rafmacalaba/data-use-mentions-extended huggingface dataset:
F-statistic|265.5|231.3|265.5|231.3|265.5|231.3|265.5|231.3|265.5|231.3|
|Mean Y (Control)|-0.059|-0.059|-0.034|-0.034|-0.062|-0.062|-0.084|-0.084|0.106|0.106|
|SD Y (Control)|0.582|0.582|0.785|0.785|0.660|0.660|0.991|0.991|0.904|0.904|

Notes: Heteroscedasticity-robust standard errors in parentheses. The Global index is composed by indexes for individuals’ knowledge, gender norms/roles and VAW attitudes; excluding individuals beliefs on others’ attitudes. Higher values of the indexes in (1) to (8) stand for more progressive stances. The index "Beliefs on others’ attitudes" reported in (9) and (10) takes higher values when the respondents believe that a larger share (out of 10) of their closest Facebook friends have progressive attitudes. Controls include: Baseline Stance Index, age, gender, education, education of household head, religion, caste, occupation, relationship status, self-assessment of the English language, frequency of watching videos online, indicators for being a student, having sisters, having male friends beating partner or female friends beaten, and city-of-residence fixed effects. All controls are measured at baseline. In Panel B, independent variables Play Drama and Play Documentary take value 1 if the respondent has played half or more of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *
|University|87.7|12.3|100|
|Above University|78.9|21.1|100|
|Total|93.8|6.2|100|

Source: Authors’ calculations using Labor Force Survey Most migrants have had little education. Around 25 percent are illiterate, and 51.4 percent received less than intermediate level schooling. It is worth noting however, that a significant share of migrants (15.5 percent) possess college degrees.

13
learning and market selection is not operating perhaps due to the lack of pro-competitive forces.<sup>10</sup> Likewise, the finding that exporting firms are better managed confirms that selection and learning mechanism can work in Croatia if firms operate in competitive conditions of an international market. More precisely, management score of a manufacturing exporter in Croatia is likely to be higher by 7 percent than an average firm. The fact that this result is mainly driven by high-tech manufacturing exporters, where management score is likely to be 13 percent higher, points to the potential for improving management capabilities among other firms and sectors.

The paper beyond this point is organized as follows: Section 2 introduces the methodology of the management survey and presents some descriptive statistics for firms in Croatia. Section 3 presents the methodology for computing management scores, and the main descriptive results on the relationship

> 9 Their results suggest that group-based intervention led to increases in firm size over the next 1.5 years, including a statistically significant increase in employment, while the impacts on firm outcomes are smaller and statistically insignificant for the individual consulting. 10 In the United States it is well known that “up-or-out” dynamics in which unsuccessful firms exit or are bought out by more successful competitors who absorb the labor and capital released by exiting firms drives productivity and business dynamism (Haltiwanger et al., 2013). In the case of Croatia, such selection dynamics does not seem to work in a similar way, and that firms that are badly managed still continue to survive.
output expands in labor-intensive sectors, particularly other agriculture, to meet the new demand from countries involved in the dispute.

Although the United States plays a major role in the world economy, accounting for close to a quarter of global GDP, the expansion of China and other developing countries has reduced its relative importance in the global trading system. In 2016, the United States accounted for 8.7 percent of all goods exports and 13.3 percent of goods imports (World Development Indicators, World Bank). With the exception of Canada, Mexico and several Central American economies, the United States is not a major trading partner for many economies. The majority of EU trade is intra-EU. Europe is Africa’s main export destination. Globally, the United States is less important than high-income Asia or Europe in terms of total trade. Faced with an increasingly inward-looking United States, the global trading system will likely rebalance and adjust.

There are a few important findings that emerge from this work:

- Retaliatory action by developing countries is the least desirable strategy in the face of new protectionist measures. No action is preferred to retaliation, as aggregate losses are found to be nearly twice as big when developing countries impose retaliatory measures.

- No action could translate into benefits for developing countries if they can respond to trade diversion opportunities in regions involved in the trade dispute.

- Trade liberalization and improved intra-regional integration could not only help offset the negative terms of trade effects of increased protectionism on developing countries, but also represent significant benefits that are, so far, untapped.

- “Turning the other cheek” could benefit countries in Latin America and the Caribbean that are deeply interconnected with the United States, the source of most of their imports.

- Faced with an increasingly inward-looking United States, the world trading system will likely rebalance and adjust, reinforcing existing trade ties and creating new ones.

