rafmacalaba/data-use-mentions-extended huggingface dataset:
||||||X||
|Update SUA|||||||||||X|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables as well as store and period fixed effects are included. Weights are fixed to the pre-period of 2006m1. Population weights refer to regressing with county-level population weights. State population weights refer to collapsing observations to the state-level and regressing with state population weights. County-level benefits refer to using county-level benefits and county-level synthetic benefits for the set of states that report benefits at the county-level. Full sample refers to using the entire sample from 2006-2015. Removing disaster payments refers to removing any variation in both benefits and participants due to disaster relief events. Policy controls refer to state-year measures of amounts paid out by transfer programs available from the BEA. The transfer amounts are logged and these programs include social security, other retirement and disability insurance transfers, Medicare, Medicaid and other vendor payments, military medical insurance, SSI, EITC, other income maintenance programs such as TANF and WIC, state unemployment insurance, other unemployment insurance, Veterans’ benefits, education and training assistance, other government transfers, transfers from nonprofit institutions, and transfers from businesses. Recalculate the SUA and update the SUA refer to the estimated effects from a specification in which benefits per population are interacted with an indicator whether states recalculate the SUA using utility data, or update the SUA relative to a base each year using the fuel CPI respectively.

Table N6: Effect of SNAP-benefit changes on prices, alternative price indices

|Method|(1)<br>Default|(2)<br>Alternative weights|(3)<br>Tornqvist|(4)<br>One-stage|(5)<br>Geometric|(6)<br>Fixed|(7)<br
<!-- Start of picture text --> Central North North−east South South−east<br>Television Radio<br>Computer<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 9: Percentage of students having access to means of online education Source: SAEB 2017 However, it also shows that about 90% of the students across Brazil has access to television and some states in Brazil, namely Amazonas, Piaui, Parana, Distrito Federal and _Maranhao_ have already begun to broadcast educational content through television. According to the student vulnerability index, published by World Bank, Roraima is one of the most vulnerable states in Brazil, where students face high dropout, less family support, higher incidence of working and lower technology. Since most of the Venezuelan children are living in Roraima, they are more susceptible to disrupted learning due to the pandemic.

School closures also mean that the social safety nets through schools are also a↵ected. Many children depended on the school meals as their only source of nutritious meals. Consequently, Law 13,987/2020 has been enacted by the federal government, allowing the resources originally allocated to providing school meals in all public schools (under the national School Meals Program—PNAE) to be used to buy basic food baskets for disadvantaged families. Before this change, many municipalities were already distributing food baskets to the families of vulnerable students (World Bank, 2020).

E↵ect of the COVID-19 pandemic on the labor market is heterogenous, a↵ecting some types of jobs more than others. Unfortunately, the risk of employment disruptions are higher in service based jobs and Venezuelans, as discussed previously, are heavily concentrated in those service sector 28
re-interviewed in 2008. These are clearly identified in the 2008 dataset, and we are able to use information on 4086 such households.

The labor market information collected in LFS and VHLSS is not directly comparable. Each dataset has its own reference period and questions. For example, while it is possible to construct indicators such as unemployment and informal employment in the LFS, this is not possible in the VHLSS. On the other hand the VHLSS have valuable information regarding educational attainment. We therefore make use of both types of data.

4
- 1) **A regression model to derive quantitative relationships between a selected drought hazard measure and household poverty outcome** for rural households in Ethiopia. This is a survey‐weighted regression model combining historical household data with historical data on drought hazard, which effectively constitutes our ‘vulnerability module’;

- 2) **Testing of the derived ‘vulnerability module’ to evaluate its robustness** , and therefore the validity of its future application onto a forward‐looking probabilistic view of drought occurrence generated from a catastrophe risk modelling framework. This evaluation is conducted through interpretation of the regression results and the application of Statistical Learning Methods (drawing from James et al (2013)) as described in Section 4.

Our evaluation of validity of the resulting damage functions will be centred around concepts of internal and external validity as described by Anttila‐Hughes and Sharma (2014). Anttila‐Hughes and Sharma emphasize that ensuring that estimates of damage functions are both internally and externally valid is the major econometric challenge in the development of general form damage functions from historical data on disaster impacts. In the context examined, internal validity is considered as the extent to which impacts statistically associated with disaster occurrence can actually be causally linked to the disaster occurrence, i.e., that the estimates are econometrically “well‐identified.” External validity is considered as the extent to which relationships estimated in the context of one disaster event can be generalized to other contexts and locations. More detail on these concepts is outlined in section 4.

