rafmacalaba/data-use-mentions-extended huggingface dataset:
**|||
|Fraction of female leaders<br>0.647|1.178*|-0.440|
|(0.678)|(0.696)|(1.075)|
|Observations<br>235,036|166,801|68,235|
|Outcome mean<br>6.962|6.711|7.575|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. Panels A–C present 2SLS estimates as specified by equations 1 and 2 and using cross-sectional data. Panel D presents 2SLS estimates as specified by equation 3 using the retrospective woman-year panel data. Column (1) shows estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the gap in votes between the winner and the runner up is less that 3 percent of votes. The outcomes in the table are as follows: indicator for whether the woman is using any contraceptive method (Panel A), indicator for whether the woman is using a modern contraceptive method (Panel B), indicator for whether the woman is using a traditional contraceptive method (Panel C), and the number of months since the last birth (Panel D). All regressions include individual controls, district controls, and state fixed effects. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. The regressions in Panel D also include year fixed effects and
Beard J (2006) DDT and human health. Sci Total Environ, **355** : 78-89.

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Cone M. Should DDT Be Used to Combat Malaria? Scientific American, May 2009 <u>http://www.scientificamerican.com/article.cfm?id=ddt-use-to-combat-malaria</u> Cox S, Niskar AS, Narayan KM & Marcus M (2007) Prevalence of self-reported diabetes and exposure to organochlorine pesticides among Mexican Americans: Hispanic Health and Nutrition Examination Survey, 1982–1984. Environ Health Perspect, **115** : 1747–1752.

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instances the supervisors in Nigeria were present at the plot at the time of measurement). This may influence the comparison of compass and rope with GPS measurement if, for example, an enumerator had to re-visit the plot in order to take the new compass and rope measurement and unintentionally identified the borders differently during the second visit. In Ethiopia, on the other hand, all re-measurements were completed at the same time of the initial measurement as the closing error was calculated before leaving the plot. The instance of closing error greater than 5%, however, was rare and therefore not expected to influence the analysis (in the Ethiopia experiment, only 5% of fields were measured more than once).

Second, the skill level of the enumerators varied. In Tanzania, the enumerators were the agricultural extension officers for the local area. These enumerators were very familiar with the agricultural practices but generally inexperienced in survey administration and the use of the particular measurement tools. These enumerators also happened to be significantly older and several had poor vision (requiring the purchase of glasses in order to read the compass and GPS). Because of the ongoing and intensive nature of the cassava measurement component of the Tanzania experiment, the extension officers were the preferred enumerators for the existing infrastructure and established relationships within the community. In Ethiopia, professional enumerators were hired based on past performance with the Central Statistical Agency and previous experience with computer-assisted personal interviewing. In this particular study the enumerators all held bachelor’s degrees and were relatively young. In Nigeria, staff from the head office of the National Statistics Bureau were trained and sent to the field rather than the enumerators used to conduct the national panel survey. In each of these studies the health, education, and skill level varied, as did the incentive structure and duration of fieldwork. Rather than discredit the comparability of the data collected from each of these studies, the consistency observed in the comparison of methods should lend confidence in the applicability across survey environments.

# **IV. Methods*
congestion, it is nonetheless a reasonably good proxy. As the statistics shown in Table A-I of Annex A indicate, on average the number of motor vehicles per kilometer of total roads at the urban level is more than ten times higher than at the national level for developing countries, and five times higher for high-income countries. Moreover, the data for 29 world cities shown in Figure 5 indicate that the vehicle-road ratio at the urban level is highly correlated with average road speeds. The figure reveals a clear negative relationship between the vehicle-road ratio and average road speed, which is generally similar to the empirical relationship between speed and the volume-capacity ratio commonly used in urban transport planning. As a proxy for the traffic level on the road network, the vehicleroad ratio is expected to increase with the relative prices of vehicles to roads at low vehicle-road ratios (such as on national total road networks). Most of the national road networks have fewer than 50 motor vehicles per kilometer of road length, so congestion on national roads is not a common problem even in high income countries. But when congestion occurs in urban areas, speeds fall and travel time cost increases, thus economically justifying more roads.

