rafmacalaba/data-use-mentions-extended huggingface dataset:
by the World Bank and the African Development Bank.

The program was launched in October 2014, and consisted of two main parts. One, the program aimed to sensitize communities living in the vicinity of each of the project forests to the detrimental effects of forest fires. Two, the program also aimed to actively engage community members in local conservation activities using a combination of technical support (by experts from the regional and national authorities) and improved coordination of management activities between adjacent communities. Communities were encouraged to participate in setting up fire barriers within forests to compartmentalize wildfires, in establishing forest management infrastructures and in protecting and monitoring the forests. Taken together, these efforts were expected to reduce both the frequency with which forest fires were started as well as their spread, and especially so at the end of the agricultural season (in November and December) when most of the forest fires take place. We use the Synthetic Control Method (Abadie and Gardeazabal, 2003; Abadie, 2021) to estimate the impact of the policy intervention over the period 2014-2019, the first five years after its inception, using remote sensing data.

Overall, we find that the project was not very effective in reducing fire-induced forest degradation. Forest fires were lower in the project forests, but only in the month of November (the month in which most of the post-harvest forest fires take place), and also in just the first four years after the start of the program. The program did not reduce forest fire occurrences in any of the other months in the dry season. And even though the project managed to decrease the November forest fires by on average 35% in the first four years, we fail to detect an increase in overall vegetation cover. This may be because the decrease in November forest fires was too small to be visible on the satellite images, or because the impact had dissipated too quickly for the forest vegetation to recover.

While the impact of the project was thus limited at best, we perform additional analyses to gain insight
providing disaster relief and assistance in recovery (Davies and others, 2014), which increases the possibility of cascading disaster effects (see next section).

The impacts of disasters can be difficult to identify in macroeconomic statistics because efforts to rebuild can create a stimulus effect that counterbalances production losses during the disaster. However, ADB (2012, p.2) reported a noticeable long-term impact on economic development in Cambodia from the 2011 floods: “Despite the positive overall economic performance of Cambodia in 2011, the flood slowed the potential for sustained economic growth due to the devastating impact at the household and macroeconomic levels.” Even in cases where resources are diverted toward recovery efforts, there could be displacement effects, for example if public services are cut to balance budgets. Alternatively, an increase in debt levels could impact financial stability, as discussed in Section 4.

Table 1 shows that large floods with substantial human and economic cost already occur in Cambodia on a frequent basis, in part because of the intensity of rainfall in the rainy season (when 80 percent of annual rainfall occurs). Paltan and others (2018) report that climate change will increase the frequency further; even under a scenario with limited climate change, what was previously a 1-in-100-year flood could occur once every 25 years or even once every five years. World Bank Group (2021) summarizes the recent literature and finds that 25 percent of Cambodia’s population is expected to be exposed to floods between 2035 and 2044.

However, there remains substantial uncertainty about both the scale and potential impacts of future floods. Phy and others (2022) note that Cambodia lacks both flood hazard mapping and risk and damage assessment. The authors recommend further data collection and model-based analysis, combined with predictions about future land use change and infrastructure development, to identify key future risks in Cambodia.
4 Note that, given a base year capital stock estimate, it is possible to estimate capital stocks beyond the base year by simply accumulating net investment in each period. Therefore, for constant _I G_ , we estimate the counterfactual series of produced capital for each country as: _G_ We calculate two versions of _K_<sup>_*_</sup> in what follows – one with _I_ = 0 (the standard Hartwick rule), and a second with _I G_ equal to a constant 5% of 1987 GDP. The choice of a particular level of genuine investment for the analysis is obviously arbitrary. We use 5% of 1987 GDP for the following reasons: (i) there is some logic to choosing the mid-point of our time series of data from 1970-2000, but 1987 is a slightly better choice, falling after the early 1980’s recession, after the collapse of oil prices in 1986, and before the early 1990’s recession; and (ii) a 5% genuine investment rate is roughly the average achieved by low income countries over time. Since the elasticity of output with respect to produced capital α is implicitly greater than 0.5 in the theoretical model of the preceding section, the choice of 5% of GDP ensures that the feasibility condition _G_ < α _F_ (0) of Proposition 3 is satisfied.

