rafmacalaba/data-use-mentions-extended huggingface dataset:
# 2010).

17We thank an anonymous referee for suggesting this way of computing this measure.

18Ideally, we need a measure that captures the competitive pressures faced by French firms in each Asian market by other local and foreign firms. To compute such measure we will need firm-product level information on the HS6 products sold by local and foreign producers in each domestic market. To the best of our knowledge this disaggregated information at the firm-HS6 product level is not available for China and the other Asian countries in our sample.

> 19 To further address the potential endogeneity issue between import tariff and export patterns, we use lagged tariff mea-

sures.

20Section III describes in detail how these measures are constructed.

21 Serious missing information problems prevented us from considering a more disaggregated product level dimension. 22To identify multinational firms, we combine our main dataset with the _Enquete Echanges Internationaux Intra-Groupe_ produced by the French Office of Industrial Studies and Statistics (SESSI). This latter dataset is based on a firm-level survey of manufacturing firms belonging to groups with at least one affiliate in a foreign country and with international transactions totaling at least one million euros for the year 1999.

23 To add information on firms’ characteristics, we match our main customs dataset with the Annual French Business Surveys (EAE), available from INSEE. This survey allows us to have information on firms’ employment and labor productivity (value added per worker). Since this implies restricting the sample to exporters with more than 20 employees which have manufacturing as their main activity, the number of observations is reduced by a half. We have no firm-level information for the Food and Beverages industry (corresponding to ISIC 15). This restricted sample covers around 14,000 observations, while the main sample has almost 28,000 observations.

24Since the basket of products is kept constant over the period, the number of observations is reduced to almost 24,394.

35
standard deviations are calculated from this database. The default assumptions in the cost data include a 7% discount rate and zero carbon tax rate. The model also incorporates other global assumptions such as average lifetime and lead construction time of different power generation technologies (IEA), CO2 emission coefficient per GWh (IEA), learning rate for renewable technologies (IRENA), and international energy price (IEA). While these global data sets are provided as defaults, users have the flexibility to revise them or use alternative data sources according to their preferences.

Regarding cumulative global power production, the model implements projections from the IEA. Users can select from three scenarios for cumulative global power production and future fossil fuel prices, sourced from the IEA’s World Energy Outlook (2022): the Stated Policies Scenario (STEPS), Announced Pledges Scenario (APS), and Net Zero Emissions (NZE). According to the IEA, these scenarios are linked to global temperature increases of approximately 2.5 ̊C, 1.7 ̊C, and 1.5 ̊C, respectively. Coupled with the learning rate assumptions from IRENA, the global production data in these scenarios are sufficient to describe the dynamics of each technology’s average LCOE in the FTT-FLEX model.

**Where available, domestically sourced technology cost data are preferred to the global estimates,** as these sources tend to better reflect local attributes like labor costs, logistic expenses, and production costs. In cases where a country intends to import equipment and external capacity, utilizing a global cost database becomes an acceptable option. The FTT-FLEX database offers pre-collected global average cost data, serving as an initial reference point for a readily accessible tool. In the next version of FTT-FLEX, users will be able to set their own discount rate to reflect local financing conditions or connect the discount rate to interest rates in a macroeconomic model (see the Discussion section).

In summary, the country specific data required to run FTT-FLEX are:

- Historical data for the
and women. About 5% of both men and women are unemployed, while about 27% of individuals are _not in the labor force_ , with slightly higher estimates for men compared to women. There are significant differences in wage and selfemployment between men and women – more women are represented in self-employment, compared to men (52% vs. 33%), while more men are in wage employment, compared to women (35% vs. 15%). The distribution of employment found in this study differs from that of the AHIES (2022), even after restricting the AHIES sample to Ashanti and Northern regions. This could be explained by the differences in sample design and the objectives of the two studies; the current study was designed to provide information on employment and informality in the regions while the AHIES focuses on providing income and expenditure information, with limited emphasis on employment and informality.

8
, making it very costly.

