rafmacalaba/datause-encoder-data huggingface dataset:
economic damage, loss, and impact of disasters are used interchangeably in various documents, including official ones. In fact, EM-DAT uses ‘estimated damage’<sup>11</sup> while NatCat’s data is labeled as ‘overall losses’. It is then useful to clarify the terminology: _damages_ are by economics definition the damages on stocks, which include physical and human capitals; _losses_ are business interruptions, such as production and/or consumption, caused by damages and can be considered as first-order losses; _higher-order effects_ , which take into account the system-wide impact based on first-order losses through inter-industry relationships; and _total impacts_ are the total of flow impacts, adding losses (first-order losses) and higher-order effects.<sup>12</sup> Whereas EM-DAT and NatCat databases used different terms for economic data of disasters, we consider both of them as damages, _i.e._ damages on capital stock.

Then, the disaster cases 13 are combined between two databases, and are screened based on the intensity in order to reduce the number of cases by eliminating smaller cases. The intensity condition is set as: damages should be greater than or equal to US$ 20 million (current), and either should be greater than 1% of current GDP for high-income countries or 2% of current GDP for low-income countries. The number of cases after this screening becomes 184. In order to be used as the input to estimate total impacts, these damage data were converted first to flow measure, _i.e._ losses, using capital-to-output ratio based on the available and estimated capital data<sup>14</sup> and the current GDP data. The derived losses are further converted to changes in final demand through dividing losses by the inverse of diagonal terms in the direct input coefficient matrix. Then, the total impact of each disaster is estimated by plugging this final demand changes into the respective accounting multiplier matrix, described below.

> 11 EM-DAT states the definition of estimated damage as: “Several institutions have developed methodologies to quantify these losses in their specific domain. However, there is no standard procedure to determine a global figure for economic impact.” (http://www.emdat.be/ExplanatoryNotes/explanotes.html)

> 12 Further discussion of terminology can be found in a companion paper, “Critical Review of Methodologies on Disaster Impact Estimation.”

> 13 These cases include climatological, geophysical, hydrological, and meteorological disasters in EM-DAT definition.

> 14 Nehru and Dhareshwar (1995), GTAP, and World Bank (2006).  Estimation of missing values was carried out using ‘Gross Capital Formation’ data from the World Bank website.

11
Table 7: Spillover Effects on Business Outcomes at Endline

|||||Control group o|nly|||
|---|---|---|---|---|---|---|---|
||Skills|Business|Proft|Extensive Margin|Intensive Margin|Emp|loyment|
||(1)<br>Index|(2)<br>Ownership|(3)<br>_>_0|(4)<br>Index|(5)<br>Index|(6)<br>Any|(7)<br>Salaried|
||||_Panel _|_A: Any Network Link_||||
|Link - Treated|-0.003|0.028<sup>_∗∗_</sup>|0.039<sup>_∗∗_</sup>|0.018|-0.034|0.015|-0.019<sup>_∗_</sup>|
||(0.030)|(0.014)|(0.015)|(0.029)|(0.029)|(0.011)|(0.010)|
|Observations|253|253|253|253|253|253|253|
|Control Mean|-0.182|0.493|0.420|-0.147|0.024|0.710|0.217|
|Adj. R<sup>2</sup>|0.158|0.133|0.089|0.073|0.045|0.037|0.010|
||||_Panel _|_B: Friendship Link_||||
|Link - Treated|0.013|0.019|0.021|0.037|0.026|0.017|-0.002|
||(0.030)|(0.016)|(0.017)|(0.048)|(0.031)|(0.014)|(0.014)|
|Observations|253|253|253|253|253|253|253|
|Control Mean|-0.026|0.635|0.571|0.005|0.019|0.782|0.147|
|Adj. R<sup>2</sup>|0.158|0.124|0.072|0.074|0.040|0.036|0.000|
||||_Pane_|_l C: Business Link_||||
|Link - Treated|0.081|0.057<sup>_∗_</sup>|0.070<sup>_∗∗_</sup>|0.080|0.129|0.025|-0.051<sup>_∗∗∗_</sup>|
||(0.078)|(0.030)|(0.032)|(0.094)|(0.094)|(0.028)|(0.014)|
|Observations|253|253|253|253|253|253|253|
|Control Mean|-0.031|0.627|0.555|-0.021|0.058|0.786|0.159|
|Adj. R<sup>2</sup>|0.162|0.130|0.081|0.076|0.056|0.035|0.013|

