rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.2 Hurricane Damage**

## **Hurricane Windstorm Model**

To measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America. The main model output is predicted wind speed in kilometers per hour (km/h) at the height of 10 meters for each cell and tropical storm that has since 2000 affected the following countries: Costa Rica, El Salvador, Honduras, Guatemala, Nicaragua, and Panama. Further details of the model can be found in supporting information.

## **Hurricane Destruction Index**

To match the wind speed data set to our nightlight data, we construct a cell-year panel, that uses only observations from the hurricane with the highest recorded wind speed during a given year.<sup>3</sup> Approximately a quarter of the cells in our sample experience non-zero wind speed.

We convert wind speed into potential damage using the transformation and parameters proposed by Emanuel (2011). This transformation imposes a threshold below which damage is unlikely to occur, it guarantees that damage will approach unity for very high wind speeds, and it accounts for the physical property that wind power (the rate of increase of kinetic energy) from a hurricane is proportional to the third power of wind speed. The damage index _f_ is given by: where _Vit_ represents wind speed in cell _i_ and year _t_ , _VT_ is the threshold below which damage is unlikely to occur, it is set just below severe gale speeds 50 knots ( _≈_ 92 _._ 6 km/h), and _VH_ is the wind speed at which half of all structures are expected to be destroyed 150 knots ( _≈_ 277 _._ 8 km/h). Additionally, because physical damages may be higher non-linear

> 3We also calculate a cell-year panel using the
- 75-

4.a <!-- Start of picture text --> 4.b<br><!-- End of picture text --> <!-- Start of picture text --> 01M mm mmmm<br>7 . ,3125 6 7<br>6~~~~~~~~~~~~~~~~~~6<br>a~~~~ ~ ~~ ~~ ~~~~~~~ J I- i/o'; 4.wnX<br>2 "K9< X -" , 23o"<br>2 ____ I 2 Du? liii<br>1953 195 15U ,11 1512 1%3 154 151 1 1917 116 119 1m 1971 1m<br>IN -U<br>I Iw _ __ _ _ _ _ U _ _ _ _ .- I SS- Se<br>1913 1959 153 1%1 151 1913 1914 1515 1515 ? 1516 1%5 1M 1971 172<br>U0 is 3"Is<br>In i uc s A a<br>-RU - -M. S<br>/ 1 4~~~~~~~~~~~~A<br>ii. WREI cli Exh3 1R t n<br>-In -us -"DEOFF. Ooauto acL ~~~~~~~~~~~~~~~-s Ofeu xhgRt 251<br>195W 1959 151 1 11 1513 I514 155 1 M 1 1n 1 197 1971, 192<br>-4. ~ CA .a /ura Scon $ 3oac,llo<br><!-- End of picture text --> Anumal Data DEVOFF Devaluation Rate in of ficdel £xchaoge Itatc REX : Real Exchange Race tndex CAB : O.crent AccOunlc Balances i,liow _UJS$_ WRA Real Wage Index (SowuCC: fundaCioti
**Table 1: FPM coefficients, actual and theoretical transfers**

|Population|FPM<br>coefficient|Actual<br>transfers|Theoretical<br>transfers|Obs.|
|---|---|---|---|---|
|6,793-10,188|0.6|13.91|13.11|5211|
|10,189-13,584|0.8|18.20|17.65|3982|
|13,585-16,980|1|22.48|22.28|2931|
|16,981-23,772|1.2|26.77|26.78|3931|
|23,773-30,564|1.4|31.12|31.17|2312|
|30,565-37,356|1.6|35.35|35.60|1412|
|37,357-44,148|1.8|39.55|39.80|907|
|44,149-50,940|2|43.81|44.43|542|
|Total|-|23.42|23.13|21168|

_Notes:_ Actual and theoretical FPM transfers are in hundred thousand Brazilian _reais_ at 2000 prices.

