rafmacalaba/datause-encoder-data huggingface dataset:
|2010|France (south)|25|1,500|1,070|71|
|---|---|---|---|---|---|
|2010|Portugal (Madeira)|43|1,350|70|5|
|2010|Germany, Czech Republic|16|1,300|500|38|
|_America_||||||
|2001|USA: Tropical Storm Allison (Houston, TX)|25|6,000|3,500|58|
|2001|Argentina|1|750||<1|
|2005|Canada (Alberta)|4|860|190|22|
|_2005_|_USA: Hurricane Katrina (Gulf Coast)_|_1,322_|_125,000_|_62,200_|_50 *_|
|2007|Mexico (Tabasco)|22|2,500|350|14|
|2008|USA (Midwest)|24|10,000|500|5|
|2008|Brazil|131|750|470|63|
|2009|USA, Canada (Red River)|3|1,000|75|7|
|2010|Colombia|195|2,000|140|7|
|2010|Brazil|72|870||<1|
|2011|Brazil|842|<1,000||<1|
|_Australia_||||||
|2007|Australia (East Coast)|9|1,300|780|60 *|
|2008|Australia (Queensland)|2|1,700|1,340|79|
|2010/11|Australia (Queensland, Victoria)|30|>10,000|5,000|≈50**|
|_Africa_||||||
|2000|Mozambique, Zimbabwe, South Africa|1,000|715|50|7|
|2001|Algeria|750|300||<1|
|2007|Sudan|150|300||<1|
|2009|West Africa|215|300||<1|

**_© Wolfgang Kron, Head of Research, Hydrological Hazards Munich Re, Munich, Germany_**

Source: Kron 2011 Notwithstanding these issues, reported economic and insurance losses are seen to have increased over time as shown below in Figure 8. The total losses in exceptional years such as 1998 and 2010 can exceed $40 billion. Clearly the cost of flooding will be related to the nature of exposed assets and tends to be higher, per capita, in wealthier nations. It may therefore be more constructive to consider economic and financial losses in relative terms, as a proportion of national GDP. In this way, the hardship caused by flooding may be more accurately represented. In the longer term, some argue that major natural disasters can be seen as a spur to economic activity and technological renewal (Kim 2010; Crompton and McAneney 2008; Noy and Bang 2010). However, the success of countries in achieving growth through rebuilding and redevelopment after tragic disasters is limited by the capacity of nations to invest in rebuilding or by the volume of international aid and capital investment. In addition, such investment may divert funds from other development projects.

12
there are some good reasons for such arguments. Although, as discussed earlier, it is not as simple as some seem to think to design the ‘right’ taxes on alcohol and tobacco, there is much to be said for rationalizing and increasing such taxes on both revenue and health grounds. Moreover, increased expenditure on public health, especially for low‐income persons, is also a generally good idea in developing countries in terms of both equity and increasing productivity. The idea of tying increases in ‘good’ taxes that themselves have beneficial health benefits to expanded ‘good’ expenditure on improving low‐income health thus seems both logical and attractive. Equally importantly, since the presumed progressivity of expanding health spending compensates at least to some extent for the regressivity of the taxes, tying the two together may make the tax reform more politically acceptable, as the recent Philippine reform has demonstrated.

Other instances of earmarking excises on alcohol and tobacco, not always for health purposes, may be found in the region. For example, Indonesia allocates a 2% excise for health to local governments and since 2014 has also earmarked a 10% tax for regional governments. Thailand allocates some alcohol and tobacco taxes not only to health but to local provinces and even for public TV broadcasting. The Republic of Korea earmarks part of the alcohol excise to education, and also imposes several special earmarked taxes on tobacco including one collected by the Ministry of Health and Welfare with the revenue earmarked to finance public health information. Taiwan, China, also earmarks some tobacco tax receipts to a variety of medical services including health promotion. Cambodia even earmarks some excise taxes for public lighting.<sup>13</sup> Nonetheless, despite the frequency with which such arguments are made by proponents of increasing health expenditures as well as by politicians attempting to make tax increases more palatable by associating them with such expenditures, in reality few countries earmark revenues from taxes on alcohol and tobacco taxes to health‐related activities. One reason is simply because, apart from the short‐term political push ‐‐perhaps essential in some cases‐‐ such earmarking may provide for excise tax reform, there is no long‐term rationale for tying this source of revenue to health expenditures ‐‐ let alone to such completely unrelated activities as public lighting and public TV, as has been done in other ASEAN countries (Box 2) or to education and local finance as in Korea (Bird and Jun 2007).

