rafmacalaba/data-use-mentions-extended huggingface dataset:
lustrates that the rebels located disproportionately in high altitudes as part of their war strategy (FMLN, 1984) and that rivers often marked the boundaries of their territories.

# **III.C Development outcomes**

The long-term development impacts of guerrilla territorial control are measured using 2013 night light luminosity (as a proxy for local economic activity) and 2007 population and household census data.

_Night light luminosity_ . Data on night light luminosity comes from the Defense Meteorological Satellite Program Operational Linescan System. This data was obtained from the US National Oceanic and Atmospheric Administration (NOAA) web page. It has a resolution of 30 arc seconds _×_ 30 arc seconds (i.e., approximately 1 km _×_ 1 km) and spans 1992 to 2013. The main results use data for 2013 as it is the last year of data available from the Operational Linescan System (OLS) flown by the U.S. Air Force Defense Meteorological Satellite Program (DMSP). To study the durability of effects, we also used individual years between 1992 and 2013.

_2007 Population and Household Census_ . The General Directorate of Statistics and Censuses ( _Direcci ́on General de Estad ́ısticas y Censos_ , DIGESTYC) provided anonymous microdata from the 2007 census for this study. The data includes the socioeconomic characteristics of all households and individuals, including but not limited to labor market outcomes; educational attainment; material ownership (e.g., having a car, a TV, etc.); use of public services (electricity, sewerage, and others); migration; and other characteristics of all dwellings in El Salvador.

_2007 Census Cartography_ . DIGESTYC also provided maps of the tracts for the 2007 census. Each tract represents a small area with a fixed geographic perimeter. In 2007, the average tract in our sample included 131 households and 473 individuals. Small tract units facilitate accurate identification of guerrilla territorial control, which is approximated using the geographic coordinates of the tract centroids.

In sum, we explore the effects
underlying model, bootstrapping techniques can be used to simulate the impact that specific shocks or cyclical impulses have on the frequency distribution of credit losses. The re-sampling approach is very flexible and lends itself to many alternative simulation exercises aimed, for example, at measuring the exposure to credit losses of portfolios composed of loans of different size, maturity and rating granted to a specific industry or geographic region. An example of how the aggregate results for the banking system can be broken down and separately analyzed for banks of different size is presented, with reference to the Mexican case, in the Appendix.

In line with the methodological aim of this paper, we have conducted a reasonably basic bootstrapping exercises aimed at simply replicating the distribution of credit losses prevailing at a specific period of time<sup>11</sup> and in three countries; namely Argentina, Mexico and Brazil. We view this as a first step and suggest later what follow-up work might be of interest on a wider country sample.<sup>12</sup> In each of the three countries the Central Bank maintains a Public Credit Registry (PCR) that contains information on a very large number of loans in the financial system. The objectives and design of these databases is the subject of Miller (2003) and in a companion paper we explore further the design issues, given the potential of these databases as tools to analyze credit risk (Majnoni, Miller, Mylenko, and Powell, 2004). The first step of the procedure consists of extracting, from the PCR, a large pool of performing loans to the (non-financial) corporate sector at a particular date. This pool

> 11 Typically we consider the default experience over a 12-month period.

> 12 Simple modifications of the sampling procedures (Carey, 2002) or of the selection of loans extracted from the Credit Registers would enable to select predefined risk profiles and visualize very effectively the different risk embedded in specific forms of risk exposure.

6
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persons are somewhat more attentive to situations where their voice is being trampled in view of the fragility of their situation. The story is different for the OECD that mostly includes developed countries. To some extent, unemployed persons in OECD may be more under pressure to become active as the cost of living (housing, heating, various insurances, etc.) is generally higher in OECD relative to MENA countries. The expenses are actually diverse and the costs often significant. Unemployment benefits are rarely sufficient to allow them to face these costs and have a decent life. In this context, unemployed persons in OECD may be more focused on finding a job given the greater level of job opportunities, and thus be somehow less attentive to some situations where their voice is violated.

