rafmacalaba/datause-encoder-data huggingface dataset:
**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, and Vietnam, the panel of respondents is still too young to enter the labor force. These data are already providing important insights into the importance of SEM skills, but as of yet, they do not include labor market outcomes; only the latest (yet-to-be-released) round of

> 5 They find that four coefficients are positive and statistically significant at the 90% level of confidence, three coefficients that are negative and statistically significant and 42 coefficients that are small and imprecise. As the standard errors are not adjusted for multiple-hypotheses testing, even the statistically significant results are likely due to chance. The authors write, “ _It must be noted that the noncognitive skills measures are a function of scores on three to five items each. We believe the limited number of items for each (noncognitive skill) scale could be limiting the reliability of these measures and obscuring the true relationship between noncognitive skills and earnings._ ” (Valerio et al., 2016: 26).

6
# _3c. Endogeneity and Instrumental Variables_

It is evident that causation may run both ways between skill differences, which is our primary determinant of interest, and foreign investment. For example, an increase in inbound FEI may raise the level of skills in Singapore due to professional training within MNEs. Moreover, flows of investment may be accompanied by the transfer of skilled engineers and managers within the firm between countries. Thus, the skill-differences variable is likely to be endogenous to investment to some degree. To address this issue we develop instruments that plausibly are correlated with skill differences but not correlated with the regression error term.

We incorporate two classes of instruments. First is the vector of labor force participation rates for males and females in the partner country, which we take from the World Bank’s _World Development Indicators_ . The idea is that as the prevalence of skills rises, labor participation increases for both males and females. Thus, for French investment in Singapore these instruments are the labor participation rates for males and females in France, whereas for Singapore’s investment in India it is the corresponding Indian labor force participation rates.

A second class of instruments relates to labor-market conditions abroad. For regressions involving bilateral FEI stocks between Singapore and the industrialized economies we employ the contemporaneous unemployment rates in China and the Philippines. The idea is that changes in aggregate labor demand in those countries should be correlated with labor-market conditions, including occupational mix, in Singapore and the developed economies, but should be exogenous to decisions to change their bilateral FEI stocks. Similarly, for bilateral skill-ratio differences between Singapore and the developing 18
$60 scholarships. In more than two-thirds of schools the “last” child to receive a $45 scholarship has a value of the score between 20 and 30; this is also the case in more than half of the schools for children who receive a $60 scholarship. Second, the left-hand panel of the figure shows that, if anything, the impact of the $45 scholarship _rises_ as the cut-off falls at lower values of the composite dropout-risk score, corresponding to children in families who are poorer (although the change in slope is not statistically significant). We therefore conclude that our results on the diminishing marginal returns to transfer size are very unlikely to be driven by the fact that children who were offered $60 scholarships were, on average, somewhat poorer than those who were offered $45 scholarships.

# **4. Conclusion**

A great deal is known about the impacts of CCT programs on a variety of outcomes, including school enrollment. Yet remarkably little is known about what features of program design account for the observed CCT program effects. In this paper, we focus on one important element of program design— namely, the amount of money that is transferred. We find clear evidence of diminishing marginal returns to program size. Indeed, in most specifications we cannot rule out the null hypothesis that households that received a transfer that was one-third larger than that received by other households had the same enrollment response to the program.

This is an important finding, and one which has obvious policy implications. It is particularly noteworthy because the amounts transferred by the CSP program in Cambodia are very small compared to those transferred by other CCTs, in particular in Latin America: The $45 transfer accounts for approximately 2 percent of the consumption of the median recipient household in Cambodia, while the comparable value is 22 percent for recipients of Oportunidades, and 6 percent for recipients of the Bolsa Familía program in Brazil and the Bono de Desarrollo Humano program in Ecuador.<sup>16</sup> This suggests the

> 16 These figures are based on Fiszbein and Schady (2009), table 3.2. They are broadly consistent with those reported elsewhere (for example, Schultz 2004 on PROGRESA, and Schady and Araujo 2008 on the Bono de Desarrollo Humano). The exact values depend on the survey used, in particular the year it refers to (because many programs have expanded dramatically, taking on new population groups with different underlying characteristics) and whether

