rafmacalaba/datause-encoder-data huggingface dataset:
# **TABLE A3.  Asian Development Bank (ASD)**

## **A3: Asian Development Bank (ASD)**

http://www.adb.org/ The Asian Development Bank is a multilateral development finance institution dedicated to reducing poverty in Asia and the Pacific. Established in 1966, the ADB - headquartered in Manila and with 26 offices worldwide - is currently owned by 63 member countries, mostly from the region.

The Asian Development Bank develops its own "Country Policy and Institutional Assessments" for its own Client sample. Similarly to the World Bank's CPIA, the ASD Indicators annually assess the quality of Asian Development Bank borrowers’ policy and institutional performance in areas relevant to economic growth and poverty reduction. Data is publicly disclosed only on a limited basis.

In the table below we list the variables included in each of the governance indicators. We use data from 2000, and from 2002-2005.

|**Table A3: Asian Developm**|2005<br>**ent Bank(26 Asian**|2004<br>**count**|2003<br>**ries)**|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Competence of civil service|X|X|X|X|X|..|..|
|Budget Management|X|X|X|X|X|..|..|
|Efficiency of Public Expenditures|X|X|X|X|X|..|..|
|Management of public debt|X|X|X|X|X|..|..|
|**Regulatory Quality**||||||||
|Trade policy|X|X|X|X|X|..|..|
|Competitive environment|X|X|X|X|X|..|..|
|Factor and products markets|..|X|X|X|X|..|..|
|**Rule of Law**||||||||
|Property rights|X|X|X|X|X|..|..|
|**Control of Corruption**||||||||
|Anticorruption and Accounting Institutions|X|X|X|X|X|..|..|

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# **Figure 13. Transition risks (fossil fuel exports) and macro‐financial risks**

A. Fuel exports (% GDP) vs. macroeconomic risks, 2017 <!-- Start of picture text --> B. Fuel exports and macroeconomic risks, by region<br><!-- End of picture text --> <!-- Start of picture text --> 45 100% 0<br>40 KWT 90% 3 4<br>80% 7 1<br>35 OMN AZE 70% 1 8 7<br>30 COG 60% 1<br>ARE 5<br>25 50% 1<br>6 3<br>20 SAU DZA KAZ MOZ 40% 8<br>IRN 30% 3 6<br>15 NOR SGP<br>RUS 20% 5<br>10 LTU NGA GHA 10% 3 4 3 1<br>5 LUXISRAUSCHNKOR JAM 0% 1 1 0<br>SWE EAP ECA LAC MENA SA SSA<br>0 CHE NZL UKR<br>10 30 50 70 90 low both high climate<br>EIU economic risk index (100=High), 2017<br>C. Fuel exports (% GDP) vs. banking risks, 2017 D. Fuel exports and banking risks, by region<br>45 100% 0<br>40 KWT 90% 3 6 4<br>80%<br>35 OMN AZE 9 7<br>30 COG 70% 2 0 1 2<br>25 ARE 60% 3<br>20 SAU DZA MOZ 50% 9<br>NOR KAZ 40% 6 3 4<br>15 SGP RUS IRN 30%<br>10 MYS LTU GHA NGA 20% 7 4 1 1<br>50 NZLSWE LUX CHE AUSCHLISR 10%0% 2 5 1 0 2<br>10 30 50 70 EAP ECA LAC MENA SA SSA<br>EIU banking risk index (100=High), 2017 low both high climate high banking high both<br>E.Fuel exports (% GDP) vs. public debt risk, 2017 F. Fuel exports and public debt risks, by region<br>45 100% 0<br>40 KWT 90% 3 5 4<br>1<br>80%<br>35 OMN AZE COG 70% 1 2 8 7<br>30 3<br>60%<br>ARE<br>25<br>50% 1<br>20 SAUKAZ DZA MOZ 40% 6 10 3 3 4<br>15 NOR SGP IRN 30%<br>RUS<br>NGA<br>10 GHA 20% 5 5 1 1<br>5 SWE AUSISR 10% 3 3 2<br>NZLLUXCHECHL 0% 1<br>0 JOR<br>EAP ECA LAC MENA SA SSA<br>10 30 50 70<br>EIU country index (100=High), 2017 low both high climate high debt high both<br>Fuel exports (% GDP), 2017<br>Fuel exports (% GDP), 2017<br>Fuel exports (% GDP), 2017<br><!-- End of picture text --> Note: Panels A, C, and E: Countries above (below) country median in Notre Dame Readiness indicator in green (red). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low macro‐ financial risks, low climate‐high macro‐financial risks, high climate and macro‐financial risks) by region. Thresholds are country median values of EIU indicator or climate variable as of 2017 (historical country median for the case of fuel exports). Numbers in columns are the number of countries in each category. EIU data for Saudi Arabia as of 2016.

