rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 4** Impact of Treatments on Sexual Activity

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|A. Female Data|||||
|Sexual activity index<br>treatment x post|-0.125**<br>|0.065<br>|-0.000|5,182|
|treatment x post x Goal|(0.058)<br>-0.027<br>(0.081)|(0.064)|||
|Ever had sex|||||
|treatment x post|-0.047<br>(0.031)|0.046<br>(0.031)|0.372|5,182|
|treatment x post x Goal|-0.009<br>(0.041)||||
|Currently has partner|||||
|treatment x post<br>treatment x post x Goal|-0.110***<br>(0.036)<br>0.027<br>(0.056)|0.000<br>(0.044)|0.337|5,182|
|Total sex partners ever|||||
|treatment x post|-0.025|0.088**|0.491|5,182|
||(0.037)|(0.038)|||
|treatment x post x Goal|
9|16.8|35.8|34.2|
|Household-level EB model|3.7|4.6|32.0|18.6|11.1|17.5|

_Notes: Mean poverty refers to the average of area headcount rates prior to benchmarking. Mean MSE is the mean across areas of the mean squared error, estimated using a parametric bootstrap approach, multiplied by 10,000_ Table 9 shows the evaluation results against the area poverty rates derived from the census. Average relative bias is highest for the Fay-Herriot estimator in both countries, and lowest for the direct estimates. Of greater interest for evaluating accuracy, however, are the results for correlation and root mean squared error. In both countries, the small area estimates are far more accurate than the direct estimates, but the comparisons between the household level model and Fay-Herriot model is mixed. In Sri Lanka, the household model estimates are more highly correlated with the census than the FayHerriot estimates by a moderate amount (0.88 vs 0.84), but slightly less strongly correlated in Tanzania (0.876 vs. 0.883). The Fay-Herriot predictions have a lower root mean squared error in Sri Lanka, reflecting fewer large outliers, but the root mean squared error is virtually equal in Tanzania. Overall, with regards to accuracy, both the household level model and the Fay-Herriot model perform well.

The household model estimates are substantially more precise than the Fay-Herriot model and the direct estimates, but the tighter confidence intervals around the household model estimates may lower coverage rates. The last column of Table 8 shows the coverage rate, which for the household model in Sri Lanka is 84.3%. This is slightly higher than the Horvitz-Thompson estimates (82.2%), moderately higher than the clustered direct estimates (76.1%) but substantially lower than the Fay-Herriot estimates (91.8%). For Tanzania, the coverage rate for the household level model is 74
.01|(0.02)|[1.00]|0.02|(0.13)|1,422|
|Economic Optimism (Z-Score)|-0.38**|(0.19)|[0.38]|0.00|(1.00)|995|

**_Notes_** :

- The table shows the regression results on pre-specified social capital outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Crime Index (Extensive Margin) is defined as a normalized index from two indicators for having been stolen from or gone through an attempt to steal any cash, household items, or livestock from the FR and another indicator if someone physically assaulted the FR last 12 months. Crime Index (Intensive Margin) is defined the same as Crime Index (Extensive Margin) but the number of times if robbery and assault is used instead of the indicators. Security Compared to 2011 (Z-Score) is defined as a normalized index from FR’s answer to whether the security better or same or worse for him/her now compared to January 2011 (referring to the place and circumstances of his/her life at that time). Gender Equality Index is defined as a normalized index constructed from four items in which the FR strongly agrees, agrees, disagrees, or strongly disagrees. The four items are "A married woman can work outside the home if she wishes", "Husbands should have final say in all decisions concerning the family", "A woman can be a president or prime minister of a Muslim country", and "Women and men should have equal rights in making the decision to divorce." Each item is normalized and then the resulting index is normalized again where higher values indicate views in accordance with more gender equality. Religiosity Change in Community (Z-Score) is defined as a normalized index from the FR’s responses to whether he or
8) we repeat the exercise using the growth rate of consumption per capita instead of the growth rate of GDP per capita as the dependent variable. As can be seen, the main result is that the sum of coefficients on the current and lagged level of the RRA is negative and significant at the conventional confidence levels. Quantitatively, the estimates in columns (4) and (9) suggest that a one standard deviation increase in the absolute relative rate of assistance (0.23) reduced GDP per capita and consumption growth by over one and half percentage points per annum. Hence, our analysis suggests that distortions to the relative price between agricultural and non-agricultural products had a significant negative effect on Sub-Saharan African countries' GDP and consumption per capita growth rates.

