rafmacalaba/data-use-mentions-extended huggingface dataset:
a positive impact on child health and maternal mental health. Three randomized controlled trials of improvements in housing conditions provided by the TECHO NGO in Uruguay, El Salvador, and Mexico estimate a positive effect of the intervention on satisfaction with one’s own shelter and overall quality of life (Galiani, et al., 2016). They also provide evidence that child health might be affected in some settings.

Our observational data for 2015 and recall data for 2010 are consistent with the possible detrimental effects associated with worse living conditions. For most refugees, forced displacement implied a transition from living in houses and apartments to living in nonstandard facilities such as collective centers, worksites, and abandoned buildings, including dwellings built for purposes other than human habitation, such as garages and storage rooms. Refugees living outside camps in 2015 and 2016 – in Lebanon, this includes the entire population of refugees – have experienced an improvement in housing since that time. The share of households living in houses and apartments has increased, especially in Kurdistan and Jordan, where almost all out-of-camp refugees are now living in standard housing (Figure 7). In Lebanon, the rates upon arrival and currently show little or no improvement, roughly 75 percent and 78 percent, respectively. As a consequence of the process that led to these living conditions, refugees tend to live in crowded 7
to be positively associated with both productivity and firm growth. The Enterprise Survey’s Overdraft indicator is positively associated with both the productivity level and firm growth (it is significant when regressed alone and insignificant when all three groups of BE indicators are included). A one-standard deviation increase of the Overdraft measure is associated with an increase in labor productivity and TFP by 14 and 8 percentage points, respectively. The Global Competitiveness Financial Development indicator is positively associated with firm growth, and positively and significantly associated with TFP (but not log labor productivity, LP). Increasing it by one standard deviation is associated with a nearly 1-percentage point increase of employment growth and 13- percentage point increase of TFP. In contrast, _Doing Business’_ Getting Credit indicator is not significantly associated with firm growth, and is negatively associated with 16
07|0.09|0.03|0.04|
|Offer letter|0.16|0.18|0.11|0.07|0.03|
|Document from chief|0.43|0.42|0.48|0.47|0.38|
|Other/no document|0.17|0.17|0.14|0.16|0.18|
|**Size and land allocation (% of area)**<sup>b</sup>||||||
|Farmer own estimation (ha)|19.74|11.72|28.42|42.02|171.51|
|Mapped area (ha)|26.22|10.19|30.98|67.62|441.35|
|Cultivated area (ha)|9.71|7.08|13.19|18.06|51.47|
|Number of farms|6,703|5,010|1,221|325|147|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey _Note:_<sup>a</sup> Information was collected only from owner respondents. Only 3% of farms expanded their farm size (via rental or sale) after establishment and at 1% of land acquired initially, the extent of such expansion remained limited.

19
# Figure 2. Inequality <u>declines after taxes and social spending</u>

A. Poland. Gini Coefficient B. Emerging markets: Gini Coefficient (pensions as deferred income) <!-- Start of picture text --> 0.55 0.60<br>0.53<br>Old-age pensions as<br>Brazil<br>deferred income<br>0.55 Chile (2009)<br>0.50<br>(2009)<br>Old-age pensions as<br>Mexico<br>transfers 0.50 (2010)<br>0.45<br>0.41 Uruguay<br>0.45 (2009)<br>0.40 Georgia<br>(2013)<br>0.35 0.40<br>0.34 Armenia<br>0.35<br>(2011)<br>0.35<br>Russia<br>0.30 0.28 Poland (2010)<br>0.30 (2014)<br>0.25 0.25<br>Market Income Disposable Consumable Final Income Market Disposable Consumable Final Income<br>Income Income Income Income Income<br><!-- End of picture text --> Source: Own estimates using PHBS 2014.

