rafmacalaba/data-use-mentions-extended huggingface dataset:
Three distinct data sets were used to measure surface water quality. These data sets are from the Mekong River Basin Commission (MRC), the Central Water Commission (CWC) of India, and GEMStat of the United Nations Global Environmental Monitoring system for freshwater (UNGEMs). Table 2 summarizes these data sets. The MRC data span four countries (Cambodia, Lao PDR, Thailand, and Vietnam) and cover the main tributaries of the Mekong River. Of the 121 stations with data on EC, 23 are in Cambodia and Lao PDR, 21 are in Thailand, and 54 are in Vietnam (figure 1).

In India, the Central Water Commission or CWC (under the umbrella of the Ministry of Water Resources) maintains a network of 375 river monitoring stations throughout India that cover almost all major river basins in the country. Water samples are taken at specific time intervals (monthly or quarterly), and then sent to regional laboratories for analysis.

Finally, the GEMStat data from UNGEMS are used for the global analysis. These data are collected by the United National Environmental Programme (UNEP) and self‐reported by participating countries. GEMStat contains over 3 million observations for 224 water quality parameters in 71 countries. EC is one of the most documented pollutants in GEMstats with 167,914 observations of EC in 1,719 stations and 71 countries. As with the Indian CWC data, frequency of observations varies significantly across countries and monitoring stations. Some monitoring stations have multiple observations per month while others have seasonal or annual observations (e.g., one station in Japan has 1,721 observations of EC only). For all the data sets, observations at a single monitoring station are collapsed at the annual level. This leaves us with a panel data set of year/station observations.

# 2.2 Agricultural Productivity and Land Use

To measure changes in agricultural productivity, we use a satellite‐based estimate of net primary production (NPP). NPP is linearly related to the amount of solar
LAYS above. A different approach could be to choose a basket of top-performing countries and adjust learning relative to the mean performance in that basket, as follows: The intuition of the _LAYS_ adjustment is the same as before. The main difference is that for some of the countries in the basket (those whose value of _L_ is above the mean for the top performers), by construction the _LAYS_ estimate is now **greater than** their average years of schooling (since for them R��� �1).

Figure 4.5 compares _LAYS_ values using these two different numeraires—the mean of the top 5 performers (in terms of learning per year) versus the score of the top performer (Singapore)—based on both the TIMSS (left panel) and PISA (right panel) mathematics scores. Figure 4.6 shows how average years of schooling compares to _LAYS_ in the two cases, again for TIMSS (top panel) and PISA (bottom panel). Since learning per year averaged across the top 5 performers will be less than that among the top performer, using the former of course yields _LAYS_ estimates that are larger. Nevertheless, as Figures 4.5 and 4.6 show, the impact of changing the numeraire in this way is very small.

**Figure 4.5:** **_LAYS_ using top 5 performers versus top 1 performer (Singapore) as the numeraire**

<!-- Start of picture text --> TIMSS PISA<br>0 5 10 15 0 5 10 15<br>LAYS (Numeraire = Singapore) LAYS (Numeraire = Singapore)<br>Source: Authors’ analysis of TIMSS 2015, PISA 2015, and Barro‐Lee data.<br>Notes: Illustration for case where learning starts at Grade 0 for TIMSS and age 6 for PISA.<br>15 15<br>10 10<br>5 5<br>LAYS (Numeraire = Top 5) LAYS
There is little, if any, _new_ information that congresspersons seek from lobbies in order to raise their re-election chances. And there is little, if any, new information that agriculture lobbies possess. At least in the agriculture case, then, we do not see a role for informational lobbying. Our experiment is different from Hansen’s (1991), who does find an important role for informational lobbying. He considers a longer period in history during which there were points in time when Congress sought new information from lobbies. At those threshold points, informational lobbying may have led to marked shifts in how Congress began to view agricultural policy. However, on a continuing basis over the 1991-2000 period we see little role for new information.