**Table 2: Preschool enrollment and household income, 2001**

||% of total<br>enrollment|Mean|10th percentile<br>Household inc|Median<br>ome (mont|90th percentile<br>hly)|
|---|---|---|---|---|---|
|Enrollment in public preschool|0.68|586.93|120|400|1200|
|Enrollment in private preschool|0.32|1079.26|280|950|3980|

_Notes:_ Data come from _Pesquisa Nacional por Amostra de Domicilios_ (
|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Authors’ estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

Additional covariates were tested (e.g. openness for trade) and results are robust. In the GMM specification (S1), lags 2 to 8 of changes in the share of working age population were used. In the GMM specifications (S2) and (S3), lags 2 to 8 of changes in the share of working age population and the initial per capita GDP were used. Geographic and time variables were used as instruments. Results are also significant when reducing the number of instruments. Using the GMM estimation as a baseline (S3), the results suggest that an increase of 1 percentage point in the share of working age population would lead to an increase in GDP per capita of approximately 1.5 percentage 15
20 educational performance. Thus using the 30 directly-measured countries, they estimated a statistical relationship between their educational quality indices and schooling inputs such as primary school enrollment, average years of schooling, total expenditure on education (as a share of GDP), annual population growth and regional dummies. These schooling inputs were able to account for more that 65 percent of the cross-country observed variation in educational quality<sup>33</sup> . The resulting estimates were used to generate predicted values for an additional 52 countries.

Bosworth and Collins (2003) also constructed an alternative series on educational quality. They produced this series by re-estimating the Hanushek-Kimko relationship between the quality of education and the set of schooling inputs with updated data obtained from the World Development Indicators. In addition, they modified the estimating equation to include a measure of the quality of government institutions as one of the independent variables. The quality of institutions measure substantially improves the statistical fit and the reduced the role of several other variables.

As with our data series on the Mincerian rates of return to education, we seek to increase the number of usable observations on educational quality by filling in missing values in the Hanushek-Kimko series. We employ the identical procedure of filling in missing values as before by using the simple average of a specific country’s regional and income group average of available educational quality observations. We term this series as our _extended series_ on educational quality.

# **-** **<u>Using Country Specific Rate of Return to Education to Account for Difference in Educational Quality</u>**

Some argue that qualitative differences in schooling can be accounted for by the use of country-specific rates of return to education that were mentioned above. For country specific rates of return to accurately reflect quality differences in the stock of human capital, three assumptions are required to hold. First, global labor markets need to be perfectly competitive. Second, labor is required to be perfectly mobile internationally, and lastly, employers need to be perfectly informed
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2442 A Firms's-Eye View of Policy and<br>Fiscal Reforms in Cameroon|Bernard Gauthier<br>Isidro Soloaga<br>James Tybout|September 2000|L. Tabada<br>36896|
|WPS2443 The Politics of Economic Policy<br>Reform in Developing Countries|Richard H. Adams Jr.|September 2000|M. Coleridge-Taylor<br>33704|
|WPS2444Seize the State, Seize the Day:<br>State Capture, Corruption, and<br>Influence in Transition|Joel S. Hellman<br>Geraint Jones<br>Daniel Kaufmann|September 2000|D. Billups<br>35818|
|WPS2445 Subsidies in Chilean Public Utilities|Pablo Serra|September 2000|G. Chenet-Smith<br>36370|
|WPS2446 Forecasting the Demand for<br>Privatized Transport: What Economic<br>Regulators Should Know, and Why|Lourdes Trujillo<br>Emile Quinet<br>Antonio Estache|September 2000|G. Chenet-Smith<br>36370|
|WPS2447 Attrition in Longitudinal Household<br>Survey Data: Some Tests for Three<br>Developing-Country Samples|Harold Alderman<br>Jere R. Behrman<br>Hans-Peter Kohler<br>John A. Maluccio<br>Susan Cotts Watkins|September 2000|P. Sader<br>33902|
|WPS2448 On "Good' Politicians and**"Bad"**<br>Policies: Social Cohesion,<br>Institutions, and Growth|Jo Ritzen<br>William Easterly<br>Michael Woolcock|September 2000|A. Kibutu<br>34047|
|WPS2449 Pricing Irrigation Water: A Literature<br>Survey
are not always clearly defined. The measurement of ‘casual labor’ in LSMS‐ISA surveys is, for instance, problematic. Most labor

> 13 One variant, common in LSMS‐ISA surveys, is open questions _(‘How many hours in the [reference period] did you spend on [activity]?’)_ rather than closed, binary questions ( _‘Did you work on [activity] in [reference period]?’_ ). Open questions aim to capture the employment status and time use in a single question.

> 14 Questions related to the reference period are worded slightly differently across surveys. Surveys either refer to the _‘last 7 days’_ or ‘ _the last week’_ . Whether respondents wrongly interpret _‘last week’_ as the last full week ranging from Monday to Sunday and whether this affects the employment data is unclear.

