rafmacalaba/datause-encoder-data huggingface dataset:
possibilities, we turn back to our main calculations to get a sense of the magnitude of this issue.

Again, we highlight that these cost estimates are underestimated in the sense that we only consider the lost productivity in the labor market, thus not estimating the costs incurred to women’s health, the possible implications for the child’s future productivity as indicated by studies that show that children of adolescent mothers have lower school attainment rates, the social costs of unwed adolescent mothers, and so forth. Also, due to data availability, we cannot account mothers under the age of 15 or those who have paid the ultimate price of adolescent pregnancy: the girls who die from its complications.

# **6. A better path for girls: Conclusions and policy implications**

Social inclusion of adolescent girls that keeps them on a path to achieving their maximum human potential will result in significant economic growth. This paper has presented simple non-parametric methodologies to roughly quantify the costs incurred by societies as a result of the social exclusion of adolescent girls. The estimates are limited to the opportunity costs, which measure the losses in terms of potential productivity gains and income young girls could have achieved if they were employed, if they had delayed pregnancy, or if they had attained higher educational levels. Using secondary data drawn from the International Labour Organization, World Bank, and World Health Organization, we estimate the costs in several African countries (Burundi, Ethiopia, Kenya, Malawi, Nigeria, Senegal, South Africa, Tanzania, and Uganda) and a few Latin American (Brazil and Paraguay), and South Asian (India and Bangladesh) and East Asian (China) countries.

The rough estimates showed that marginal investments in girls can have a substantial impact on GDP growth and well-being. If girls just completed one higher level of education, the total value of productivity generated over the work life of those girls is equivalent to nearly one year’s GDP (Burundi), equivalent to GDP growth that is 2 percentage points higher in each year that these more educated girls would be working. The additional growth would be equivalent to more than 25 percent of annual GDP in the other African countries in the sample (except Ethiopia), or an increase in growth rates by one to 0.5 percentage points annually. While girls’ contribution to overall output is much 21
them. Select summary statistics derived from the database are shown in Table 2. (Note that for the year 2023, the database covers the period January through June.) In total, the database counts about 90 million tweets.

# **3.3 Identifying Gender of Users**

Twitter does not indicate whether a user account belongs to an individual or an organization, and in the case of individuals, whether the user is female or male.<sup>3</sup> Accordingly. we have to determine these user attributes on our end. There are a variety of existing methods that have been developed for this task. We proceed in two steps. First we classify users as either individuals or organizations. Second, for users identified as individuals, we impute their gender. These two steps involve separate imputation methods that have been optimized for these individual tasks.

For the first step, we adopt the approach put forward by (Cetinkaya et al., 2023), which classifies accounts as either organizations or individuals. The predictors include the user account’s profile metadata, such as name, bio description, location, whether the bio includes an URL, time on Twitter, as well as Twitter account statistics, such as the number of tweets, retweets, following count, followers count, media count, and the number of likes.

The publicly available “Demographer” dataset is used by the authors to train the model and evaluate out of sample performance. These data contain both Twitter user identifiers and a variable indicating whether the account belongs to an individual or an organization for a total of 214,236 user accounts. The study conducts a 7-fold cross validation test, where 6 _/_ 7 _th_ of the data is used for training and the remaining 1 _/_ 7 _th_ of observations are used for out of sample prediction. The reported accuracy is about 97% (i.e., only 3 percent of user accounts are incorrectly classified). We also applied

> 3For simplicity, we do not consider non-binary genders.

9
36

# **Appendix 2: Survey descriptions, variable calculations, and additional data**

The total household expenditure in each country is computed using a uniform procedure. Monthly total household expenditure is defined as the sum of the following components:

- (1) <u>Food expenditures. Expenditures for all food items using recall periods of varying lengths</u> are prorated to a 30-day level.

- (2) <u>Imputed food expenditures. Imputed values of non-purchased but consumed food items</u> from the recall period are prorated to a 30-day level.

- (3) <u>Non-food expenditures. Expenditures on non-food items are prorated to a 30-day level</u> where required. Where both annual and monthly expenditures are available, the following practice is followed:

- <u>Monthly. For expenditures on items such as fuel and light, entertainment, non-</u> institutional medical, personal, toiletries, consumer services, rent, and commuting, the monthly recall values are used.