In the empirical section a reduced form approach is used to explore the association between lobbying spending and agricultural protection. This is the first evidence on such an association in the case of agriculture, and the nature of the exercise and the evidence is exploratory. The main message is evidence of a strong association. Whether the association is via informational channels or whether lobbying is payment for services are questions that are not directly addressed, for they require more theory-based investigation. Gawande and Hoekman (2004) undertake one such examination of the quid pro quo hypotheses. Perhaps, other second generation studies will use more formal structural models to investigate the relative importance of informational versus quid pro quo lobbying in the context of US agricultural policy.

# **4. Econometric Evidence**

# _A. Data_

Econometric evidence on the association between lobbying spending and protection in agriculture is presented in this section, using panel data for 44 commodities across 1985-2001. The USDA’s Production, Supply and Distribution database PSD Online (at <u>http://www.fas.usda.gov/psd/complete_files/default.asp) was the source for trade and</u> production data in order to construct the import penetration ratio. Time series data over 19852001 for farm products at the
out each year, with a 4-5 year cycle. The firm-level survey is complemented with analysis from other studies, as wells as discussions and meetings with key stakeholder groups. For example, indicators on entry regulation estimate the procedures, time, cost and minimum capital requirements to register a business formally, thus highlighting the microeconomic and institutional conditions inhibiting productive investment. These assessments will ultimately feed into operations and technical assistance.

Several of the investment climate surveys also contain detailed information on transportation constraints including trade facilitation programs. There are data on questions such as the number of competitors, suppliers, customers, and percentage of firms that find transportation and customs and trade regulations to be “major” or “very severe” constraints, infrastructure 6 __________________________________________________________________________________________________________
6 the actual height variations along local vertical structures. Since VE can still include vertical vegetation structures such as trees or hedges, all VE-candidate pixels dominated by vegetation are masked out. For this purpose, an ensemble of support vector regression models is trained separately on the NDVI ~~m~~ ax layer for all K ̈oppen-Geiger climate zones to estimate the percent impervious surface (IMP) within each WSF2019 pixel. Based on the resulting WSF-IMP, all VE candidate pixels showing a value _<_ 10 (meaning that more than 90 % of the pixel area is covered by vegetation) are masked out. To produce the final BH product, all local height differences observed for the remaining VE pixels are then spatially aggregated from their 12 m resolution to a 90 m cell by calculating the mean of all VE heights observed within this cell.

As reported in Esch et al. (2022), the BH layer of the WSF3Dv1 shows certain inaccuracies due to the systematic underestimation for the heights of high-rise ( _>_ 50 m) buildings. In dense, high-rise areas, complex and compact morphological structuring leads to multiple scattering, layover, and shadow effects causing ambiguities in the SAR signal, which ultimately results in errors in the absolute heights provided by TDX-DEM.

Thus, information from Emporis (EM) for the year 2019 is now integrated to create an enhanced version of World Settlement Footprint 3D (WSF3Dv2). EM is the most complete collection of global skyscraper and high-rise building data (see Appendix Section B for details). They rely on information provided by the industry for “thousands of cities worldwide. Emporis (2022) collects information about the full life-cycle of each building. The database (last accessed 09-07-2022) contains information for 781,136 completed tall buildings.

Technically, the improvement of the BH layer is based on a correction factor (fxy) calculated using the relative differences between spatially overlapping BH values derived from WSF3Dv1 (BHv1), and EM building height values (BHEM) provided for the exact geographical
gaps are particularly acute for the Pacific Island countries: Solomon Islands is the only Pacific Island country (out of a total of 12) where data are available between 2015 and 2019. Overall, data availability is better in East Asian countries. However, even when data are reported, the attrition rates fluctuate substantially year by year. For instance, primary teacher attrition rate for Malaysia were 6.7% in 2015, 3.1% in 2016, 2.2% in 2017, no data in 2018, and 3.5% in 2019 (WB Edstats Database).