To strengthen the above result of a significant long-run growth effect of distortions to the relative price between agricultural and non-agricultural products, we report in Table 12 estimates that are based on 5-year, non-overlapping panel data. Before discussing those results, we would like

> 14 See, for example, Bond et al. (2010) for an application of this strategy to testing whether the positive effect of an increase in the level of investment is limited to the level of GDP per capita, or whether it also has an effect on the GDP per capita growth rate.

23
firms, which each own at least 19 vessels. Medium-firm vessels belong to firms that own 2 to 10 vessels. Singleton vessels belong to a firm that owns only one vessel. Data are for the North-Central zone only. Landings data are used to calculate the number of active vessels each season. Landings and vessel length data are from PRODUCE (Englander, 2022).

Large-firm vessels account for 78% of the closures policy treatment effect, which is greater than their share of total juvenile catch in the fishery (70%). Large-firm vessels also catch a higher fraction of juveniles and underreport percentage juvenile by a greater amount than medium-firm or singleton vessels (Table D2).

Second, I find that above-median-length vessels have larger treatment effects than belowmedian-length vessels. The increase in juvenile catch because of the closures policy is 59% for above-median vessels, compared to 23% for below-median vessels. Above-median vessels account for 91% of the closures policy treatment effect, which is greater than their share of total juvenile catch in the fishery (83%).

It is difficult to determine whether above-median-length vessels respond more to closures because they are large, so have more flexibility in the length of their fishing trips, or because 43
Our municipal budget execution dataset is organized into three tables: _commitment_ , _verification_ , and _payment_ .

In the _commitment_ table, one observation is a commitment by one agency in a given municipality. Commitments vary from very large expenses, such as the commitment for the entire wage bill of the mayor’s office in a month, to very specific commitments such as the acquisition of replacement parts for a car. Each commitment is often linked to four levels of “functional programming” that map the nature of a commitment (such as Transportation > Road Transportation > Road Recovery > Recovery of a specific road in a given street), as well as a text that explicitly describes the nature of the commitment.<sup>16</sup> After an initial commitment, officials can increase or decrease the amounts committed as well as annul them. Across municipalities in our dataset, the quality of tracking these actions after initial commitment varies, but we include initial, increases, annulments, adjustments, and net amounts for each commitment when these are available. Each commitment is also linked to a unique identifier (id_empenho_bd) that allows users to connect a commitment to verification and payments linked to them.

In the _verification_ table, observations are verifications by some agency in a given municipality, which are always linked to a specific commitment. The key information available for each verification is the date when they happen, the initial value verified, any adjustments to the original value and the final value. One commitment might generate one or more verifications, so each verification is uniquely identified by the variable id_liquidacao_bd and can be connected to commitments by the variable id_empenho_bd.

Finally, for the _payment_ table, each observation is a payment made to a specific entity. Payments can often (but not always) be linked back to a specific verification. The key information available in the payments table is the date of payment and values. The dataset also includes variables with the names and “document number” of payees - these are often unique national identifiers for
(active) bribers are firms that purposefully engage in such informal dealings as a way of reducing their regulatory burden, obtaining a public market or an exclusive license, or gaining an advantage over competitors. Demand side (passive) bribers are firms that accept to participate in the bribe transaction process with public officials, potentially due to threats or vulnerability that lead to being targeted by officials and that force the illicit payment. Some firms will not, however, abide by these informal rules and despite requests (or threats) by public officials, will not accept to pay bribes, potentially for ethical or moral reasons. We identify such firms as "incorruptible" and use the latter, in particular, for a falsification test.

Businesses might expect various advantages from these informal transactions. In this paper, we focus empirically on two types of benefits that could be associated with informal payments to officials: (i) reduced tax and customs obligations, and (ii) securing public contracts.

# **II.1 Data**

The data used in this paper come from the WBES, a collection of a firm-level surveys of a representative sample of business environments mainly in developing and transition countries. Questionnaires cover a wide range of business environment topics such as infrastructure, competition, access to finance, as well as crime and corruption.<sup>10</sup> We use the standardized questionnaires and dataset over the period 2002-2006 from which we exclude advanced economies.<sup>11</sup> The survey data contain rich details on bribes and benefits expected for firms and allow controlling for a large set of observable determinants of corruption. To construct our four categories of firms’ bribe behavior, we make use of two questions relating to bribe transactions. First, we determine if a request for a bribe was made by public officials. To measure the incidence of a bribe request from an official, we rely on a set of questions in the survey asking firms whether a gift or informal payment was expected/requested
# **_The Oportunidades program_**

The Oportunidades program is a conditional cash transfer program in which bimonthly cash transfers are provided to households in extreme poverty. The cash transfers are conditioned on the attendance of children in school, health care visits for all members of the household, and attendance at information sessions on primary health care and nutrition. Money for schooling constitutes the largest part of the conditional cash transfer. The total amount that a household receives depends on the number, age, and sex of its children. On average, households receive approximately 20 percent of their household consumption from such cash transfers.