> 10. See Gallego, Montero, and Salas (2013) for a brief discussion on the use of traffic counts to evaluate car use. Certainly, vehicle speed is also an alternative measure to analyze congestion. However, there is no comprehensive data in Bogotá to conduct a reliable PYP assessment using car speeds. Constructing a vehicle speed database would be relevant for policy-making given that congestion is such that Bogotá is considered the second worst metropolitan area to drive in according to Waze (2015).

7
# **Table 2: Estimation results: Group trade viability**

||Dependent variable:<br>Income increase from group trade<br>||
|---|---|---|
||[iii]|[iv]|
|Constant|-2.136<br>(0.411)|-1.831<br>(0.364)|
|COVI|0.058<br>(0.353)||
|COST|0.402<br>(0.294)||
|FREQ|0.677 **|0.687 **|
||(0.339)|(0.311)|
|VOLU|-0.055<br>(0.333)||
|NEWC|0.593 *|0.621 *|
||(0.360)|(0.366)|
|PRIC|0.577 **<br>(0.291)|0.633 **<br>(0.285)|
|A|0.752 **|0.765 **|
|QUL|(0.337)|(0.313)|
|FREE|0.748 **<br>(0.330)|0.860 ***<br>(0.320)|
|Observations|142|142|

_Note_ : Estimation results for location dummies omitted. The superscripts *, **, and *** denote significance, respectively, at the 90%, 95% and 99% level.

One surprising finding from the econometric analysis is that increased quality requirements of group trade (QUAL) are significantly related to higher trader incomes, while it would be expected that meeting higher quality standards is costly and would, hence, reduce income. However, this outcome might be the result of multicollinearity between the
is encouraging. KPI derived from Big Data provide new insights, more actionable than the traditional LPI (e.g., dwell time). The following directions could reinforce the value of the KPIs. The first is making the production sustainable by strengthening the data partnerships with industry. The discontinuation of TradeLens means that other data partnerships are needed to access comparable data, either direct partnerships with the shipping lines or purchasing the data from commercial providers.

The second direction is to explore the potential of new indicators from the same dataset (see section 3). A third area is producing the established overall LPI on a 1-5 scale from Big Data. It is possible that the full set of KPIs can accurately help predict the survey-based LPI. A respondent to the classical LPI survey is influenced by his/her actual experience of timeliness, speed and delays of supply chains when rating a partner country. This information is objectively captured by the new KPIs. In statistical terms, the tracking data may constitute a “sufficient statistic” of the LPI, and the survey-based LPI will hence be discontinued.

Finally, establishing more data partnerships and obtaining more data are desirable. Having global or largescale sources on international rail or road movements or truck movements would enrich the set of indicators. Since shipment tracking databases compile waybills or manifests, they include information on the nature and value of goods and freight costs, adding new insights to the current ones focused on speed of trade and connectivity.

19
**Table 1: Sample of Economies**

|||**_Developed Ex_**<br>|**_porters (28 Econ_**<br>|**_omies)_**<br>||
|---|---|---|---|---|---|
|_Australia_|_France_|_Ireland_|_Malta_|_Qatar_|_Switzerland_|
|_Austria_|_Germany_|_Israel_|_Netherlands_|_Singapore_|_Taiwan, China_|
|_Belgium_|_Greece_|_Italy_|_New Zealand_|_Slovenia_|_United Kingdom_|
|_Denmark_|_Hong Kong_<br>_SAR, China_|_Japan_|_Norway_|_Spain_||
|_Finland_|_Iceland_|_Kuwait_|_Portugal_|_Sweden_||
|||**_Developing E_**|**_xporters (31 Econ_**|**_omies)_**||
|_Brazil_|_Czech Republic_|_Latvia_|_Peru_|_Slovak Republic_|<br>_Vietnam_|
|_Bulgaria_|_Egypt, Arab_<br>_Rep._|_Lithuania_|_Philippines_|_South Africa_||
|_Chile_|_Estonia_|_Malaysia_|_Poland_|_Korea, Rep._||
|_China_|_Hungary_|_Moldova_|_Romania_|_Thailand_||
|_Colombia_|_India_|_Morocco_|_Russian_<br>_Federation_|_Tunisia_||
|_Croatia_|_Indonesia_|_Oman_|_Saudi Arabia_|_Turkey_||