_Note:_ Coefficients are ANCOVA estimates that control for the outcome at baseline, and baseline network size. All Indeces are Anderson indeces of the respective outcome groups using the swindex command in Stata. Columns 2-3 are binary indicators. Any Employment is a binary variable equal to one if the woman reports being either employed by someone else or being self-employed. Salaried Employment is a binary varibale equal to one if the woman reports being employed by someone else. We report White robust standard errors in parentheses. These results correspond to spillover effects for the control group at endline.

34
distribution of rewards. In relation to this, it is worthwhile to draw attention to a recent estimate of the reliability of data pertaining to the global disease burden and projections about what the global disease burden will be at some future point.

The best data comes from countries with the strongest vital registration systems - i.e., for the reporting and recording of each death and its cause, among other things. Unsurprisingly, however, such systems are usually weakest and often absent in developing world countries. There is less confidence in current disease burden estimates in poor countries, and the authors of the GBD [Global Burden of Disease] studies urge ‘great caution’ in the use of their projections of future disease burden in places like SubSaharan Africa in particular (Murray and Lopez, 1996:331). For the purpose of a fullpull program, then, that data is weakest in the very places where it is wanted most. (Selgelid 2008:138).

#  _Prudential appeal_

Sonderholm (2009b) has raised worries about the prudential appeal of the HIF. This point of criticism requires an example to get under way. A is a drug that immediately reduces the symptoms of diarrhea in infants and keeps the symptoms at bay for up to four weeks. It is successful in 40% of cases and comes in the form of two pills that cost US$ 2 to produce. B is a drug that immediately reduces the symptoms of diarrhea in infants and keeps the symptoms at bay for up to four weeks. It is successful in 90% of cases and comes in the form of a powder that needs to be dissolved in 25 centiliter of clean water that should be drunk by the infant. Moreover, B should be stored at refrigerator temperature. The production cost of B is 1⁄4 of that of A.

29
Indeed, Monte Carlo simulations suggest that the application of log-linear estimators to this type of data-generation process tends to produce substantial biases in the coefficients compared to the Poisson estimator, which controls for a constant correlation between the conditional mean of each observation and its regression-error variance (Santos Silva and Tenreyro 2005). Furthermore, if the data-generation process is characterized by overdispersion (a rising ratio of variance over conditional mean) then the Negative Binomial estimator could be preferable. The data on bilateral FCS seems to be characterized by the same type of systematic heteroskedasticity that afflicts bilateral trade data, and consequently we subject all of our empirical models to the three estimators, namely OLS, Poisson, and Negative Binomials. Santos Silva and Tenreyro argue that the Negative Binomial estimator might not be desirable if the smaller observations are more prone to measurement errors than the larger observations.<sup>4</sup> To some extent, the use of outward FCS reported mainly by OECD countries might help deal with this concern, but to err on the side of caution, we report results from all three estimators. The following sections discuss the empirical models and the corresponding data.

# **3. Modeling the Determinants of FCS across Countries**

Our approach is similar to the one proposed by ET, which is to utilize bilateral FCS in China and India in estimations of the determinants of FCS around the world. However, we use the KCM instead of the gravity model as a benchmark for our regressions, since it is grounded in formal FDI theory (Carr, Markusen, and Maskus 2001). This model allows for both horizontal and vertical motivations for FDI and was estimated in a number of

> 4 Santos Silva and Tenreyro (2005) warn against estimating models were V[y/x] is a function of higher powers of E[y/x] when dealing with trade data. These models might give excessive weight to observations with low trade values, which may be ridden with measurement errors. This observation is less valid for our outward FCS data, since it is collected by OECD countries.