- <u>Annual. Expenditures on such items as clothing, bedding, footwear, education,</u> medical (institutional), durable goods, life insurance premiums, vehicle insurance premiums, and membership fees are prorated from their annual levels to 30-day levels.

The surveys provide information on certain large expenditure items (such as furniture; household appliances including refrigerators, air conditioners, washing machines, televisions, DVD players; expensive jewelry; automobiles; personal computers; ceremonies; and taxes and cesses). Expenditures on these items are removed for two reasons: first to avoid misrepresentation of households in their respective income quintiles since large expenditures are not made regularly, and second to enable cross-country comparison, given that the various categories of large expenditure items were treated differently (being included in some surveys but not in others). Where information is available, food items, both purchased and nonpurchased, are deflated by a cost-of-living index defined for foodstuffs in order to remove geographical and temporal variations in the cost of living. Details of deflation procedures are included in the notes below on each country The datasets are divided into five population quintiles based on monthly per capita expenditure levels derived from the above definition of total household expenditures. Each quintile contains the same number of _individuals_ and not households. As well as national quintiles, separate urban and rural quintile groups are defined by drawing households from the national quintile, depending on whether they resided in an urban or rural area. The numbers of people in these quintile groups are no longer the same and depend on the relative numbers of urban and rural households in the nationally defined quintile. Both the quantity and implicit price data (for the first six countries) are examined for outliers and households with extreme values are excluded from analysis.

All the surveys contained some missing observations. Table A2.1 provides the numbers of observations at three levels:

1. The total number of households interviewed (full sample).
# **1. INTRODUCTION: RAISING THE PROFILE OF MIGRATION**

The purpose of this paper is to provide guidance to interested researchers and development practitioners on how to measure the impact of migration and remittances on income poverty, inequality and human capital (or, in general, “welfare”).

Migration is a huge phenomenon. The share of migrants in industrial countries’ populations doubled over past three decades and remittances flows to developing countries are larger than foreign investment or overseas aid. In many developing countries the percentage of the population working abroad and the percentage of GDP represented by remittances run into double digits.

Policymakers in affected countries do not always fully appreciate the challenges and opportunities which migration brings to the development process and, when they do, are often in the dark about which policies can enhance the impact of migration on development. Sound research and successful advice based on it can raise the profile of migration in governments’ agendas and inform policy choices.

This paper deals mostly with international migration. Many of the impacts on welfare and issues of measurement and estimation methods discussed here apply equally to internal migration. However, the policies needed for countries to maximize the welfare benefits of international migration can be quite different from the policy responses needed for internal migration.

This note has four general messages: • The research question should be given adequate consideration. In principle, a broad question, e.g. “what is the impact of migration on poverty/human capital?” (as opposed to a narrower question about, say, the impact of remittances only) is more appropriate, given the complexity of relationships involved.

• The pervasive endogeneity in decisions surrounding migration requires more advanced techniques, e.g. instrumental variables, which deal with this issue explicitly.

• Since remittances are private money and migration is a private decision, policy recommendations should spell out the rationale for interventions, pointing to market failures that cause the private returns to migration and remittances to diverge from the social returns.

• A realistic contribution to current knowledge can be made through collecting better migration data and evaluating the interaction between migration and government policies.

In the next section, we discuss channels by which migration impacts welfare and consider typical questions asked in operational research on linkages between migration and poverty or human capital. In the third section we consider appropriate methodological approaches, given the analytical challenges of establishing and measuring causal relations that include migration. In the fourth section we consider how to convert research into policy recommendations.