_Notes: Developed reflects high-income countries using the World Bank country income classification in 2000, the start of our sample. Developing reflect non-high-income countries._ 14
and each loan type. We do not observe interest rates at the loan level, but anecdotal evidence suggests that interest rates are fairly standardized within products for a given lender. To capture losses, we then subtract the amount in default 24 months after the initial loan application, which could be zero in case of no defaults. We picked 24 months to allow six months for the loans to be issued and 18 months for the termination of loans with the maximum duration.<sup>5</sup>

# **3 Results**

We now describe our identification strategy and results. As our partner bank applied a strict threshold rule based on the EFL score, across which both the likelihood of loan approval and loan terms vary discontinuously, we apply a regression discontinuity (RD) design. Specifically, letting be the borrower's EFL score, be the EFL cutoff, (1) be the outcome of interest for borrower ii conditional on being above the EFL cutoff and ii (0) ss ss̅ YY be the outcome of interest for borrower conditional on being below the EFL cutoff, we ii ii YY estimate by approximating lim ii (Hahn et al., 2001). We estimate the EE(YYii(1) −YYii(0)|ssii = ss̅) lim ssii→ss̅<sup>+ EE(YYii(1)|ssii) −lim</sup> ssii→ss̅<sup>−EE(YYii(0)|ssii)</sup> variety of bandwidths around and polynomial functions of various orders. ssii→ss̅<sup>+ EE(YYii(1)|ssii)and</sup> ss<sup>lim</sup> ii→ss̅<sup>−EE(YYii(0)|ssii)using a</sup> In our main results we estimate a linear model on either side of the threshold. ss̅ Normalizing the threshold , and letting represent the
1. **Limited human, technical, and legal resources.** Subnational tax agencies often struggle with enforcement due to inadequate IT systems and limited human resources. In 2020, for instance, Tres de Febrero’s tax audit department operated with just five employees overseeing 130,000 TSG payers and over 4,000 businesses subject to a local turnover tax (TISH). This team relied on data from a 1980s IBM mainframe to determine taxpayer debts and payments, leading to high costs for identifying debts and noncompliance, making large-scale audits infeasible. Given their limited resources, local tax agencies typically focus their enforcement efforts on a few large businesses that generate most of the income and tax revenue, rather than overseeing tens of thousands of households. For example, in 2020 ten companies accounted for half of the business tax revenue, compared to 25,000 properties that made up half of TSG’s revenue.

2. **High political cost.** The political economy of enforcing a local property tax is contentious (Cabral and Hoxby, 2012), especially in high-inflation contexts. In the Province of Buenos Aires, municipal elections occur biennially, and mayors tend to avoid comprehensive tax enforcement campaigns on delinquent households during odd years to maintain voter support. Moreover, mayors have been reluctant to update the cadastral value of properties (i.e., the tax base) for the same reason (Christensen and Garfias, 2021). Consequently, the property tax’s share in the overall tax structure has declined over time.

3. **Financial disincentives to compliance.** Frequent debt regularization programs and a partial adjustment of delinquent tax debt can further disincentivize timely tax payments. Until 2022, the municipality charged an annual simple interest rate of 12% on late payments, significantly below the inflation rate. Additionally, the municipality has implemented payment plans with preferential interest rates and debt forgiveness ( _moratorias_ ) in four of the last five years. These actions incentivize taxpayers to postpone payments to reduce the real
% in food-producing areas, but increases the probably of conflict in no crop producing areas.

This discussion highlights the complex and essentially contested connections between roads, violence, and resource rents, and the necessity to control for the possible effect that minerals may have on the link between roads and violence.