Source: EIU Credit Risk Model, GermanWatch, Notre Dame GAIN, WDI. Staff calculations.

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# **Optimal Targeting under Budget Constraints in a Humanitarian Context**

_Chiara Gigliarano_<sup>_1_</sup> _and Paolo Verme_<sup>_2_</sup>

**JEL** : I31; I32; I38

**Keywords:** Food vouchers; Refugees; Receiver Operating Characteristics Curves; Targeting.

> 1 Department of Economics, Università dell'Insubria, Varese, Italy.

> 2 Fragility, Conflict and Violence, World Bank Group. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Department for International Development (DFID), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees” (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of DFID, the WBG or UNHCR. The authors would like to thank Lidia Ceriani, Xavier Devictor, Ugo Gentilini and Phillippe Leite for very useful comments.
Both the REER index and the share of primary commodities in total exports are retrieved from the UNCTAD database.<sup>13</sup> Labor tax data are from the World Bank, Doing Business project.<sup>14</sup> The proxy variables for governance are from the World Bank, Worldwide Governance Indicators, and the remaining proxy variables are from the World Bank, World Development Indicators database.

# _4.2.2 Results_

The results show that of the eight (8) factors analyzed, only three (3) have positively evolved to support structural change in Guinea. The three factors are: the share of primary commodities in total exports, access to electricity, and control of corruption. The growth rate of the share of primary commodities in total merchandise exports has declined over time. While during the period 2006-10, the share of primary commodities in total merchandise exports has grown by 0.6 percentage point per year on average, this has declined by 1.6 percentage points during the period 2011-15. Thus, between 2006-10 and 2011-15, the dynamics of primary commodity exports should have been favorable to structural change. The average annual growth rate of access to electricity has increased by 0.4 percentage point between 2006-10 and 2011-15.

**Table 3.** Guinea: Dynamics of drivers of structural change

||Averag<br>growth rat|e annual<br>e<br>annual|Variation of<br>growth rate|average<br>Positive<br>(+)/Negative (-) evolution|
|---|---|---|---|---|
||200610|2011-15|||
|Primary<br>commodity’s<br>share<br>of exports|0.6|-1.6|-2.2|+|
|Real effective exchange rate index|8.9|11.7|2.8|-|
|Labor tax and contributions (% of<br>profit)|9.0|15.9|6.9|-|
|Access to electricity|3.9|4.3|0.4|+|
|Gross primary enrollment rate|1.5|1.2|-0.3|-|
|Life expectancy|1.3|0.9|-0.4|-|
|Control of corruption|-7.8|2.1|9.9|+|
|Government effectiveness|16.2|3.1|-13.1|<br>-|

Source: Authors’ calculation based on data from different sources as described in the text.

> 13 <u>https://unctadstat.unctad.org/FR/Index.html</u>

> 14 <u>https://www.doingbusiness.org/</u>

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- 13 -

As usual (e.g., McFadden 1974b), the utility is assumed to comprise a deterministic term, _V_ , and a random error _ε_ : _U ij_ ( _m_ )  _Vij_ ( _m_ )   _ij_ ( _m_ ) . When the error term is distributed according to the type I extreme value distribution, the probability that a firm chooses mode m is given by: The following linear specification is assumed for the deterministic part: where _Xij_ contains firm- and transaction-specific characteristics.

Our data were collected by carrying out an enterprise survey in February to May 2016. The survey aimed at about 500 firms focused on light manufacturers and agribusinesses in major urban areas of 18 regions.<sup>5</sup> Based on the customer databases of the two rail companies as well as with freight forwarders and clearing agents, firms were randomly selected in each major urban area. The subsample size was determined in proportion to the size of each city. In the survey, each firm was asked about not only its basic characteristics, such as year of establishment and ownership, but also its chosen transport mode and transport spending for each type of transaction.