Even though one can think of a tenuous ‘benefit’ rationale for some such linkages – for instance, between tobacco taxes and expenditures attributable to lung cancer or those devoted to anti‐smoking

> 13 This information comes from the sources cited in Box 2 as well as ITIC (2013).

20
# How Delayed Learning about Climate Uncertainty Impacts Decarbonization Investment Strategies<sup>∗</sup>

Adam Michael Bauer<sup>†1,2</sup> , Florent McIsaac<sup>2</sup> , and St ́ephane Hallegatte<sup>2</sup> 1 _Department of Physics, University of Illinois Urbana-Champaign, 1110 W Green St Loomis Laboratory, Urbana, IL 61801_

> 2 _World Bank Group, Washington DC_

Originally published in the Policy Research Working Paper Series on _April 2024._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL:** E10, Q50, Q54, Q58

**Keywords:** green investment, adjustment costs, stochastic modeling, climate risk, carbon price

> ∗The authors thank Yongyang Cai, Kevin Carey, Simon Dietz, Roy Dong, Sam Okullo, Cristian Proistosescu, Adrien Vogt-Schilb, and Gernot Wagner for helpful discussions related to this work. The authors also thank Mar ́ıa Reyes Retana Torre for verifying the reproducibility of our results. The authors acknowledge financial support from the Climate Support Facility of the World Bank. AMB acknowledges financial support from a National Science Foundation Graduate Research Fellowship (Grant No. DGE 21-46756). The views expressed in this paper are the sole responsibility of the authors. They do not necessarily reflect the views of the World Bank, its executive directors, or the countries they represent. All remaining errors are the sole responsibility of the authors.

> †Corresponding author: adammb4@illinois.edu
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# Do Fiscal Multipliers Depend on Fiscal Positions?

Raju Huidrom, M. Ayhan Kose, Jamus J. Lim, and Franziska L. Ohnsorge<sup>∗</sup> Key Words: Fiscal multipliers, fiscal position, state-dependency, Ricardian channel, interest rate channel, business cycle JEL Codes: E62, H50, H60

> ∗Huidrom: World Bank, Development Prospects Group; rhuidrom@worldbank.org. Kose: World Bank, Development Prospects Group; Brookings Institution; CAMA; CEPR; akose@worldbank.org. Lim: World Bank, Development Prospects Group; jlim@worldbank.org; Ohnsorge: World Bank, Development Prospects Group; fohnsorge@worldbank.org. Earlier drafts benefited from comments by Syud Amer Ahmed, Jean-Louis Arcand, Raphael Espinoza, Alejandro Izquierdo, Aart Kraay, Sergio Kurlat, Jaime Marquez, Rahul Mukherjee, Ugo Panizza, Assaf Razin, Luis Serven, Cedric Tille, Carlos Vegh, Guillermo Vuletin, Sebastian Weber, Kei-Mu Yi, Hakan Yilmazkuday, Charles Wyplosz, and seminar participants at the Graduate Institute-Geneva, the 2015 Midwest Macro Conference, and the 2016 Conference on Fiscal Policy and the Macroeconomy at Johns Hopkins SAIS. We thank Kiwako Sakamoto for excellent research assistance. Ethan Ilzetski kindly shared data.
The objective of this paper is two-fold: (i) to analyze the extent of inequality of opportunity among Egyptian children; and (ii) to identify the main circumstances that are beyond the control of children and affecting their development outcomes. Specifically, the paper aims to answer a simple question: what are the chances that an Egyptian child will have adequate access to critical basic services regardless of his or her circumstances at birth, such as gender, place of birth, and family background? It draws on the concepts and ideas developed in the _World Development Report_ (WDR) 2006: _Equity and Development,_ WDR 2007: _Development and the Next Generation,_ and the methodology developed in the recent and growing literature on inequality of opportunity (see, for example, John Roemer, 1998; Barros et al., 2009). The study follows a life cycle approach that identifies an individual’s outcomes and opportunities from conception to adulthood and analyzes the extent to which these outcomes are determined by circumstances beyond individuals ́ control. We use data from Demographic and Health Survey (DHS) and the Household Income, Expenditure and Consumption Survey (HIECS) over time.<sup>1</sup> In the particular case of Egypt, the essence of this paper can be illustrated by visualizing the following scenario. Imagine Amal, a six-year-old girl living in rural Suhag (in Upper Egypt), one of the poorest governorates. Amal has four siblings and lives with her widowed mother who has no formal education and works in subsistence agriculture. Now in turn imagine Karim, a boy, also six years old, living in Heliopolis, an upscale neighborhood in Cairo, Egypt’s largest megacity. Karim has one sibling and lives with his mother and father, both graduates from the American University of Cairo. Karim’s father works for a multinational corporation and his mother in the Egyptian civil service. What are the chances that both Amal and Karim will have access to basic services and early childhood development interventions that will have profound impact on their long term educational, health and labor market outcomes? The paper analyzes the difficulties faced by children like Amal due to circumstances beyond their control, such as their gender, place of birth, and family