Resource depletion is estimated as the sum of total rents on the extraction of the following commodities: crude oil, natural gas, coal, bauxite, copper, gold, iron, lead, nickel, phosphate, silver and zinc. While the underlying theory suggests that scarcity rents are what should be invested under the Hartwick rule (i.e. price minus marginal extraction cost), the World Bank data do not include information on marginal extraction costs. This gives an upward bias to the hypothetical capital stock estimates under the genuine investment rules.

There is another clear divergence between our empirical methods and the theory of the preceding section. In the autarkic Dasgupta-Heal economy presented
bonds are positive across pre-merger and announcement months. These studies, however, do not distinguish between absolute and systemic size as potential determinants of market discipline on a bank.

Demirgüç-Kunt and Huizinga (2010b) consider absolute and systemic size as separate determinants of the market-to-book value of a bank’s equity for a sample of international sample of banks over the 1991-2008 period. Bank market-to-book value is found to be negatively related to either size measure, while it is more negatively related to systemic size in countries with large public deficits, as evidence that systemically large banks can be too large to save. The present paper’s finding that the sensitivity of bank interest expenses to bank risk is greater for systemically large banks also suggests that these banks can be too large to save, and thus it is consistent with the evidence from bank stock pricing in our earlier paper.

The remainder of this paper is organized as follows. Section 2 discusses the data. Section 3 presents the empirical results. Section 4 concludes.

# **2. The Data**

In this study, we examine an international sample of banks over the 1991-2009 period. Income statement and balance sheet information on individual banks is taken from Bankscope. A full list of variable definitions and data sources is given in the Appendix. Our sample is restricted to publicly-listed banks to ensure relatively high data quality. With data from many countries, we can distinguish between a bank’s absolute size and its systemic size, i.e. its size relative to the national economy. To measure a bank’s absolute size, we define the assets variable as the log of total assets in constant dollars. To gauge systemic size, we take a bank’s liabilities-to-GDP ratio, denoted liabilities over GDP. This ratio corresponds is to a country’s maximum expenditure in a bank bail-out relative to its GDP, if all of a bank’s assets go completely sour.

In Table 1 with summary statistics, we see that liabilities over GDP has a sample mean of
the following statement (translated into English here): _”281,000 irregular Venezuelan migrants have been legalized through the Permiso Especial de Permanencia, which grants them a work permit, access to social programs (such as a subsidized health regime), and access to financial services.”_ Figure F.1 illustrates how the information was presented. The survey was executed between October and December of 2022. The sample size was 1,040 individuals and included Colombian residents who were older than 21 years old and living in Bogot ́a.

The survey followed a structured sequence. First, respondents provided demographic and socioeconomic information, including age, sex, marital status, economic stratum, occupation, formal employment status, education level, student status, and news consumption related to national politics. Second, we included the social desirability bias scale from Crowne and Marlowe (1960) to standardize and control for potential social desirability effects. Third, participants took part in the experiment. They were given five minutes to read informational flyers without any verbal explanation from the enumerators. Both control and treatment groups received flyers with general migration statistics. However, the treatment group also received additional information about the PEP program, detailing work permits, social benefits, and access to financial services.

After reading, participants answered questions on voting intentions, social capital, and attitudes toward Venezuelan migrants. The final section of the survey assessed knowledge of the PEP program. Respondents who reported familiarity with the PEP were asked: (i) Who is eligible for the PEP? (ii) How many Venezuelans currently hold the PEP? (iii) What are the benefits of the PEP? (iv) What rights does the PEP grant to migrants?

Those who stated they were unfamiliar with the PEP were instead asked about the Estatuto Temporal de Protecci ́on (PTT), the most recent regularization policy for Venezuelan migrants. If respondents were unaware of both the PEP and PPT, they were asked: (i) Can Venezuelan migrants regularize their
in health care spending, which they interpret as human capital investment.

Bargaining power may also increase in asset holdings. This idea is explored by Doss (2006). Using data from the Ghana Living Standards Surveys in 1991/92 and 1998/99, the author finds that in households where women own a larger share of assets (land, savings, and business assets), the share of expenditures on food and education is larger, but the share of alcohol expenditures is smaller. The share of medical expenses, on the other hand, decreases with female asset ownership. The authors argue that medical expenses are primarily curative and speculate that women with higher bargaining power are able to keep their families healthier.