At this stage, it is important to check the reliability and the strength of the instruments used. To this end, Table 9 reports the results of the Anderson and Rubin (1950) test of overidentifying restrictions (OIDR) that checks the validity of the instruments and the Stock and Yogo (2005) test that checks the weakness of the instruments. The latter is based on the Cragg and Donald (1993) minimum eigenvalue (CDME) statistic, the value of which is compared to the critical values relating to the Limited Information Maximum Likelihood (LIML) size of a nominal 5% Wald test.<sup>44</sup> This testing approach corresponds to the Stock and Yogo (2005) second characterization of a weak instrument. We do not consider the first characterization due to both the estimation method and the number of instruments.<sup>45</sup> We see from Table 9 that the null hypotheses of validity of the instruments are not rejected, saying that the instruments used with both the MENA and OECD datasets are valid. As to the strength of these instruments, we see that the values of the CDME statistics are higher than the respective critical values, thus rejecting the null hypothesis that the instruments used with both datasets
background on the position of village heads. Section 3 presents the study’s data. Section 4 analyzes whether the village heads indeed selected poorer households in the village. Section 5 explores what village heads prioritize when making selections. Section 6 analyzes what information they use. Section 7 explores the question of acceptability of the village heads’ selection for other community members. Section 8 concludes.

# 2. Road Maintenance Groups Program and Beneficiary Selection

In rural Lao PDR, remote villages are connected to laterite highways via dirt access roads. Access roads are built by government-hired contractors but typically fall into extreme disrepair and often become unusable within a couple years due to a harsh rainy season. The Road Maintenance Group (RMG) program is a pilot program set up by Lao PDR Poverty Reduction Fund (PRF)<sup>4</sup> with the dual objectives of providing an income-earning opportunity to in-need households and extending the lives of the access roads. The RMG program trains participants to carry out road maintenance activities that do not require heavy machinery and subsequently employs them to provide basic road maintenance services throughout the year. PRF designed the program to be part-time. In order to support women’s empowerment, PRF only hires women to work in RMGs.

The villages participating in the RMG program are poor and have few opportunities for wage work. Compared to rural households in the nationally representative sample in the Lao Expenditure and Consumption Survey (LECS) V, collected in 2013, RMG villages were poorer across a range of indicators including housing composition, ownership of durables, and nutrition.<sup>5</sup> Only 17% of working-age individuals in these villages had paid work outside the household at the start of the program. The RMG jobs were therefore highly attractive to populations living in these villages, but the program aimed to allocate the RMG spots to the poorest households in the villages.

The village heads played an important role in this allocation. First, the village heads were responsible for publicizing the program and inviting
the possibility of addressing those questions with the proper tools of economics.

The primary purpose of this study is to analyze in detail the content of PTAs as an essential step to inform the debate on their rationale and impact. Building on the methodology developed by Horn, Mavroidis and Sapir (2010), we collected information for all PTAs in force and notified to the WTO in 2015. Specifically, the database contains information on the inclusion of 52 policy areas and their legal enforceability in 279 PTAs among 189 countries.<sup>4</sup> To our knowledge, this database offers the most comprehensive data available in terms of the number of trade agreements, countries and policy areas covered.<sup>5</sup> Some novel stylized facts emerge from the analysis of the new data on PTAs. First, the new database can inform on the changing content of PTAs. In addition to tariff reductions, more than half of the PTAs in the database include legally enforceable regulations in some policy areas that fall under the current mandate of WTO. These provisions (referred to as “WTO plus” or “WTO+” in the literature) include areas such as customs regulations, export taxes, anti‐dumping, countervailing measures, technical barriers to trade (TBT), or sanitary and phytosanitary standards (SPS), among others. Provisions outside the WTO mandate (usually called “WTO extra” or “WTO‐X”) include a wide‐ranging set of policy areas from investment to

> 2 Note that the term PTAs will be used throughout the paper and is preferred to the term ‘regional trade agreements (RTAs)’ since some of these agreements are not necessarily between countries within the same region or in regional proximity. 3 Recent surveys include Freund and Ornelas (2010), WTO (2011), and Limao (2016).

> 4 The full list of provisions and trade agreements are reported in Tables 1 and Appendix Table 3 and discussed in detail in Section ‐ 2.
GFW vessel characteristics data and only match our vessel characteristics data to GFW fishing hours data on the following vessel identifiers: registry name, MMSI (called SSVID by GFW), call sign and IMO.

For vessels that do not match perfectly on these four variables between the two datasets, we develop a matching criterion with two indices: the vessel name as the primary fixed matching index and other indicators as the flexible matching parameter with the priority MMSI _>_ call sign _>_ IMO. This matching criterion means we first match vessels by name and MMSI, then match unmatched vessels by name and call sign, and finally match remaining unmatched vessels by name and IMO.<sup>12</sup> Figure B1 illustrates this process and states the number of vessels in each category.