The peak and decline of the vehicle-road ratio for national paved roads is likely to be related to an increasing demand for higher quality roads, and possibly for vehicle speed, as incomes increase. Paving roads is an obvious means of improving road quality, which reduces vehicle operating costs. Speeds are typically higher on paved roads than on unpaved roads, and the demand for speed (lower travel times) increases with income and the value of time.
**_<u>________________________________________________________</u>_**

# _<u>International Air Transport Association (IATA)</u>_

The International Air Transport Association offers two main statistical publications. “World Air Transport Statistics” contains data on individual IATA member airlines, including number of passengers carried, freight tons carried, kilometers flown for international, domestic and systemwide operations by year, fleet information, aircraft utilization, financial results, number of employees by airline, as well as summary analyses of main trends in passenger and freight traffic.

“Custom Statistical Reports” allow customers to order and configure air traffic reports for information on specific markets - passenger or freight-, on specific locations -airports or regionsand for the periods of interest. Regional Outlook provides with highlights on the air transport activity in one specific region for one specific year. Industry Outlook provides with international air traffic figures for one specific year. Airport Outlook provides with international traffic information for one specific airport for one month or one year.

<u>ICAOData.com</u> is IATA’s new website which increases the availability and visibility of the ICAO statistical data on the air transport industry. The website delivers ICAO’s air transport statistics in a user-friendly interface allowing for easy access and analysis. The database contains detailed financial, traffic, personnel and fleet information for commercial air carriers. It also holds Traffic by Flight Stage (TFS) information for air carriers. Financial and traffic data for airports and air navigation service providers will be added later in 2005. On-flight Origin/Destination statistics will also be added in 2005.

# **_2.4.2 Maritime Transport Costs_**

Sources for data on Maritime Transport Costs include the following:

# _<u>Maersk Sealand</u>_

Online business services offer: Schedules by location or vessel, Rates of exchange applicable to shipments, port to port freight rates and surcharges, cutoff times for ports within a country/region, and standard
:10.1111/j.1467-9914.2010.00494.x

- Cameron, Lisa. 2007. Can a public scholarship program successfully reduce school drop-outs in a time of economic crisis? Evidence from Indonesia.  Economics of Education Review, Volume 28, Issue 3, June 2009, Pages 308-317. Available online at <u>https://www.sciencedirect.com/science/article/abs/pii/S027277570900003X</u>

- Carvalho, Shelby  and Susannah Hares. 2020. More from Our Database on School Closures: New Education Policies May Be Increasing Educational Inequality. Available online at <u>https://www.cgdev.org/blog/more-our-database-school-closures-new-education-policiesmay-be-increasing-educational</u>

- <mark>Ceyhan, Esra and Aykut Ceyhan. 2007. Earthquake survivors' quality of life and academic achievement six years after the earthquakes in Marmara, Turkey.</mark> _<mark>D</mark> isasters_ , _31_ (4), pp.516-529. Available online at <u>https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-</u> = - -

- <u>7717.2007.01023.x?casa_token kceYondf7KIAAAAA:2L9BgjcROIt gtt2OelHj6 8dOuYaRoOMO6HgGJFCXB9WH3KVldLPc-bHFq47MTgiBErGQqBLE-76-IG</u>

- Cooper, Harris, Barbara Nye, Kelly Charlton, James Lindsay, and Scott Greathouse. n.d. “The Effects of Summer Vacation on Achievement Test Scores: A Narrative and Meta-Analytic Review.” _Summer Vacation_ , 42.

- Cummiskey, Christopher and Jonathan Stern. 2020. Calculating the Educational Impact of COVID-19 (Part II): Using Data from Successive Grades to Estimate Learning Loss. Available online at <u>https://shared.rti.org/content/calculating-educational-impact-covid-19-part-ii-usingdata-successive-grades-estimate</u>

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|
|# of Casual Clients Seen||-9.66|||-10.12||
|||(7.58)|||(7.73)||
|Activity Controls|No|No|Yes|No|No|Yes|
|Observations|7385|7385|7385|6681|6681|6681|
|Number of Women|190|190|190|190|190|190|
|_p_-value for death today +|_-_|_-_|_-_|0.16|0.16|0.14|
|deathyesterday= 0|||||||

Notes: All variables are measured at the daily (rather than client) level, as we are not able to match clientspecific transfers to client-specific activities. Regressions are fixed effects regressions with controls for the day of the week and the month of the year. Standard errors are clustered at the individual level. Sickness is an indicator variable equal to 1 if respondent reports a cough, fever, malaria, typhoid, diarrhea, cuts, burns, or other illnesses. Sexual activity controls include vaginal sex, anal sex, oral sex, kissing, massage, stripping, whether the women gave a client company, whether the woman had unprotected sex, and whether she engaged in any other activities. All sexual activity variables measure the number of clients with whom the woman engaged in the given activity at least once, since we did not record the number of repetitions for all activities.

