rafmacalaba/datause-encoder-data huggingface dataset:
7 _I. 2.3. Sources of Risk for Banks_ In their traditional banking business - lending financed by deposits from customers or the wholesale markets - the main sources of risk for banks, as well as their regulation, were credit risk in their loan books and internal control systems. Credit risk was accounted for in the 1988 Basle Capital Accord, which provided for the first time minimum credit risk-based capital standards. The Basle framework now serves as the standard for all banks in numerous countries within and outside the G-1O group.

Banks may also be exposed to securities market risk because they have lent to investment firms and they hold securities as collateral, because they engage in trading business off their own balance sheets, or because they have securities subsidiaries or affiliates. Whereas, though, the first of these exposures can be dealt with, at least in principle, through regulatory limits on large exposures, the other two exposures inextricably link the bank's solvency to its securities operations. In effect, therefore, the bank's capital stands behind its securities unit.

Recent attempts to incorporate market risk into the framework of risk-based capital standards are largely based on the deregulation of interest rates, the dismantling of capital controls, and the relaxation of banks' authorized range of activities<sup>13</sup> . These developments have permitted the rapid growth in securities, foreign exchange, and derivatives (especially over-the-counter or OTC) trading by banks. Whereas exchangetraded derivatives are extensively regulated by government agencies, it is the unregulated nature of OTC derivatives trading, as well as its fast growth 4, that is causing concern.

the existence of widely divergent classes of assets and instruments, which can greatly vary bank risks, has rendered the use of total assets an increasingly imperfect proxy for the relative risks of an institution.

12 Modem capital regimes often classify regulatory capital into two Tiers: "core" capital, incorporating the highest elements (for example, equity and disclosed reserves); and "secondary" or "supplemental" capital, incorporating elements that have the capacity to absorb unexpected losses but are less permanent in nature - for example, various debt instruments such as subordinated debt. See The World Bank (August 1992), and Berger et al. (1995) for a discussion on the role and concept of capital in financial institutions.

**13** See Weston and Gray (December 1994).

**14** The standard way to judge the size of OTC derivatives is by reference to the notional amount outstanding for particular types of derivatives. The notional amount is the face value of the principal of the
||World<br>Memo Items:|65,630|112,287|146,451|203,577|74,450|110,459|138,436|199,274|-8,821|1,828|8,014|4,302|-0.6|0.1|0.1|0.0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|Industrial Countries|50,937|84,676|103,062|139,185|41,998|77,186|91,243|135,381|8,939|7,490|11,819|3,804|0.8|0.3|0.3|0.1|
|2|Middle-income, all|12,287|24,195|38,365|57,311|19,785|23,208|39,192|52,444|-7,498|987|-827|4,867|-3.4|-0.6|-0.1|0.1|
|3|Oil Exporters|539|1,813|3,931|5,865|5,225|6,133|11,093|17,048|-4,686|-4,320|-7,162|-<br>11,183|-4.4|-4.5|-3.6|-2.8|
|4|Civil Conflict States|105|102|211|343|241|239|898|1,129|-137|-136|-687|-786|-0.4|-0.7|-4.2|-3.0|
|5|Small Islanders<br>Other Middle-|80|251|148|135|259|398|423|593|-179|-147|-274|-458|-2.9|-1.6|-1.5|-1.8|
||income|11,564|22,029|34,075|50,968|14,059|16,438|26,779|33,673|-2,495|5,590|7,296|17,295|-3.1|0.0|0.5|0.7|
|6|Low-income, all|1,450|1,936|4,689|6,859|2,585|2,399|4,647|7,402|-1,135|-463|42|-543|-2.0|-0.6|0.0|-0.2|
|7|Oil Exporters|230|137|323|503|909|457|1,221|2,125|-679|-320|-898|-1,622|-3.4|-2.8|-4.8|-4.6|
|8|Civil Conflict States|160|101|108|160|203|208|353|559|-43|-107|-245|-399|-2.2|-2.5|-4.0|-4.0|
||Other Low-income|1,060|1,698|4,258|6,197|1,473|1,734|3,073|4,718|-413|-36|1,185|1,479|-1.1|0.2|1.1|0.7|
|Note:<br> <br> <br>|Food is defined as raw fo<br>1 = Industrial countries; 2<br>countries for middle and l<br>Exclude those small islan|od, excludi<br>= Middle-i<br>ow-income<br>ds econom|ng all cash<br>ncome; 6 =<br>accordingly<br>ies which a|crops, proc<br>Low-incom<br>.<br>re colonize|essed food<br>e; 3&7 = Oi<br>d and depe|products a<br>l exporters<br>ndent territ|nd seafood<br>for middle<br>ories.|(see detail<br>and low-inc|ed classific<br>ome respec|ations in A<br>tively; 5 =|ppendix Ta<br>Small isla|ble 1).<br>nd econom|ies; 4&8 =|Civil conflict||||

