rafmacalaba/datause-encoder-data huggingface dataset:
Data on macroeconomic performance, fiscal stance, and other types of external shocks (used as controls in part of the analysis) come from various sources. Real GDP per-capita is measured in constant 2000 U.S. dollars and obtained from the Bank (2008) World Development Indicators (WDI). The terms-of-trade index is the ratio of export prices to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1) and updated using the terms-oftrade data from WDI.

Data on government expenditures and revenues come from WDI, IFS, and EIU. Data on total government debt come mainly from Panizza et al. (2008), complemented with data from WDI, IFS, and EIU. Government expenses are cash payments for goods and services incurred by the government, including wages compensation and interest payments. Revenues include receipts from taxes, social contributions and fees, excluding grants. Data on a country’s CPI and inflation rate come from WDI. Official assistance and grants are not included in our analysis so that expenditure and deficit are measured before grants. This is an advantage considering that movements in them may generate movements in deficits that are not related to fiscal sustainability. Official assistance is not included separately as a variable due to its unavailability for developed countries that constitute a major part of our sample.

Finally, data on money market, discount, and deposit interest rates come from the International Monetary Fund (2010) International Financial Statistics. To increase the crosscountry coverage of our sample, we select from the three definitions the interest rate series with the longest spell during the sample period, with preference for the money market rates when two or more series had the same coverage. Summary statistics for these variables for the sample of countries during the period of analysis are presented in Table 1. To improve coverage on all macroeconomic and disaster variables, the final sample used in the econometric analysis below is restricted to the post Bretton Woods, 1975-2006 period.

Table 2 takes a first look at the data by comparing, within the sample, the average macroeconomic performance in years with and without disasters. The results show that expenditures grow slightly faster in years with Geological and Climatic disasters, but not significantly so. The year of a geological disaster, expenditures grow 5.6 percent on average, compared to only 2.6 percent for the remaining years. However, both averages have wide 12
<!-- Start of picture text --> Ethiopia 0.6 Tanzania<br>0.8<br>0.4<br>0.6<br>0.4 0.2<br>0.2 0<br>0 -0.2<br>-0.2<br>-0.4<br>-0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-1 -1<br>-1.2<br>-1.2<br>1.5 Uganda<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>-2.5<br>1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003<br>1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002<br><!-- End of picture text --> Note: Undervaluation zone is above zero. Source: Nguyen (2014) and Rodrik (2008).

26
|Lower MangokyAgricultural Development Project|
|---|
|Lower MangokyAgricultural Development Project|
|HighwayProject(05)|
|HighwayProject(05)|
|Industrial Development Bank Project(01)|
|Industrial Development Bank Project(01)|
|Petroleum Exploration Project|
|Petroleum Exploration Project|
|Water Supplyand Sanitation Project(01)|
|Water Supplyand Sanitation Project(01)|
|Agricultural Credit Project(01)|
|Agricultural Credit Project(01)|
|Agricultural Credit Project(01)|
|Studyof the Plain of Antananarivo Technical Assistance|
|Studyof the Plain of Antananarivo Technical Assistance|
|Studyof the Plain of Antananarivo Technical Assistance|
|Mangoro ForestryProject(02)|
|Mangoro ForestryProject(02)|
|Mangoro ForestryProject(02)|
|Accountingand Audit Organization and TrainingProject|
|Accountingand Audit Organization and TrainingProject|
|Accountingand Audit Organization and TrainingProject|
|Accountingand Audit Organization and TrainingProject|
|Accountingand Audit Organization and TrainingProject|
|Village Livestock and Rural Development Project(02)|
|Village Livestock and Rural Development Project(02)|
|Village Livestock and Rural Development Project(02)|
|Village Livestock and Rural Development Project(02)|
|Village Livestock and Rural Development Project(02)|
|Agricultural Institutions Technical Assistance Project|
|Agricultural Institutions Technical Assistance Project|
|Agricultural Institutions Technical Assistance Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Lac Alaotra Rice Intensification Project|
|Tsimiroro HeavyOil Exploration Project|
|Tsimiroro HeavyOil Exploration Project|
|Tsimiroro HeavyOil Exploration Project|
|Tsimiroro HeavyOil Exploration Project|
|Tsimiroro HeavyOil Exploration Project|
|Tsimiroro HeavyOil Exploration Project|
|HighwayProject(06)|
# **2.3. Drilling deeper – bank-level evidence**

