rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.11%|2.41%|5.92%|4.44%|0.05%|2.74%|
|T2B|6.77%|4.34%|2.03%|1.76%|6.92%|5.07%|2.28%|1.73%|
|T2C|3.89%|2.67%|0.00%|4.64%|4.03%|3.17%|0.00%|4.63%|
|Selected|8.50%|5.25%|92.58%|0.02%|8.60%|6.07%|86.39%|0.05%|

Notes: The table shows averages of estimates for each treatment arm obtained from 1,000 simulated samples drawn from the observed experimental data. Columns (1) and (5): mean posterior probability of a successful call. Columns (2) and (6): standard deviation of the posterior success probability. Columns (3) and (7): probability that the treatment arm is optimal. Columns (4) and (8): posterior policy regret in terms of engagement success probability.

and we append the bootstrapped sample to the observed wave 1 data to estimate a hierarchical Bayesian Binomial GLM as described in Eq. 2. We carry out 1,000 draws that simulate an RCT and 1,000 draws that simulate an exploration sampling experiment. For the simulated RCTs, we bootstrap a wave 2 of equal-sized treatment arms. For the simulated exploration sampling experiments, we use the treatment assignment shares derived from the original wave 1 posterior distributions.<sup>27</sup> For each sample draw, we calculate the posterior mean and standard deviation of _θ_<sup> ̄</sup><sup>_k_</sup> , the probability
’s unpaid domestic work is 2.3 hours greater than that of young men’s, while the difference increases to 3.9 hours per day among prime working age individuals and decreases again to 2.7 more hours for mature adults.

Men and women of prime working age work more hours in total—in the market or at home—and allocate less time to leisure and personal care than any other age group. These patterns are closely related to lifetime events such as family formation, including marriage and parenthood. Similarly, women of all ages specialize in domestic chores and men specialize in market work, but the degree of specialization varies across age groups. For example, young men allocate fewer hours to market work than their mature adult counterparts and the same if true among women when it comes to unpaid domestic chores.

# _Time Use by Marital Status_

In the analysis that follows, we break down the sample according to sex and three categories of marital status: single or widow/widowed, married or cohabiting, and separated or divorced.<sup>12</sup> The choice of categories was carefully determined based on the classification used in the 19 countries.

> 12 The West Bank and Gaza survey only distinguishes between married and not married people. Not married people are included in the single/widowed category.

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they participate in on-the-job training programs. This is slightly more common among women (63.1%) than men (47.7%). Most of this training is of the apprenticeship type (86%), but men are slightly more likely to be trained in new technologies than women.

Men are more likely to be union members. They are also more frequently employed in smallsized firms (between 1 and 9 employees), whereas women tend to be more frequently employed in medium-sized firms (between 10 and 19 employees). The former represent about 40% of employed men and the latter group about 30% of employed women. The share of men and women in largesized firms is more or less the same, at about 11%. Again the greater risk aversion of women explains why they tend to gather in medium-sized firms. The equal shares of men and women in large firms might depend on their tendency to apply more frequently gender neutral recruitment policies.

In the SWT database, unfortunately, there is no definition of firm’s ownership. Respondents are requested to declare their firm’s name which may help indicate whether they work in the private sector. These represent 24% of the sample. In addition to the firms’ name, respondents are requested to say whether they are: self-employed (11.9%) or “working for family income” (55.8%), which we take to mean that they are employed by a family business. The rest (8.4%) do not provide the name of their company. Surprisingly, the largest number of employed individuals in the sample “work for 9
# **Choosing Parameters for Clustering Crash Reports into Unique Crashes**

Multiple people often tweet about the same crash. In order to cluster crash reports to unique crashes, we cluster by the kilometer and time distance between reports. To determine optimal kilometer and time parameters, a team manually determined which crash reports refer to the same crash. The dataset was double coded by different team members, resulting in two “truth” datasets. To judge whether crash reports refer to the same crash, the team used the location of the crash, the time of the tweet and looked for details about the crash in the tweet itself (e.g., extent of injuries, types and numbers of vehicles, etc.).