Quisumbing (2003) uses relative education and assets at marriage as measures of bargaining power. Data from Bangladesh, Ethiopia, Indonesia, and South Africa are used to show that bargaining power matters for expenditure shares. The results differ a lot by country, however. For example, in Bangladesh and South Africa women's assets increase expenditure shares on education, while in Ethiopia men's assets have this effect. Similarly, female assets have a negative affect on food budget shares in South Africa. In some countries, women's asset ownership increases boys' schooling relatively more than girls' schooling, while in others the reverse is true.

Another form of female empowerment is political empowerment. Chattopadhyay and Duflo (2004) study how the gender of political leaders affects public good provision in rural India using data from a natural experiment. In India, one third of the local government head positions are randomly reserved for women. The authors find that leaders invest relatively more in infrastructure that is relevant to the needs of their own genders. Namely, female leaders favor

> 9 The authors hypothesize that women use their increased bargaining power to spend more time with children and work less in the market, which leads to a decline in food consumed outside the home.

8
.compromissoeatitude.org.br/legislacao-sobre-violencia-contra-as-mulheres-no-brasil/</u>

> 6 There are two main sources of VAW data: data from specialized surveys and administrative data. The highest prevalence rates are obtained by specialized surveys that follow WHO ethical and safety standards. However, this type of data is expensive and not available in a continuous basis in Brazil. On the other hand, administrative data from security and health services are more accessible. However, they do not capture well non-physical forms of violence and tend to underestimate the problem due to underreporting and lack of technical capacity of service operators.

_3_
>households|
|Wave 4|May 2017 -<br>August 2017|7 out of 10 states: Same as Waves 2 and<br>3.|Revisited urban<br>households from Waves 1<br>and 2|

Source: High Frequency South Sudan Survey

# **Prices and Inflation**

The consumption section of the household survey (HFSSS) collects information on items’ unit prices and quantities. As with all data collected from sample surveys, the household-reported prices are subject to sampling errors. Item non-response and measurement error will also lead to biased estimates (Dahlhamer _et al_ ., 2003; Garner _et al_ ., 2009). However, household-reported prices have a key strength: knowing precisely the prices paid by households who make expenditures themselves has an advantage in that it captures the parallel exchange rates, showing households’ real purchasing power. This is particularly important in the context of South Sudan with a strong parallel exchange market.

Based on the strengths and weaknesses of the three price data sources, we decided to use household-reported prices because it covers the entire sample and has price information for all items consumed by the household. Thus, for our analysis, inflation is calculated based on unit price household survey data (using Laspeyres price index). In addition to using the total inflation variable, we also break it down into food price inflation and non-food price inflation to explore which of the two might be driving the results.

4
and child nutrition in Nepal." _Economic Development and Cultural Change_ 65(2): 167-188.

- Trinh, T. A., A. Posso, and S. Feeny. 2020. “Child labor and rainfall deviation: Panel data evidence from rural Vietnam.” _The Developing Economies_ 58(1): 63-76.

- Van Daalen K. R., S. S. Kallesøe, F. Davey, S. Dada, et al. 2022. “Extreme events and genderbased violence: a mixed-methods systematic review.” _Lancet Planet Health_ e504-e523.

- Vu, T. M. 2022. "Effects of heat on mathematics test performance in Vietnam." _Asian Economic Journal_ 36(1): 72-94.

- Walsh, B. S., S. R. Parratt, A. A. Hoffmann, D. Atkinson, R. R. Snook, A. Bretman, and T. A. R. Price. 2019. “The Impact of Climate Change on Fertility.” _Trends in Ecology & Evolution_ 34(3): 249-259.

- WMO (World Meteorological Organization). 2021. _Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019)_ . WMO 1267. Geneva.

- World Bank. 2015. _World Bank Group Gender Strategy (FY16-23): Gender Equality, Poverty Reduction and Inclusive Growth_ . Washington, DC: World Bank.