While the aggregate data already give us some indication of the shallowness of African banking systems, bank-level data provide more detailed insights. Comparing a sample of 307 banks from low and lower-middle income countries in Africa and 720 banks from nonAfrican developing countries shows significantly higher liquidity ratios for African banks. Specifically, the ratio of liquid assets to short-term funding and deposits is 42.9% for African banks, as compared to 29.3% for non-African banks. Similarly, African banks are better capitalized, with an equity-asset ratio of 14.3%, compared to 13.3% for non-African banks. These comparisons give a picture of African banks that are well capitalized, over-liquid and provide only limited lending to the real economy, as highlighted previously by Honohan and Beck (2007) and Beck et al. (2011).

As discussed above, African banks are less efficient than banks in other developing regions of the world and financial services are therefore more expensive. A lot of discussion in this context has focused on interest rate spreads and margins, i.e. the difference between lending and deposit interest rates. But what drives high interest rate spreads in Africa? There are two different ways to analyze spreads and margins; one is a decomposition of spreads into the different components, while the other analyzes the relevant underlying bank-, industry-, and country-level traits.

**Table 1: Decomposition of interest rate spreads in Uganda in 2008**

||All Banks|Domestic|Foreign|
|---|---|---|---|
|Average Lending Rate|16.72|18.44|15.24|
|Average Deposit Rate|1.97|2.31|1.90|
|Spread|14.75|16.13|13.34|
|Overhead Costs|4.66|2.74|6.22|
|Loan-loss Provisions|0.72|0.38|1.01|
|Reserve Requirements|0.22|0.26|0.21|
|Taxes|2.51|3.34|1.64|
|Profit Margin|6.65|9.42|4.26|

Source: Cull and Trandafir (2010).

14
**Table G4: Relationship between food security measures and height-for-age z-scores (Pakistan): Limited Controls Version**

|VARIABLES|(1)|(2)|(3)<br>Height-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|Per capita kcal|0.040<br>(0.043)||||||
|1-SSR||0.072<br>(0.050)|||||
|1 - SSEXR|||0.065<br>(0.050)||||
|DDS||||0.115**<br>(0.053)|||
|PC expenditure|||||0.051<br>(0.048)||
|Food share||||||-0.024<br>(0.047)|
|Rural|-0.149|-0.122|-0.16|-0.122|-0.148|-0.155|
||(0.114)|(0.114)|(0.114)|(0.114)|(0.114)|(0.114)|
|Punjab|-0.997***|-0.976***|-1.005***|-1.010***|-1.004***|-1.006***|
||(0.250)|(0.250)|(0.250)|(0.251)|(0.251)|(0.251)|
|Sindh|-1.300***|-1.242***|-1.271***|-1.304***|-1.285***|-1.293***|
|Khyber Pkahtoon|(0.254)|(0.257)|(0.254)|(0.254)|(0.254)|(0.254)|
|<br>Khaw|-1.664***<br>(0.290)|-1.613***<br>(0.291)|-1.659***<br>(0.289)|-1.730***<br>(0.293)|-1.674***<br>(0.292)|-1.669***<br>(0.292)|
|Constant|-1.203***|-1.256***|-1.203***|-1.208***|-1.204***|-1.197***|
||(0.254)|(0.255)|(0.253)|(0.254)|(0.254)|(0.254)|
|Observations|2,098|2,098|2,098|2,098|2,097|2,097|
|R-squared|0.025|0.026|0.026|0.028|0.026|0.025|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure in total expenditure.

76
Gondim, F. S., Campos, M. O., Flores, T. R., França, G. V., & Medeiros, A. C. (2022). 2019 National Health Survey: progress in monitoring. _Epidemiologia e Serviços de Saúde_ , 31(Special Edtion), 1-3. doi:10.1590/S2237-9622202200001.especial Higgins, S., and Lustig, N. (2018). Allocating Taxes and Transfers and Constructing Income Concepts. Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty, 219.

Higgins, S., and Pereira, C. (2014). The Effects of Brazil’s Taxation and Social Spending on the Distribution of Household Income. Public Finance Review, 42(3), 346–367. <u>https://doi.org/10.1177/1091142113501714</u> Higgins, S., Pereira, C and Cabrera, M. (2020). CEQ Master Workbook: Brazil (2008-2009), CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University). May 2020.