The framework for damage function development described above, allows for the application of the derived functions out of the context in which they were derived. This is a fundamental feature of catastrophe risk models, as their purpose is to provide a forward‐looking view of risk beyond the historical events used in their development.

**2. A methodology for deriving vulnerability relationships for the impact of drought on poverty in Ethiopia**

For our chosen area of focus – the impact of drought hazard on
br>|all initial NTL<br>|Sample: Lar<br>|ge initial NTL<br>|
|VARIABLES<br>|(1)<br>Linear|(2)<br>Quadratic|(3)<br>Linear|(4)<br>Quadratic|
|α1;Lagged NTL<br>|0.563***<br>(0.00947)|0.917***<br>(0.0210)|0.570***<br>(0.0911)|1.201***<br>(0.297)|
|α4;(Lagged NTL)<sup>2</sup><br>||-0.393***<br>(0.0147)||-0.392**<br>(0.164)|
|α2;Total Inflow<br>|-9.09e-07***|-4.09e-06***|1.55e-05**|-1.55e-05|
||(7.78e-08)|(3.87e-07)|(6.09e-06)|(0.000128)|
|α3;(Lagged NTL)*Total Inflow<br>|3.01e-06***|1.34e-05***|4.04e-06|0.000347|
||(1.67e-07)|(1.40e-06)|(5.61e-06)|(0.000285)|
|α5;(Lagged NTL)<sup>2</sup>* Total Inflow<br>||3.44e-06***<br>(9.64e-07)||-9.81e-05<br>(0.000161)|
|α6;(Total Inflow)<sup>2</sup><br>||1.39e<sup
which act as the primary sampling units from which households are selected.<sup>24</sup> Individuals within households are enumerated in a full roster; individuals are tracked if they leave a household, though this does result in some individual-level attrition. Starting in 2010, 5,000 households in 500 enumeration areas were selected for the panel dataset; however, due to attrition, by the fourth round (2018–2019), a refresh sample of 360 enumeration areas was required.

Due to this sampling design, two points merit some attention. First, non-random attrition poses a challenge to identification. If, for example, attriting individuals are more or less likely to be those that engage in a particular type of economic activity, estimates will be accordingly biased. A related but distinct concern is that attrition itself is related to violent events, which would make treatment endogenous to violence in the sample. Second, the sampling procedure for the fourth round of the GHS included new enumeration areas. Since the main treatment (e.g., exposure to violent events around the farmer-herder conflict) is determined by location, a panel including those new enumeration areas would be inappropriate and therefore, the refreshment enumeration areas are excluded from all analysis of panel data, where individuals (households) appear over multiple rounds of the GHS. We discuss the relative risk to bias from attrition and our approach to account for non-random attrition below.

The second data source is a panel of violent events taken from the Armed Conflict Location and Event Data (ACLED) project (Raleigh _et al._ , 2010). The ACLED project provides several pieces of detailed information, including the approximate date and geo-coded location of an incident, as well as reported information on primary and associated actors in each event. The recorded events are based on accounts from media, NGOs, international organizations, partner reports, and new media (e.g., social media such as Twitter or Facebook). A single account can include several events (
use the Raven’s Colored Progressive Matrices (CPM) to measure a child’s cognitive ability. The Raven’s CPM is a measure of fluid intelligence or problem solving ability, and it does not require formal schooling to be able to answer the questions (Raven, Raven, and Court, 1998). The test does not depend heavily on verbal skills, making it relatively “culture free” (Borghans, Duckworth, Heckman, and ter Weel, 2008). In the Raven’s test, the child respondent is asked to select the image that is missing in order to complete a picture.

> 13 Age seven is the official school starting age in Burkina Faso, but many children start school at a later age. 14 Due to the low primary school enrollment rates in Burkina Faso, the program intervention focused exclusively on primary schooling as opposed to also covering secondary schools.

> 15 Immediately prior to the baseline survey, we conducted a household census in every village to collect information from each household about living structure (flooring, access to latrine), ownership of assets (plow, cart, draft animals, motorcycle, radio), whether the household head ever attended school, whether the household grows cotton, and whether there is a weekly village market. We combined this information with a Burkina Faso nationally representative household survey (INSD Burkinabe Survey on Household Living Conditions – 2003) to calculate a

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# **2.2.Methodology**

The major technical challenge in generating global poverty profiles is combining surveys from different countries and years. Since the availability and frequency of household surveys differ significantly from one country to the next, it is not feasible to produce a global profile that maintains adequate geographical coverage of the poor using data from one particular year. This raises the challenge of how to reliably describe the characteristics of the poor based on national surveys collected in different years.