As discussed above, only a small number of firms are currently directly using railway services for shipping their freights, because of the diminished rail operations and the poor quality of the services. In the current analysis, both direct and indirect rail users are considered as beneficiaries. Some are directly using rail services, and others have

> 5 Arusha, Dar es Salaam, Dodoma, Iringa, Bukoba, Kigoma, Moshi, Babati, Musoma, Mbeya, Morogoro, Mwanza, Sumbawanga, Shinyanga, Singida, Songea, Tabora, and Tanga.
We calculated the impact of higher investment growth. The investment growth path is taken from the Ministry of Economy and Development’s projections up to year 2025 and extrapolated afterwards. These forecasts imply that the ratio of investment to potential GDP is expected to increase from 23 percent<sup>6</sup> in 2017 to 34 percent by 2028. Since 1990, more than 70 countries have experienced such an increase. Other than the well-known example of China, other relevant examples include Indonesia, India, the Republic of Korea, Saudi Arabia and Turkey. All these countries experienced over a 11 percentage points increase in their investment rates in 11 years.

The increase in the investment/GDP ratio raises potential growth by 0.2 percentage point in 2020 and 0.6 percentage point in 2028. The cumulative impact of the higher investment rate is around 4 percent of potential output during 2018-2028.

# **D. Productivity**

Russian TFP growth began slowing down around the global financial crisis period in 2008-09, and declined to 1.3 percent in 2017. The decline of TFP growth in Russia is supported by firmlevel data (World Bank 2016): firm-level TFP has been declining since 2007 in all sectors. At the same time, productivity dispersion, especially in the service sector, has risen since 2005, which may imply increasing economic distortions. These economic distortions ultimately create an uneven playing field, limiting the entry and expansion of more efficient firms and the exit of less efficient ones. It is expected to decline further to 1.1 percent in 2028, as the declining share of young working age population will weigh on TFP.

We assumed substantial strengthening of institutions for regulation of private enterprises and successful implementation of policies focused on (1) leveling the playing field for firms by stimulating competitive markets and eliminating barriers to the entry of productive firms and the exit of unproductive incumbents; and (2) upgrading firm capabilities, such as innovation, and technology adoption and better managerial skills, in order to help firms, especially start-ups and SMEs, become more competitive.

> 6 23 percent refers to the investment to GDP ratio using 2010 PPP numbers. Russia’s nominal investment to GDP ratio was 21.6 percent in 2017.

14
across rich vs. poor countries and across small vs. large countries is estimated with income and country size assumed to be constant over time; only the number of required documents is allowed to vary temporally. This is discussed in more detail below.

All standard errors used for computing the significance levels of the estimated coefficient values are Huber-White robust. Throughout the paper, significance level is denoted by<sup>***</sup> (1 percent or less),<sup>**</sup> (5 percent or less) and<sup>*</sup> (10 percent or less).

A formal definition of all the variables used in the paper is provided in Table 1. Summary statistics of the main variables and the correlation between the main explanatory variables are provided in Table 2 and Table 3, respectively.

# 2.1 _Dependent variable_

The dependent variable equals the log of the time (recorded in calendar days) it takes to clear all procedures to export and import ( _Timeit_ ). The subscripts _i,t_ denote the country and year, respectively. As mentioned above, the time span for the variable is 2005 to 2010. However, since we use lagged values of the explanatory variable, number of required documents, we lose the first year (2005) values of _Time_ . The data source for the variable is World Bank’s Doing Business project. We note that _Time_ includes the time cost of all procedures as well as the waiting time between procedures (for example, during unloading of the cargo). Hence, only part of the variation in the time cost of shipments across countries and time can be explained by the variation in the number of required documents.

