0|
|Canada|8.0|9.0|8.0|
|Germany|5.0|5.0|5.0|
|UK|10.0|7.0|7.0|
|US|7.0|9.0|9.0|
|Chile|8.0|6.0|5.0|
|Greece|1.0|3.0|5.0|
|Mexico|8.0|5.0|5.0|
|Peru|8.0|5.0|7.0|
|Spain|5.0|6.0|4.0|
|Turkey|8.0|4.0|4.0|
|**East Asia**|**5.2**|**4.4**|**6.1**|
|ECA|4.7|3.8|6.0|
|Latin America|4.3|5.1|5.8|
|OECD|6.3|5.0|6.6|

Score ranges from 10 (highest) -0 (lowest).

While on average the region scores well in terms of the coverage of some key aspects in the securities laws—including disclosure—the extent of disclosure in practice varies quite widely across countries. Figure 8 below shows an analysis done by Standard
# **1. Introduction**

In December 2003, bank regulators estimated that there was one ATM in Bangladesh for every 1.7 million citizens, and one deposit-taking bank branch for every 22,000 people.<sup>1</sup> In Bolivia, there was one ATM for every 21,000 people, and just one deposit-taking bank branch for every 65,000 people. Bangladesh and Bolivia, though, are notable as early sites in which microfinance first took root and grew rapidly. Was the lack of financial depth in the banking system a help or a hindrance to microfinance? This paper is a first attempt to gauge how the presence of formal, regulated providers of financial services in an economy affects the profitability and outreach of financial institutions targeted narrowly to the under-banked and unbanked.

Microfinance banks (“microbanks”) target low-income communities, and most make loans without requiring collateral (or are far more flexible than most mainstream commercial banks about the kinds of collateral required to secure loans). They are micro not because of their institutional scale but because of the scale of typical transactions with customers. Loan sizes range from under $100 to roughly $5000, and operational scale varies from several hundred customers to several million. The most famous microbank, Grameen Bank, serves nearly 8 million customers in Bangladesh with an average loan balance of $79in 2007.<sup>2</sup> If the growth of microfinance has demonstrated nothing else, large numbers of low-income borrowers can be served while achieving a remarkably high level of repayment. Billions of dollars in loans to over one hundred million borrowers are outstanding, and data from top lenders show that only 2-3 percent of those are delinquent in recent years (Cull, Demirgüç-Kunt, and Morduch, 2009b).<sup>3</sup> The industrial organization of microfinance and the broader banking sector has received little attention so far, but as central banks set the stage for the rapid expansion of

> 1 Calculation is from Beck et al (2007), Table I.

> 2 Armendáriz and Morduch (2005) describe the economics of microfinance, and Cull, Demirgüç-Kunt, and Morduch (2009b) describe recent trends and data.  Data on Grameen Bank loan balance are from the Mix Market, as of December 2007 (www.mixmarket.org/en/demand/demand.show.profile.asp?ett=1658, accessed November 22, 2008). Grameen Bank reports having 7.9 million customers as of July 2009 (www.grameen-info.org/index.php?option=com_content&task=view&id=453&Itemid=527, accessed September 1, 2009).

> 3 These calculations are for a sample of leading microfinance institutions that serve 18 million borrowers. Loans are defined as delinquent if they are at least thirty days overdue.

2
# Contents

|Su<br>Pos|mry..c<br>. ....<br>...<br>..<br>. ...<br>, _<br>............<br>*****<br>***<br>**<br>**********<br>tscript 1988...|
|---|---|
|Sec|tion|
|1|Introduction......o<br>l...|
|2|When Uncertainty<br>Matters in Public Project Appraisal..9*ooo*****oo..2|
||2.1 Theoretical perspectives........<br>2|
||2.2Practical considerations....-. 0<br>....|
||2.3 World Bank practice.l.5.......o.........|
|3|Measuring Uncertainty in Project Components......................17|
||3.1 Uncertainty in variables external to the project<br>...............<br>17|
||3.1.1 GDP annual percent change. ....<br>18|
||3.1.2 Conditional forecasting precision...<br>l...........<br>ooeeo19|
||3.2 Uncertainty in project variables......*eo*oo#.e..<br>...<br>.....<br>22|
||3.2.1Quantitiesoo...o..o....o............<br>23<br>3.2.2Prices*e*******9.o..o....o.<br>o....<br>24|
||3.2.3 Other variables...<br>.....................<br>25|
|4|Workable Procedures for Uncertainty<br>Accounting... ....<br>*.........25|
||4.1 Pragmatic methods for computing risk adjustments<br>.......<br>*..o26<br>4.1.1 A 'rough-and-ready'<br>approach...p<br>.. 26<br>4.1.2 A stochastic simulation approacho.. ......... .*#**.29|
||4.2A case study illustration....o.<br>.o . .<br>o.<br>.<br>oo..<br>..<br>.*<br>..<br>. ..<br>.32|
|5|Conclusion......o...o... o..ooo.oo.o...<br>oo..<br>o....**.**....<br>ooe.oo..<br>36|
||5.1Implications for price forecasters..ooo.o.<br>......<br>..<br>....<br>....<br>37|
|6|References<br>...........<br>o.o.............<br>o.oo...40|
|Ap|pendix|
|1|Mean and Variance of Simple Functions of Normal Variables.e.....o..46|
|2|A Monte Carlo Study of Proportional<br>Risk Deductionso.o...6*6.....50|
jointly significant).<sup>29</sup> As a further robustness check, we re-estimated the effect of our treatments on attendance, but restricting the sample used to construct the share of girls present to names registered on the class roll at the first spot check. The first spot checks were carried out within the two first months of school (between February 25 and March 31), and so well before any end-of-trimester transfers were paid. The class rolls called by the independent surveyor were slightly updated between spot checks for various reasons. A few girls changed classes or schools during the year, some names were updated to match the girl’s used name when it did not match that with which she was recorded in the school register, or to match the name used at home in the case of girls included in the household survey sample. Estimates obtained by restricting the spot checks data to girls with exact name matches from the first attendance check roll are presented in Table 7. These results are near-identical to those obtained in the main analysis, thus confirming that selection through school switches is unlikely to be biasing our results.