PovcalNet, the primary source of international poverty estimates maintained by the Research Department of the World Bank, reports global and regional poverty rates for particular reference years. For the reference year 2013, for example, PovcalNet uses approximately 50 surveys conducted that year. For the rest of the countries, the available survey data are “lined up” to 2013 using a complex procedure. First, the welfare measure in each country is multiplied by a constant scale factor to account for changes in welfare between the survey year and 2013. In most countries, the scale factor is the real growth between the survey year and 2013 in per capita household final consumption expenditure (HFCE), which come from the World Development Indicators Database. For the others, mostly in Sub-Saharan Africa, HFCE is either not available or has not been used, so real growth in GDP per capita in local currency units is used instead. Second, after this scale factor is applied to the welfare aggregate, extreme poverty in 2013 is calculated as the share of the sample population whose welfare falls below $1.90 per day in PPP terms.

Two assumptions underpin this line-up method. The first is that the relative distribution of welfare across households remains constant over time; and the second is that HFCE or GDP growth provides a reasonable approximation to the growth in survey consumption means. World Bank researchers have attempted to validate these assumptions in both Africa and India.<sup>17</sup> In India, the relative distribution of welfare across households changes little across years
arrest, we will have two records for that individual, one for each charge. If they are then arrested again in 2011 for another charge, we will have a third entry for them. Each of these incidents can be linked to the same individual with their unique ID and each incident has a unique incident ID.

We drop all arrests and charges for which we do not have court outcome data (i.e., the

> 7About 25% of the arrests do not have a date of offense recorded, so we conduct our analysis using the date of arrest. For the arrests for which we have both date of arrest and date of offense, these dates are the same 84% of the time. We run robustness checks using the date of offense and find broadly similar results.

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imputing the totality of consumption in certain areas based on data from other areas it imputed a randomly different fraction of consumption across all enumeration areas covered in the survey (Pape and Mistiaen, 2018, 2015). Food and non‐food consumption items were first into a core and multiple optional modules. Each household was then asked only about the core items and those items in one of the optional modules, and consumption of items in the remaining optional modules was estimated through multiple imputation. The imputation does not suffer from bias caused by different covariate distributions, since data on every one of the optional consumption modules are collected within each enumeration area. Furthermore, because a majority of consumption is accounted for by a relatively small set of items collected for each household, additional variance introduced by the imputation is minimized.

This section will describe the rapid survey consumption methodology, a more detailed treatment and simulations can be found in (Pape and Mistiaen, 2018, 2015). First, food and non‐food consumption for household _i_ are estimated by the sum of expenditures for a set of items where y�� and y�� denote the food and non‐food consumption of item _j_ in household _i_ .20 Previous consumption surveys in the same country or consumption surveys in neighboring / similar countries can be used to estimate food shares.<sup>21</sup> In South Sudan, the item assignment could draw from the NBHS 2009 survey.<sup>22</sup> The list of items was partitioned into 1 core and 4‐optional modules each with _mk_ items:

> 19 Survey of Well‐being via Instant and Frequent Tracking.

> 20 As the estimation for food and non‐food consumption follows the same principles, we neglect the upper index _f_ and _n_ in the remainder of this section.

> 21 In a case where a previous survey is not available the items can be randomly assigned to the module. This would result in larger standard errors but would not introduce bias.

>
# **5. VF Financial Information and Performance**

The discussion of VF performance begins with an examination of the financial position of Village Funds, as reported in the 2010 survey and summarized in Table 10. Information relevant to the balance sheet is given in the first two panels, and income and expenditure data near the bottom of the table. The first two columns of numbers show the median and mean amounts per village fund, _for those reporting values for these variables_ . The working assumption is that when values are not reported, they are really zeros; thus, for instance, while the mean donation received by the 50 village funds that got funding from NGOs was 101,000 baht, this represents an average of just 2,000 baht per village fund when averaged over all VFs.

By far the most important source of capital is the initial contribution of a million baht by the government, which mainly occurred in 2002-2004. In the first couple of years of the VF, some Village Funds received additional working capital from the government, which pushed the mean contribution up to 1.165 million baht (for those Village Funds reporting). Two other sources of funding are relatively important: the first is borrowing from banks such as the BAAC. Over one in five Village Funds have borrowed from the established banks, and the median amount borrowed by these VFs was a million baht. The other important source of capital is savings deposits by VF members, which averaged 161,000 baht, or about a tenth of total loanable funds.

Most capital is used to lend to VF members, although at the time of the survey – May 2010 – about threetenths of all VF funds were held as deposits in the banking system. Some of this money was thus available for loans later in the year – for instance, for emergency purposes – and some of it remained idle, in the sense of not being lent to villagers, for the whole year.