The below table shows summary statistics of the maximum distance and time between any two crash reports in the same clustered or individual crash. Before calculating the statistics, outliers were removed (we define an outlier as a crash cluster where reported crashes occurred over 24 hours or over 5 km from each other). Across both truth datasets, around 52% of tweets were clustered with another tweet, meaning that 48% of tweets are the only tweet reporting one crash.

Table S6: Clustered Tweets Truth Data Summary Statistics

|Variable|Min|Quartile 1|Median|Mean|Quartile 3|Max|
|---|---|---|---|---|---|---|
|||Tru<br>|th Dataset <br>|1<br>|||
|Hours Dif|0|0.133|0.55|1.68|1.693|23.776|
|KMs Dif|0|0|0.013|0.213|0.138|3.328|
|N Tweets|2|2|2|3.324|3|44|
|||Tru<br>|th Dataset <br>
DSF. But in contrast to the 2017 LIC DSF, which, like its earlier incarnation, decomposes the change in the ratio of nominal public debt to GDP, d, during a given year into contributions attributable to primary fiscal deficits, growth rates, interest rates and exchange rates, the calculations here focus directly on pd, r and g as driving the trajectory of d and providing early warning about its sustainability.

Table 3 uses the identity that the current account deficit is the difference between national investment and national saving. It breaks this identity down into a public and private component, demonstrating that public deficits—the difference between public investment and saving—drive the current account deficit and hence external debt dynamics. In Ghana’s case, the central government deficit in line 1 of the table is the main driver of the CAD and therefore of external debt dynamics. Indeed, as the external DSA table in IMF (2021) shows, the rise in external debt-to-GDP between 2019 and 2020 of 5.9 percentage points is almost entirely due to its PPG component, which went up by 5.7 percentage points.

**Table 3: Government Deficit Drives Ghana’s Current Account Deficit 2019-20**

|**Percent of GDP**|**2019**|**2020**|
|---|---|---|
|1. Government deficit|7.2|15.7|
|2. Private deficit|-4.4|-12.6|
|3. Current Account Deficit|2.8|3.1|
|_4. PPG % of external debt_|_89_|_90_|

**_Notes_** : Data from Table 1 and the External DSA in IMF (2021). “Deficit” is defined as investment minus savings. A positive (negative) number in the table refers to a deficit (surplus). Footnote 3 for Table 1 in the IMF report notes that private saving includes public
Table 2 Impact of Major Hurricanes on GDP per capita (SS ≥ 1)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Linear<br>Regression|Linear<br>Regression<br>correcting for<br>Serial & Spatial<br>Autocorrelation|Panel Data<br>with Controls<sup>1/</sup>|Panel Data<br>correcting for<br>Dynamic<br>Models|
|MSWS|‐0.00274|‐0.00861*|‐0.00839**|‐0.00894**|
|Standard Error|(0.00434)|(0.00469)|(0.00349)|(0.00399)|
|R‐squared|0.314|0.415|0.076|n.a.|
|IHD|‐0.0126*|‐0.0148**|‐0.0148***|‐0.0160***|
|Standard Error|(0.00540)|(0.00642)|(0.00320)|(0.00375)|
|R‐squared|0.365|0.458|0.157|n.a.|
|WEI|‐0.00498|‐0.00994**|‐0.00983***|‐0.0105***|
|Standard Error|(0.00569)|(0.00447)|(0.00330)|(0.00394)|
|R‐squared|0.319|0.423|0.093|n.a.|

*** p<0.01, ** p<0.05, * p<0.1

> <sup>
(1) l 0 + 1l l 2<sup>l</sup> 0<sup>l</sup> 0<sup>l</sup> tt tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ ββ</sup> tt<sup>+ εε</sup> tt where (measured at date/time t): llPP = ββ ββ<sup>lll</sup> P = Pollutant concentration measured by CAMS-3 [PM 2.5 (ug/m<sup>3</sup> ); NO2 (ppb)] M = Average satellite-based measure for the .01-degree cell surrounding CAMS-3 H = Average humidity (%)