Source: Based on partner data from UN COMTRADE Statistics.

34
# **PITFALLS AND UNRESOLVED PROBLEMS**

# **Interviewer error**

Although taking GPS readings is quite straightforward, it requires good training, otherwise surveys end up with another source of measurement error. One solution is to take multiple readings for the same location and then take their average. Some GPS receivers have an inbuilt function for doing this. For example the guidelines for collecting GPS data in DHS surveys (Montana and Spencer, 2004) recommend taking multiple readings within a five minute period and then averaging them.<sup>14</sup>

# **Datum and Coordinate Projection Problems**

Geographic coordinate systems use latitude and longitude to define the location of points on the surface of the earth. Because the earth is not a true sphere (the poles are flattened and it bulges at the equator) it is difficult to solve geodetic problems for point location. Instead, a spheroid approximation to the shape of the earth is used, and this surface, its origin and the orientation of its latitude and longitude lines make up a geodetic datum. GPS receivers typically use the World Geodetic System 1984 (WGS84) datum, which is a geocentric datum (its origin coincides with the center of the Earth) designed for making measurements world wide. However there are hundreds of other datums which may use a different center, spheroid, or reference point on the Earth’s surface in order be locally more accurate. Coordinate values resulting from interpreting latitude, longitude, and height values based on one datum, as though they were based on another datum, can cause position errors of up to one kilometer (Ramachandran, 2000) although the discrepancy will typically be less. Bennett (2006) gives the example of walking around Tiananmen Square in Beijing with a GPS receiver and then importing the measurements into Google Earth which showed a path offset by approximately 14 meters due to the difference between the WGS84 datum and the datum used by Google Earth.

In addition to these datum shift errors, which may be small enough to escape notice, another common source of error comes from mixing geographic and projected coordinate systems. These projected coordinates are designed to overcome a major weakness of latitude and longitude for distance- and area-measuring purposes, which is that these are not constant units. A

> 14 The fieldguides of Spencer et al. (2003) and Montana and Spencer (2004) provide a good starting point for researchers planning on using GPS in a household survey.

- 19 -
11 (Regression 11). We find that the microstate dummy remains significant even after controlling for the effect of greater terms of trade volatility on the volatility of overall growth. This additional volatility may be due tb several factors. Many of the microstates in our sample are located in areas prone to natural disasters such as hurricanes, and the higher growth volatility in small states may simply reflect the devastating effect of these natural forces. However, it is also possible that some of this observed volatility reflects difficulties in measuring per capita incomes, which may be particularly acute in small states where statistical institutions may be weaker than average.

# 4. Opportunities **fDr** Diversification

In the previous section we have seen that microstates experience much more volatile growth rates than non-microstates. This in part reflects their greater vulnerability to terms of trade shocks, and also the tendency of many microstates to suffer heavily from natural disasters. In this section we briefly consider the potential of small states to mitigate the adverse effects of this largely-unavoidable volatility by sharing risks with the rest of the world.