IBGE (2021). Nota técnica 03/2021. PNAD Contínua. Sobre a divulgação da Reponderação da PNAD Contínua em 2021.

IBGE (2022). Nota técnica 04/2022. PNAD Contínua. Sobre as Características gerais dos moradores em 2020 e 2021.

International Monetary Fund (2023). Government Finance Statistics Yearbook (GFSY) [Data set]. https://data.imf.org/?sk=a0867067-d23c-4ebc-ad23-d3b015045405 <mark>Kaufman, P. R., MacDonald, J. M., Lutz, S. M., & Smallwood, D. M. (1997).</mark> _<mark>Do the poor pay more for food? Item selection and price differences affect low-income household food costs</mark>_ <mark>(No. 1473-2016120710).</mark> <u><mark>https://www.ers.usda.gov/webdocs/publications/40816/32372_aer759.pdf?v=0</mark></u> <mark>Kirti, Divya et al. (2022). Tracking Economic and Financial Policies During COVID-19: An AnnouncementLevel Database. IMF Working Paper No. 2022/114. Available at:</mark> <u><mark>https://www.imf.org/en/Publications/WP/Issues/2022/06/03/Tracking-Economic-and-Financial-PoliciesDuring-COVID-19-An-Announcement-Level-Database-518896</mark></u> Lambert, Peter (2001). The Distribution and Redistribution of Income, 3rd ed. Manchester University Press.

<mark>Lara Ibarra, Gabriel, Nistha Sinha, Rana Fayez and Jon Jellema (2019). Impact of Fiscal Policy on Inequality and Poverty in the Arab Republic of Egypt. Policy Research Working Paper 8824, World Bank, Washington, DC. Available at:</mark> <u><mark>https://documents1.worldbank.org/curated/en/617221555617293604/pdf/Impact-of-Fiscal-Policy-onInequality-and-Poverty-in-the-Arab-Republic-of-Egypt.pdf</mark></u> <mark>Lara Ibarra, Gabriel, Rafael Macedo Rubião, and Eduardo Fleury (2021). Indirect Tax Incidence in Brazil: Assessing the Distributional Effects of Potential Tax Reforms.</mark> _<mark>Policy Research Working Paper 9891</mark>_ <mark>, World Bank, Washington, DC. Available at: https://openknowledge.worldbank.org/handle/10986/36783</mark> <mark>Lara Ibarra, Gabriel; Campante Cardoso Vale, Ricardo. 2022. Brazil 2020 Data Update: Methodological Adjustments to the World Bank’s Poverty and Inequality Estimates (English). Global Poverty Monitoring Technical Note; no. 21 Washington, D.C. : World Bank</mark> 42
# Macro‐Financial Aspects of Climate Change<sup>1</sup>

Erik Feyen Robert Utz Igor Zuccardi Huertas Olena Bogdan Jisung Moon JEL Classification: E69, G19, Q54 Keywords: macro‐financial risks, climate‐related risks, climate change

> 1 This paper was prepared as a _Special Topic_ of the World Bank’s October 2019 Macro‐Financial Review. The team is grateful for valuable inputs and comments by Ceyla Pazarbasioglu (EFI), Marcello Estevao, Christian Eigen‐Zucchi, Dirk Heine, Doerte Doemeland, Miria Pigato (MTI), Alfonso Garcia Samantha Cook, Martijn Regelink, Fiona Stewart, Samantha Power, Henk Reinders (FCI), Stephane Hallegatte (SD‐SCCD1), and Grzegorz Peszko (ENV‐SENGL).
problems”? On asylum seekers and elections,” _European Journal of Political Economy_ , 67, 101981.

UNHCR (2023) “Refugee statistics,” https://www.unhcr.org/ refugee-statistics/.

- (2025) “Global Trends,” _United Nations High Commissioner for Refugees_ , https:

- //www.unhcr.org/us/global-trends.

- Urbina, Maria, Sandra Rozo, Andres Moya, and Ana Ib ́a ̃nez (2023) “Least Protected, Most Affected: Impacts of Migration Regularization Programs on Pandemic Resilience,” .

- _American Economic Association Papers Proceedings_

- Williamson, Scott (2020) “Countering misperceptions to reduce prejudice: An experiment on attitudes toward Muslim Americans,” _Journal of Experimental Political Science_ , 7 (3), 167–178.