We first apply this procedure to the 2,202 vessels whose names appear in only one RFMO database. Then we manually match the remaining 14 vessels. In total, we are able to match 1,496 vessels to the GFW fishing data. If a vessel-year has observations in GFW data in some months but not in others, we add rows for those months to our panel data and assign fishing hours = 0.<sup>13</sup> Finally, we subset vessels to those present in the publicly-available version of GFW’s fishing data so that our analysis is reproducible. This filtering reduces the number of matched vessels to 1,340, of which 1,234 have a gear with a gross tonnage discontinuity.<sup>14</sup> Table 1 reports summary statistics on our estimation sample, which we believe is the largest vessel-level database ever compiled on the attributes and fishing activities of the Chinese distant water fleet. These vessels are typically large, roughly 50 meters long on average, with engine power averaging 1,036 kW. Distant water fishing vessels fish on average 221 hours per month (297 for those fishing positive hours). The range of activity across vessels and months is large
levels), and long-run implications through their impact on productivity, economic inequality and poverty. At the micro level, early sociological studies emphasized that, for the most part, remittances were financing consumption and housing expenditures<sup>3</sup> , with limited dynamic effects. More recent research has found that migration is associated with higher levels of entrepreneurship and educational attainment of the children of migrants.<sup>4</sup> This paper investigates further the impact of migration on human capital accumulation, focusing on child health outcomes. Child health outcomes are one important aspect of well-being and a key determinant of future productivity. In addition, our results provide evidence that the impact of migration exceeds just the direct effects of remittances, suggesting that consideration of the additional indirect benefits and costs of migration should be incorporated into the design of optimal migration policy.

Identification of the health effects of migration is complicated by the fact that migrants are not randomly drawn from the general population. Individuals in poor health are unlikely to be able to endure the process of crossing the border into the U.S., while the most prosperous and healthy rural Mexicans are less likely to find the benefits of illegal migration outweigh their other options in Mexico. Secondly, unobserved shocks such as crop failures or natural disasters may be both an impetus for migration and also result in worsening health conditions in the community. As a result, simple comparison of the health of migrants and non-migrants and their children will not provide reliable estimates of the effects of migration.

In this paper we use instrumental variables methods in order to account for these difficulties and provide credible estimates of the impact of migration on child health, allowing quantification of some of the benefits of migration for human capital accumulation in the sending country. A nationally representative demographic survey, the ENADID 1997 is used to provide a broad sample of migrants in rural communities with varying levels of migrant experience. Historic migration networks formed as a result of U.S. demand conditions and the pattern of
Economy: Evidence From the Syrian Refugee Crisis in Jordan,” _Comparative Political Studies_ , 54 (1), 33–76.

- Altindag, Onur and Neeraj Kaushal (2020) “Do Refugees Impact Voting Behavior in the Host Country? Evidence from Syrian Refugee Inflows in Turkey,” _Public Choice_ (10849).

- Amuedo-Dorantes, Catalina and Francisca Antman (2017) “Schooling and Labor Market Effects of Temporary Authorization: Evidence from DACA,” _Journal of Population Economics_ , 30 (1), 339–373.

- Amuedo-Dorantes, Catalina and Cynthia Bansak (2011) “The Impact of Amnesty on Labor Market Outcomes: A Panel Study using the Legalized Population Survey,” _Industrial Relations: A Journal of Economy and Society_ , 50 (3), 443–471.

- Amuedo-Dorantes, Catalina, Cynthia Bansak, and Steven Raphael (2007) “Gender Differences in the Labor Market: Impact of IRCA’s Amnesty Provisions,” _The American Economic Review_ , 97 (2), 412–416.

- Amuedo-Dorantes, Catalina, Ana Marıa Ib ́anez, Sandra V Rozo, and Salvador Traettino (2025) “Fertility Responses to Regularization Programs: Evidence from Forced Venezuelan Migrants in Colombia,” _Economic Development and Cultural Change. Forthcoming._

- Bahar, Dany, Rebecca J Brough, and Giovanni Peri (2024) “Forced Migration and Refugees: Policies for Successful Economic and Social Integration,” Working Paper 32266, National Bureau of Economic Research, 10.3386/w32266.

- Bahar, Dany, Bo Cowgill, and Jorge Guzman (2023) “Refugee Entrepreneurship: The Case of Venezuelans in Colombia,” _American Economic Association Papers Proceedings_ .

- Bahar, Dany, Ana Mar ́ıa Ib ́a ̃nez, and Sandra V. Rozo (2021) “Give me your Tired and your Poor: Impact of a Large-scale Amnesty Program for Undocumented Refugees,” _Journal of Development Economics_ , 151, 102652.

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**Figure 11. Village Public Goods Investment and Migrant Share of the Village Labor Force**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Migrant Share of the Labor Force<br>-1<br>-1.5<br>-2<br>Per Capita Public Goods Investment (log)<br>-2.5<br><!-- End of picture text --> Source: Supplemental survey conducted with RCRE in survey villages and matched to the RCRE Village Survey.