Sexual activities were missing for some women. To avoid dropping them from the regressions, we code the activity as 0 and generate dummies for whether the activity is missing. See text for more details.

1Mean of Dependent variable is mean when all shocks equal 0. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period.

* significant at 10%; ** significant at 5%; *** significant at 1%
# _Analysis of Price Stickiness_

An important concept in measuring deviations from the Law of One price is the rigidity or “stickiness” of prices over time. This is a concept examined in depth by Crucini _et al._ (2010). Price stickiness measures the probability that prices will change by more than 1 percent in a given period. Using Indian retail and wholesale data, the measure is calculated by first generating the change in commodity prices between the current and previous month at the state, district, commodity, variety, and period level.

A dummy is then created equaling 1 if the price change is either greater or lower than 0. The dummy equals 0 if the price change is 0. In other words, if prices in a month either increased or decreased the dummy will show a 1 but if prices remained the same then the dummy equals 0.

This dummy is then summed at the state, district, commodity, variety, and period level to get a count of all month in which a change in prices occurred. This is then divided by the total number of months.

Where, s represents the state, d the district, c the commodity, v the variety, and p the period.

Figure 22 displays information on the median, minimum, maximum, first quartile and third quartile for price stickiness by commodity **.** These box‐and‐whisker plots and are a good way of seeing the dispersion in the data. The plots in Figure 22 show these values for price stickiness at the commodity level for all states, district, varieties and periods under inspection. This allows for easier comparison of the distribution of price stickiness across commodities.

Price stickiness varies by commodity at both the retail and wholesale level. The median price stickiness varies by commodity. Onions have a median price stickiness around 25 percent, which may be interpreted as a 25 percent probability that onion prices will change by more than 1 percent in a given month. Within onions, price stickiness varies less than
15 _Table 2: Variable definitions_

||China Health & Nutrition Survey (CHNS)|Gansu Survey of Families and Children (GSCF)|World Bank China Health VIII project<br>baseline survey|
|---|---|---|---|
|Out-of-pocket<br>health spending|Total health care expenditures during last month, including<br>expenditures not associated with a provider visit, expenditures<br>associated with first visit to providers (if applicable),<br>expenditures associated with visit to second provider, additional<br>health care expenditures, minus expenditure reimbursed by<br>health insurance (1989 prices). The top half of 1% of<br>observations in each wave have been trimmed.|Sum of expenditure associated with doctor visits<br>and drug purchases during last 12 months.|Annual expenses on drugs, prevention<br>and health care of the family. The top<br>half of 1% of observations in each<br>wave have been trimmed.|
|High out-of-<br>pocket spending<br>Catastrophic out-<br>of-pocket<br>spending|Annualized out-of-pocket spending in excess of 5% of mean per<br>capita income for the wave in question<br>Annualized out-of-pocket spending in excess of 10% of<br>household’s own per capita income for the wave in question|Out-of-pocket spending in excess of 5% of mean<br>per capita income for the wave in question<br>Out-of-pocket spending in excess of 10% of<br>household’s own per capita income for the wave in<br>question|Out-of-pocket spending in excess of<br>5% of mean per capita income<br>Out-of-pocket spending in excess of<br>10% of household’s own per capita<br>income|
|Health insurance|A dummy indicating whether they person has any cover
One of the main advantages of the data from the EAC-I is that plot size is measured by GPS. This avoids measurement errors that lead to biased results. We aggregate crop yields at the household level. We convert production to kilograms (kg) using World Bank conversion factors. Interestingly, our data indicate that crop yields vary according to the extent to which polygyny is practiced in the commune. Indeed, as shown in Table 1 crop yields is respectively 1263.1 kg/ha, 925.9 kg/ha, 892.7 kg/ha and 1027.4 kg/ha for the EA located in the first, second, third and the fourth quartiles of polygyny’s prevalence. This distribution of crop yields by quartile of polygyny suggests a negative association between polygyny and crop yields. The economic literature documents an inverse relationship between farm size and productivity in developing countries (Bevis and Barrett, 2020; Barrett, 1996; Henderson, 2015). The difference in crop yields between highly polygynous and less polygynous communes could thus be explained by the variation in farm size. We define farm size as the sum of the land area (in hectares) that is currently cultivated by a household.