6
# **Climate volatility and poverty vulnerability in Tanzania**

Syud Amer Ahmed<sup>a</sup> *, Noah S. Diffenbaugh<sup>b,c</sup> , Thomas W. Hertel<sup>b,d,e</sup> , David B. Lobell<sup>f,g</sup> , Navin Ramankutty<sup>h</sup> , Ana R. Rios<sup>i</sup> , and Pedram Rowhani<sup>h</sup> a Development Research Group, World Bank, Washington, DC, USA b Purdue Climate Change Research Center, Purdue University, IN, USA c Department of Earth and Atmospheric Sciences, Purdue University, IN, USA d Center for Global Trade Analysis, Purdue University, IN, USA e Department of Agricultural Economics, Purdue University, IN, USA f Program on Food Security and the Environment, Stanford University, CA, USA g Department of Environmental Earth System Science, Stanford University, CA, USA h Department of Geography and Earth System Science, McGill University, QC, Canada i Sustainable Energy and Climate Change Unit, Inter‐American Development Bank, Washington, DC, USA

* Email: sahmed20@worldbank.org

KEYWORDS: climate, volatility, poverty, vulnerability, Tanzania, GCM

## **Acknowledgments**

This research was funded by the World Bank’s Trust Fund for Environmentally and Socially Sustainable Development. The authors are grateful to Tasneem Mirza for research assistance and to Hans Binswanger and Will Martin for helpful feedback. We also acknowledge the modeling groups in the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multi‐model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. The views and opinions expressed in this paper are solely those of the authors.
Figure 11: Formal and Informal Sector Wages <u>(Index, 2002=100)</u> <!-- Start of picture text --> A. Formal Sector Real Minimum Wage Rate B. Average Real Daily Wage Informal Sector<br>160 150<br>150 140<br>140<br>130<br>130<br>120<br>120<br>110<br>110<br>100<br>100<br>90<br>90<br>80<br>80<br>70 70<br>60 60<br>Tea, Male<br>Agriculture Rubber, Male<br>Industry and Commerce Coconut, Male<br>Services Paddy, Male<br> Central Government Employees Carpentry, Skilled and Unskilled<br>Government School Teachers Masonry, Skilled and Unskilled<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Own construction based on Central Bank of Sri Lanka, Annual Reports: 2007, 2008, 2009, 2012, 2013. The Index numbers are calculated on fixed weights based on the numbers employed as at 31 December 1978. The wage rates used in the calculation of Index Numbers are minimum wages for different trades fixed by the Wages Boards Source: Central Bank of Sri Lanka. Annual Report Statistical Appendix, 2007, 2008, 2009, 2012, 2013. Wage information represents payment in cash without meals. Based on monthly wages from 90 data collection centers under the CWDCS until 2009 and 102 data collection centers under the CWDCS beginning in 2010.

# Figure 12: <u>Sectoral Productivity in Sri Lanka, 2002-2012</u>

<!-- Start of picture text --> A. Output per Worker by Sectors B. Decomposition of Growth in Output per<br>Worker<br>8,000<br>7,000 2002 2012 Inter-sectoral shift<br>6,000<br>Services<br>5,000<br>4,000<br>Industrty<br>3,000<br>2,000 Agriculture<br>1,000<br>0% 10% 20% 30% 40% 50% 60% 70%<br>0<br>Agriculture Industrty Services Contribution to Change in Output per Worker<br>millions of 2005 Dollars<br><!-- End of picture text --> Source: Own estimates using WDI, DCS Labor Force Surveys. Decompositions done using JoGGS decomposition method (see World Bank, 2010) and growth accounting framework following Duma, 2007.