Interventions for young children and their mothers are particularly emphasized. Prenatal and postpartum care visits, growth monitoring, immunization, and management of diarrhea and antiparasitic treatments are provided to mothers and young children. Children between the ages of four months and 23 months must have nine periodic medical check ups. From the age of 23 months until the child turns 19 years old, household members must have at least two check ups per year. Children between the ages of six and 23 months, lactating women and low-weight children between the ages of two and four years receive milk-based and micronutrient fortified foods containing the daily recommended intake of zinc, iron, and essential vitamins.<sup>7</sup>

# _Sample design_

The selection of immediate and delayed treatment samples was undertaken in several steps (see, e.g., INSP 2005). Highly deprived localities were identified by using a deprivation index computed on the basis of relevant sociodemographic data available from national censuses. Localities with at least 500 and not more than 2,500 inhabitants, that were categorized as having high or very high deprivation and that had access to an elementary school, a middle school and a health clinic were eligible for treatment. Localities were identified, and a random sample was constructed that was stratified by locality size. Within each state, localities were randomly 11
from Douala to the borders with the Central African Republic and Chad, and it compares favorably with competing bus services on the route from Yaoundé to Ngaoundéré, for which travel by unpaved road becomes difficult in the rainy season.

**Table 6. Comparative performance ofCentral African railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Angola, CFM|580|4,045|30|950|||
|Cameroon, CAMRAIL|603|4,738|26|868|5|2|
|Congo, Dem. Rep., CFMK|18|64|10|257|14|4|
|Congo, Dem. Rep, SNCC|38|275|4|317|13|3|
|Congo, Rep., CFCO|221|3,212|27|300|11|6|
|Gabon, SETRAG|1,778|1,891|39|902|||
|Rail concessions|350|2,945|23|491|5|2|

_Source_ : AICD railways database. _Legend_ : Labor productivity = ‘000s traffic units per employee; Locomotive productivity = millions of traffic units per locomotive; Carriage productivity = ‘000s passenger-kilometers per carriage; Wagon productivity = ‘000s net tonne-kilometers per wagon.

Since 2007 the concessionaire’s productivity has improved. The share of working locomotives increased from 76.9 in 2007 to 83.4 percent in 2010. CAMRAIL’s debt service coverage ratio rose from 19
international food price increases for virtually most of the MENA countries. Only Algeria and Tunisia have low pass-through coefficients.

To get a sense of robustness of our estimates, we use Monte Carlo simulation (1,000 iterations) and bootstrap standard errors for the 6 and 12-month food price pass-through coefficients, shown in Table 3. For many countries, including Bahrain, Kuwait, Qatar, UAE, Iraq, Syria, Egypt, Jordan, Morocco, and WBG, the estimated 12-month pass-through elasticities are statistically significant at the 5 percent level. For other countries, the pass-through coefficients are not significant, possibly due to limited price transmission (i.e. policy interventions in the cases of Algeria and Tunisia) or relatively short time-series rendering the coefficients insignificant (i.e. Lebanon, Djibouti and Yemen).

We also tested the robustness of the results to the choice of the food price index. We used the FAO world food price index, which is an alternative to the World Bank‟s food price index. Both indices are similar, but the FAO index shows higher peaks in 2011. The main effect of using the FAO index is to increase the standard errors of the coefficients, while preserving the pass-through coefficients. Using disaggregated world price index data for cereal, from the FAO and World Bank, produces lower pass-through coefficients than those obtained from aggregated indices. This result is consistent with a lower share of cereal than total food consumption in household expenditures.

The market exchange rates vis-à-vis the euro work best because even in oilproducing MENA countries, a significant share of food imports is denominated in euro. The nominal effective exchange rate, which is a trade-weighted average of the nominal exchange rate, might not be a good proxy for import prices because it also contains export data. Nevertheless, the type of exchange rate choice does not impact significantly the size of the pass-through coefficients.