On the one hand, the projections presented here likely _underestimate_ the proportion of 2030 teachers who have already been recruited, for a number of different reasons. For the countries with dwindling student numbers, these countries will likely struggle to reduce the number of teaching positions (at the rate our 7
We include several variables to account for country-level variation. From the WDI database, we define access to credit as measured by the ratio of private sector credit to GDP and skills as measured by the percent of the workforce with tertiary education. Quality of infrastructure is measured by an index taken from the Global Competitiveness Report,<sup>24</sup> a survey of business leaders published by the World Economic Forum. Foreign direct investment inward and outward stock are measured as the ratios of stock to GDP for manufacturing and for service sectors, from the Eurostat SBS.

To assess the regulatory environment within each country, we employ the World Bank’s Doing Business<sup>25</sup> (DB) database. Using principal component analysis, we construct a comprehensive index of all regulatory policies, _all_DB_ .<sup>26</sup> A second variable, _DB_business_startup_ , indexes barriers to entry and exit, including the costs of starting a business, registering property, and closing a business. _DB_business_operations_ indexes the difficulty of operating a firm, including securing construction permits, paying taxes, trading across borders, and employing workers. Finally, _DB_institutional_environment_ is an index of the quality of the legal and institutional framework for enterprises, including the level of protection for minority shareholders, the quality of the credit information systems, and the cost and speed of contract enforcement. All indices are coded such that higher values indicate better regulation.<sup>27</sup> Summary statistics are provided in Appendix XII.

# **4. Methodology**

We use the following specification to analyze productivity growth in Europe.

The variable _∆_ ln(Prodi)03-08 is the annualized growth rate of labor productivity (defined as value-added per employee) for firm _i_ from 2003 to 2008.<sup>28</sup> _Size_ , is expressed in terms of number of employees on the company’s payroll, and is divided into five previously mentioned categories. Microenterprises are mostly family-owned and have a
from the Central Bank;

- managerial structure for borrowings and debt-related transactions;

- existence, coverage, and content of the legal framework;

> 24 For analysis purpose, Maldives is classified as lower-middle-income country. Maldives was classified as lowermiddle-income country when the DeMPA was conducted, which status was changed to upper-middle-income for FY12 based on 2010 GNI.

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# **I. Introduction**

Conflict affects education in several ways. It destroys infrastructure (Abdi, 1998), displaces and most tragically results in the deaths of students and teachers (Buckland, 2005), causes problems in harmonizing school calendars across war-affected regions (UNICEF, 2005) while schools remain closed for an indefinite period of time (Bruck, 1997), and has a damaging and pernicious socio-psychological impact on students (Sany, 2010). A cross-country analysis by Lai and Thyne (2007) showed that countries experiencing civil war suffer a decline in school enrolment by 1.6 to 3.2 percentage points. Evidence is growing at the subnational level that the outcomes are similar. Merrouche (2006) documented that exposure to landmines in Cambodia resulted in an average loss of .4 years of education. In a similar study, the mid-1990s genocide in Rwanda lowered the average level of educational attainment by .5 years (Akresh and de Walque, 2008). From the perspective of gender, Shemyakina (2006) finds that conflict makes no significant impact on male education rates in Tajikistan. However, females were 12.3 percentage points less likely to complete the mandatory secondary schooling compared to those who completed their education before the war broke out. A recent study, using household survey data between 2000 and 2008 from twenty-five conflict affected countries, finds that conflict leaves a legacy of fewer average years of education, decreased literacy rates and a smaller share of the population with formal schooling (UNESCO, 2010).

