rafmacalaba/data-use-mentions-extended huggingface dataset:
Assume that a household head � is confronted with the decision (denoted by ��) of whether or not to migrate to a different location. He will migrate only if the benefits expected from migrating are greater than the gains expected from not changing location (which can be normalized to zero for convenience). Let us indicate with ��∗ the unobservable latent variable measuring the gains deriving from the decision to migrate, this decision can be written as follows: where �������� is our exclusion restriction, the share of the population aged 15 and above who was not born in the village (or town) they resided in at the time of the 1984 Census, �� is a set of households’ and heads’ characteristics, and �� are district level controls. Household characteristics are assumed to affect the probability of migration; migration is a life-cycle event and as such young people and males are more likely to participate. Following the human capital model, education and skills are likely to affect the probability of migrating as more educated individuals have more chances to get high-paying employment opportunities in destination areas. For these reasons, we include gender, a second-degree polynomial in age, educational attainment, household size, household demographic (share of household members aged between 4 and 14, between 15 and 64 (separately for males and females), and 65 and above) and labor market structure (share of working-age household members who are employed and who are unemployed), current location of residence (urban/rural), and region of birth. The inclusion of region of origin fixed effects implies that we compare non-migrants with migrants from the same region of origin. As explained above, among the district level variables measured in 1984 we include the average number of years of schooling of the population aged 6 and above, the Gini index of years of schooling among the population aged 6 and above, the share of the population aged 6 and above with no schooling and the share of the employed
**Relationship to Literature:** In the United States, the returns to schooling arise, in part, from the link between earnings and cognitive skills (Altonji and Pierret, 2001). However, as Bowles and Gintis (1976) and Heckman and Rubinstein (2001) pointed out, individuals with seemingly identical years of schooling and cognitive skills are nevertheless compensated very differently in the labor market. To explain this earnings residual, they argued that schools must produce other skills that are then recognized and rewarded in the labor market. The measurement of, and returns to, these socioemotional skills has been an active area of research for at least the last two decades, with Deming’s (2017) recent contribution suggesting that the returns to some SEM skills have increased over time (relative to cognitive skills). Interestingly, to return to Bowles and Gintis’s (1976) original program of trying to discern the specific skills valued in the labor market and produced in schools, it is the studies from low-income countries that hold considerable promise. This context still provides the variation in years of schooling that we need to allow for the separate identification of labor market returns to years of education, test scores, and SEM skills.

Unfortunately, despite advances, research that tries to understand the link between labor market outcomes, schooling, and specific skills has proven difficult even before addressing complex identification problems. One set of issues revolves around the measurement of SEM skills in lowincome countries. Laajaj and Macours (2021) show, for instance, that the factor structures observed in the United States do not fit the data from low-income countries and that measures of SEM skills suffer from both low reliability and validity concerns. Similarly, when estimating returns to the seven skills measured in the World Bank STEP surveys, Valerio et al. (2016) point to serious reliability concerns.<sup>5</sup> Where SEM skills have been measured successfully, such as the longitudinal Young Lives project in Ethiopia, India, Peru
health care costs related to the treatment of tobacco-related diseases include the costs of medical care provided in outpatient facilities, hospitals, and day-care hospitals. It is calculated by disease using the information on the number of cases and the weighted average costs of the treatment for each case. The diseases considered are as follows:

- Diabetes

- Cardiovascular disease

- Respiratory disease

- Cancer

- Tuberculosis

The number of diseases associated with tobacco smoking is calculated through the use of the indicator generally accepted in the available literature on population attributable risk (PAR). The PAR is calculated based on data on smoking prevalence and on the indicator of the relative risk (RR) of disease in smokers vs. nonsmokers. For the purposes of this paper, the RR has been taken from previous studies. The details of the calculations and the sources of information are summarized in annex C. The total direct tobaccorelated medical costs in Russia in 2017 are estimated at the level of 418 billion rubles.<sup>11</sup>

> 11 Alternatively, as a robustness check, the simulations have been performed using data from a cross-country comparable database. The most complete and standard study estimating the economic cost of smoking in Russia is Goodchild, Nargis, and Tursan d’Espaignet (2018). The study uses a cost of illness approach to estimate the economic cost of smoking attributable-diseases in 152 countries. Annual direct health care costs attributable to tobacco use in Russia in 2012 amounted to 526 billion rubles.