- T  =  Average temperature (C)

- W =  Average wind speed (km/hr)

ε = Random error term Table 3 reports our estimation results. Our primary estimates use variable levels, but we also include estimates for time-differences to test the possible effect of serial correlation.<sup>6</sup> Columns 1- 2 test the relationship between CAMS-3 NO2 measures and Sentinel-5P’s measure of tropospheric column NO2 density. The S5P measure has very high significance. On the other hand, the cloud cover and weather variables have no consistent patterns of significance.

Columns 3-8 test the relationship between CAMS-3 PM2.5 and three satellite measures, controlling for weather variables. As the results show, the weather variables introduce deviations between ground- and satellite-based measures that vary in size and significance across estimates. We focus on the comparative significance of the satellite-based estimates as indicators of ground-level PM2.5.

Columns 3-4 report level and difference results for S5P’s absorbing aerosol index. We find no significance for this variable.

Columns 5-6 re-introduce the S5P NO2 measure, since nitrates from NO2 emissions are a significant source of PM2.5 (Hodan and Barnard 2004). Here we find very high significance for both level and difference
# **Multidimensionality of Land Ownership among Men and Women in Sub-Saharan Africa**

Ardina Hasanbasri<sup>†</sup> , Talip Kilic<sup>‡</sup> , Gayatri Koolwal<sup>*</sup> , and Heather Moylan<sup>⁑1</sup> JEL Codes: C81, C83, J16, Q15.

Keywords: Land Rights, Gender, Household Surveys, Machine Learning, Clustering, Malawi, Ethiopia, Tanzania, Sub-Saharan Africa.

> 1† Corresponding Author. Lecturer, Department of Economics, University of Michigan, Ann Arbor, <u>ardinah@umich.edu. ‡ Senior Economist, Development Data Group, World Bank, tkilic@worldbank.org.</u> *Consultant, Development Data Group, World Bank, gkoolwal@worldbank.org. ⁑Survey Specialist, Development Data Group, World Bank, <u>hmoylan@worldbank.org. The authors would like to thank Cheryl Doss, Mekonnen Ayano,</u> Susmita Baulia and the participants of the MAGNET research seminar and the Nordic Conference of Development Economics 2021 for their helpful comments and discussions. The research was funded by the World Bank Living Standards Measurement Study – Plus (LSMS+) program (www.worldbank.org/lsmsplus).
are able to map 280 of our observations to the 543 observations used by Deaton in his comparison of survey means to HFCE. For Ravallion, we match 54 of our estimates to his 88 observations. Given this inability to match many countries, we also consider a subsample where the match is less precise. More specifically, we include in our subsample for comparison an observation if we can match on the country within the overall reference period of the Ravallion and Deaton studies. When we consider this criterion, we match 507 of Deaton’s sample and 72 of Ravallion’s.

Table A1.1 presents the original estimates from Deaton and Ravallion’s studies, as well as our estimates for the matched samples. The point estimates for the gaps for our matched samples are generally similar or a bit larger (i.e. larger in absolute value) than the estimates from Deaton and Ravallion for the same countries and time period. In contrast to Ravallion’s original estimate for consumption surveys, our estimates for the matched samples are statistically different from zero. We do not observe statistically different estimates for income versus consumption surveys, as Ravallion did in his assessment. In our comparison with Deaton’ estimates, we generally find larger gaps using our matched data, but no evidence that these differences with his findings are statistically significant.