In this paper we estimate the average causal effect of civil war on education in Côte d‟Ivoire. In particular, we measure the effect of Ivoirian conflict, which reached its peak between 2002 and 2004, on years of education for individuals who were exposed to it in their schoolgoing age. The civil war in Côte d‟Ivoire broke out in September 2002 as a result of growing ethnic tensions and a failed attempted military coup. It divided the country into two: the rebelheld North
that rural Malawi in the rainy season approximates autarchy, 15 Data from a 2008 nationally representative survey in Malawi indicates that only 26 percent of respondents use a formal financial product, and around 60 percent had never heard of a savings account (FinScope, 2008). 16 Longer-run saving instruments are more common, typically in the form of agricultural inputs, such as livestock. 17 To illustrate, suppose that outside of the lab a participant can borrow or save at market rate _r_ and faces no financial transaction costs. A typical experiment asks the participant to choose between $ _x_ sooner or $  1  _re_  _x_ later, where<sup>_r_</sup> _e_<sup>denotes the rate of return implied by the later option. The participant may view this as a</sup> choice between Option _A_ , $ _x_ sooner and access to the interest rate _r_ , and Option _B_ , $  1  _re_  _x_ later and access to the interest rate _r_ . If _re_  _r_ , then the set of allocations of consumption between sooner and later is strictly larger under option _B_ than under option _A_ . Thus, for any monotonic preferences, option _B_ is preferred. Analogously, if _re_  _r_ , then _A_ is preferred for any monotonic preferences.

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have been in the absence of OBA voucher subsidies for comparison with the outcome of the actual interventions. However, since the design was not implemented as planned, the analytical approach adopts a post hoc definition of the counterfactual. Two definitions are used: (1) respondents in the 2010–11 survey who had never used the maternal health voucher (nonvoucher clients), and (2) respondents from villages that were included in both baseline and follow-up surveys, but where no voucher clients were present in 2010–11.

The outcomes of interest include targeting of beneficiaries, knowledge, health service access and utilization (new use and general use of ANC, health facility delivery, and PNC), outof-pocket expenses, and socioeconomic inequities in service utilization. An analysis of targeting 16
For Senegal our universe includes 9,631 establishments. We collected data for 1,786 establishments randomly selected from the RGE-ANSD. Table A.6 and Table A.7 provide the information on the distribution of firms in the population and the sample for Senegal.

Table A.6: Population Distribution, Senegal

|Region|Size|Agri-<br>culture|Food<br>Processing|Wearing<br>Apparel|Other<br>Manuf.|Wholesale<br>& Retail|Other<br>Services|Total<br>Region|
|---|---|---|---|---|---|---|---|---|
|Dakar|Small|72|273|809|859|1126|979|4930|
||Medium|9|75|19|114|125|281||
||Large|9|22|0|48|26|84||
|Diourbel|Small|18|84|182|204|214|80|816|
||Medium|1|9|1|7|8|5||
||Large|1|1|0|0|0|1||
|Kaolack|Small|26|36|242|175|91|50|820|
||Medium|50|12|3|18|63|26||
||Large|11|1|0|0|8|8||
|Kolda|Small|480|28|74|87|64|51|819|
||Medium|21|1|1|1|4|6||
||Large|1|0|0|0|0|0||
|St. Louis|Small|125|43|60|116|96|70|688|
||Medium|65
country to request their collaboration. However, only _Telefonica Ecuador_ had the technical capabilities to implement the data collection we proposed. In addition, this company was the only one with a large presence in the Venezuelan mobile phone market and, thus, could be expected to attract the majority of Venezuelans seeking to obtain cell phone service in Ecuador.

> _Telefonica Ecuador_ keeps millions of geocoded records of phone calls and data use in the corresponding denominators. All canton-year aggregates have been computed using the survey weights.

> 14On July 25 2019, the Government of Ecuador issued the Executive Decree 826 which establishes the implementation of a migration census and the requirements to access a humanitarian visa for migrants that entered legally but have overstayed their permits.