26
prices: using price data, Balkan and Tumen (2016) find that consumer prices of goods typically associated with informal labor declined following the influx of Syrian workers in those regions. Likewise, Tumen (2018) documents that the wages for young workers in new low-skill jobs have also declined in those regions.

Adopting a more structural approach, Loayza et al. (2018) also analyze the labor market effect of Syrian refugee flows on Turkey’s labor market. Their model considers both the location choices of refugees within Turkey and the decisions of companies to hire informal workers. Their analysis suggests that the inflow of refugees increases informal employment among low-skill native workers, but also increases the skilled-unskilled wage premium and induces regional migration of native workers to other regions.

> 7The negative effect on female workers is in line with the findings in Ruiz and Vargas-Silva (2018) and Ruiz and Vargas-Silva (2016). Using data for Tanzania, these authors showed that the arrival of refugees negatively affected the labor force participation of low-educated women.

5
br>literacy of adult) – 3 outputs (literacy of youth, first<br>levelcomplete, secondlevelcomplete)|.98|1.0|1.0|.98|.99|.99|

Rows 4 and 5 of Table 8 allow comparing the impact of adding the variable teachers per pupil as an additional input. In Africa the change is dramatic, while in ECA and MNA there is no significant change. Further analysis is required to explain this differential response to the inclusion of this input.

In health there are multiple combinations of inputs (public expenditure, private expenditure, and literacy of adults) and outputs (life expectancy at birth, immunization DPT, immunization measles, and Disability Adjusted life expectancy (DALE)). The combinations we selected produce rankings that are more homogeneous. The rank correlation is in the range of .65 to .98 (Tables 10-12). Bangladesh appears as efficient, as well as Niger, mainly due to the inclusion as of the ( low ) levels of literacy of adults as an input, and hence, making these countries to appear as efficient.

**Table 10. Health Attainment: Multiple Inputs, Multiple Outputs**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Bangladesh, Malaysia, Costa<br>Rica, Kuwait, Morocco, Oman,<br>Mauritius, Niger|Bangladesh, Costa Rica,<br>Kuwait, Malaysia, Morocco,<br>Mauritius, Oman, Niger|
|Least efficient|Russia, Belarus, Namibia,<br>Romania, Estonia, Croatia,<br>Lituania,, Hungary, Jordan|Namibia, Togo, Ethiopia,<br>Mozambique, Cote d”Ivoire,<br>Cameroon, Congo, Central<br>African Republlic, Nigeria,<br>Uganda|

22
--|---|
|De<br>og pic category|1994|1996|
|...................|||
|single|70.3|73.4|
|husband and wife|62.1|60.6|
|Single:..........................|||
|childless|38.1|38.7|
|childless + parents|38.8|45.8|
|with children|28.1|22.1|
|with children + parnts|30.8|33.0|
|childless|38.8|33.3|
|childless + parents|37.3|41.5|
|with children|26.3|29.6|
|with children + parents<br>|27.2<br>|35.0<br>|
|_*: A '''ts 'X::~~~~~~~~~..._<br>.<br>* ::: w:^fiw<br>::Y ............ . .................. ...........|_._**_......._**_._**_......._**_....._<br>_. . . . . _.._. . ._..|_._|
|Source: The Moscow Longit<br>Hous|ehold Survey in Dec|-1994 and Jan-1996.|

Some
diversi...cation for a given ...rm, incorporating the number of products exported by ...rm i to the world market in the initial year of the trade relationship, NProductsci. Transport costs are proxied with bilateral distance between origin country c and destination country j in logs, Distancecj<sup>12</sup> . We also include a dummy variable that equals one if importing and exporting countries share a border, Contiguitycj. Bilateral trade can be fostered by countries’cultural proximity. Similarity in culture can indeed increase the quality of the match between varieties produced in exporting country and tastes of consumers in the destination country. We control for this proximity by introducing two dummies, respectively equal to one if a language is spoken by at least 9% of the population in both countries, Com_languagecj, or if both partners have had a colonial relationship, Colonycj. Appendix 8:E provides summary statistics of the main variables used in our analysis.

Equation (2) is estimated under partial likelihood (Cox, 1972). Since there may be unobserved variation across exporter-sector pairs–to avoid biasing the standard errors downwards–in all tables we report robust standard errors clustered at the exportersub-sector (HS4) level. The coe¢ cients can be interpreted as semi-elasticities, as they measure the percentage point change in the hazard rate as a result of a unit change in the right-hand side variables.