# **3 Data**

## **3.1 Political Violence**

Our outcome variable is political violence in the Democratic Republic of Congo (DRC). Figure 3 gives a first impression of the cross-sectional distribution of political violence as measured by the two most popular datasets, the ACLED and the UCDP.<sup>2</sup> It is clearly visible that many regions are extremely violent, and few regions are completely peaceful. This motivates an approach in which we make all of the DRC our test sample instead of only the most conflict-affected regions in the East of the country: Ituri, Nord-Kivu, and Sud-Kivu.

> 2See Eck (2012) and Raleigh et al. (2023) for a discussion of the relative merits of these two datasets in conflict detection and measurement. Another important source of data, Kivu Security Tracker, which tracked and publicly reported incidents of violence in Eastern DRC, ceased operations and was put offline in 2023 after a critical security vulnerability was discovered in its database.

8
Only a quick glance at data from the CDM market reveals that offset prices (per ton of CO2‐e) achieved by project hosts have varied a lot across both projects and host countries, and have often (or even typically) been substantially below the respective (policy bloc‐internal) quota prices. Several reasons exist why we could observe offset prices well below quota prices. First, the buyer often contributes expertise, or even perhaps project finance, in implementing the investments necessary for a successful CDM project, and thus in practice provides capital to the host, and where the return for the buyer can be reaped (in part) through a lower offset price. Secondly, uncertainty related to eventual delivery of CERs from CDM projects (or similarly, Emission Reduction Units – ERUs – from JI projects) at project start‐up can lead to highly variable offset prices, even for actually delivered CERs.

Still, the situation in the CDM market strongly indicates the possibility that bargaining between project hosts and buyers is widespread. Implications could then differ from those under standard, friction‐less market conditions largely dealt with above. A substantial share of the bargaining surplus is then likely to go to offset demanders in the policy bloc. If this is the case, the CDM conveys additional benefits upon the policy bloc when compared to those accounted for above (from the cited papers).

Such situations, with project search and bargaining, leads to a more complicated picture around CDM‐ type mechanisms, as compared to my discussion above. I will in this section discuss some of these complications and changes in market function. It is, in particular, not entirely clear whether such bargaining can lead to a separate justification for “net mitigation”: I will here argue that it does not.

At a general level, such bargaining leads to rent sharing between individual buyers (firms in policy‐bloc countries) and sellers (project hosts in fringe countries). This will generally result in project hosts being paid lower prices for
|America including<br>Brazil, Chile and<br>Mexico|5 countries<br>1980-1999|Rivera-Trujillo|SPF (DF)|Finds that technological change effect (2.2%<br>annual average) is higher than technical<br>efficiency for most countries.<br>South American have lower growth rather<br>than North America.|
|Sub-Saharan Africa|9 countries<br>1970-1990|<br>Mbangala<br>and<br>Perelman(1997)|Sequential DEA|Small companies and companies with higher<br>passenger sharesperform better.|
|Sub-Saharan Africa|10 countries<br>1970-1990|Mbgangala<br>(2004)|Sequential DEA<br>and Malmquist<br>TFP index|Provides a regional ranking of operators.|
|Sub-Saharan Africa|1980-2001 10<br>national<br>railways|Mbgangala<br>(2004)|Sequential DEA<br>and Malmquist<br>TFP index|Updates previous ranking of operators.<br>Shows that Interconnection and multimodal<br>transportation need to be developed.|
|Argentina and Brazil|Argentina:<br>1993-1999 5<br>freight,<br>5<br>passenger<br>Brazil:<br>1992-<br>1996 6<br>freight<br>companies|Estache,<br>Gonzalez<br>and<br>Trujillo (2001)|TFP index|TFP growth rates paths revealed the strategic<br>behavior<br>of companies<br>in<br>a<br>price-cap<br>regulatory regime.|
|Brazil|6<br>freight<br>companies<br>1992-2001|Estache,<br>Perelman<br>and<br>Trujillo (2004)|DEA<br>and<br>Malmquist TFP<br>index<br>with<br>quality-quantity<br>decomposition
We conclude by suggesting future areas of research, notably on the role of group inequality and inference, and suggest ways to improve the measurement of conflict in the village census.