One of the potential benefits of financial openness is that it allows countries to share risks with the rest of the world, by holding claims on assets located outside their borders whose returns are not perfectly cor.related with returns to domestic assets. The magnitude of these benefits depends on how volatile are shocks to the domestic economy, and the extent to which they are uncorrelated with shocks abroad. Small states are particularly well-situated to benefit from such risk sharing arrangements, for two reasons. First, small states suffer large shocks, as documented in Section 3. Second, in contrast to the often-heard view that small states are particularly susceptible to cyclical fluctuations in large states, we find that the shocks experienced by small states are not unusually correlated with the world business cycle. We illustrate this point with Regression 12, which regresses the correlation of per capita GDP growth in a country with OECD average real per capita GDP growth on the same set of dummies as before, as well as the logarithm of average per capita CiDP (to capture the stylized fact document by Kraay and Ventura (1998) that business cycles in poorer countries tend to be less correlated with the world average cycle), and a microstate dummy. The microstate dummy is insignificant, suggesting that microstates are not unusually correlated with the OECD cycle. However, it is important to note that growth rates in neighbouring microstates may be highly correlated, especially to the extent that growth rate volatility reflects natural disasters such as hurricanes. This suggests that regional
Table 6.2. Individual and household estimates by the unweighted, current UNPS and <u>calibrated GWSM base estimators</u>

||Unweighted<br>|UNPS<br>|Calibrated<br>GWSM<br>base<br>|Offi<br>|cial Statistics<br>|
|---|---|---|---|---|---|
||Mean|Mean|Mean|Mean|Source|
|Individuals||||||
|Female|51.1|50.8|51.1|51.0|Census (2014)|
|% children below 18 years old|51.2|52.1|55.0|55.0|Census (2014)|
|Literate population (+10 yrs)|80.7|80.8|81.7|72.2|Census (2014)|
|Working population|76.8|76.1|80.1|78.8|UNHS (2016)|
|Poverty headcount|20.4|19.3|19.9|21.4|UNHS (2016)|
|Households||||||
|Share of urban households|25.3|25.1|22.9|25.0|Census (2014)|
|Size of the household|4.8|5.0|4.8|4.7|Census (2014)|
|Share of agricultural households|78.7|80.1|79.4|80.0|Census (2014)|
|Number<br>of<br>rooms<br>in<br>the<br>household|2.2|2.3|2.1|2.4|UNHS (2016)|
|Household owns a cellphone|74.0|72.9|75.4|74.3|UNHS (2016)|
|Household<br>has<br>access<br>to<br>electricity|15.4|15.1|15.6|21.0|Census (2014)|
|Household has access to safe<br>water|75.2|72.2|71.7|72.0|Census (2014)|
|Household owns the dwelling|81.9|82.9|81.1|71.8|UNHS (2016)|
|House has brick walls|67.2|63.8|64.2|66.6|UNHS(2016)|

Table 6.3 breaks down the share of working population by area of residence. The calibrated GWSM base estimator (82.0%) seems closer to the official statistics than the current UNPS estimates (78.8%) in representing the official statistics in rural areas (82.6%). As indicated earlier, these differences, although small, are statistically significant. More controversial are the estimates in the urban areas, where we do not see a best estimator.

<u>Table 6.3. Employed population by area of residence</u>

||Unweighted|UNPS|Calibrated GWSM<br>base|<br>Off|icial Statis|tics|
|---|---|---|---|---|---|---|
||Mean|Mean|Mean|Mean|Year|Source|
|Rural|79.6|78.8|82.0|82.6|2016|UNHS|
|Urban|67.5|67.4|72.7|69.0|2016|UNHS|

Having concluded the analysis of statistics at the national level, we now turn to the study of poverty by area of residence (Table 6.4), which does not show concrete differences among the estimators. The incidence of poverty is much higher in the rural areas than urban areas.