A common feature of survival data is censoring. First, we observe ‡ows in the ...rst year of our sample but do not know how long they have been in existence. Second, we observe ‡ows in the ...nal year of our sample but do no know how long they will continue to exist. The problem of right-censoring is accounted for in the Cox estimation procedures<sup>13</sup> . Left-censoring presents a more serious problem. Given the short time span, our approach is simply to ignore left censoring in our main estimations. As a
# **Acknowledgments**

This paper is a product of the Urban Strategy and Analytics Global Solutions Group in the Social, Urban, Rural and Resilience Global Practice (GPSURR). For their excellent comments and guidance, we are grateful to William Kingdom, Sudeshna Ghosh Banerjee, Paul Kriss, Catherine O’Farrell, Fernanda Ruiz Nunez, Jordan Schwartz, Bernice Van Bronkhorst, Marianne Fay, Sheila Kamunyori, Abdu Mwonge and Wendy Ayres. We are also grateful for the inputs from clients and partners at a multi-sectoral workshop held in Nairobi on Feb 14, 2017 where this study was discussed. The paper has benefited from funding from the Global Partnership for Output-Based Aid (GPOBA). The underlying dataset was drawn from the Kenya State of the Cities Baseline Survey (2016) which was funded by the Government of Kenya, World Bank, Government of Sweden, Cities Alliance, and Bill and Melinda Gates Foundation.

2
>130<br>190<br>180 Real Per Capita GDP (1990=100; LHS)<br>120<br>170<br>Net Barter Terms of Trade (2000=100; RHS)<br>160 Global Financial Crisis<br>110<br>150<br>140 100<br>130<br>120 90<br>110<br>100 Convertibility Crisis 80<br>90<br>70<br>80<br>70<br>60<br>60<br>50 50<br>Source: World Development Indicators, author’s calculations<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> 36 IMF December 2003 Staff Report on Argentina.

37 Buera et al, 2011, highlight this period as one of two brief periods where fiscal management and growth performance rose above the slower trend post-1974.

17
treatment and control households in the survey sample (Rs 34,339) corresponds to approximately 77% of India’s 2010 annual per capita income of Rs 44,345 (US$ 1,002).

# **5 Data Description**

The analysis draws on data from two main sources. The first set of data consists of bank lists on all debt relief accounts in the sample districts. As a transparency measure, all banks and credit cooperatives were required to disclose details about all accounts qualifying for full or partial debt relief. This information was posted on public notice boards of local bank branches throughout the country, and several banks also published detailed beneficiary information on their websites. The second set of data comes from a detailed household survey of debt relief beneficiaries, conducted in late 2009, approximately one 14
**2,160**<sup>**i**</sup>|
|Conversion technology||Traditional<br>fermentation|Advanced solid<br>state<br>fermentation<br>(ASSF)|Traditional<br>fermentation|
|Investment cost<sup>j</sup>|Yuan/tonne (ethanol)|4325<sup>k</sup>|n.a.|4325<sup>i</sup>|
|Ethanol total production cost|Yuan/tonne|**5,017-6,802**|**5,444-5,968**|**4,887-5,796**|
|Share of feedstock cost|%|56.95-68.25|70.54-73.12|55.8-62.74|
|Designated<br>fuel<br>ethanol<br>wholesale price from January<br>1, 2008 to April 7, 2011|Yuan/tonne||||
|-<br>Min<br>-<br>Average<br>-<br>Max||4,956 (January<br>6, 238<br>7,816 (April 7,|15, 2009)<br>2011)||

Note: a. as mentioned in Table 2, there are other utilizations for cassava and sweet potato in China, therefore their assumed prices are equal to current average market prices, which are not only for energy use, but also for the use of food, feed, starch, and so on; b.as mentioned in Table 2, there is almost no sweet sorghum market in China, therefore, the pricing mechanism of sweet sorghum is cost-based pricing involving farmer’s profit; c. Feedstock price is assumed to be the average value from 2007 to 2008, data from Sun et al (2008); d. data from Wei, et al.