Source: Armenia: Younger et al (2016); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2010) Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Russia: Lopez-Calvo et al (2016); Uruguay: Bucheli et al (2014); Poland: <u>own estimates using PHBS 2014.</u> <u>Figure 3. Decline in Inequality from Market to Disposable Income</u> <!-- Start of picture text --> 0.00<br>-0.05<br>
>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01

# **5.5 Other explanations**

In addition to underestimating wage growth, youths may also under-value coverage by social security programs, either because they are not aware of these programs and / or heavily discount these future benefits. In the endline survey we ask participants whether they know what constitutes a formal job: 31% of the control group did not known, suggesting that a lack of knowledge may be a partial explanation for low rates of formal work. The share is reduced to 17% in the treatment group, indicating that the wage incentive helps to raise awareness. We also ask how much of their salary participants would be willing to have deducted in order to be covered by social security programs. The average (median) response is 17% (14%) of their wage.<sup>33</sup> This is considerably lower than the legally mandated social security contributions, which stand at around 26% of formal wage rates (Levy, 2010).<sup>34</sup> However, it is unclear whether this means that job seekers underestimate the _marginal_ benefit

> 33In the treatment group the willingness to pay is 55 pesos (5.7%) higher. However this difference is not significant (p-value: 0.136).

34This gap can help explain why formal firms in Mexico often hire workers informally (de la Parra, 2017).

35
of Education focused on the most vulnerable schools in municipalities with relatively poor populations. The reform’s implementation was meant to give priority to these schools. In Chile, these municipalities are those with the lowest levels of female labor participation. As a result of the design of the reform’s implementation, our estimates probably underestimate the true impact of FSD on female labor participation. These results should thus be interpreted as understating the true effect of the Full School Day on mothers’ labor participation.

In conclusion, in this article we find that lengthening the school day has a positive and significant effect on single mothers’ labor supply with no younger children. This result is remarkably interesting because the adequacy of this policy both strengthens the benefits mother enjoy from their participation in the labor market and improve the economic well-being of families. In addition, these results are subject to a number of interpretations that highlight the importance of future work in this area: for example, the need to give priority to greater availability of childcare for young children and the complementary of FDS with these policies.

# REFERENCES

Adser, A. 2005. “Where Are the Babies? Labor Market Conditions and Fertility in Europe.” IZA Discussion Papers No. 1576.

<mark>Amuedo-Dorantes, C., and A. Sevilla. 2014. “Low-Skilled Immigration and Parenting</mark> <mark>Investments of College-Educated Mothers in the United States Evidence from Time-Use Data.”</mark> _<mark>Journal of Human Resources</mark>_ <mark>49 (3): 509–39.</mark> Antecol, Heather. 2003. ‘‘Why Is There Cross-Country Variation in Female Labor Force 23
Kremer, M., A. Onatski, and J. Stock (2001). Searching for prosperity. NBER Working Paper 8250.

Lokshin, M. and M. Ravallion (2002). Rich and powerful? Subjective power and welfare in Russia. World Bank Policy Research Working Paper 2854. Long, J. and J. Freese (2003). Regression Models for Categorical Dependent Variables Using Stata. Texas: Stata Corporation.

McBride, M. (2001). Relative-income effects on subjective well-being in the cross-section. Journal of Economic Behavior and Organization 45, 251-278. Munoz, J. (2001). Timor Loro Sa’e Living Standards Survey Sampling Design and Implementation. World Bank, mimeo. Oswald, A. J. (1997). Happiness and economic performance. Economic Journal 107, 1815-1831.

Pollack, R. and T. Wales (1979). Welfare comparison and equivalence scale. American Economic Review 69.2, 216-221. Quah, D. (1993). Empirical cross-section dynamics in economic growth. ropean Economic Review 87, 426-434.

Ravallion, M. and M. Lokshin (2001). Identifying welfare effects using subjective questions. Economica 68, 335-357.

Ravallion, M. and M. Lokshin (2002). Self-rated economic welfare in Russia. European Economic Review 46.8, 1453-1473. Ross, C. and J. Mirowsky (1992). Households, employment and the sense of control. Social Psychology Quarterly 55, 217-35.

Sen, A. (1999). Development as Freedom. Oxford: Oxford University Press. Shorrocks, A. (1978). The measurement of mobility. Econometrica 46, 10131024. Veall, M. and K. Zimmermann (1996). Pseudo-R2 measures for some common limited dependent variable models. Journal of Economic Surveys 10, 241— 259.