> 1 See next section on data and methodology for description of the data sources.

3
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Policy Research Working Paper 9067

# **Abstract**

Embedding management and operational practices survey in a broader firm capabilities survey, this paper finds that an average firm in Croatia scores 0.532 on structured management practices, which is farther from the frontier (0.615 in the United States). This average, however, masks the wide heterogeneity in management practices among firms. Relative to advanced countries, a large share of firms in Croatia are badly managed. Management is particularly worse in services and more so in non-knowledge intensive services. Better managed firms show superior performance: improving the management score from the 10<sup>th</sup> decile to the 90<sup>th</sup> decile is expected to improve sales per employee by 36 percent, profits by 33 percent and the probability to innovate by 11 percent. Likewise, better managed firms more likely use sophisticated technologies and have a higher probability of accessing external finance. What drives firms to improve their management practices? As elsewhere in the world, global linkages of firms matter. However, unlike the evidence in advanced countries, management capabilities in Croatia is negatively associated with firm age, especially in services, indicating the possibility of allocative inefficiency, where learning and selection mechanism does not weed out the badly managed firms perhaps due to the lack of pro-competitive forces.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- _Definition of risk limits:_ setting ceilings for borrowing capacity, funding maturity gaps, exposure to a single source or market and to unsecured borrowing<sup>54</sup> . The bank should set the limits according to its business in terms of location, complexity of the activity, nature of the products, currencies, and markets served, that is, it may set limits on an intra-group basis. The limit framework should also include measures to ensure that, under stress, the liquidity available will exceed the liquidity needs.

- _Information_ : The MIS must be able to provide reliable, comprehensive, and timely information. IT systems are crucial for providing senior management with timely and forward looking information on the liquidity position of the bank and to monitor compliance with the limits and policies established. MIS should be able to:

- Calculate liquidity positions: in all currencies--per branches and subsidiaries, and on an aggregate group basis.

- Provide liquidity positions on an intra-day basis, day-to-day basis, and longer time periods, to be used by liquidity risk management.

- Capture all sources of liquidity risk, including contingent risks.

- Deliver granular information under stress scenarios.

- _Execution and post-execution_ (cost-effectiveness of measures taken) _reporting_ .

- _Intra-day liquidity management_<sup>55</sup> that ensures a bank meets its liquidity payments obligations and receives customers’ inflows on time. Intra-day liquidity management requires appropriate anticipation and planning of flows throughout the day to make sure that cash and collaterals will be available for use in payment and settlement systems in normal and stressed times. Thus, it should:

- Measure the expected daily liquidity inflows and outflows.

- Identify key counterparties, times and circumstances that may pose particular liquidity needs.

- Monitor key positions frequently and forecast potential funding shortfalls.

- _Multiple Currency Liquidity Management._ Cross-currency liquidity management makes institutions heavily reliant on the smooth functioning of foreign exchange markets, and those markets did not always function as expected during the 20072008 crisis. Thus, special attention should be paid when a liquidity event occurs because transferability, convertibility or country risks can abruptly change.

- _Internal audit_ that regularly reviews the implementation and effectiveness of the liquidity strategy.