In summary, although our findings differ substantially when considering our overall sample, the gaps are very similar when considering subsamples from overlapping periods and matched samples of the previous studies of Ravallion and Deaton. The overall larger gap that we find then is driven by the more recent data, especially post 2000. This likely reflects the fact that as more countries have grown into middleincome countries, which we find is associated with larger gaps (a trend also observed by Deaton), the overall average gap becomes larger.
# **3. Data and Empirical Strategy**

# **3.1Data**

The trade data is drawn from the US International Trade Commission (USITC).<sup>3</sup> The USITC database provides exhaustive information on imports of HS 8-digit products from each country to the US in each year from 2001 to 2020, years of AGOA eligibility of countries and the list of AGOA eligible countries and products. The list of countries whose eligibility has been revoked is published by the US State Government and Accountability Office. The list also provides information on the dates they become ineligible and the dates their AGOA eligibility is reinstated.

Our dependent variable is the log export of a particular product from an AGOA eligible country into the US from 2001 to 2020. If nothing is reported, exports are set to zero. For the regressions that look at the extensive margin a dummy variable is created that takes the value of 1 if exports to the US are positive. Table 2 presents the exports before and after the suspension for the countries that were suspended from AGOA between 2001 and 2020 and thus represent the treatment group in our analysis. Three key observations can be gleaned from the table. First, the suspended countries are quite heterogeneous in terms of the size of their exports to the US, ranging from US$0.1 million (Guinea-Bissau and South Sudan) to US$706 million (Côte d’Ivoire). Second, only a handful of countries claim AGOA preferences for a significantly large share of their exports (e.g., 83 percent in Madagascar). Third, countries that rely heavily on AGOA for their access to the US

> 3 Our sample is limited to AGOA eligible countries only, to provide a reasonable control group.

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driven by climate change and displacement from the country’s ongoing conflict with Boko Haram in the northeast. Simultaneously, settled communities have expanded, and dry farming techniques have lengthened their land and water use throughout the year. In response to these tensions, three Nigerian states (Benue, Ekiti, and Taraba) passed outright bans on open grazing in 2016 and 2017. These bans exacerbated previous tensions and directly contributed to a peak of violence in the first half of 2018.

We combine detailed panel data of households and individuals from Nigeria’s General Household Survey (GHS) with data on violent events from the Armed Conflict Location and Event Data (ACLED) project (Raleigh _et al._ , 2010). The GHS data contain four survey rounds, each including two seasonal visits—post-planting and post-harvest. This means that individuals can be observed in (up to) eight separate periods between 2010 and 2019. Herder-related violence typically follows a seasonal pattern, with events worsening as herders remain to graze their cattle in areas past May, when they historically moved north.<sup>5</sup> With these data, we leverage variation across time and space using the presence of herder-related violent incidents within a given radius around households (i.e., 10 km) and within a given time frame (i.e., within the last month) as an indicator of exposure to herder-related violence.<sup>6</sup> The granularity of these data allows us to include fixed effects at the level of a comparatively small geographic area, which combined with time and individual-level fixed effects, allows us to estimate changes in economic activities associated with herder-related violence while ruling out confounding variation between narrowly defined locations over time. Therefore, our identification strategy relies upon the fact that exposure to these violent events varies meaningfully, even within narrowly defined geographic areas. This empirical approach helps ensure that we are comparing households with similar agro-ecological and economic conditions. In particular, we include fixed effects by river sub-basins
where **βj,i** is the adult equivalent for individual _j_ in household _i_ . We use a single calorific equivalence scale based on OECD recommendations and we assign to each child in the age group 0- 18 years a value of β= 0.3 of an adult, and a value of β= 0.5 to each other adult in the household which is not head of the household. Against this background, the total consumption variable to be used in the econometric specification is constructed as the sum of household food and non food consumption scaled by equivalents. Consumption is expressed in March 2007 prices using the annual inflation rate for rural areas provided by the Malawi Statistical Office.