In the full sample, the mean value of _Time_ equals 3.98 and the standard deviation is .51. Averaging over time, the value of _Time_ is highest for Iraq (5.27) and lowest for Panama (2.89). Computing the annual change in the value of _Time_ , the change averages -.047 or about 1.2 5
# **List of Figures**

<!-- Start of picture text --> Figure 1: Sri Lanka Growth and Poverty<br>A. GDP Growth B. Poverty Indicators<br>(Annual percentage change) (percent of population)<br>10 24 22.7<br>22<br>20<br>8<br>7.46 18<br>15.5<br>6.28 16<br>6<br>14<br>5.69<br>12<br>4 10 8.2<br>8 6.1<br>5.1<br>6<br>2<br>3.1<br>4<br>1.6 1.7<br>2 1.0 0.9 0.5 0.3<br>0<br>0<br>Headcount Poverty gap Severity<br>-2<br>2002 2006/07 2009/10 2012/13<br>Source: World Development Indicators Source: Own estimates based on HIES 2002, 2006/7, 2009/10<br>and 2012/13.<br>Note: Comparable regions and comparable consumption<br>aggregate<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Figure 2: <u>Growth Incidence Curve: Per capita consumption, 2002-2012/13</u> <!-- Start of picture text --> 5<br>4<br>3<br>2 Growth Incidence<br>Mean growth rate<br>1<br><!-- End of picture text --> Source: Own estimates based on HIES 2002 and 2012/13. Notes: Comparable regions and comparable consumption aggregate.

16
disease itself. Abay et al. (2020) investigate the economic consequences of the pandemic by using Google search data to estimate the eects of the crisis on the demand for dierent services across 182 countries.

The remainder of the paper is organized as follows. Section 2 provides a simple conceptual framework that explicitly anchors the comparison between lost-years and povertyyears on standard social welfare analysis. Section 3 presents mortality and poverty estimates as of early June 2020, rst for a set of six countries (three developed and three developing) for which Covid-19 mortality data disaggregated by age are available (Section 3.1) and then for the rest of the world, using the age-distribution of deaths from population pyramids and infection-to-fatality ratios documented by Salje et al. (2020) and Verity et al. (2020) (Section 3.2). In both samples, the number of poverty-years is almost always at least 10 times larger than the number of lost-years. In many cases, the PY/LY ratio is above 100 or even 1,000. This suggests that, for most countries in the world, the welfare losses from the Covid-19 pandemic arise disproportionately from increases in poverty. This section also documents that the trade-o between human lives and poverty costs varies substantially with countries' GDP per capita, with developed countries facing both much larger mortality costs and much smaller poverty costs than developing countries - suggesting that the best policy responses may dier by country.

Section 4 then compares the estimated welfare consequences as of early June to a counterfactual no-intervention scenario in which contagion only stops when herd immunity is reached.<sup>11</sup> This permits a cleaner comparison of potential mortality burdens across dierent countries, since looking only at current mortality is hampered by the fact that countries are in dierent stages of the epidemic. In most countries, we nd that the number of lost-years under no-intervention are considerably larger than the sum of the lost-years and poverty-years generated as of early June. This implies that, even if we conservatively assume that the no-intervention scenario has no poverty consequences, its negative welfare consequences are larger than the current welfare consequences as of early June.<sup>12</sup> This strongly suggests that no-intervention was - or would have been - a suboptimal policy, particularly in richer countries. Section 5 concludes.

# 2 A simple conceptual framework

This section briey explains how our empirical comparison between years of life lost (LY) and additional years lived in poverty (PY) can be interpreted in terms of a standard

> 11We follow Banerjee et al. (2020), whose no-intervention scenario assumes that herd immunity for Covid-19 comes about when 80% of the population is immunized.

> 12Coming up with a convincing counterfactual under alternative policies than the one actually implemented is, of course, challenging. Nonetheless, several papers are now trying to model how behaviors and macroeconomic outcomes change during pandemics, using such counterfactuals (see Eichenbaum et al. (2020) or Garibaldi et al. (2020)).

5
group Gini component.<sup>7</sup> This inference should not imply a disinterest in actual distribution at the top; indeed, we will assess the empirical support for it in section 4.