13
.348**|-94.276**|-113.860**|-110.443**|417.745**|
||(1.998)|(44.156)|(46.013)|(46.173)|(49.689)|(172.440)|
|Number of observations|<br>44,539|<br>44,539|<br>43,616|<br>41,444|<br>36,944|<br>36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.513|
|Standard errors in brackets. All<br>Sample size varies due to missi|standard error<br>ng data.|s are Huber-|White robust|and clustered o|n country-tim|es-industry.|

40
of stimulation and excessive stress (Walker, Wachs, Grantham-McGregor, et al., 2011). These factors affect children’s brain development and behavior. Over time, these effects accumulate, further increasing inequality across generations (World Bank, 2005). These inequalities in early childhood are particularly troubling because they occur before children have any control over their circumstances, and thus are purely inequality of opportunity (Roemer, 1998). Essentially, young children face an unequal ‘playing field’ for development.

These deficits and inequalities in early childhood development (ECD) are preventable. Studies have shown that investments in ECD, especially in the prenatal period through the first years of life, can be the most effective and cost-efficient way of preventing inequality. Quality ECD programs for the poorest can lead to major gains in the lives of disadvantaged children and reduce economic disparities later in life (Heckman & Masterov, 2007; Heckman, 2006). For example, investments in early education have reduced achievement gaps, needs for special education, and crime rates, among other benefits (Temple & Reynolds, 2007). To be most effective and cost-efficient at reducing inequalities, ECD interventions should target the many risks which children experience and should begin early in life while brain development is still at its most flexible. This is more effective than attempting to remedy cumulative deficits at a later stage in a child’s life (Engle, Fernald, Alderman, et al., 2011).

Shortfalls in ECD have historically been a challenge in Morocco, with children facing unequal development opportunities due to the circumstances of their birth (El-Kogali & Krafft, 2015; Krafft & El-Kogali, 2014). However, despite the scale of this problem, analyses for Morocco have only used data from 2003/04. More recent survey data have not been used to conduct research, identify developments over time, and update outcomes. This paper attempts to address this gap in the literature. It examines the roots of inequality in human and economic development in Morocco
farms overall, 68% and 61% of large teff and maize farms were established after 2007 with a corresponding shift in the level at which negotiations were held. With 55 ha per permanent employee, the average farms employs has less than 5 permanent workers; wheat being the least and teff the most employment intensive crop in our sample.<sup>9</sup> With 37% on average, the share of those reporting payment of lease fees ranges from 73% for wheat to 30% for sorghum. While only 6% overall, the share of farms who access credit is 15% for teff but only 5.6% for sorghum. The fact that some two-thirds of large farms (59% for sorghum, 73% for wheat, 80% for maize, and 88% for teff) claim to provide benefits to surrounding small farmers further justifies exploration of spillover effects.

Table 2 provides information on area cultivated, yield, fertilizer and seed use, for the four cereal crops of interest (maize, sorghum, teff, and wheat) based on 2013/14 data for smallholders as well as commercial farms of different sizes. Lack of data on input quantities and prices makes productivity comparison difficult but in all crops except teff, smallholders’ yields (27.1, 21.3 and 21.9 Q/ha for maize, sorghum, and wheat, respectively) are 75% of or less of those by commercial farms. While part of this difference may be due to

> 9 As flower farms are not included in our sample, coffee is the most employment intensive crop among large farms.

9
<!-- Start of picture text --> Fig. 2b: Spatial mismatch by sectors<br>0.35<br>0.30<br>Unorganized Plants<br>0.25<br>Unorganized Employment<br>0.20<br>0.15<br>Organized Employment<br>0.10<br>0.05 Organized Plants<br>0.00<br>1989 1994 2000 2005<br>Urban spatial mismatch index<br><!-- End of picture text -->
# **What Drives Short-Run Labor Market Volatility in Offshoring Industries?**