- <u>Table 6.4. Poverty head count ratio by area of residence</u>

|Unweighted|UNPS|Calibrated|Official Statistics|
|---|---|---|---|
|||GWSM base||

28
3 Then in the third section the two systems of paid-extension in Nicaragua are described. Section four applies the analytical framework to available data from two private extension programs in Nicaragua. The paper is concluded with suggestions for policy and directions of future research.

# A **Conceptual Framework for Comparing Paid-Extension Performance**

In evaluating the performance of paid-extension, two comparisons have to be addressed. First, a comparison between paid-extension and public extension performances and second, a comparison among alternative paid-extension programs. Although the analysis should not be detached from the objectives for which paid-extension was initiated on the first place, there are several basic economic rules that should always hold.

Extension inputs and outcomes of public extension services can be measured in many ways. They can be measured in monetary terms such as expenditures on fuel, salaries, and training, and in physical terms, such as number of extensionists or extensionist man-hour employed in the program. Extension output also can be measured in a variety of ways, including the number of farmers contacted by extensionists, farmers' participation in extension activities, changes in agricultural practices due to the provision of extension, improved farm-level physical performance (yields, crop varieties, inputs), and increased farm-level profitability. In the case of private extension, there is also a need to compare between private and public expenditures on extension. We distinguish among four categories of analysis: individual producer level analysis, agency-level analysis, government-level analysis, and social-level analysis. In the following sections we provide a detailed analytical framework for each category, which in turn is the basis for measuring the cost and some performance indicators of paid-extension in Nicaragua.

# **Individual producers**

The impact of paid-extension on individual producers can be estimated in several ways, compared to performance at the no-extension or at the public extension stage. First, by improving technical efficiency, where increase in profit at any given combination of other inputs Xi and X2 is observed (Figure 1). Second, by improving allocative efficiency, where at a given technical efficiency extension increases profits due to a better economic allocation of scarce inputs XI and X2 (see Figure 2).
||Obs.|Total|Q1|Q2|Q3|Q4|Q5|
|---|---|---|---|---|---|---|---|
|**(A) FAT: Pre-COVID characteristi**|**cs**|||||||
|Tech sophistication|1,063|1.78|1.10|1.36|1.73|2.06|2.77|
|Brazil|1,063|0.12|0.02|0.06|0.09|0.07|0.36|
|Vietnam|1,063|0.45|0.01|0.31|0.67|0.79|0.52|
|Senegal|1,063|0.44|0.97|0.63|0.24|0.15|0.13|
|Small|1,030|0.52|0.71|0.71|0.50|0.45|0.24|
|Medium|1,030|0.26|0.21|0.21|0.30|0.29|0.30|
|Large|1,030|0.21|0.07|0.08|0.21|0.26|0.47|
|Agriculture|974|0.15|0.28|0.17|0.09|0.08|0.09|
|Manufacturing less exposed|974|0.15|0.07|0.14|0.18|0.17|0.21|
|Manufacturing more exposed|974|0.18|0.20|0.15|0.18|0.16|0.19|
|Services less exposed|974|0.32|0.27|0.32|0.35|0.42|0.26|
|Services more exposed|974|0.12|0.11|0.17|0.12|0.11|0.08|
|Age 0 to 5|1,030|0.20|0.16|0.21|0.23|0.28|0.14|
|Age 6 to 10|1,030|0.23|0.18|0.26|0.25|0.21|0.23|
|Age 11 to 15|1,030|0.20|0.18|0.18|0.21|0.24|0.20|
|Age 16+|1,030|0.37|0.49|0.35|0.32|0.27|0.43|
|Export|1,063|0.21|0.12|0.13|0.23|0.23|0.36|
|Foreign owned|1,063|0.12|0.02|0.02|0.13|0.23|0.21|
|Work experience|1,010|17.97|18.61|16.88|15.72|15.88|22.59|
|Post-graduate degree|1,063|0.11|0.01|0.09|0.10|0.11|0.24|
|Studying abroad|1,063|0.19|0.04|0.09|0.18|0.33|0.34|
|Experience in large business|1,033|0.26|0.16|0.18|0.22|0.31|0.46|
|Management score|1,063|0.13|-0.19|-0.10|0.20|0.25|0.52|
|**(B) COV-BPS: Post-COVID perfor**|**mance**|**and resp**|**onses**|||||
|Average change in sales (%)|1,063|-45.32|-53.27|-49.10|-40.30|-38.69|-44.13|
|Increased use of digital platforms|1,052|0.41|0.12|0.36|0.51|0.53|0.58|
|New investment in digital solutions|1,058|0.14|0.10|0.15|0.12|0.13|0.21|
|<br>More work from home|1,016|0.12|0.06|0.17|0.10|0.10|0.19|
|More online sales|706|0.13|0.07|0.18|0.10|0.09|0.20|
|Increased use of any digital|1,063|0.47|0.24|0.45|0.53|0.54|0.62|
|Change in mobility|1,063|-33.81|-56.92|-41.61|-22.81|-17.28|-27.20|