Winkelmann, L. and R. Winkelmann (1998). Why are the unemployed so unhappy? Evidence from panel data. Economica 65, 1-15. World Bank
# **Figure 2a**

<!-- Start of picture text --> China Data: Average Days to Clear<br>Customs for Imports, by Industry<br>12<br>10<br>8<br>Days 6<br>4<br>2<br>0<br>apparel and consumer electronic electronic vehicles and<br>leather goods products components equipment vehicle parts<br>Industry<br><!-- End of picture text -->

# **Figure 2b**

<!-- Start of picture text --> Brazil Data: Average Days to Clear<br>Custom for Import, by Industry<br><!-- End of picture text --> <!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Textiles Garments Shoes and Electronics Auto-parts<br>leather<br>products<br>Industry<br>Days<br><!-- End of picture text --> 13
)|(0.23)||(0.22)|(0.23)|||
|Proportion of principal in default at 12 months post maturity|0.00|0.02|0.00|0.02|0.03|0.76|0.01|
||(0.19)|(0.21)||(0.20)|(0.21)|||
|Missed a due payment (=1)|0.00|0.02|0.20|0.01|0.03|0.89|0.18|
||(0.47)|(0.48)||(0.48)|(0.48)|||
|Got a new loan (=1)|0.00|-0.01|0.70|-0.01|-0.01|0.90|0.76|
||(0.45)|(0.46)||(0.46)|(0.46)|||
|Number of observations|922|1,553|2,475|582|971|1,553|1,893|

Notes: In Panel B, we obtain residuals after regressing default outcomes on the 18 observable characteristics from Table 1 for the Standard Contract group, controlling for treatment assignment probability. P-values based on regressions that control for treatment assignment probability; for additional details, see Section 4. Note on outstanding principal: 238 borrowers have a slightly incorrect version of the outcome variable “remaining outstanding principal.” Outstanding principal comes from FMSD administrative data. FMSD also provide us with the loan’s repayment status over time. We see exactly when the lender classifies a loan as delinquent and subsequently “cancels” the loan. The initial administrative data did not report payments after the loan was “
agreement (Permenko No 8 of 2019, Article 6). In 2022, there were 44 KUR distributors. However, the majority of KUR loans are disbursed by one distributor, Bank Rakyat Indonesia (BRI), which distributed approximately 70 percent of KUR loans in 2022.<sup>7</sup> The fiscal cost of the KUR subsidy program amounted to approximately 0.1% of GDP in 2018-21, rising to 0.2% in 2022-23. Total spending on KUR in 2023 amounted to approximately US$2.76 billion, with the majority of the expenditure program derived from the cost of the interest subsidies and a small portion derived from the cost of the credit guarantees. The total fiscal cost of the KUR program since the introduction of interest rate subsidies in 2015 totals approximately US$12 billion. Earlier estimates found the cost of interest subsidies to be approximately ten times the cost of the credit guarantee.<sup>8</sup> The program claims approximately 90%

> 6  For Islamic financial institutions the proper term is margin subsidy. Throughout this paper, for simplicity, “interest rate subsidy” is used to refer to the interest rate subsidy given to commercial banks and to the equivalent “margin subsidy” given to Islamic financial institutions.

> 7  Data drawn from the SIKP as reported on the CMEA’s website: http://sikp.kemenkeu.go.id/dashboard.

> 8  Indonesia Economic Quarterly, March 2017. “The New Kredit Usaha Rakyat (KUR) Program.”

**10**
household sample was drawn from the same set of 252 enumeration areas (EAs) that were visited concurrently for the Cambodia Socio-Economic Survey (CSES 2019-20) in the last quarter of 2019.<sup>9</sup> In each of these 252 EAs, six additional households were selected at random from the complete CSES listing of households, amounting to a total Cambodia LSMS+ sample of 1,152 households.<sup>10</sup> Within each household, all adult household members aged 18 years and above were targeted for private interviews that elicited self-reported information on education, health, labor, ownership of and rights to selected physical and financial assets, and 24-hour time use.<sup>11</sup> Survey design and implementation was anchored in the latest international recommendations for individual-disaggregated data collection on these topics. The fieldwork was conducted by 21 field teams, each made up of one male and one female enumerator. This allowed teams to aim, to the best extent possible, for gender matching between the enumerators and respondents, and to conduct at least some of the interviews simultaneously. The self-reporting rate across modules was high: In modules on assets and time use modules, all respondents reported for themselves, while in modules on education, health, and labor, 90 percent of men and 95 percent for women self-reported.