2
Table 6: Accuracy of Population Density Predictions, by Sector and Nature of Indicators

|Imagery Type|In-sample _R_<sup>2</sup>|Out-of-sample _R_<sup>2</sup><br>A. Nati|Number of <br>Candidate<br>onal Sample|variables<br>Selected|No. of villages|
|---|---|---|---|---|---|
|A. Rural and estate||||||
|1. No satellite imagery|0.757|0.646|35|23|12,686|
|2. Low resolution, public|0.806|0.726|35|25|12,686|
|3. All Public|0.877|0.808|39|19|12,389|
|B. Urban||||||
|1. No satellite imagery|0.595|0.493|35|3|1,284|
|2. Low resolution, public|0.654|0.540|35|7|1,284|
|3. All Public|0.690|0.569|39|6|1,048|
|||A. 55 s|ub-districts|||
|A. Rural and estate||||||
|1. No satellite imagery|0.752|0.688|35|8|978|
|2. Low resolution, public|0.815|0.754|35|8|978|
|3. All Public|0.898|0.838|39|8|935|
|4. All Public + Propriety texture|0.919|0.882|46|24|935|
|5. All|0.942|0.869|67|41|881|
|B. Urban||||||
|1. No satellite imagery|0.487|0.413|35|3|382|
|2. Low resolution, public|0.637|0.511|35|5|382|
|3. All Public|0.665|0.525|39|7|340|
|3. All Public + Propriety texture|0.740|0.668|46|8|340|
|5. All|0.860|0.804|67|26|336|

Note: The results are based on a Poisson regression model whose dependent variable is census village population density, and the independent variables are selected based on LASSO regularization among the following: Relevant satellite imagery sources as described in panel B in Table 1, district fixed effects, a binary indicator for urban villages, and log village area. The models with “No satellite imagery” use only the district fixed effects, an indicator for urban villages, and log village-area as independent variables. “Low-resolution public” indicators refer to the low-resolution and public indicators as defined in Table 1. “All public” indicators refers to all the public imagery based indicators, including the high-resolution imagery based indicators described in Table 1. “Proprietary texture” indicators include very high-resolution texture features based on Digital Globe from Engstrom et al. (2017) as defined in Table 1. “All” imagery refers to all public and proprietary texture indicators, and object identifiers, such as cars, roofs etc., as defined as in Table 1. The out-of sample _R_<sup>2</sup> is obtained from stata’s crossfold command using five-fold cross-validation.

24
results, privately operated airports tend to be more efficient, with a TE score that is on average 6% to 8% points higher than publicly operated airports.

Another important feature that distinguishes airports is the importance of aeronautical activities in their operation. As expected, the importance of these activities, summarized by the share of aeronautical revenues in the total airport revenue, plays a negative effect on efficiency (although this effect is statistically significant only when we use TE-VRS scores as the dependent variable). In other words, airports in which non-aeronautical (i.e. commercial) activities are more important tend to be more efficient. The estimated marginal effect indicates that, on average and holding the other variables constant, a 10% increase in the share of aeronautical revenues appears to be related with a loss in technical efficiency of nearly 2%.

GDP per capita seems to have a positive effect on airport efficiency. However, **its** estimate is only significant in the VRS model (with regional dummies). In this case, when GDP per capita increases 10,000 USD higher the technical efficiency of airports is expected to increase 6%. Finally, tourism expenditure is not significant in the three specifications.

**Table 6: Truncated regressions - Marginal effects**

|**Explanatory factors**|**TE-VRS**<br>**With regional**<br>**dummies**|<br>**TE-VRS**<br>**Without regional**<br>**dummies**|**TE-CRS**<br>**Without regional**<br>**dummies**|
|---|---|---|---|
||**Marginal**<br>**effect**<sup>**(std)**</sup>|**Marginal**<br>**effect**<sup>**(std)**</sup>|**Marginal**<br>**effect**<sup>**(std)**</sup>|
|**Institutional framework**||||
|Private airport (dummy)|0.064 (0.036)*|0.082 (0.035)**|0.068 (0.041)|
|Regulation authority (dummy)|0.048 (0.048)|0.041 (0.050)|0.083 (0.059)|
|**Socioeconomic environment**||||
|GDP per capita|0.006 (0.002)***|0.001 (0.001)|0.001 (0.001)|
|Tourism expenditures per capita|- 0.033 (0.049)|- 0.005 (0.033)|- 0.045 (0.036)|
|**Population concentration**||||
|Population > 5,000,000 (dummy)|0.169 (0.025)***|0.201 (0.027)***|0.173 (0.031)***|
|**Airport characteristics**||||
|Hub airport (dummy)|0.122 (0.028)***|0.099 (0.031)***|0.153 (0.031)***|
|Aeronautical Revenues|- 0.150 (0.081)*|- 0.183 (0.085)**|- 0.134 (0.102)|
|**Control variables**(dummies)||||
|Asia|0.059 (0.047)|-<br>-|-<br>-|
|Europe|- 0.200 (0.059)***|-<br>-|-<br>-|
|Canada and US|- 0.201 (0.069)***|-<br>-|-<br>-|
|Year 2006|- 0.023 (0.023)|- 0.107 (0.024)|- 0.210 (0.274)|
|LR test|Chi<sup>2</sup>(11) 110.3***|Chi<sup>2</sup>(8) 80.8***|Chi<sup>2</sup>(8) 56.7***|
|Observations|251|251|251|