Table 1: Descriptive statistics of FAT and COV-BPS data. Computations do not use sampling weights. Quintiles (Q) are based on technology sophistication pre-COVID-19. Q1 = Bottom 20%; Q2 = Second quintile; Q3 = Third quintile; Q4 = Fourth quintile; Q5 = Top 20%.

10
22

**Table 3: OLS regression on the economic determinants of official international remittances**

|_Variable_|_Dependent variable_<br>_recei_|_=Per capita international remittances_<br>_ved by country in 2003_|_Dependent var_<br>_international remitt_|_iable = Pooled data_<br>_ances received by c_<br>_2003_|_, Per capita_<br>_ountry in 1998 or_|
|---|---|---|---|---|---|
||_(1)_|_(2)_<br>_(3)_|_(4)_|_(5)_|_(6)_|
|Distance (miles) from developing country to<br>ittdi i US OECDE|-0.657|-0.680<br>-0.631|-0.644|-0.672|-0.671|
|remance-senng regon (, -urope<br>or Arab Gulf)|(-2.38)*|(-1.98)*<br>(-1.54)|(-3.19)**|(-2.55)**|(-2.35)*|
|**_Economic variables_**||||||
|Per capita GDP (constant 1995 dollars)|9.587|10.551<br>7.258|9.701|10.186|7.755|
||(3.08)**|(3.13)**<br>(2.23)*|(4.19)**|(3.89)**|(3.12)**|
|Per capita GDP<sup>2</sup>|-0.654|-0.781<br>-0.516|-0.653|-0.732|-0.538|
||(-2.95)**|(-3.24)**<br>(-2.25)*|(-3.98)**|(-3.93)**|(-3.02)**|
|Gini coefficient||4.213<br>1.143||3.060|0.081|
|||(2.63)*<br>(0.75)||(2.62)*|(0.07)|
|Poverty headcount ($1.00/person/day)||-0.328<br>-0.347||-0.118|-0.163|
|||(-1.33)<br>(-1.45)||(-0.61)|(-0.79)|
|**_Human capital, demographic variables_**||||||
|Share of population over age 25 with secondary|-0.558|-0.030<br>-0.074|-0.710|-0.177|-0.293|
|education|(-1.67)|(-0.07)<br>(-0.18)|(-2.78)**|(-0.54)|(-0.91)|
|Population density (people per square kilometer)|0.384|0.657<br>0.372|0.411|0.593|0.318|
||(2.01)*|(2.92)**<br>(1.41)|(2.98)**|(3.61)**|(1.62)|
|**_Financial variables_**||||||
|Country credit rating||-1.179|||-0.735|
|||(-1.01)|||(-0.80)|
|**_Regional variables_**<br> (East Asia omitted)||||||
|Europe, Central Asia||-1.067|||-1.854|
|||(-0.63)|||(-1.62)|
|Latin America, Caribbean||1.908|||1.352|
|||(1.34)|||(1.39)|
|Middle East, North Africa||1.262|||0.859|
|||(0.67)|||(0.70)|
|South Asia||1.288|||0.772|
|||(0.98)|||(0.87)|
|Sub-Saharan Africa||-0.041|||-0.442|
|||(-0.03)|||(-0.40)|
to select beneficiaries to cover households that were just above the eligibility threshold before the shock. In reality, shocks often consist of a combination of these two extremes.