# 2.2.1. Time use module

Survey modules that collect data on daily time use — the more common approach being a recall-based 24hour time use diary (see Floro, 2021, Seymour et al., 2020, for a discussion)<sup>12</sup> — can complement standard labor survey modules and can provide additional detail on the distribution of time and effort. Time use modules can improve our understanding of (i) how time is allocated, among men and women, across unpaid and paid activities, domestic and care work, and leisure; (ii) the extent of time allocation to simultaneous activities; and (iii) the
28%|14%|37%|30%|29%|14%|
|**HS 2-digit Sector**|9%|18%|7%|5%|24%|46%|12%|17%|17%|16%|
|**Residual**|81%|58%|90%|93%|54%|41%|51%|55%|55%|70%|

Note: The variance decomposition shown is based on measures in the country-year-product (HS 2-digit) level dataset (file CYH2.dta) averaged across the 2006-2008 period for each country except Kuwait for which averages are across the 2009-2010 period and Portugal for which averages are across the 2003-2005 period.

# **_Stylized Fact 5: There is a tremendous skewness in exporters as total exports of developing_**

# **_countries are largely dominated by multi-product multi-destinations exporters but these account for a very small share of the number of exporters._**

The recent trade literature shows a dominant role for firms that export many products to many destinations in explaining trade flows (see, for example, Bernard, Jensen, Redding, and Schott, 2009 on the US; Eaton, Kortum and Kramarz, 2008 on France; and Amador and Opromolla, 2008 on Portugal). As mentioned in Section 3, a component of the Database is a set of matrix tables showing for each developing country and year the distribution of exporters and of total exports by the number of HS 6-digit products exported and the number of destination markets served that can provide an insight onto the importance of multi-product multidestination exporters across developing countries and over time.

Using the information from developing countries over the period 2006-2008, we calculate for each country the average share of exporters and of total exports accounted for by singleproduct single-destination firms and by firms exporting to more than four countries and to more than four destinations and show them in Table 8. Single-product single-destination firms 31
<!-- Start of picture text --> Filtered vs Normal Price Series: Growth RatesKerala, Trivandrum: Coconut Oil Filtered vs Normal Price Series: Growth RatesKarnataka, Bangalore: Rice<br>Domestic Domestic<br>International International<br>Note: Smoothed series are estimated using Christiano‐Fitzgerald band‐pass filter<br>Source: Author’s calculations<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1<br><!-- End of picture text --> The timing with which these “boom” and “bust” international price cycles are transmitted to domestic markets matter since the commodities in question are an important element in the consumption basket of poor Indian households. To identify the average transmission time that “busts” (troughs) take to transmit to domestic retail prices we use standard time series filters. Figure 10 plots these results for the four commodities for which domestic retail prices can be mapped to international prices (groundnut oil, rice, bananas and coconut oil).<sup>9</sup> These charts plot the extent of transmission in the vertical axis (the portion of the shock that is actually transmitted), and the time it takes for the trough to transmit (international to domestic trough distance in months). Results show that on average it takes five to fourteen months for international troughs to transmit, with substantial heterogeneity by city. Interestingly, the time it takes for shocks to transmit is independent of the portion of the shock that is passed through to domestic prices.

Figure 10: Time it takes for international troughs to transmit to domestic markets <!-- Start of picture text -->
Automation and Performance in a Manufacturing Developing Country Massimiliano Calì<sup>_∗_1</sup> and Giorgio Presidente<sup>_†_2</sup>

> 1 _World Bank_

> 2 _Oxford Martin School, Oxford University_

**Keywords** : Robots, Automation, Development, GVC, Employment, Productivity, Indonesia.

**JEL classification** : O14, J23, J24, L11, F63.

> _∗_ The authors thank Erhan Artuc, Paulo Bastos, Aufa Doarest, John Morrow, Bob Rijkers and Pengfei Zhang for helpful comments, Nabil Ryandiansyah for outstanding research assistance and Mauro Testaverde for sharing automatability data in south-East Asian countries. Giorgio Presidente gratefully acknowledges funding from Citi. The views expressed in this paper are of the authors and do not necessarily reflect those of the affiliating institutions.