***, **, and *: Below the 1%, 5% and 10% statistical significance thresholds, respectively.

15
# **Annex 4.3. Stakeholders involved in land allocations and transactions**

||**Land del**|**ivery cha**|**nnels**|**Mai **|**nareas of inte**|**rvention **|
|---|---|---|---|---|---|---|
||||||**Bamako’s**|**’**|
|**Stakeholders involved**|**Customary**|**Public**<br>**and**<br>**para-**<br>**public**|**Formal**<br>**private**|**Bamako**<br>**District**|**peri-**<br>**urban**<br>**area**|**Bamakos**<br>**Rural**<br>**hinterland**|
|Government institutions||*|*|*|*|*|
|ACI||*||*|*||
|**Ld**<br>_Communes(Mairies_)||*||*|*|*|
|**an**<br> <br>Individuals|||*|*|*|*|
|**suppliers/sellers**<br>Customaryland holders<sup>~~(1)~~</sup>|*||||*|*|
|Informal land developers|*||||*|*|
|Formal land developers||*|*|*|*|*|
|Central and local<br>Government officers|*|*|*|*|*|*|
|Merchants and traders|*|*||*|*|*|
|**Ld h**<br>Individuals|*|*|*|*|*|*|
|**an purcasers**<br>Informal land developers /<br>coxers|*|*|||*|*|
|Formal real estate<br>developers||*|*|*|*|*|
|Village authorities|*||||*|*|
|Peri-urban_commune_<br>authorities(_Mairies_)|*|*|||*||
|_Communes_in Bamako<br>District||*||*|||
|**Institutions**<br>**governing land**<br>_Préfet:Sous_<br>_-Préfet_|*|*|||*|*|
|**allocations and**<br>**transactions**<br>Central<br>Council of<br>Ministers|*|*|*|*|*|*|
|<br>government<br>DNDC /<br>DRDC<br>Koulikoro|*|*|*|*|*|*|
|Cadastre /<br>CARPOLE|*|*|*|*|*|*|
|<br>Brokers|*|*|*|*|*||
|**Intermediaries**<br>Coxers|*|*||*|*|*|
|Land surveyors|*|*|*|*|*|*|
|**Professionals**<br>Notaries||*|*|*|*|*|
|Urbanplanners||*|*|*|*||
|**Credit / Finance institutions**||*|*|*|*||

_(1) When customary owners do not sell customary land but land which has already been extracted from the customary channel, they are classified as “individuals”._ 48
triangular distribution is applied.

**Table D-1: Sensitivity Analysis on Nominal Imports by Changing Armington Elasticities (%)**

||Ca|se 1: World||Case 2: World||
|---|---|---|---|---|---|
|importers|Base|Mean|S.D.|Base<br>Mean|S.D.|
|1 AUS|0.3|0.3|0.2|0.2<br>0.2|0.3|
|2 CAN|-2.2|-2.2|0.2|-1.9<br>-1.9|0.2|
|3 CHL|0.1|0.1|0.2|0.3<br>0.3|0.3|
|4 CHN|36.9|37.2|6.3|28.5<br>28.8|8.5|
|5 HKG|2.5|2.5|0.4|0.8<br>0.9|0.3|
|6 IDN|19.1|19.2|3.3|11.7<br>11.7|3.8|
|7 JPN|0.6|0.6|0.6|0.6<br>0.6|0.9|
|8 KOR|16.9|16.9|2.1|12.9<br>13.0|2.9|
|9 MEX|15.0|14.9|2.4|11.7<br>11.7|3.3|
|10 MYS|10.3|10.2|1.4|7.9<br>7.8|2.0|
|11 NZL|0.4|0.3|0.2|0.6<br>0.6|0.3|
|12 PER|27.0|27.2|4.9|15.8<br>16.0|5.2|
|13 PHL|78.0|77.2|12.9|61.2<br>62.4|16.1|
|14 RUS|97.3|98.2|14.3|58.2<br>58.3|12.9|
|15 SGP|0.9|0.9|0.3|1.3<br>1.3|0.6|
|16 THA|41.1|41.1|6.0|29.5<br>29.7|7.2|
|17 CTP|0.3|0.3|0.3|0.5<br>0.5|0.4|
|18 USA|-0.4|-0.4|0.4|-0.5<br>-0.5|0.5|
|19 VNM|37.5|38.3|5.4|23.6<br>26.1|0.7|
|20 EU15|-1.0|-1.0|0.1|-0.9<br>-0.9|0.0|
|21 ROW|-0.2|-0.2|0.2|-0.5<br>-0.5|0.1|