- Williamson, Scott, Claire L. Adida, Adeline Lo, Melina R. Platas, Lauren Prather, and Seth. Werfel (2021) “Family Matters: How Immigrant Histories Can Promote Inclusion,” _American Political Science Review_ , 115 (2), 686–693, 10.1017/S0003055420001057.

- Zhou, Yang-Yang and Guy Grossman (2021) “When Refugee Exposure Increases Incumbent Support through Development: Evidence from Uganda,” _OSF Preprints_ , 7, .

- 10.31219/osf.io/94tpy

- Zhou, Yang-Yang, Guy Grossman, and Shuning Ge (2023) “Inclusive refugee-hosting can improve local development and prevent public backlash,” _World Development_ , 166, 106203.

- Zhou, Yang-Yang and Jason Lyall (2025) “Prolonged contact does not reshape locals’ attitudes toward migrants in wartime settings,” _American Journal of Political Science_ , 69 (1), 210–222, https://doi.org/10.1111/ajps.12862.

55
slightly lower for origin countries in Africa and in low- and lower-middle income countries, and higher for GCC destination countries. The proportions are similar across time as well (table B.1).

Table 1: Structure of migration and BLA datasets

||Full sample<br>(1)|African origins<br>(2)|LLMIC<br>(3)|UMHIC<br>GC<br>(4)|C destinations<br>(5)|
|---|---|---|---|---|---|
|||Bilater|al migratio|n data||
|Share of non empty corridors|0.386|0.352|0.370|0.397|0.353|
|Share of regular corridors|0.122|0.091|0.103|0.135|0.088|
|||Bilateral la|bor agree|ment data||
|Share of corridors with BLA|0.016|0.008|0.011|0.019|0.034|
|Share of regular corridors with BLA|0.089|0.055|0.063|0.103|0.192|
|||Migrant sto|ck (in milli|on) in 2020||
|All corridors|261.1|36.8|119.8|141.3|29.8|
|Regular corridors|242.0|33.4|108.7|133.3|27.3|
|Corridors with ever a BLA|81.8|8.1|34.9|46.9|16.5|

_Notes_ : The sample includes 39,006 origin-destination corridors for 7 periods from 1960 to 2020 at ten-year intervals. Regular corridors are corridors with at least 10 migrants in the corridor for each of the periods in the data.

# **3 Empirical specification**

## **3.1 Baseline analysis**

To investigate the impact of signing Bilateral Labor Agreement (BLAs) on migration flows, we rely on a structural gravity model for migration derived from the Random Utility Framework (Beine, Bertoli and Fern ́andez-Huertas Moraga, 2016). This framework posits that migration flows between two locations **i** (origin) and **j** (destination) depend on the relative attractiveness of **j** over **i** net of the migration cost between **i** and **j** . Consequently, the implied baseline empirical equation to be tested is written as: where _Mijt_ is the migration stock from country _i_ to country _j_ measured at time _t ∈{_ 1960, 1970, 1980, 1990, 2000, 2010, 2020 _}_ . _BLAijt_ indicates whether _i_ and _j_ have signed a BLA at any time in the decade preceding _t_ . That is, _BLAij_ 2000 indicates whether country pair _ij_ signed a BLA between 1991 and 2000. The _αit, αjt,_ and _αij_ represent the origin-time, destination9
# **Estimating the Half-life of Theoretically Founded Real Exchange Rate Misalignments**

## **Megumi Kubota**<sup>*</sup>

### **JEL Classification:** F31, F41

**Key Words:** Misalignment, Fundamentals, the HBS Effect, an Unrestricted VECM, A Half-life

**Sector Board:** EPOL

> *Kubota: The World Bank, Poverty and Economic Management, Macroeconomics 1 Unit, Europe and Central Asia (ECA). 1818 H Street, NW, Washington, DC, 20433 USA; mkubota@worldbank.org; tel: +(1)202-473-4844. The author is grateful to Michael Wickens as well as César Calderón, Ronald MacDonald, Neil Rankin and Peter N. Smith. The data is updated although this paper is based on the University of York Discussion Paper No. 2009/24 and of her PhD thesis “Real Exchange Rate Misalignments” at the University of York in 2009. This paper was presented at the 2013 Annual Conference of Royal Economic Society at Royal Holloway, University of London. The views expressed in this paper are those of the author and do not necessarily reflect those of the World Bank or its Board of Directors. The usual disclaimer applies and all errors are hers.
these are precisely the teachers who have more power and who are therefore harder to hold accountable. Second, absence is higher in remote schools that are farther away from Ministry of Education offices, and are probably less subject to official supervision. Third, we sometimes find higher absence among teachers in communities where the students’ parents are less educated, which may reflect communities that are less able to monitor and enforce performance of teachers.