8
<!-- Start of picture text --> i taeoot<br>)<br>AFGHANISTAN ’<br>> Gilgit<br>Swat Valley ee Approximate<br>" — Lineof Control<br>KHYBER Fari | ae<br>* KABUL PAKHTUNKHWA i<br>Peshawar 1 3<br>Peshawar o OF ISLA‘AMABAD*,<br>ISLAMABAD H :<br>CAPTAL we<br>TERRITORY Seyi,<br>PUNJAB<br>Lahore -<br>IBRD 44424 | MAY 2019<br><!-- End of picture text -->
instead treated as secondary treatment effects within the control set.<sup>13</sup> 12 Without these leads, part of the impact of fiscal rule adoption on the primary balance would be absorbed by the fixed effects, leading to a downward bias in the coefficient estimates of the fiscal rule indicator. In the leads-and-lags structure of the rule indicator, we consider all instances of rule adoption, not only the initial set of rules. Moreover, we include a separate variable to capture the effects of subsequent rule adoptions. Including a variable that captures the presence of fiscal rules—but setting it to zero in the first year of adoption—yields similar results as to including second time adoptions in the leads-and-lags structure. Moreover, differentiating between rule frameworks that remained unchanged within their first ten years and those that were amended yields similar outcomes (results are available on request).

13 We assume that the primary balance does not respond the same to later changes to the fiscal rule framework, as the response is conditional on the initial adoption of the rule(s). Therefore, investigating the impact of later rule adoptions (or subsequent rule modifications) would require focusing on countries with an existing rule, while also controlling for the time since the rule was first adopted. Accounting for fiscal rule intensity—such as the number or design of the initial rules—adds further complexity to the analysis. As such, we exclude later changes from the shock indicator to achieve a clearer understanding of the implications of rule adoption for countries. For an in-depth discussion of second treatment effects and their empirical implications, see de Chaisemartin and D’Haultfœuille (2023).

11
<!-- Start of picture text --> Burkina Faso<br>“\<br>2 ry ay<br>‘' }<br>H t‘<br>= f ‘<br>’ \<br>‘ \<br>Nn ,' ‘‘<br>é \<br>If ‘x<br>bon ><br>°o<br>2 o 2 4 6<br>Lag of consurnption per cupits<br>[----- Nondean season = Laan aeascr ]<br><!-- End of picture text --> <!-- Start of picture text --> Niger<br>, ¥<br>‘‘ ’‘1<br>o ui ‘<br>‘ ‘<br>i‘ ti<br>3. 4 ‘<br>a<br>( ‘<br>¢ .<br>TM ’‘ ‘<br>/<br>’/<br>i ¢<br>e -<br>71 i] 1 2 3 4<br>Log of commuenptionper capeta<br>~~> Non-lean season ———_ Laan season<br><!-- End of picture text --> <!-- Start of picture text --> Senegal<br>=<br>v'<br>‘<br>‘<br>2 ‘ s<br>)‘<br>‘‘<br>)‘<br>'<br>: ‘‘‘‘ '<br>‘\<br>‘‘<br>Nn ’f<br>ul<br>G<br>-<br>o Be<br>38 a 2 4<br>Lag of consumption per capita<br>[----- Noreen sesso Lean aeaace
in a Large Commercial Farm Environment: Evidence from Ukraine. The World Bank Economic Review 32, 287-306 Dhillon, J.S., Figueiredo, B.M., Eickhoff, E.M., Raun, W.R., 2020. Applied use of growing degree days to refine optimum times for nitrogen stress sensing in winter wheat. Agronomy Journal 112, 537-549 George, J., Adelaja, A., Weatherspoon, D., 2020. Armed Conflicts and Food Insecurity: Evidence from Boko Haram's Attacks. American Journal of Agricultural Economics 102, 114-131 Graubner, M., Ostapchuk, I., Gagalyuk, T., 2021. Agroholdings and Land Rental Markets: A Spatial Competition Perspective. European Review of Agricultural Economics 48, 158-206 Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850-853 Johnson, D.M., Rosales, A., Mueller, R., Reynolds, C., Frantz, R., Anyamba, A., Pak, E., Tucker, C., 2021. USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses? Remote Sensing 13, 4227 Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A., 2017. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters 14, 778-782 Kvartiuk, V., Herzfeld, T., Bukin, E., 2022.
. When financial aid is merit-based, the coefficient on _DAID_ may be biased upward. Hence, students enroll not because they receive financial aid, but because they are academically proficient, which makes them eligible for student financial aid. Second, there are some interpretation difficulties. With regard to the student support variable, the amount of the financial support, its conditions (e.g. merit-based, or need-based), and timing of the student aid (some of the financial support may have been granted for primary and secondary education) are not