_Ex-post power calculations_ . In Table 8, we report ex-post power calculations using the means and standard deviations of the outcomes studied in this paper in the control group, for 80% power. The last column reports the Minimum Detectable Effect (MDE) as a share of the control group’s mean, showing that the experiment is well-powered for our three schooling outcomes, teacher absenteeism and self-reported monitoring quality, but not for being ever married and self-reported empowerment. This bolsters our confidence in the results for which we find consistent significant effects, while confirming the inconclusiveness of our findings for early marriage and self-reported empowerment.

29 Individual coefficients (p-values) are: 0.004 (0.784), -0.013 (0.316), 0.0152 (0.237) for the information, parent cash and girl vouchers arms, respectively, and the joint F-test p-value is 0.183.

35
9 urban areas may disproportionately benefit the poor if they live predominately inside or downwind of major cities. For estimating effects on poor households, one needs household level survey data with income levels, transportation budget shares, and residence location relative to pollution concentrations.

# B. DEFINING INSTRUMENTS

The possible policy options for reducing mobile source pollution (and congestion) are nearly limitless because, as noted above, there are so many "entry points" for affecting emissions: the vehicle characteristics supplied, the vehicles demanded, the fuel characteristics supplied, the maintenance of the vehicle and its emissions control system, and the choice of mode, trip length, timing and location, residential location, and employment location.

To organize the set of instruments that a model should be able to consider, we use a modified version of a taxonomy from Eskeland and Jimenez (1991). This taxonomy classifies policies by their type (economic incentive versus command and control),5 by the control variables (whether prices, quantities, or technologies are being controlled), and by directness of control (either direct, say by a fee on emissions, or indirect, say by a gasoline tax that reduces emissions by reducing driving). Our taxonomy adds one more distinction -- if the policy is direct, whether it directly influences emissions or congestion. The entry point for the policy is obvinus from the description of the instrument.

5. An economic incentive policy is defined as one that prices addition emissions or kilometers traveled, either through taxes, subsidies, or tradable permits. Command and control policies may be considered everything else, including, in this case, public sector policies to increase road capacity or buy more buses.
**Solicit specific topics of interest.** Motivation, perception of relevance, and other behavioral factors have also been shown to be an important factor in whether participants benefit from financial education or advice (Varcoe et al., 2005; Hung and Yoong, 2010). More work is needed in these areas in order make financial education programs more relevant and effective.

**Be creative with the delivery format of financial education programs.** Some evaluations suggest that information might be best conveyed through interactive or experiential program designs, such as actually opening up a bank account, or through computer or video games.  A number of interventions convey information via a dramatic or fictional medium, such as radio or television soap operas (e.g. Spader et al., 2009). Other formats include formal courses, seminars and workshops, individual counseling, and DVD instruction. The optimal format, of course, would also depend on the type of information being delivered, and the target audience. Individuals of different geographic, socioeconomic, and cultural backgrounds may find certain formats more engaging or informative than others. More research should be done in this area as well.

**Implement complementary interventions to achieve objectives** . With regard to increasing financial access and take-up of financial services, such as savings accounts, financial education may not be as beneficial as other interventions, such as subsidies (Cole et al. 2011). Some have also argued that consumer protection measures may be more effective than improving individual financial literacy in preventing fraud. Other studies indicate that behavioral factors, such as impatience, may play a bigger role in financial outcomes than financial illiteracy (Hastings and Mitchell, 2011).

**Consider supply-side failures.** Financial education and other consumer side interventions to increase take-up may be misguided in cases where the products are fundamentally flawed. In this case, stronger regulation and consumer protection measures would be required. For instance, the FinScope survey in Kenya indicates that some individuals may mistrust insurance products for legitimate reasons, because they either believe them to be fraudulent or because they’re poorly explained.

41
# **Table A1 – Infrastructure Access Indicators in MENA countries.**

|Country|Mobile & Fixed<br>line telephones<br>(per 100 people)|Roads paved (%<br>of total roads)|Improved water<br>source (%<br>population with<br>access)|Improved<br>sanitation<br>facilities (% of<br>population with<br>access)|Electrification<br>rate (%<br>population<br>connected to<br>electricity)|
|---|---|---|---|---|---|
|Algeria|90.5||85|94|98.1|
|Bahrain|211.2|-|-|-|99.0|
|Djibouti|6.82|-|92|67||
|Egypt|65.4|-|98|66|98.0|
|Iran|94.2|72.8|||97.3|
|Iraq|52.2||77|76|15.0|
|Israel|162.6|100|100||96.6|
|Jordan|98.8|100|98|85|99.9|
|Kuwait|117.7||||100.0|
|Lebanon|47.8||100||99.9|
|Libya|48.2|||97|97.0|
|Morocco|82.7|61.9|83|72|85.1|
|Oman|125.4||||95.5|
|Qatar|152||100|100|70.5|
|Saudi Arabia|163.3|21.5|||96.7|
|Syria|50.4|100|89|92|90.0|
|Tunisia|95||94|85|98.9|
|United<br>Arab<br>Emirates|242.3||100|97|91.9|
|West Bank Gaza|37.1|100|89|80||
|Yemen|18.2|8.7|66|46|36.2|
|Source: WDI,|2005, 2006 a|nd 2007 and|OECD/IEA, 20|07 for electrifi|cation rates.|

24