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0.293|0.240|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. All the independent variables are subjected to a one-period lag. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 19982017 period. The pre-crisis period includes Q1 1998-Q2 2008 and the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

20
on total deaths by external causes. However, when we break down to individual causes, we find that homicide is strongly _negatively_ correlated with unemployment. What this means is in counties with higher job losses, the number of deaths from homicide is actually lower. Moreover, the relationship is robust and not driven on outliers (scatterplot not shown). The unemployment and crime relationship has been widely studied in the literature, with mixed results. Our finding is related to that of Raphael and Winter-Ebmer (2001), who found murders and rape procyclical. The authors postulated that higher incidents of rape during economic upswing is due to greater frequency of interactions between potential victims and offenders when a larger proportion of the population is working. However, the authors do not propose a rationale for murders. Note that Ruhm (2015a) found mortality from homicide having an unstable relationship with unemployment.

Table 5.4a: Unemployment and mortality by external causes (IV with controls)

|VARIABLES|External<br>Causes|Transport<br>Accidents|Non-<br>Transport<br>Accidents|Suicide|Homicide|
|---|---|---|---|---|---|
|Instr. Job Losses|-0.121|-0.455|0.413|-0.490|-2.645***|
||(0.201)|(0.281)|(0.263)|(0.322)|(0.642)|
|Constant|-0.0928|-0.102|0.0238|0.153|0.489|
||(0.141)|(0.204)|(0.192)|(0.216)|(0.454)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Observations|2,213|1,971|2
Policy Research Working Paper 5960

# **Abstract**

This note details simulations of the distributional impacts of the 2009 financial crisis on households in Latvia. It uses household survey data collected prior to the crisis and simulates the impact of the growth slowdown. The simulations show that Latvia experienced a sharp rise in poverty, widening of the poverty gap, and a rise in income inequality due to the economic contraction in 2009. The 18 percent contraction in gross domestic product (affecting mainly trade hotels and restaurants, construction, and manufacturing) likely led the poverty head count to increase from 14.4 percent in 2008 to 20.2 percent in 2009. The poverty gap, which measures the national poverty deficit, was simulated to increase from 5.9 percent in 2008 to 8.3 percent in 2009. The analysis finds that the results are robust to most assumptions except post-layoff incomes, which substantially mitigated household welfare. The authors also simulate the impact of Latvia’s Emergency Social Safety Net components and find that the Safety Net likely mitigated crisis impacts for many beneficiaries. The simulations measure only direct short-run impacts; hence, they do not take into account general equilibrium effects. Post-crisis income data from a different data source suggest that poverty rates increased by 8.0 percentage points between 2008 and 2009. As a result, the authors suggest that their ex-ante simulation performs reasonably well and is a useful tool to identify vulnerable groups during the early stages of a crisis.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at majwad@worldbank.org or mazam1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of
of Agriculture. In contrast to the RFMOs, which can obtain detailed vessel data from member countries based on compliance rules, some GFW vessel characteristics are machine-learning-predicted values (Kroodsma et al., 2018). We believe that RFMO vessel characteristics are more reliable than GFW’s for this reason. We ignore

> 11 We have slightly fewer vessels in our data compared to the 2,705 legally authorized distant water fishing vessels at the end of 2020 mentioned in the Chinese Fisheries Statistical Yearbook. The reason for this is that our dataset only includes distant water fishing vessels from seven RFMOs and Rongcheng; we are missing distant water fishing vessels not present in these data. For example, vessels authorized to fish in another country’s waters, and not in any regions managed by RFMOs, could be authorized by the MOA without being registered in an RFMO database.

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wider set of variables available in the survey along with statistical learning methods that yield information that would not be available from fitting the model only once using the original sample (e.g. Hastie et al., 2009; James et al., 2013; Athey and Imbens, 2019).

# **Data**

The analysis relies on the cross-sectional variation across households in two Multiple Indicator Cluster Surveys (MICS): the 2014-15 MICS in the Republic of Congo and the 2014-15 MICS in São Tomé and Príncipe. The MICS is an international household survey program that collects information about health, nutrition, education, and early development of well-being of children and their families in developing countries.

4