Quantile regression models were used to quantify outcomes at the median and at the .25 and .75 quantile: this choice allows for a type of robust regression analysis which reduces the influence of outliers. The dependent variable used in the analysis is given by the ratio of the variation of consumption between the final round and the baseline round over the average total consumption measured at t0 and t1. This is an alternative measure of consumption variation used in place of the more traditional difference in logarithmic consumption at t1 and t0. The econometric specification can be written as: where the dependent variable, defined as [(c1-c0)/( c1+c0)]*2, is regressed on a constant and a set of taste shifters Zi like the household composition as well as age, sex, health and marital status of the head of the household. Moreover, a set of variables Xi measuring idiosyncratic shocks at household and village level affecting the level of income, such as crop pests, livestock stolen and price shocks, was used. The transfer T is also part of the regression. Under the perfect risk sharing hypothesis, the coefficients on idiosyncratic shocks and on the transfer should be equal to zero. If this is not the case, then it means that transfers and other shocks to income have an impact on the household
|0.30|0.96|0.26|
|(fees, uniform, books)|(295.41)|(173.22)|(283.11)|||||

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors in parentheses, clustered at the village level. (2) Data are from the pre-intervention PAL survey and the 2003 Formato 911 school databases. (3) Child consumption data was only collected for children aged 1 to 4 in the preprogram survey. (4) Math and Spanish grades are self-reported recalls of the student's most recent report card, sample includes students currently attending school. (5) A household is defined as indigenous if one or more members speak an indigenous language. (6) Expenditure is the value of non-durable items (food and non-food) consumed in the preceding month, measured in pesos.
cut-off grade is set ex-post based on the distribution of grades, which mitigates the risk of score bunching around the cut-off.

To address potential spatial correlation in learning outcomes within schools, we cluster standard errors of regressions that use test scores as the outcome at the school level.

# **3.2 Weather Data**

We utilize the ERA5-Land dataset to obtain weather information at a spatial resolution of 9 km grid spacing. This dataset, which is available from 1950 onwards, provides hourly information on surface variables such as daily maximum temperature and daily total precipitation for each school in our sample.

To ensure the accuracy of our weather data in relation to the experiences of students, we construct a 10 km buffer around the school’s geographic location<sup>11</sup> . We base this on the assumption that students typically reside close to their schools. This assumption is supported by findings from the Living Standard Measurement Study (LSMS) surveys, which indicate that about 90% of students in Ethiopia walk to school, and take less than one hour to reach their school from their homes. Additionally, we re-weight the climate data by population size (from Center for International Earth Science Information Network - CIESIN (2018)) within the buffer, to account for variations in population density.

Overall, our use of the ERA5-Land dataset and the construction of geographic buffers allows us to accurately capture the relationship between extreme temperatures and learning in our study. Our results are robust to variations in buffer size (of 5 km and 20 km) and population density.

# **3.3 Other Data**

## **3.3.1 Household Survey**

We use the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data for Ethiopia (also known as the Ethiopia Socioeconomic Survey (ERSS)) in order to investigate the time-use patterns of household members as well as the impacts of shocks to agriculture on food consumption within the household (CSA, 2012, 2014,
a number of countries, following the advice of IMF country desks and national authorities.<sup>3</sup> In Figure 1, we present broad trends in remittance receipts for several different regions. One notable feature is the rapid increase over the last decade, particularly for Latin America. This increase is likely to reflect rising numbers of migrant workers around the world. In addition, however, it may reflect technological developments and increased competition in the financial services industry, reducing the cost of sending remittances, especially through the formal financial sector. This could have both encouraged remittances in general and, as argued above, led to a shift in transactions from the informal and into the formal sector. Indeed, data from household surveys imply that the informal sector is now quite small and has been declining in several Latin American Countries.

In addition to not capturing the informal sector, remittance data in the balance of payments do not always include transactions through money transfer providers. One study of 40 central banks in developing countries around the world indicates that about 60 percent do not record data from money transfer providers that do not settle through banks (de Luna Martinez 2005). Transactions by money transfer providers that are settled through banks and are properly identified as remittances will still be recorded. Because of this under-recording, a portion of formal remittance transactions will end up in the statistical discrepancy in countries that do not collect information from money transfer providers. Our estimates of the informal sector are based on costs of using formal versus informal modes and thus will not incorporate misrecorded flows. Total

> 3Please see the Appendix for a fuller discussion of the definition of remittances and of the various adjustments made to the data.

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