### World Bank Policy Research Working Paper 3384, August 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * World Bank. The authors can be contacted at pbarron@worldbank.org, kkaiser@worldbank.org, and mpradhan@ worldbank.org. A version of the paper was presented at MOST UNESCO-LIPI (Indonesian Institute of Sciences) and UNDP Conference on Conflict in Asia-Pacific: State of the Field and the Search for Viable Solutions, 22nd-24th October 2003.  The work was supported by DfID, AusAID and Dutch trust funds. Thanks to Juana C. Brachet, Scott Guggenheim, Bert Hofman, Ben Olken, Frances Stewart, Mohammed Zulfan Tadjoeddin, Jeremy Tobacman, , Bridget Welsh and Michael Woolcock, for their comments on drafts of this paper.  Special thanks to Yulia Herawati and Kristen Stokes for excellent assistance. This version was presented at the Cornell Conference “75 Years of Development Research,” May 7-9, 2004.

1
|0.237|0.264|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Dependent variable is weight in kilograms. Results are the year coefficient from a regression of sample weight on state dummies and cohort (birth-year) fixed effects, so the reported coefficient on (survey) year captures the linear (annual) time-trend in weight gain within cohort. Columns 5-8 include height as a control to (partially) capture attrition related to nutritional status. Columns 3 and 7 restrict the female sample to only ever married women, making the 2005 and 2015 survey rounds comparable to the 1998 survey round. Columns 4 and 8 exclude the 1998 survey round so the estimated trend is more directly comparable to the one for men, who only have anthropometric data starting in 2005.
Wilde 2018, Armand et al. 2017), research has not yet examined behavioral factors that may contribute to the persistence and spread of malaria in the long-term.

This paper uses novel data to causally estimate a constraining factor for elimination: the reintroduction of malaria into elimination zones by population movement. While this phenomenon has been documented in at least 61 countries by the epidemiology literature, this has not been done in a causal framework (Justin Cohen et al. 2012, Lu et al. 2014). In this research, I quantify the negative externality of mobility empirically using a similar setting in Senegal. This paper leverages policy simulations based on these estimates to show how aggregated big data on individuals' geolocation can inform more cost-eective targeting strategies to reduce transmission generated by population mobility, which would be a complementary component of a campaign to successfully eliminate malaria.

The negative externality from travel is generated when people are unaware of their risks to others because they do not know that they are disease vectors.<sup>3</sup> Yet given the benets of travel, an information campaign is unlikely to cause people to internalize the externality and choose not to travel to prevent infecting others. Measuring the size of the externality and identifying those people that contribute the most can allow for targeted policies that can help address this market failure.

The main challenge in estimating the size of the externality from mobility is that while disease transmission may respond quickly to changes in migration patterns, existing survey data that record these patterns are often infrequent or do not have coverage across a country.<sup>4</sup> Therefore, the only strategy available to policymakers to address this externality is using incidence in the previous year to identify where and who to target. This paper is able to signicantly improve on this strategy by utilizing a new source of data to track population 3As will be described in more detail in the next section, the long incubation period for malaria allows people to travel without knowing they are infected
intensity|change, respe|s, and a cubic|kets.|
|Beating justifed if wife:<br>Goes Out<br>w/out Permission|(2)|-0.080<br>(0.044)<br>[0.071]|0.582|6.64<br>18,067|ian Demographic and Health Survey (in years 2005|from 0 to 5 of acceptable reasons for domestic vi|0 otherwise. The sample includes all married wom|instrumented with the free primary education (FP|measures for MTI regions without and with script|r and zone fxed efects, zone-specifc linear trend|n in parentheses; _p_-values are shown in square brac|
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|ptable Reason<br>stic Violence|(1)|-0.399<br>(0.171)<br>[0.020]|2.80|7.94<br>17,658|n data from t|column 1 is t|elieved to be t|cted level of s|MTI<br>_y_<br>, which de|ssions include|he zone level a|
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