# **Evidence from Northern Mexico during 2007-2009**

David S. Kaplan Daniel Lederman Raymond Robertson Key words: trade shocks, labor market dynamics, adjustment costs JEL codes: F16, J63, J31 Kaplan: Inter-American Development Bank, LMK and IZA, dkaplan@iadb.org. Lederman: World Bank, PRMTR, dlederman@worldbank.org. Robertson: Macalester College, <u>robertson@macalester.edu. The authors gratefully</u> acknowledge financial support from the World Bank executed Multi-Donor Trust Fund on Trade and from the World Bank’s Latin American and Caribbean Regional Studies project on Labor Markets and Business Cycles. We are also indebted to Judith Frias and Eduardo Alcaraz for help with the IMSS data. The authors received invaluable feedback on preliminary results presented at seminars in the World Bank’s Development Research Group, Trade and International Integration unit (DECTI), the Inter-American Development Bank’s Labor Markets analysis unit (LMK), the 2010 LACEA-TIGN meetings, and the 2011 Eastern Economic Association meetings. We particularly would like to acknowledge the insightful comments received from Tomas Castagnino, Julian Messina, Pravin Krishna, Marcelo Olarreaga, Carmen Pages, and Rodrigo Wagner.

.
Uganda) and even higher (Botswana and Niger). From the utility’s perspective, underpricing can amount to losses valued as much (and even more than) 100 percent of the utility’s turnover (see annex 14 for details).

Because electricity subsidies are typically justified by the need to make services affordable to lowincome households, a key question is whether subsidies reach such households. Results across a wide range of African countries show that the share of subsidies going to the poor is less than half their share of the population, indicating a very pro-rich distribution (figure 21). This result simply reflects the fact that connections to power are already highly skewed toward more affluent households. In SSA as a whole, access to power among the bottom three quintiles of the budget distribution is no more than 12 percent on average compared to 72 percent in the top budget quintile.

To put these results in perspective, one must compare them with the aims achieved by other forms of social policy. Estimates for Cameroon, Gabon, and Guinea indicate that expenditures on primary education and basic health care reach the poor better than do power and water subsidies (Wodon 2007) Figure 21. Extent to which electricity reaches the poor <!-- Start of picture text --> Nigeria<br>Gabon<br>Congo<br>Côte d'ivoire<br>Cape Verde<br>Togo<br>Sao Tome<br>Senegal<br>Cameroon<br>Mozambique<br>Ghana<br>CAR<br>Guinea<br>Burundi<br>Burkina<br>Chad<br>Malawi<br>Uganda<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9<br>Measure of distributional incidence<br><!-- End of picture text --> _Sources_ : Banerjee and others 2008; Wodon 2007. _Note_
data flows among different stakeholders. At advanced levels of data maturity, the goal is to optimize the system for the best possible outcomes.

Kitzmueller _et al._ (2021) offer a discussion of the data systems and efforts to improve them using three real-life country examples—Ghana, Mexico, and Estonia that correspond to the three levels of data development—and well illustrate each country’s current position regarding statistical capacity with their SPI score. Jolliffe _et al._ (2023) argue that data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Carpenter _et al._ (2022) and Reddy _et al._ (2023) describe various South-South and North-South initiatives to improve local statistical capacity (in medical statistics), ranging from setting up joint graduate study programs and research projects to consulting consortiums for rapid response to the COVID19 pandemic. Using volunteer data collected by other stakeholders, including citizen scientists, private sector companies, and other organizations, could encourage the public to further participate in building up statistical capacity, filling SDG data gaps, and enhancing government ability to monitor public service delivery and better protect the environment (Conrad and Hilchey, 2011; Meijer and Potjer, 2018; Fritz _et al._ , 2019).

Yet, while the amount of data produced by the public sector is increasing rapidly, the full potential of data to improve development outcomes has not been realized yet because of challenges of suboptimal data quality or lack of data on vulnerable groups. This limitation is most highlighted during times of crises. For example, recent studies on the COVID-19 pandemic suggest that vulnerable groups, including poorer households, women, children, and refugees, were most affected (Dang and Nguyen, 2021; Sumner et al., 2022; Li _et al._ , 2023). In this regard, the importance of measurement of statistical capacity has been recognized in the SDG indicator framework with the
0.492|
||(0.033)|(0.052)|(0.031)|(0.053)|
|Age 6|0.346|0.558|0.085|0.802|
||(0.029)|(0.051)|(0.035)|(0.043)|
|Age 7|0.709|1.251|0.140||
||(0.083)|(0.180)|(0.061)||
|Nobs|5110|3898|1213|4794|
|Number of panels|50|50|49|50|
|sigma v|0.595|0.830|0.360|0.795|
|sigma u|0.177|0.263|0.084|0.198|
|Predicted value if comparison|0.110|0.006|0.488|0.013|
|Predicted value if treatment|0.191|0.020|0.629|0.028|
|Mean if comparison|0.218|0.140|0.507|0.171|
|Mean if treatment|0.290|0.164|0.647|0.203|