Another indicator that accountability matters for absence is the gap between teacher attendance in public and private schools. In their nationally representative sample of Indian schools, Kremer _et al_ (2005) find that private‐school teachers have absence rates one‐third lower than their public‐school counterparts in the same villages – despite the fact that private‐school salaries are only one‐fifth to one‐quarter that of public‐school salaries. In Pakistan, Das, Pandey, and Zajonc (2006) find an even larger attendance gap: public‐school teachers are absent 3.2 days per month, compared with an already high 1.8 days per month for private‐school teachers. A plausible explanation for this difference is that private schools can fire teachers for poor effort, whereas public schools cannot. In India, despite the very high absence rate, only one in 3000 public‐school head teachers had ever fired a teacher for excessive absence (Kremer _et al_ 2005).

These insights from the data are consistent with analyses of the incentives for performance faced by many teachers. Vegas (2007), in her discussion of teacher pay structures, notes that the great bulk of compensation is unrelated to assessments of how well the teacher is performing, in terms of either effort or student outcomes. The analysis of teacher incentives and management by Alcazar _et al_ (2006) shows that Peru fits this characterization, with little incentive for performance. Even without pay incentives, the possibility of promotion could provide motivation to at least the better teachers, but in practice promotions are awarded not only on the basis of merit but also on the basis of connections and corruption.

In short, teachers tend not to be held accountable for their performance in the classroom. Not all teachers take advantage of this lack of accountability, of course. Most of the time, most teachers are at their posts, trying to teach in what are often difficult conditions. The problem is that when teachers do lack the professionalism and sense of duty to meet their responsibilities, they are not usually held accountable.

# **_Absenteeism signals broader accountability problems_**

Above, we discussed some estimates of the costs of excessive teacher absence in terms of student learning. But beyond those costs, high levels of absence are likely to signal a broader accountability and incentive problem that has other costs as well.
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# **DISASTER RISK FINANCING AND CONTINGENT CREDIT: A DYNAMIC ANALYSIS**

DANIEL CLARKE<sup>1</sup> OLIVIER MAHUL<sup>2</sup> JEL codes: G22, G32, H60, O16.

Keywords: Disaster risk financing, contingent credit, developing countries, natural disasters, dynamic financial analysis, insurance.

> 1 Department of Statistics and Centre for the Study of African Economies, University of Oxford _._

> 2 Program Coordinator, Disaster Risk Financing and Insurance, GCMNB and GFDRR, World Bank.
If decisions do not always conform to mainstream commentators' views on how predation should be identified or when price discrimination is anticompetitive, there is ample attention to issues economic theory suggests are central to the analytical task. International trade law, in contrast, has been strikingly impervious to even the most elementary aspects of economic analysis. Antidumping law is exemplary. (pp. 880-1)

# **Many possibilities for procedural error**

The legal basis for an investigation of dumping duty is a detailed examination of price data, involving literally hundred of choices as to how to adjust for one factor or another: differences in product characteristics between the export and the home market sales, differences in credit terms, in dealing through a distributor versus selling directly to users, exchange rate variations, etc. Likewise, an investigation of injury involves many such considerations, and administrators, in order to avoid being drowned in detail, must depend on rules of thumb."2 An antiduniping investigation is thus a long sequence of technical adjustments, not guided by any overarching economic or legal sense of objective. Furthermore, the GATT code has significant "transparency" requirements: if an investigation specifies what it did in each instance in which an adjustment is made, it runs the risk of being at odds with what the panel concludes is the relevant detail in the code. And if the investigation is vague about these adjustments, it risks violating the transparency requirements.

# **Free trade, not protection depends on the loopholes**

While it is instinctive to presume that those seeking import restrictions have been winning by deceit and trickery, cynically exploiting loopholes and pressing vulnerable members of Congress to

> **12** Hudec, Kennedy and Sgarbossa (1993) provide documentation.

11