# **3.3.1 Data and Measurement for Mechanisms’ Outcomes**

This sub-section presents the variables we use as the mechanism of our results. Descriptive results of these variables are reported in Table 2, and are shown by monogamous and polygynous households. For this table, the marriage type is constructed at the household level based on the EAC-I data.

# **_(i) Livestock Sales_**

We use the detailed information on livestock ownership and production, included in the EAC-I, to construct a binary livestock sale variable equal to 1 if the household has sold one or more of its livestock and 0 otherwise. Table 2 shows that nearly a quarter of households (24.1%) in Mali have traded livestock. In terms of comparison, polygynous households are slightly more likely
positively related to revenues (Khwaja and Iyer 2014). As shown in Figure 2, there is a negative correlation between revenues and the share of agriculture in GDP in our data that is consistent with these findings.

In addition, more international trade relative to GDP is commonly considered to be an important determinant of revenue potential. Since taxes from imports and exports represent important sources of revenues that are relatively easy to collect even with weaker tax administrations, trade openness is considered to be positively related to tax base and revenue potential (Gupta 2007; Aizenman and Jinjarak 2009; Khwaja and Iyer 2014).<sup>6</sup> This view is supported by empirical evidence (Leuthold 1991; Tanzi 1992; Gupta 2007; Le et al. 2012) and also consistent with the positive correlation between revenues and trade openness observed in our data (see Figure 2).

Finally, a higher age dependency ratio<sup>7</sup> tends to reduce countries’ revenue potential as it indicates a lower share of productive population and, hence, a narrower tax base. In accordance with this view, empirical evidence shows that a higher age dependency ratio is associated with lower levels of revenue mobilization, including in developing countries (Le et al. 2012; Khwaja and Iyer 2014). The correlation in our data shown in Figure 2 (Section III) is consistent with these findings.

# **III. Empirical Strategy**

The analysis uses non-parametric frontier analysis (DEA) to quantify efficiency in domestic revenue collection at the country level. The obtained efficiency scores are then used as the dependent variable in a regression framework based on country panel data.

> 5 Countries with a larger share of agriculture in GDP also tend to have lower demand for public goods and services since most high-value public services are based in cities (Tanzi 1992; Le et al. 2008).

> 6 Note that, in general, higher trade openness may have an ambiguous effect on revenue mobilization. On the one hand
find that nearly half of all respondents are not aware of the existence of a credit bureau or the consequences of non-repayment. When consumers do not fully internalize these costs, this can increase the moral hazard costs of debt relief policies.

In microfinance, one factor that works against the widespread use of default as de facto debt relief is the loss of social capital that occurs when non-repayment is publicly observable (Banerjee, 2013; Gine and Karlan, 2014; Diep-Nguyen and Dang, 2020). In the context of our model, this would correspond to a higher non-pecuniary cost ( _σ_ ) of default. For this reason, even microfinance lenders that have moved away from traditional group lending in favor of individual liability loans often still retain regular community meetings to raise the perceived cost of default.

## **3.2 Debt Forbearance**

Debt forbearance refers to policies that allow borrowers to postpone loan repayments for a given period of time without incurring penalties or a negative entry on their credit record, while leaving the amount of debt outstanding unchanged. Historically, forbearance has often been employed on an ad hoc basis in response to economic downturns in which a large number of borrowers face liquidity constraints and the nature of the underlying shock is thought to be transitory.

> 6 There is also a lack of comprehensive cross-country data on debt relief programs. One notable exception is the World Bank “Covid-19 Finance Sector Policy Responses Database”, which contains information on financial support programs worldwide and includes information on debt relief programs.

14
Ros, Jaime (eds), _The Oxford_ .

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