(Note) Base, Mean and S.D. in the columns indicate the simulation in the base case, the average (mean) value of the sensitivity analysis simulations, and standard deviation of the sensitivity analysis simulations, respectively.

It is found that the standard deviation for the nominal trade change is relatively large. This indicates that the simulation results are sensitive to the values of Armington elasticities. For example, the estimated large increase in the imports of Russia and Philippines in Case 1 are subject to the possibility to be around 30 to 40 percent, wih the confidence interval of the three standard deviations (i.e. 89% confident). One should be careful to draw the implication from the simulation that the impacts on nominal trade to Russia and Philippines more than 70 percent and 80 percent. Rather, the implication is that these impacts would be large, perhaps more than 30 percent.

39
to highly-visible global supply chains, but such bottlenecks can also occur at the local scale, and even for small businesses.

As mentioned in Hallegatte (2008), hereafter H08, these ripple-effects can be labeled “backward” or “forward.” Backward ripple-effects arise when the impact propagates from clients to suppliers, i.e. when a business cannot produce, and thus reduces its demand to its suppliers, reducing their own activity (even in absence of direct damages). Forward ripple-effects arise when the impact propagates from suppliers to clients, i.e. when a business cannot produce and thus cannot provide its clients with inputs needed for their own production.

This paper focuses on these inter-industry interactions and on their role in natural-disaster aftermaths. It proposes a new version of the ARIO model, which was first presented in H08. This new version is better able to represent production bottlenecks and ripple-effects, through the taking into account of inventories, production dynamics, and heterogeneity within sectors. The next section summarizes existing tools to assess output losses due to natural disasters and highlights shortcomings on which progress is most necessary. Section 3 presents the new version of the model (ARIO-inventory) and explains how it answers some of the identified needs. Section 4 presents the case study on Louisiana and the landfall of Katrina, and the sensitivity analyses that highlight the importance of modeling heterogeneity to avoid overoptimistic estimates of supplyside shock consequences. This section does not aim at providing a new estimate of the cost of Katrina, but it emphasizes the importance of a few uncertain mechanisms. Section 5 concludes.

# **2 Existing modeling approaches**

Post-disaster interindustry ripple-effects have already been the topic of intense research (West and Lenze, 1994; Rose et al., 1997; Brookshire et al., 1997; Haimes and Jiang, 2001; Cochrane, 3
Table 4: Lagged IV results

||No lags|1 lag|2 lags|3 lags|
|---|---|---|---|---|
|Mean PM2_._5|-0.0075<sup>_∗∗_</sup>|-0.0066<sup>_∗_</sup>|0.0022|0.0009|
||(0.0033)|(0.0036)|(0.0029)|(0.0032)|
|N|6,142|5,628|5,114|4,596|
|Sum of lags||-0.008|0.009|-0.002|
|||(0.006)|(0.009)|(0.010)|
|**Weather controls:**|||||
|Temperature|Y|Y|Y|Y|
|Precipitation|Y|Y|Y|Y|
|Relative humidity|Y|Y|Y|Y|
|Surface pressure|Y|Y|Y|Y|
|Wind speed|Y|Y|Y|Y|
|Wind direction|Y|Y|Y|Y|
|**Fixed efects:**|||||
|Fiscal year|Y|Y|Y|Y|

Notes: The outcome is the first difference within a district of the log of annual GDP measured at the district level. We instrument for annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population with inversions and inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. We winsorize GDP growth at 1%. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

21