> 54 Office of  Comptroller of the Currency, ‘The Challenges of Sound Liquidity Risk Management: OCC Expectations and Policy for Community Banks’ , May 2001 55 CEBS, page 41
purchased of a wide range of food items, which were further classified into 17 different food groups. Village prices are proxied by village level mean unit value for each of these food groups, which are constructed by averaging the household level unit value of these food groups within each village. I control for log village price of rice, pulses, big fish, small fish, and egg in the regression functions<sup>14</sup> . Table 1 indicates substantial variation of village prices across rounds, while high and low prices of different food groups are observed at different times of the year. For instance, while the mean rice price is lowest in round 2 and highest in round 3, for pulses the highest is observed in round 2 and lowest in round 4, implying seasonality (if any) differs for different food groups.

**Household size and composition** : Household size is based on number of individuals present in the household in each round. Consistent with nutritional requirements at different stages of life-cycle and activity patterns, males and females are categorized into: children ( _<_ 10 years), adolescents (10 _≤ age <_ 18 years), and adults ( _≥_ 18 years). Dummies for each of these six age-sex categories (with adult male as omitted category) and a dummy for whether a female is pregnant or not, or lactating or not in a given round are used in the econometric analysis. Log household size and share of males and females in each of these age groups (with adult males’ share as omitted category) are used to control for household size and composition. Compared to HES sample, on average an IFPRI sample household is consists of 1-2 more people with highest share of individuals in the adult category. As expected, there is very limited variation of household size and composition across rounds within one year. The lactating women outnumbered pregnant women, and these statuses vary across rounds.

**Household landholding, expenditure, and poverty** : Household landholding is a time invariant variable for which information was collected in the first round. The share of landless households is lower in IFPRI than in HES samples. The distribution of landholding is also less unequal in IFPRI sample compared with HES sample with higher share of households with 1-7.5 acres in IFPRI sample. The mean per capita landholding in IFPRI sample is 0.23 acre (which varies slightly across rounds due to limited variation in household size).

Both samples are similar in terms of mean per capita monthly expenditure, which varies significantly across rounds (p-value of t-tests are not reported) in IFPRI sample with the lowest value observed in round 2 and highest value in round 3.

The absolute and hard core poverty lines based on direct calorie intake (DCI) method are 2122 kcal and 1805 kcal per person per day, based on which the poverty incidence is higher in IFPRI compared to HES sample. HES regional upper and lower poverty lines based on cost of

> 14A comprehensive set of village prices were initially included. Subsequently, I have included the food prices that appear to be significant most of the times for the set of nutrients analysed in this paper.

16
- [15] Galiani, Sebastian, Stephen Knack, Lixin Colin Xu, and Ben Zou. 2017. “The Effect of Aid –

- on Growth”. _Journal of Economic Growth_ 22(1), 1 33.

- [16] Guardian. 2009. “G20 Declares Door Shut on Tax Havens.”2 April. http://www.guardian.co.uk/world/2009/apr/02/g20-summit-tax-havens. Accessed on 17 February, 2020.

- [17] Huizinga, Harry and Gaetan Nicodeme, 2004. “Are international deposits tax-driven”. _Journal of Public Economics_ 88, p. 1093-1118.

- [18] Johannesen, Niels. 2014. “Tax Evasion and Swiss Bank Deposits”. _Journal of Public Economics_ 111, p. 46-62.

- [19] Johannesen, Niels and Gabrial Zucman. 2014. “The End of Bank Secrecy? An Evaluation of the G20 Tax Haven Crackdown”. _American Economic Journal: Economic Policy_ 6(1), –

- 65 91.

- [20] Johannesen, Niels and Jukka Pirtill ̈a. 2016. “Capital Flight and Development: An Overview of Concepts, Methods, and Data Sources”. WIDER Working Paper, No. 2016/95.

- [21] Johannesen, Niels, Thomas Tørsløv, and Ludvig Wier. 2018. “Are Less Developed Countries More Exposed to Multinational Tax Avoidance? Method and Evidence from Micro-Data”. WIDER Working Paper, No. 2016/10. ( _World Bank Economic Review_ , forthcoming).

- [22] Johannesen, N. and Stolper, T., 2017. “The deterrence effect of whistleblowing–an event study of leaked customer information from banks in tax havens. ”Working Paper

- [23] Johannesen, N., Langetieg, P., Reck, D., Risch, M. and Slemrod, J., 2018. “Taxing hidden wealth: The consequences of US enforcement initiatives on evasive foreign accounts. ”NBER Working Paper, No. 24366.

- [24] Kersting, Erasmus K. and Christopher Kilby. 2016. “With a Little Help from My Friends: Global Electioneering and World Bank Lending”. _Journal of Development Economics_ 121, –

- 153 165.