13
# **Tables**

Table 1: **Tone of migration-related news at destination and migrants’ movements**

||Sh|are of migran|ts in the FM|P|
|---|---|---|---|---|
|||who are lo|ng-stayers||
||(1)|(2)|(3)|(4)|
|Tone of the news at destination|-0.0314**<br>(0.0172)|-0.0312**<br>(0.0177)|-0.0312**<br>(0.0177)||
|Tone of the news at destination (related to)|||||
|- Discrimination||||-0.0290*|
|||||(0.0165)|
|- Sea incidents||||-0.0180|
|||||(0.0200)|
|- Economic conditions||||-0.0109<br>|
|||||(0.0260)|
|- Crime||||0.0089|
|||||(0.0227)|
|- Government announcements||||0.0128|
|||||(0.0203)|
|- Other||||-0.0073|
|||||(0.0222)|
|Intensity of night lights within 5 km||0.0097|0.0099|0.0030|
|||(0.0593)|(0.0592)|(0.0643)|
|Number of conficts within 5 km||-0.0101|-0.0102|-0.0166|
|||(0.0179)|(0.0179)|(0.0182)|
|FMP with migrants planning to|||||
|remain in Libya (1 = Yes)|||0.0019|0.0129|
||||(0.0270)|(0.0280)|
|return home (1 = Yes)|||0.0045|0.0132|
||||(0.0369)|(0.0411)|
|FMP FEs|Yes|Yes|Yes|Yes|
|Year-month FEs|Yes|Yes|Yes|Yes|
|Country of destination FEs|Yes|Yes|Yes|Yes|
|Country of origin FEs|Yes|Yes|Yes|Yes|
|Number of Observations|1,568|1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_, where</sup><sup>_βx_is the point estimate associated to control variable</sup><sup>_x_, while</sup> _sd_ ( _x_ ) and _sd_ ( _y_ ) indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news in the top destination countries for migrants in FMP _i_ in month _m−_ 1 in year _t_ . For the definitions of the variables: _Tone News Destinations Discrimination_ ; _Tone News Destinations Crime_ ; _Tone News Destinations Economic conditions_ ; _Tone News Destinations Sea incidents_ ; _Tone News Destinations Government announcements_ , and _Tone News Destinations Other_ see Section C. All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

24
the di¤erential e¤ect of ...nancial development across industries, they interact the country level of ...nancial development with the industry level of external ...nance dependence and control for industry and country ...xed e¤ects. This allows to isolate the impact of ...nancial development on industry growth after controlling for cross-country and withincountry di¤erences as well as omitted variable bias. Industry-level measures of external capital dependence are based on U.S. ...rms’...nancial data. The indicator of a sector’s reliance on outside ...nance is de...ned as the ratio of capital expenditures minus cash ‡ow from operations to capital expenditures for the median ...rm in each industry taking the average across years. Thus, the measure of external ...nance dependence captures technological demand for ...nancing for each industry and is assumed to be similar across countries.

The paper relies on the Rajan and Zingales estimation strategy to isolate a channel through which ...nancial development may promote export survival. We do not build on Rajan and Zingales to compute our risk measure. Crucially, our proxy for ...nancing needs of agri-food products is a measure of their sanitary risk. It is an export based rather than a production based measure, computed using rejections of exports at the EU borders. Thus, our proxy captures the need for ...nancing associated with exporting to stringent developed countries market. Since our focus is on African ...rms ability to sustain in the export markets, capturing the di¤erentiated e¤ect of ...nancial development requires an export based measure of ...nancing needs.

# 3.1 The Sanitary Risk Index

This section details the construction of the Sanitary Risk Index, and motivates its use as a measure of ...nancing needs at the product level. The risk index is computed using data from the EU Rapid Alert System for Food and Feed (RASFF). The RASFF database reports all agri-food shipments to the EU between 2001 and 2008, that have su¤ered rejection due to food safety reasons. The database provides rejections by product, exporting country, importing country (EU member state) and year. The index is the coe¢ cient on the product dummy, �k in the following regression: where "ck is an error term. For a product k and an exporter c, the dependent variable is the combined count of noti...cations from all EU member states between 2001 and 2008<sup>3</sup> . The unit of observation is an exporter � product pair and the regression is crosssectional. To avoid picking up on any particularities generated by exporting countries’

> 3There is indeed, consistent di¤erences in the number of noti...cations among notifying EU states. In an average year, Germany with 20% of noti...cations is among the top notifying countries, while Ireland only account of 0.21% of noti...cations. Aggregating the number of noti...cations across all importing (notifying) countries and all years, smooths temporal ‡uctuations and reduces the e¤ects of outliers.

7
**_Misleading Headlines._** All headlines focused on COVID-19 as a theme, and we

generated new headlines based on existing headlines rather than using headlines from published articles. This approach was adopted to ensure that all headlines were completely novel to participants whereas participants could vary in their familiarity with real-world headlines.