We also focus on social assistance programs that aim to mitigate the immediate impact of the shocks on households through an emergency or rapid response, leaving aside the broader range of programs along the disaster risk management continuum that extends from preparedness through response into recovery (Macdonald et al 2015). This is to ensure that we are comparing the relative efficiency of different design options to achieve the same objective. Our focus on the poor and poverty reduction leads us to restrict the analysis to social assistance programs, specifically cash transfers, leaving aside for the moment the question of the relative effectiveness of a response through social insurance programs and their delivery systems.

In what follows, we anchor our analysis to the COVID-19 shock and subsequent social protection response. Accordingly, we consider as a “baseline” shock the COVID-19 pandemicinduced crisis that affected all countries in the world, but whose impact within each country varied depending on the movement and economic restrictions imposed to contain the spread of the virus. To simulate the social protection response, we use World Bank microsimulations of the impacts of the shock and subsequent responses in Albania, Moldova, and North Macedonia during the first COVID-19 wave (World Bank 2020c, 2020b, 2020a) as the starting point. We chose these countries because they are all upper middle-income countries of relatively the same size in Europe and Central Asia, with established social protection systems.

Next, for a fixed budget and for each type of shock, we consider different compensatory strategies in the form of cash transfers. We focus on the most common responses and program expansions observed during the pandemic, and on additional responses that could be easily implemented by policy makers. Again, our baseline scenario is the actual social assistance response to the first wave of the COVID-19 pandemic in Albania, Moldova, and North Macedonia. To analyze the quality of responses, we consider various alternatives under equivalent budgets, presented in Table 2. In summary, we consider broadly three types of shocks: the baseline “COVID-19” shock; a homogeneous, “proportional” shock reducing all labor incomes but maintaining the income distribution unchanged; and a heterogeneous, “random” shock altering the labor income distribution. For each shock, we study the quality of different responses by comparing post-response welfare measures including the poverty headcount, the squared poverty gap and the Gini coefficient.

11
Four main freight-forwarding companies participated in this survey. Panalpina, a Swiss company, provided their offices in 56 countries. Maersk Sealand, of Denmark, completed 28 surveys in northern Europe and East Asia. SDV International Logistics, of France, completed the questionnaire in 24 countries in west and central Africa. And Manica, of South Africa, covered the 10 southern African countries. Independent freightforwarders completed the survey in the remaining 18 countries, as well as second set of answers in other countries. Overall, 345 trade facilitators responded, with at least 2 per country. This provided an opportunity to compare the answers and, where differences arose, seek further clarification. After processing all questionnaires, we conducted follow-up conference calls with _all_ respondents to confirm the coding of the data.

In addition to surveying freight-forwarders, surveys were completed by port authorities and customs officials in a third of the sample (48 countries). As ports and customs constitute a portion of the exporting procedures, they answered only the relevant sections of the questionnaire and provided information on the existing laws and regulations governing their activities. This allowed a further check on the quality of the information supplied by the main respondents, the freight-forwarders.

The data are collected as part of Doing Business, a World Bank project that investigates the scope and manner of business regulations.<sup>7</sup> Doing Business collects and analyzes data in nine other areas, for example starting a business, hiring and firing workers, enforcing contracts, paying taxes. In addition to exporting procedures, the trade survey covers importing, as well as some specific aspects of trading across borders, for example recent measures to improve security and their impact on the time and cost to ship cargo. The surveys and data are available at www.doingbusiness.org.

> 7 The survey was designed by Uma Subramanian and conducted by the Doing Business team.

6