**Table 2 Time Living with Parents Last Year**

||**0-2 Years**<br>**Old**|**3-5 Years**<br>**Old**|**6-12 Years Old**|**13-15 Years Old**|
|---|---|---|---|---|
|||**Panel**|**A: Rural Areas**||
|**Months lived with father last year**|6.66|7.1|7.16|6.31|
|**Months lived with mother last year**|9.14|8.85|8.55|7.07|
|**Number of obs.**|873|1107|2094|705|
|||**Panel B**|**: Urban Areas**||
|**Months lived with father last year**|7.51|9.03|8.9|8.42|
|**Moths lived with mother last year**|9.16|10.67|10.2|9.66|
|**Number of obs.**|713|858|1472|446|

Data source: CFPS 2016
- 25 -

The key to successful administration is to adopt the system to its environment: to the characteristics of the tax base, the skill level available, and the sources of readily accessible data. In general, this implies simple and somewhat arbitrary procedures, and little teliance on interagency information sharing.

# 1. Developing Appropriate Administrat.ve Procedures

# Discovery and Identification

The administration of property taxes is based on system of property records, termed the fiscal cadastre. Each record in the cadastri contains, for a specific property, (i) an identifying number, permitting the record to be linked to a parcel on the ground; (ii) the data to be used in determining the property's value; and (iii) the data used for billing (generally consisting of an owner or occupant's name and address). The yield of the property tax depends to a great extent upon the completeness of the cadastre (in terms of having a record for every parcel) and the accuracy of information contained on each record.

Historically, many countries have relied on owner declarations to compile their fiscal cadastre. Legislation would require all property owners to supply the government with a list of properties owned, their location, and the characteristics of each property to be used in determining their value. This approach worked poorly, however. Owners would submit incomplete lists (laying claim only to properties where ownership was disputed) and consistently under reporting the characteristics to be used in calculating value. Most
defined in step 1, set the levels of the data to the observed values while keeping all the other covariates at reference values as defined in the previous step, then generate a new set of predictions.

4. For each set of predictions generated in the previous step, subtract the reference predictions generated in step 2. Then, from this prediction difference, calculate the lowest value in each district across all time periods and subtract these.

5. Standardize the prediction differences generated in the previous step to [0,1] by dividing them by their sum, then multiply by probabilities predicted when all data are set to observed levels.

> only 8 assessment cycles are available for cross-validation and errors are heavily driven by the first two periods. Again, good performance was established in a holdout, binarized outlooks scoring respectively 9 _._ 3 and 16 _._ 3 points while the model-based predictions missed the outcome by only 1 _._ 6 and 3 _._ 2 points. Finally, few assessments are available for the Republic of Yemen which has stayed near 100% crisis in all but the first period, no difference was ever recorded between near-term preliminary assessments and final outcomes in Uganda, and Zambia never experienced crisis. When focusing on the remaining 16 countries for validation, RMSE in the near-term was identical for model-based predictions and binarized outlooks, while on the 8-month target the model reduced RMSE by 48% increasing only 0 _._ 6 points compared to the binarized near-term outlook.

> 29“Markets” includes all features calculated from food prices and “Conflict” all features derived from the conflict data. The Environmental Factors are divided in two groups: “Rainfall” and “Agricultural Stress”, the latter combining all the features derived from NDVI and evapo-transpiration.

> 30It would also be possible to construct specific alternatives with which to compare observed values such as conflict counts of 0 rather than a non-zero average.

18
21 _km_<sup>2</sup> ) with a monthly periodicity. This study standardizes the series from 1960 to 1979 and calculates the standardized average of temperature for each census tract from 1975 to 1979.

- **Historical crop yield:** Agro-climatic yield rasters were obtained from the Global AgroEcological Zones version 3.0 (GAEZ v 3.0) project and are available at https://www.gaez. iiasa.ac.at. The data has a spatial resolution of 5 arc-minutes (9 _km_<sup>2</sup> ) and a yearly periodicity. We used the 30-year average starting in 1961 of the most relevant crops in terms of consumption and exports for 1990 (i.e., coffee, cotton, rice, beans, and sugarcane).

- **Roads and railways in 1980:** the map outlining the road and railway network in 1980 for El Salvador was obtained from the United States Library of Congress and is available at https://www.loc.gov/resource/g4840.ct000627/. This map was made by the

> 40Documentation of the R tool can be found at https://www.rdocumentation.org/packages/spatialEco/ versions/1.3-7/topics/tri

54