The designed headlines were adapted from prior research testing the effectiveness of inoculation against COVID-19 misinformation (Basol et al., 2021), and reflect the three common tactics used in misinformation that were also the focus of the inoculation training (i.e., emotional appeals, false experts, and fake documents/images). Additionally, the misleading headlines were designed to address themes similar to misinformation that had spread online according to various fact-checking sources (e.g., AFP Fact Check, <u>Africa Check) to enhance ecological validity.</u> **_Judgements of Misinformation._** Adapted from Roozenbeek et al. (2022), participants were presented with six headlines: three using misinformation tactics and three that did not use common misinformation tactics. When presented with each headline, participants were asked to respond to the question ‘ _Does this headline use any misinformation techniques_ ?’ on a 4-point scale: Definitely is misinformation, Probably is misinformation, Probably is not misinformation, Definitely is not misinformation. For ease of interpretation, ratings were scored such that higher scores represent stronger belief that a headline was misinformation ( _Definitely is misinformation_ = 4 and _Definitely is not misinformation_ = 1). We chose phrasing that highlighted the term misinformation to increase relevance to policymakers and practitioners, but it is worth noting that much prior research on inoculation measures perceptions of misleadingness rather than misinformation classifications per se (e.g., Roozenbeek et al., 2022).

In line with prior literature, we compute three scores to assess accuracy in misinformation detection (Basol et al., 2021; Maertens et al., 2021). First, we calculate a measure of 14
<!-- Start of picture text --> 75-<br>50-<br>£<br>iriss£<br>25-<br>a ee ee<br>FD GDP growth Inflatmatonmaton Military Naturalswansreswansrere<br>Coefficient<br><!-- End of picture text --> <!-- Start of picture text --> 2- . .<br>.<br>eo 8 . ,<br>0- .<br>.<br>-2- .<br>.<br>2010qq 2015qq<br><!-- End of picture text --> <!-- Start of picture text --> 2- . .<br>75-<br>.<br>eo 8 . ,<br>50- 0- .<br>iriss£ .<br>25-<br>-2- .<br>a ee ee .<br>FD GDP growth Inflatmatonmaton Military Naturalswansreswansrere 2010qq 2015qq<br>Coefficient<br>80" 2- ee<br>.<br>60-<br>. ce<br>2 O- + * 15<br>2 40-<br>£<br>wya<br>20-<br>-2- .<br>arr ft ee44 ee eeett .<br>FIA FID FIE GDP growth Inflationnatenate Military Naturalaurariesresaurariesresres 2010pp 2015dd<br>Coefficient<br><!-- End of picture text --> <!-- Start of picture text --> 80"<br>60-<br>2<br>2 40-<br>20-<br>arr ft ee44 ee eeett<br>FIA FID FIE GDP growth Inflationnatenate Military Naturalaurariesresaurariesresres<br>Coefficient<br><!-- End of picture text --> <!-- Start of picture text --> 2- ee<br>.<br>. ce<br>O- + * 15<br>-2- .<br>.<br>2010pp 2015dd<br><!-- End of picture text -->
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40
**Folicy, Research, and External Affairs**

# **Welfare and Human Resources**

WPS 460 _This_ papera product of the Welfarc and Human Resources Divisiun, Population and Human Resources Department and the African Food Security Unit -was written as a background paper for the _Food Aid in Sub-Saharan Africa_ study. Copies arc available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Angela Murphy, room S9-114, extension 33750 (28 pages with tables).

written as a background paper for the _Food Aid in_ Food aid averages only 10 percent of total financial aid to developing countries, but in certain African countries - Botswana, Cape - Verde, Mauritius, and Mauritania it represents more than half the food available for consumption.

What is the relationship of food aid to food production and to commercial imports? Threc main hypotheses have been advanced: Food aid is an addition to local food supplies that ultimately lowers prices and acts as a disincentive to local producers. The immcdiate effects may be small, but a lagged response can be generated.

Food aid displaces commercial imports and does not add to domestic food supplies. If there is ful displacement, prices should not change and there wiU be no effect on incentives.

* Food aid is deternined to some extent by local food production. But in the medium run it can generate a positive supply effect that increases the level of production.

Lavy applied vector auto-regression (VAR) analysis to data for Sub-Saharan Africa to test these hypotheses. The issue is not whether food aid is good or bad but how it can be used to promote economic development and improve the nutrition of the food-insecure.

Lavy found that food aid has a significant positive effect on food production. Any disincentive induced by the additional supply of food is offset by the positive effects.

The total net increase in food supply following an increase in food aid is, however, of lower magnitude than expected - because food aid tends to replace almost an equivalent amount of regular food imports.

The extent to which an increase in food aid will lead to a drop in prices and output depends on whether it leads to a net increase in the food supply. If commercial imports decline as food aid increases, the disincentive effect is mitigated.

Food aid is more likely to have a positive effcct in countries that use fertilizer intensively. One possible explanation for this is that countries that enjoy a relative abundance of regular food aid can use the resources made available through reduced food imports to invest more in - the agricultural sector which is more likely when such an investment is a condition imposed by the aid donors.

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