> _†_ Corresponding author: giorgio.presidente@oxfordmartin.ox.ac.uk
We next consider the age structure of the district, which is also referred to as the ―demographic dividend‖ in the Indian context. The propensity to start firms changes over the lifetimes of individuals (e.g., Evans and Leighton 1989), and the age structure of a district can have an additional effect on the entry rate. For example, Bönte et al. (2009) document an inverted-U shape between regional age structures and entrepreneurship rates in Germany, and Glaeser and Kerr (2009) found high manufacturing entry rates in cities that had a disproportionate share of workers aged 20-40 years old. We construct the demographic dividend measure as the ratio of working age population to non-working age population using 2001 population census counts. The inverse of this measure is commonly known as a dependency ratio.

Third, we include a measure of population density. Higher population density again reflects some measure of local market size, but it also goes beyond to include the competition for local resources. While local sales are easier with a denser market, higher population density is also associated with higher wage levels and higher land rents. Density has also been linked to 11
# **Method**

The goals of this analysis are to create data on road crashes with times and locations and understand how these incidents cluster in the city, which allows for the spatial prioritization of urban investments in road safety. The technical challenges this study addresses are: i) improve the protocols for geolocation, ii) apply applications of AI to classify tweets reporting crashes and identify their location from multiple geographical references, iii) cluster the crashes geographically and identify areas with many crashes. See the Supplemental Information (SI) for the detailed methodology. The components are as follows:

1. **Scrape data.** We scrape 874,588 tweets posted by Ma3Route, an existing urban mobility platform with 1.1 million followers, since its inception in May 2012 through July 2020 (see SI for examples of tweets and for a figure of the daily number of tweets across time).

2. **Develop and augment a gazetteer.** We build a gazetteer of landmarks for the five counties that constitute the Nairobi metro area using: OpenStreetMap, Geonames and Google Places. The gazetteer includes the landmark name, geocoordinates and type of landmark (e.g., school, bus stop). We use consecutive combinations of 2 and 3 words (known as n-grams) and skip-grams of landmarks in the gazetteer, alternate spellings and abbreviations, and splitting of landmarks with select punctuation (e.g., slashes, parentheses, commas). We innovate by developing alternate names that exclude the landmark type from the name (e.g., excluding “Hotel” from the name).

3. **Develop a truth dataset.** We develop a truth dataset to train the algorithm. Taking all tweets for July 2017 - July 2018, we restrict tweets to the ones most likely related to a crash based on a broad list of words and their variations. Each tweet is manually coded, indicating (1) if the tweet reported a crash and (2)
vaccinated, representing an over 50 percent increase. The two other intervention arms that were provided (referring to expert advice and social norms) did not have a consistent impact on people’s willingness to get vaccinated. The main findings of the online randomized survey experiment did not vary substantially across the demographics of respondents, though female respondents and those in the Southern region were more likely to respond to the social norms treatment. These types of respondents were also less likely to participate in the online experiment relative to the general population. As a result, after population weights were applied (in terms of sex, age and location) the social norms treatment became statistically significant.

By considering the findings of the representative phone survey alongside the online randomized survey experiment, this study provides rich insights into key factors underpinning vaccine hesitancy. Drawing on the combination of the phone survey and the experiment also affords increased ability to assess the generalizability of our results. Both the phone survey and the experiment present very similar descriptive statistics that show there are high levels of COVID-19 vaccine hesitancy in PNG, primarily because of respondents being concerned about side effects from the vaccine, and that people claim health experts are the most likely to be able to change their mind. These patterns hold across the surveys, even though different platforms were used (that is, phone and online) at different points in time, and also hold across most demographic characteristics. As a result, the findings are robust to alternative approaches to weighting (or analyzing the data without survey weights). This suggests that the main findings are likely to hold across the general population in PNG and potentially in other countries facing similar challenges.

There are several implications that flow from our findings for policy makers as they work to address vaccine hesitancy. We provide comprehensive evidence that building greater trust in COVID-19 vaccines 3