21
These data are then combined with household surveys to precisely identify levels and determinants of informality.

We find that in 2014/15, i.e., after LTR completion, 5.6% and 1.54% of Kigali’s residential and agricultural land parcels, respectively, were transferred through a registered sale each year (for the other provinces, figures are 0.27% and 0.07% for residential and agricultural land, respectively). A total of US$2.6 billion of mortgage lending is secured by 49,694 mortgages—65%, 30%, and 5% by residential, agricultural, and commercial land/property. Quarterly data on numbers of registered land transactions show that posting of sector land managers (SLMs) had a very positive impact on the level of registered land transfers, showing that such data can be used not only to performance monitoring and change management but also the impact of interventions that are gradually introduced across administrative units.

Linking such data to household survey evidence at the parcel level produces reliable estimates of informality. Doing so shows that, Rwanda’s success in first-time registration notwithstanding, in rural areas a substantial share of land transfers remain informal. Low awareness of relevant regulations, subdivision restrictions, high registration fees, and travel cost to reach relevant offices are some underlying factors. Given the high level of land market activity in the country, dealing with this issue will be important.

The paper is structured as follows. Section two reviews conceptual arguments and empirical evidence on costs and benefits of land registration and draws out implications for sustainability of land administration. It also describes Rwanda’s legal and institutional structure, key elements of the country’s program of land tenure regularization program, including efforts to integrate it into the country’s decentralized system of governance, as well as data sources. Section three provides evidence on the level and incidence of informal transactions. Section four provides data on and the cost of registering transfers, compares them to households’ stated willingness to pay, and draws out implications on its determinants. Section five concludes with recommendations for policy and research.

# **2. Literature and country background**

We discuss the conceptual framework for efforts at land regularization, challenges traditionally faced when trying to apply it in African contexts, and the advances made by Rwanda in implementing a national land tenure regularization program. While this has had positive impact and yielded large benefits, sustained enjoyment of these benefits will require that the registry provides up to date and authoritative information on land rights.

3
of prospective charter entrants (for instance, by lowering application costs and providing information on charter best practices) while applying tight admission and oversight standards is a welfare-enhancing mechanism for charter expansion.

While informative, our counterfactuals must be taken with caution for several reasons. First, they only reflect short-run policy effects and ignore strategic responses from non-charter schools. Second, our results reflect the institutional environment for charters in D.C., where funding is relatively high vis-a-vis other states and the charter law is permissive. Third, we do not model the relationship between school funding and quality. Nonetheless, the counterfactuals stress the role of private initiative (facilitated by low application costs) in the charter sector. They also stress the regulator’s role (through its approval and oversight activies) in attaining a high-quality charter sector.

Our findings are informative for D.C., particularly as the city adjusts to a rising number of families with school-age children and highly educated parents. They are also informative for school reform in other large, U.S. cities. Finally, they are relevant for countries with private operation of public schools, such as Colombia, the UK and Spain (Patrinos et al 2009), and for nascent charter efforts in developing countries such as Uganda and Morocco.

# **References**

- [1] Altonji, J., C. Huang and C. Taber. 2010. “Estimating the Cream Skimming Effect of Private School Vouchers on Public School Students.” _Nation Bureau of Economic Research_ , Working Paper 10-12.

- [2] Angrist, J. D., P. A. Pathak, and C. R. Walters. 2013. “Explaining Charter School Effectiveness.” _American Economic Journal: Applied Economics_ , 5(4): 1-27.

- [3] Bayer, P. and C. Timmins. 2007. “Estimating Equilibrium Models of Sorting Across Locations.” _Economic Journal,_ 117(518): 353-374.

- [4] Berry, S., J. Levinsohn, and A. Pakes 1995. “Automobile Prices in Market Equilibrium,” _Econometrica,_ 63(4): 841-890.