> 13 The increased probability of enrollment of 24 % from the regression based on household data is similar in magnitude to the response in the SOFES questionnaire, where 29% of students would not have enrolled in university in the absence of student loans.

16
The second approach focuses on supply constraints that hurdle beneficial production expansions. An argument often cited by policy makers and industrial leaders alike to explain the timid export responses to exchange rate depreciations in Pakistan is the ‘lack of export surplus’. This argument implies that firms struggle to ramp up production when conditions improve. One source of the difficulty lies in shallow credit markets. The fact that exporters are more dependent on external finance than domestic firms is well documented (Amiti and Weinstein 2011; Manova 2013). The need for financing working capital lies in the fact that there is a substantially longer lag between production and payment for exports than for domestic transactions. This is due to the longer transit and customs clearance times and implies that exporters are more reliant than domestic firms on working-capital finance. Exporters also require greater access to external finance to cover the larger costs associated with international transactions. Similarly, long-term finance is also crucial due to the needs of exporters to invest in technology to remain competitive. Pakistani exporters are no exception. When we interviewed an apparel firm that had re-purposed production into personal protective equipment in the context of the COVID-19 pandemic, the manager claimed that to scale up production, they needed to switch from manual to automatic stitching. The stitching machine enabled workers to stitch 100,000 masks per day, while the manual process only allowed for 20,000. The technology could be sourced from China for US$60,000. Yet, even though the investment project had a positive net present value, there was no credit available to carry it out. The extent to which this anecdote is representative of the reality of exporters is to be tested. During 2003-2017, the ratio of credit to private sector to GDP was 21.7 percent, suggesting credit tends to be scarce and is a plausible and important supply constraint.<sup>7</sup>

> 7 Domestic credit to GDP ratio is obtained from World Development Indicators, World Bank.

11
# 4. Methodology and exposure results

This section explains the methodological details of how exposure was modelled based on the data sources described in the previous sections. Results will be presented only selectively as overview figures. For full results and a discussion of their significance, please refer to the main report (Rentschler et al. 2020).

The quantitative analyses described in this section were conducted using the programming language Python, and relied extensively on the free and open-source libraries Rasterio, Shapely, and GeoPandas.

## 4.1. Accounting for flood protection measures

As described in section 2.1, the flood maps used in this analysis do not incorporate information about existing flood protection infrastructure. In the event of heavy rain or a coastal surge, the underlying simulations assume that water flows freely in the natural terrain without being obstructed by any manmade structures such as dikes. However, in many provinces this is not the case as the Vietnamese government has invested significantly in flood protection infrastructure. Ignoring the existing protection systems and using the flood maps as they are would thus lead to an overestimation of flood exposure.