As it is visible in Table 3, a decline in international food prices does not transmit into domestic food markets and virtually in all MENA countries prices
, (2) hopeless, (3) restless or fidgety, (4) so depressed that nothing could cheer me up, (5) everything was an effort, and (6) life is meaningless (worthless). Respondents can choose among the following: all the time, most of the time, some of the time, little of the time, or none of the time. The answers are then scored on a five-point

> 16 Most studies examining labor market outcomes in Australia use a small sample of refugees. For example, Correa-Velez et al. (2015) use a sample of 233 refugees and find that length of time in Australia and informal networks, among others, were significant predictors of employment. Focusing on refugees living in Sydney, Waxman (2001) shows that those who had pre-immigration paid job experience, completed study/job training and better job searching knowledge/language skills are more likely to participate in the labor force. Newman et al. (2018) find correlation between social support and psychological well-being using a sample of 190 refugees in Melbourne.

52
receive (VV) and dual cross-polarization vertical transmit horizontal receive (VH). Since October 2014, this data has been available every 6 days at a 10-meter resolution.

floods from permanent or seasonally occurring surface water, we utilize the historical Landsatderived monthly water probabilities dataset produced by the European Commission’s Joint Research Centre (Pekel et al., 2016). Flood confidence is categorized as high if both VV and VH Z-scores fall below the identified thresholds, and as medium if only one of these polarizations is below the thresholds. We classify floods in areas not designated as permanent open water (with a probability of water greater than 95%) or with a historical inundation probability less than or equal to 25%. For each case study, we preselect a historical reference period based on existing knowledge of past flooding events in the respective area.

##

Event Data Project (ACLED) database (ACLED, 2023), covering the period from January 2012 to December 2023 for Nigeria and from January 2016 to December 2023 in Mozambique. For the purpose of this study, conflict, as defined by the WBG (2024) is, “a state of acute insecurity resulting from the use of lethal force by a group— encompassing state forces, organized non-state entities, or other irregular bodies— driven by a political purpose or motivation. Such force may manifest

> 1 Pixels with no cloud-free observations are excluded.

> 2 A pixel is around 100m2, we tested without buffer, 500m and 1km and chose a 500m buffer to introduce more variation of nightlight intensity within flooded pixels.

> 3 A time series depicting both variables is provided in Figure 16 in the appendices for Nigeria.

> 4 The corresponding time series for is presented in Figure 17 for Nigeria.

4
, China, Taiwan, and Malaysia the main destination countries.

As batches of worker names were received from the PPTKIS, they were entered by project staff onto an Excel worksheet in the order listed by the PPTKIS, and a random number generator used to assign individuals to treatment status. Since batches of workers were often not of size divisible by four, and were of varying numbers, and that the only information available on the workers was basic data supplied by the PPTKIS, we did not stratify the randomization. The sample of 400 migrant workers was randomly assigned into one of the following groups:

- Treatment A: Financial literacy training is provided to the migrant worker only

- Treatment B: Financial literacy training is provided to the migrant worker’s household member only

6
basis in data for WBG travel in Calendar Year 2009 (CY-09). In calculating the carbon footprint from a given amount of air travel one first needs to find the class-specific per-passenger footprint. As noted, seat configurations in airplanes differ between travel classes, with premium-class seats occupying a larger space in a given plane than seats in coach class. Fewer passengers can then fit into a given space in first and business classes than in economy class. It is also crucial to recognize that these seat configurations are endogenous: they are chosen by airlines to maximize profits, and such a procedure implies that more space is generally is given in any aircraft to premium-class passengers. Seats could be spaced differently; as increased premium class passengers are accommodated, coach-class passengers are crowded out more than one-to-one. In addition, load factors are generally lower on first and business classes than on economy class.

The model in section 3 showed that when space and not weight considerations for passengers is the main constraining variable for airlines (so that passenger weight plays a very small role), the correct class-specific carbon footprint is (close to) proportional to the average space taken up by passengers traveling on different classes. When by contrast weight considerations play a larger 10
# Trade Policies and Sea and Air freight: The Impact of COVID-19 Lockdowns on Imports and Exports<sup>∗</sup>

Socrates Majune<sup>†</sup> and Angella Faith Montfaucon<sup>‡</sup> _JEL Classification_ : F10, F13, F14, O53 _Keywords_ : Lockdown policies, Indonesia, COVID-19, Sea cargo, Air cargo, Non-tariff Measures

> ∗The authors would like to thank Devaki Ghose and Csilla Lakatos for helpful comments, Bayu Agnimaruto for updating the Indonesia non-tariff measures data and Bradley Robert Larson for the COVID-19 data update. This paper has partially benefited from financial support from the Umbrella Facility for Trade trust fund, financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors or the countries they represent. All errors are our responsibility.

> †World Trade Organization and University of Nairobi skmajune@uonbi.ac.ke

> ‡World Bank amontfaucon@worldbank.org, corresponding author