- [5] Betts, J. R., & Tang, Y. E. 2011. “The Effect of Charter School on Student Achievement: A Meta-Analysis of the Literature.” _Center on Reinventing Pub. Education._

- [6] Bifulco, R. and C. Buerger. 2012. “The Influence of Finance and Accountability Policies on Charter School Locations.” _Syracuse University,_ Working Paper.

- [7] Booker K., Sass T., Gill B. and Zimmer R. 2011.“The Effects of Charter High Schools on Educational Attainment.” _Journal of Labor Economics,_ 29(2):377-415.

- [8] Buckley, J. and M. Schneider. 2007. “Charter Schools: Hope or Hype?” _Princeton and Oxford: Princeton University Press_ .

- [9] Cardon, J.H. 2003. “Strategic Quality Choice and Charter Schools.” _Journal of Public Economics_ , 87 (3-4): 729-737.

- [10] Carranza, J. E., J. Houde and R. Clark. 2011. “Dynamic Entry and Firm Reconfiguration in Canadian Gasoline Markets.” Working Paper, ICESI University.

38
floor). Also, once a family was deemed eligible for the program, it was included in the program for three years before eligibility was reassessed.

Parker and Skoufias (2000) also analyze women‟s time use related to time spent satisfying program requirements. They find that women spent a significant amount of time engaging in activities such as taking children to school or to health clinics, in order to satisfy program requirements. There was also some evidence that women spent less time engaged in domestic work.

A recent paper by Dubois and Rubio-Codina (2009) further analyzes the same _Oportunidades_ data and examines in detail the impact of the program on the time mothers and older sisters devoted to taking care of children under the age of 3, where the analysis is restricted to 4,036 mothers age 18 or over with older daughters (age 12-17) still living in the household, with children younger than three, and without elderly or sick relatives that might require care. The study finds support for the existence of substitution effects, namely that mothers in the treated households were more likely to substitute for their older daughters‟ time devoted to childcare.<sup>24</sup> Dubois and Rubio-Codina (2009) find that daughters devoted more time to schooling and less time to caring for children and that the total amount of time devoted to childcare increased. They maintain that the program augmented the human capital of children both by keeping teenage girls in school and through more and arguably better “mother-provided” childcare. Corroborating evidence that _Oportunidades_ increased women‟s time burden comes from focus group interviews where mothers often said they were doing work that was previously done by children who were now attending school (Adato et al., 2000). Some limitations of the Dubois and Rubio-Codina (2009) study are that it was not established that mothers‟ care was better in promoting child development. If mothers were more apt to combine their care activities with other household activities (such as cooking), the care might not necessarily have been better. Also, it was unclear whether the increase in time devoted to childcare was accompanied by a decrease in some other productive activity.

Although the Mexican CCT program did not appear to significantly affect mothers‟ labor supply behavior, Parker and Skoufias (2000) find that it substantially reduced the labor supply of children and adolescent girls and boys in both salaried and nonsalaried work. Behrman, Parker

> 24 When all mothers are pooled together, there is no significant effect of treatment on maternal participation in childcare; but when treatment is interacted with whether the other has offspring age 12-17, there is a positive and statistically significant increase in mother‟s participation in childcare.

49
TABLE 8: Impact of leaders’ exposure on ECD outcomes, by leaders’ prior beliefs

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|ITT Leader|-0.08<sup>_∗_</sup>|-0.09|-0.08|
||(0.05)|(0.06)|(0.05)|
|Agreeing leader|0.07|-0.10|0.15<sup>_∗_</sup>|
||(0.07)|(0.09)|(0.08)|
|ITT leader X Agreeing leader|-0.06|0.16|-0.16|
||(0.08)|(0.11)|(0.10)|
|Observations|2051|624|1427|
|ITT Leader + ITT leader X Agreeingleader = 0|0.04|0.44|0.01|

_Note:_ Agreeing leader is a binary variable indicating that the closest leader agreed with at least 4 out of 5 program messages on parenting practices at baseline. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old, and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1, _∗∗_ p<0.05, _∗∗∗_ p<0.01 42