As part of the main report to which this paper contributes, a team of experts conducted a comprehensive assessment of flood protection measures in Vietnam. For all coastal provinces, the flood protection system was divided into stretches that were classified according to the flood events the infrastructure would be able to withstand, based on characteristics such as the type of the structure or its height.<sup>5</sup> For each province, the current state of existing flood protection measures is assessed. Based on these results, the share of modelled flood extent that existing flood protection infrastructure would stop has been determined per province, return period and flood type. The results hereof are shown in table 3.3 and 3.4, where the numbers represent the share of modelled flood area that would be flooded considering the existing protection infrastructure. Therefore, it decreases the flood extent in those places where dike systems and other protection infrastructure
growth also exposed some non-poor to negative dynamics, making them poor. Panel data also reveal that many middle-class households were exposed to significant risks. In the period 2005-2008, only 45 percent of the population in Egypt remained out of poverty and near-poverty. This means that 55 percent of Egyptians experienced poverty or near-poverty between 2005 and 2008, even though the poverty and near-poverty rates at a given point of time within his period fell from 46 percent to 36 percent.

This high mobility is a reflection of real economic phenomena and not a statistical artefact. The paper looks in detail at the statistical indices of mobility and mobility profiles, and finds that new HIECPS data 2005-2008 generate reasonably robust estimates of mobility. In the face of strong mobility upward and downward mobility even considerable increases of inequality observed over the period are minor factors of social welfare dynamics.

20
(0.433)|
|N||327|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01.  Dependent variable is stove use, which is 1 if stove is still in use and 0 otherwise.

# _4.2. Stove use in the longer-run_

Our analysis of stove use in the very long run is presented in this sub-section. We look both at the intensive margin, which in this case is households that have not abandoned their Mirt stoves, and at the extensive margin using selection models to adjust for abandonment, mainly due to breakage. Our analysis focuses attention on period 5, with the objective to analyze very long-run effects. Controls are shown in Table 3. All variables are from our baseline survey conducted in 2013 and include a mix of socioeconomic and cooking metrics.

16
.0388|-0.0207|0.0178|0.0346|0.0209|0.0481|0.0504|
|terms of trade (lagged)|(0.760)|(-0.392)|(0.207)|(0.653)|(0.243)|(0.494)|(0.516)|
|Change in the log of|0.0718|0.6618 **|-0.2826|0.1064|-0.3075|-0.4325|-0.4525|
|real output (lagged)|(0.381)|(3.334)|(-0.816)|(0.480)|(-0.873)|(-1.041)|(-1.079)|
|Independent|0.0143|0.0674 **|0.0485 **|0.0100|0.0511 **|0.0441 **|0.0488 **|
|term|(1.149)|(7.903)|(5.844)|(0.842)|(3.306)|(4.768)|(3.074)|
|Adjusted R<sup>2</sup>|0.045|0.017|0.025|0.044|0.024|0.024|0.023|
|F test|16.12|6.51|7.15|12.10|5.39|5.46|4.39|
|Number of<br>observations|969|969|724|969|724|724|724|

Note: Data on average labor costs per worker are from the _Banque de Donnees Financieres,_ which is composed of about two thousand medium and large firms. There
presented in Table 1 and based on responses to a phone follow-up survey carried out in December 2018. In the survey we only ask adoptionrelated questions to farmers who say at least one of their plots had a Fall Armyworm infection during the current season; for this reason, the analysis in Table 1 focuses on practices included in the training module and only applicable once a plot is infected. Before getting to the discussion of results, the reader should have an important caveat in

> 15An alternative way to investigate the relationship between training and adoption is to use the random price assigned by the _price_ lottery to the decision maker as an instrument for training. When I implement this IV specification (not reported) I find that coefficient magnitudes are quite similar to those obtained estimating Eq. 10 but, predictably, noisier.

> 16The OLS estimator _β_ 1 in this case is a weighted estimator, where weights are given by the conditional variance of the treatment variable. The conditional variance of _Trainingv_ (conditional on each possible value of _wDz,v_ ) is highest when, within a bin of _wDz,v_ , the share of treated and untreated is equal. So bins of _wDz,v_ in which everyone is treated or everyone is not treated are going to receive lower weights than bins in which treated and non-treated have similar shares, and hence higher conditional variance (see Chapter 3 in Angrist and Pischke (2009)). In the data, bins such that _wDz,v ∈_ [6 _,_ 000 _,_ 12 _,_ 000] are the ones with the most similar treatment shares and hence are weighted more; if _wDz,v <_ 6 _,_ 000 most or all villages are untreated; if _wDz,v >_ 12 _,_ 000 most or all villages are treated.