- [25] Klitgaard, Robert. 1990. “Tropical Gangsters”. New York: Basic Books.

- [26] Knack, S., 2000. “Aid dependence and the quality of governance: a cross-country empirical analysis.”The World Bank.

- [27] Kraay, Aart. 2012. “How Large is the Government Spending Multiplier?”. _Quarterly Jour-_ –

- _nal of Economics_ 127(2), 829 887.

- [28] Kraay, Aart. 2014. “Government Spending Multipliers in Developing Countries: Evidence from Lending by Official Creditors”. _American Economic Journal: Macroeconomics_ 6(4), –

- 170 208.

21
# **Friend or foe or family? A tale of formal and informal plants in India**<sup>**1**</sup>

**Ejaz Ghani**<sup>**2**</sup> **, Stephen D. O’Connell**<sup>**3**</sup> **, and Gunjan Sharma**<sup>**4**</sup>

JEL Classification—C; D; H; J; L; O; R. Economic Policy (EPOL)

> 1 The authors are grateful to Ana Fernandes, Shanthi Nataraj, Onno Ruhl, Denis Medvedev, Henry Bagazonzya, Luke Simon Jordan, Mandakini Kaul, Michael Haney, Niraj Verma, and Yannick Saleman. They are not responsible for any remaining errors. 2 Ejaz S. Ghani. The World Bank, Washington DC, 20433. E-mail: Eghani@worldbank.org.

3 Stephen D. O’Connell. CUNY Graduate Center, Email: soconnell@gc.cuny.edu.

4 Gunjan Sharma. University of Missouri. Email: gunjan.sharma@gmail.com.
32 Table A2

|APPLICATION<br>D|ISPLACED<br>FUEL|ERR<br>1985|NP<br>ERR<br>1986|W@<br>r-.25<br>i-.13|
|---|---|---|---|---|
|| Mt. Kenya Safari Club|| Fuel Oil|18||<br>9|<br>-37631|
|| Nyali Beach Hotel<br>|| Fuel Oil|I<br>21||<br>11||-52401|
|| Diplomat Cafe<br>|| Electricity||<br>>60||<br>44||<br>32551|
|| Kenya Brewery<br>|| Fuel Oil|1<br>15||<br><5|I-154711|
|| Elliot's Bakery<br>|| Gas Oil||<br>>60||<br>23||802161|
|| Dandora Creamery<br>|| Fuel Oil|1<br>23|1<br>10|I<br>-44041|
|| Health Center<br>|| Electricity||<br>>60||<br>30||<br>65671|
|| Health Center<br>|| Kerosene||<br>>60||<br>>60||461721|
|| University Dormitory|| Gasoil||<br>45||<br>14||3111841|
|| Rural Dormitory<br>|| Wood||<br><15||<br><5||-23311|
|IResidential<br>|IElectricity|1<br>>60|;<br>30||<br>l|
||<br>Tariff A<br>|<br>l|<br>l|<br>l|<br>I<br>8541|
||<br>Tariff D<br>|l|l|l|l<br>2901|

77. In general, the applications which displace kerosene, electricity, and (marginally) gasoil still appear favorable using revised 1986 fuel prices. Due to the extremely low prices of both fuel oil and wood, SWH applications that displace these fuels Lo not appear to be cost effective. Table A2 shows that the economic rate of return for most applications displacing petroleum products has dropped to less than 50% of the rate of return calculated at 1985 oil border prices. This significant reduction in economic viability is due solely to the 32% (on average) drop in petroleum products border prices.

78. From the consumer's perspective, the financial viability of the sample applications is indicated by the NPW column in Table _A2._<sup>_2_</sup> _'_ Installations with a negative net present worth are not financially viable under the assumptions used. The NPW for the proposed Mt. Kenya Safari Club installation under 1985 retail prices of fuel oil (fuel saved) and electricity (pumping costs) was US$ 420. This compares with a NPW of -US$ 3763 using 1986 retail prices. The difference between these two indices of financial viability is due solely to the 25% drop in the retail price of fuel oiL 28/ lbe NPW was calculated at discount rate of 25%, for systems financed @ 13% interest over 10 **years,** assuming no increase in fuel **prices,** 25% duty on imported parts and no **sales** tax on **solar** equipment. A 20% premium on **foreign** exchange was used throughout the analyss.