The sample is the same as in Table 4a. School participation is averaged by child. The estimation model is two-sided tobit with random e¤ects at the school level. Predicted participation is at the mean of the dependent variables. Pre-program presence is based on one observation of school participation in term 1, 2000, prior to the implementation of the program. A constant was included in the regressions but the estimates are not reported. Standard errors are in parentheses.

23
Justice Research Institute (UNICRI) has also initiated surveys in several countries on violence against women. Both the Demographic and Health Surveys conducted by Macro International and the Centers for Disease Control’s Reproductive Health Surveys have begun to incorporate questions on violence in their surveys (Kishor and Johnson 2004).

There is some evidence that large-scale surveys designed primarily for other purposes such as the Demographic and Health Surveys are more likely to under-estimate the prevalence of violence than studies that focus specifically on violence. Characteristics of violence-specific surveys such as specialized training of interviewers, greater emphasis on privacy and safety of respondents, and multiple opportunities to disclose violence have been found to have a positive effect on women’s reporting of violence (Ellsberg, Heise et al. 2001; Jansen, Watts et al. 2004).

# **1.3 A Causal Framework for Understanding Gender-Based Violence**

GBV is a complex phenomenon, shaped by forces that operate at different levels. An ecological framework that combines factors which operate at the individual, relationship, community and societal levels thus provides an appropriate lens through which to examine
|
|Non-settlement IDP|-0.048*||-0.076***|
||(0.028)||(0.028)|
|Non-IDP|||-0.100***|
||||(0.023)|
|Constant|-0.256**|0.114|0.314|
||(0.116)|(0.205)|(0.196)|
|Observations|1,902|6,341|8,243|
|R-squared|0.583|0.299|0.368|

_Source:_ Estimated by authors based on Somali HFS 2017-18 _._ Note: Robust standard errors in parentheses. Estimations include regional dummies. *** p<0.01, ** p<0.05, * p<0.1. _Note:_ Following the Durable Solutions Analysis Guide (Joint IDP Profiling Service [JIPS], 2018), the variable mechanisms to obtain or replace lost documents refers to access to support systems that grant or restore personal or other documentation such as IDs, land and housing titles.

We find some association between gender of the household head and poverty emerges for IDPs, controlling for other individual and household characteristics. Male headship remains associated with higher poverty for IDPs but not for non-IDPs. Among non-IDPs widow-headed households emerge as a group that with high risk of poverty but this result does not hold for IDPs.

21
Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. _Educational Psychologist_ , 44(3), 159-175.

Rimm-Kaufman, S. E., Curby, T., Grimm, K., Nathanson, L., & Brock, L. (2009). The contribution of children’s self-regulation and classroom quality to children’s adaptive behaviors in the kindergarten classroom. _Developmental Psychology_ , 45(4), 958-972.

Rivkin, S. G., Hanushek, E. A. and Kain, J. F. (2005). Teachers, Schools, and Academic Achievement. _Econometrica,_ 73 (2), 417-458.

Rockoff, J. E. (2004). The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data. _American Economic Review_ , 94 (2), 247-252.

Samejima, F. (1969). Estimation of Latent Ability Using a Response Pattern of Graded Scores (Psychometric Monograph No. 17). Richmond, VA: Psychometric Society.

Snilstveit, B., Stevenson, J., Menon, R., Phillips, D., Gallagher, E., Geleen, M., and Jobse, H. (2016). The Impact of Education Programmes on Learning and School Participation in Low and Middle-Income Countries: A Systematic Review Summary Report. Systematic Review Summary 7, International Initiative for Impact Evaluation, London.

Spilt, J. L., Hughes, J. N., Wu, J. Y., and Kwok, O. M. (2012). Dynamics of teacher-student relationships: Stability and change across elementary school and the influence on children’s academic success. _Child Development_ , 83, 1180 –1195.

Staiger, D. O. and Rockoff, J. E. (2010). Searching for effective teachers with imperfect information. _Journal of Economic Perspectives_ , 24 (3), 97-118.

Stallings,