16
|Land|0.00584|-0.00153|0.00541|-0.00156|-0.00514|
||(0.00400)|(0.00242)|(0.00480)|(0.00682)|(0.00775)|
|High school|0.0622|-0.0777|0.183|-0.0840|-0.124|
||(0.0879)|(0.0593)|(0.0981)|(0.138)|(0.149)|
|Non-mud house|0.0519|0.0234|0.0678|0.0459|0.0358|
||(0.0629)|(0.0474)|(0.0842)|(0.104)|(0.112)|
|_N_|242|452|181|181|154|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table 3: Probit regression for _H_ versus _L_ players, by pair status. Marginal probabilities that a player chose _Stag_ are shown, with standard errors of the marginal effects in parentheses.

it would be natural for a person who adheres to the culture of honor to interpret the loser’s payoff as an insult to his honor and to retaliate. As shown in the anthropology and sociology literatures, the culture of honor is much stronger among high-caste men than among low-caste men. To test that this is indeed true of the communities from which we drew our samples, we implemented in 2013 a vignette-based survey of the norms for retaliation. The survey was conducted in 22 hamlets in the same area in which we had conducted the Stag Hunt experiment. The design follows a format developed by social psychologists for measuring moral attitudes (see, for example, Haidt, Koller and Dias
day, suggesting a click-through rate of 4.3 percent for abstract reads and 1.1 percent for downloads.<sup>11</sup> This seems consistent with the intuition that as an academic‟s blog expands readership to a larger and larger audience, the additional readers are less likely to be interested in the academic papers.

Finally, we should note that the estimates in Table 1 show the average impacts of being linked to by these blogs. In practice, there appears to be considerable heterogeneity in the spike in blog traffic caused by a particular blog. For example, just taking the difference in abstract views in the month blogged about compared to the mean abstract views over the months before the paper was blogged about gives a range of +33 to +2908 over the 31 papers linked by Marginal Revolution in our sample (25<sup>th</sup> percentile to 75<sup>th</sup> percentile range is 69 to 314). It is likely the size of the increase reflects a combination of the interest in the topic to the general blog reader, and the manner in which the blog links to the paper (e.g. full post about a paper vs. single line link; positive, neutral or negative link, etc.). Unfortunately there are insufficient data in our sample to explore this phenomenon systematically.

# **3. Probit Analysis of Influence**

When economic bloggers discuss what the benefits of blogging are, much of the discussion tends to be self-centered – they view it as something beneficial to themselves. Thus blogs are viewed as providing a space to catalog and flesh out ideas, get feedback and a sense of popular applications for ideas, and to disseminate their own ideas to a wider audience (Nash, 2008). Yet it is difficult to quantify or measure many of these effects, although several bloggers attest that

> 11 Blog traffic statistics from http://www.gongol.com/lists/bizeconsites/
|-0.755|
||(0.625)|(0.614)|(0.631)|(0.525)|(0.532)|(0.496)|(0.491)|(0.499)|
|Exports (%, cell average)*GDP|0.059|0.034|0.044|0.061|0.078|0.075|0.072|0.079|
|per capita (logs)|(0.069)|(0.068)|(0.070)|(0.058)|(0.059)|(0.055)|(0.055)|(0.071)|
|Herfindahl-Hirschman Index|-9.772**|-11.543***|-12.715***|-13.130***|-15.763***|-10.768***|7.869|8.584|
||(4.495)|(4.053)|(4.110)|(3.871)|(4.275)|(3.803)|(7.985)|(7.882)|
|Courts Obstacle (cell average)|0.000|0.151|-0.049|0.456|0.589|0.120|0.043|-0.040|
||(0.932)|(0.903)|(0.843)|(0.794)|(0.833)|(0.827)|(1.426)|(1.421)|
|Women can work the same night|<br>-8.005***|<br>-7.419***