The inequality possibility frontier can also serve as a measure of inequality with a clear intuitive economic meaning. Normally, measures of inequality reach their extreme values when all but one individual appropriates the entire income of a community. Such extreme values are obviously just theoretical and devoid of an economic meaning since no society could function in such a state. Moreover, that one person who appropriated the entire income would soon remain alone (all others, living for a short while at zero income, having died), and after his death inequality would fall to zero, and society indeed would cease to exist. The inequality possibility frontier avoids this problem by charting maximum values of inequality compatible with the maintenance of a society (however unequal), and thus represents maximum inequality that is dynamically sustainable.<sup>8</sup>

# **3. Social Tables and Inequality Measures**

Income distribution data based on household surveys are, of course, unavailable for pre-industrial societies. The earliest household surveys of income and expenditures date from the late eighteenth century in England and the mid nineteenth century in other countries. We believe that the best estimates of ancient inequalities can be obtained from what are called _social tables_ (or, as William Petty (1676) called it more than three centuries ago, _political arithmetick_ ) where various social classes are ranked from the

> 7 Moreover, in the empirical work below, we shall be using mean incomes of social classes to calculate the estimates of ancient inequalities, thus making an assumption equivalent to the one made in the derivation of the inequality possibility frontier.

> 8 We owe this interpretation to Martin Ravallion.

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# **Annex Table: Example of detail of project documents: Monitorable objectives at various levels in the World Bank – People’s Republic of China Hubei Shiman Highway Project to Integrate a central province with poorer regions**

# **Social objectives**

- Increased output of agriculture and industrial goods

- Increased school enrollment

- Increase of number of persons served by medical facilities

# **Project objectives**

# <u>Roads improvements</u>

- Kilometers of roads maintained with new techniques,

- Frequency of maintenance

- <u>Roads management</u>

Number of staff trained Management, financing systems installed <u>Safety and Safety Management</u>

- Installation of a safety management plan

- Numbers of vehicles inspected

# **Work schedule**

Percentage of existing road freight and passenger transport enterprises systematically screened in accordance with safety requirements, procedures and monitoring arrangements of the Safety Review Process:

- Targets: Year 4, 50%; Year 5, 75%; Year 6, 100%.

- Frequency of reports: Annual reports.

# **Results objectives, improvements of**

- Average daily traffic on different routes

- Number of days roads, bridges are closed

- Breakdown rates

- Increased availability of common carrier services; trucks and busses

- Transport costs, rates charged

- ––––––––––––

Source: World Bank (2004) Project Appraisal Document, Report Number: 27681CHA, May 25, 2004 11272 – 2376 - 8876 40
# **_4.2 Population and Economic Development._**

The Brantas basin is considered a strategic basin of Indonesia and East Java. Comprising 24.6% of East Java’s land area, it has 23% of the province’s forest land and 55.8% of its arable land (see Table 1). (Ramu 2004) Most arable land in the basin is used for productive farming (38%), while the remainder is used for forest, settlement, and non-agricultural activities. (Rusfandi 2000) The agricultural economy centers on paddy cultivation, nearly all of which is irrigated (Table 2). In 2000, the Brantas basin made up 32% of East Java’s total rice production and 5% of that of Indonesia. Aside from rice, important food and cash crops include maize, cassava, soybean, peanuts, tobacco, coffee, and sugarcane.

**Table 1:  Land Use Data (hectare)**

|**Location**|**Forest**|**Plantation**|**Agricultural**<br>**Land**|**Homestead**|**Others**|**Total**|
|---|---|---|---|---|---|---|
|East Java|1,346,500|859,600|1,157,100|564,960|864,038|4,792,198|
|Brantas Basin|309,400|31,400|645,800|170,100|23,300|1,180,000|

**Source:** Land Use Plan, East Java.

**Table 2: Production of Major Crops (1000 metric tons)**

|**Location**|**1970**|**1980**|**1990**|**2000**|
|---|---|---|---|---|
|**_Rice_**|||||
|Indonesia|26.39|29.77|45.18|51.90|
|East Java|4.66|6.28|8.23|9.22|
|Brantas Basin|1.43|2.29|2.43|2.99|
|**_Maize_**|||||
|Indonesia|2.60|4.01|6.73|9.68|
|East Java|1.05|1.69|2.58|3.49|
|Brantas Basin|0.03|0.40|0.49|1.12|
|**_Cassava_**|||||
|Indonesia|10.69|13.53|15.83|16.09|
|East Java|3.33|4.03|3.71|3.62|
|Brantas Basin|0.09|1.03|0.86|0.95|

**Source:** Statistical Year Book-2001, Central Bureau of Statistics.

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