The main source of a VF‟s revenue is interest
the policy variables in ∆ _t_ .

Third, we use cross-sectional bilateral export data for 180 + 1 products and _J_ ( _J −_ 1) country pairs from UNCTAD. These cross-sectional data are obtained from averaging annual export data for the period 2017-2019. Then, we construct a ( _J × J_ )( _K_ + 1) matrix of bilateral trade flows,<sup>1</sup> We combine these data with the _S_ (sector-by-sector) input-output matrix for the United States in

> 1Note that we consider _K_ + 1 products here, in order to account for the bulk of products (a residual category) beyond the 180 sensitive ones in focus here.

2
# _4.3. Benchmark simulation and retrospective fit_

We run a benchmark simulation to compare the outputs of the model with the data. Past evolution of average income, total population and income distribution are derived from Census data. Results are presented on Figure D.8 of Appendix D, which graphically shows that the overall fit is reasonable. We run a retrospective simulation (i.e., running the model “backwards”) starting in 2011, and compare the outputs of the model 2001 estimation with Census and property price data for the same year. We present the details and results for this retrospective simulation in Appendix D, which shows that the model appropriately replicates changes in housing prices over time. The good fit provides confidence that the model can reasonably be used to simulate the future evolution of Cape Town’s spatial structure.

# **5. Scenarios and prospective simulations**

We consider scenarios of income growth and population trends aligned to those which inform the City’s Land Use Scenarios underpinning its medium-term infrastructure master plans (City of Cape Town 2017, Medium Term Infrastructure Investment Framework). The anticipated twentyyear supply of State-subsidized housing is based on the City of Cape Town’s Housing Pipeline as contained in its Integrated Human Settlement Framework (2013) (see Appendix F). All the other parameters remain constant.<sup>35</sup> We prospectively run simulations for the period 2011-2040.

# _5.1. Urban growth boundary_

There has long been discussions in policy circles of Cape Town being a sprawling city. Against a backdrop where Cape Town’s urban footprint was estimated to have expanded by over 1,000 hectares a year during an unprecedented housing boom during the late 1990s and early 2000s, the City introduced an Urban Growth Boundary (or ’Urban Edge’) as a policy guideline and then, in 2012, as a statutory instrument. It was delineated to include sufficient developable land to accommodate future growth for at least 10 years and was thus not immediately binding
# **3.1 Data sources and main variables**

Data in this paper were collected from three sources. Information on portfolio holdings by Colombian Pension Funds was provided by the Superfinanciera de Colombia (SFC), a supervisory agency within Colombia’s Finance and Public Credit Ministry which oversees all financial, insurance and pension services in the country. The data include detailed security allocations for all private pension funds on a monthly basis between January 2004 and December 2014. Figure 1 presents the time series of pension funds monthly cumulative net flows into stocks (solid line) normalized by the size of the domestic stock market. PFAs have consistently increased their participation in the stock market, with the exception of a few short-lived periods of net outflows. By December 2010, these Colombian Pension Funds had 32% of their $59.6 billion invested in domestic stocks, which amounted to 13% of total market capitalization.

We augment our dataset by including monthly trading activity from the Colombian Securities Exchange. The data include monthly trading volume in each stock by all PFAs. The information, however, is not disaggregated by PFA and only includes industry totals. Figure 1 displays PFAs monthly trading volume as a percentage of the total amount traded in the stock exchange (dash lines). On average, pension fund trades represent over 12% of the value traded in each month. Overall, not only do these funds make up for a significant portion of the domestic equity market, but they also represent a large fraction of the trading volume.

We use Compustat Global for daily stock prices and firms’quarterly balance sheet information.<sup>11</sup> To avoid stocks with small trading activity, we restrict our sample to stocks that trade at least 25% of the business days when the stock was active. We also limit our sample to stocks in which at least one PFA had any holdings during the period. Our final sample includes 57 different stocks, which represent 93% of total stock market capitalization and 95
(0.001)|(0.001)|(0.001)|
|Observations|41459|41459|41459|41459|9459|9459|9459|9459|99613|99613|99613|99613|

_Note:_ Robust standard errors in parentheses. Marginal effects in the Logit/Multinomial logit model are reported. Sample is restricted to _employed_ males aged 20-59. Emp. is short for Employed and SE is short for Self-employed. To combine the HIES and the RMS sample in Column 1, sample weights are applied. Data source: Bangladesh HIES 2016-17 for nonmigrants and RMS 2018-19 for return migrants; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.