# **5 Conclusion**

Faced with escalating protectionist measures and a potential trade war between major economies, policymakers in developing countries are
debt service/GDP adjusted for GDP<br>growth|
|Current account|(1) change in real exchange rate<br>(2) level of the real exchange rate<br>(3) drift of the real exchange rate<br>(4) variance of the real exchange rate<br>(5) deviations from PPP in the real<br>bilateral exchange rate<br>(6) deviations from trend in the real<br>exchange rate<br>(7) deviations from historical average of<br>the real exchange rate<br>(8) real exchange rate squared|(9) trade balance/GDP<br>(10) current account/GDP<br>(11) exports/GDP<br>(12) exports/imports<br>(13) change in exports<br>(14) change in imports<br>_(15)_saving/GDP<br>(16) investmentlGDP<br>(17) change in the terms-of-trade<br>(18) change in export prices|
|International|(1) OECD real GDP growth<br>(2) international interest rates|(3) U.S. interest rates<br>(4) foreign price level|
and test sets (Mullainathan and Spiess 2017).<sup>17</sup> However, in our case, LASSO conducts variable selection in a stable manner with the most important variables being selected in the vast majority of model runs.

# _2.2.3 OLS specification_

<mark>As mentioned above, we benchmark the performance of our two ML algorithms against OLS. The OLS model we use for comparison is as follows:</mark> where,

- _Yi_ is Average Student Learning Gain for Teacher i

- _X1_ is a vector of Student controls such as student age, baseline score, whether a student attends private tuitions in that subject<sup>18</sup> and percentage of students belonging to the lowest asset quartile

- _X2_ is a vector of school level covariates such as pupil teacher ratio, school location (urban/rural), annual entitlement grant per school etc.

- _X3_ is a vector of school level variables that affect the management and governance of teachers

- _X4_ is a vector of controls representing teacher characteristics (who teachers are) such as gender, experience and academic qualifications

- _X5_ is a vector representing our variables of interest divided into 3 categories: (i) what teachers do (measured through the TEACH Classroom Observation Tool and Student surveys) (ii) what teachers believe (measured through modules in Teacher surveys representing mental models) and (iii) what teachers know<sup>19</sup> (teacher assessment scores etc.)

- _εi_ represents an idiosyncratic error term

# **2.3 Limitations**

Our study suffers from three main limitations. First, we cannot fully address the issue of non-random matching of students, teachers, and schools. A rich set of teacher, student, and school level controls helps, as does the reliance on two cohorts (Grades 2 and 3). Nonetheless, despite our use of a very detailed set of teacher variables, the problem of unobserved variable bias remains. Non-random matching is sufficiently mitigated
Administrative data offer a representative measure of disease incidence in each district, enabling the estimation of the broader impacts of the campaign, including indirect or spillover effects on individuals not directly exposed to it. As a result, this district level analysis also facilitates more precise evaluations of the campaign’s cost-effectiveness.

# **4.3.3 Outcome Measures**

We evaluate the impact of the social media campaign on three outcome categories: (1) behaviors associated with malaria prevention and response as reported in surveys, (2) malaria incidence through self-reported surveys, and (3) objective malaria incidence from the health facility HMIS data. Below we list the primary behavioral and health outcomes and explain their construction. The questions underlying the outcomes are reported in Appendix B.

**Self-reported outcomes** **_during_ the campaign.** Respondents in the experience survey were asked every 17 days a series of questions that captured the adoption of preventative behaviors and response actions related to malaria. From these questions, we constructed the following outcomes: (i) the share of times a respondent reported to use a bed net or other protection measures, (ii) the average share of household members who used a bed net, (iii) any fever/malaria case in the household, and (iv) medical seeking and testing behaviors. We aggregated the responses for every individual across survey waves by considering the _mean_ for outcomes (i-ii) and the _maximum_ for outcomes (iii-iv). In the analysis, we weight observations by the number of survey waves answered by the respondent.

**Self-reported outcomes** **_after_ the campaign.** In the cross-sectional survey, we inquired about the occurrence and number of individuals who tested positive for malaria since August 2020 (the campaign launch date), households’ concerns about contracting COVID-19 or malaria in the upcoming year, intentions to purchase mosquito nets, and whether mosquito nets had been treated with insecticide.

**Health facility recorded outcomes.** These data
not fit the age criteria; or b) individuals living in a rural area who declared to receive a pension, fit the age criteria and whose pension value was higher than 1.5 minimum wages. 26F<sup>27</sup> All rural pension beneficiaries are assumed to receive a 13<sup>th</sup> salary.

**In-kind transfers.** The in-kind transfers included in the analysis provide a monetized estimation of the public services received by Brazilian families. There are two main transfers included in the study:

- _Education_ **_._** The in-kind transfers from access to public education were modeled based on the direct identification of beneficiaries, i.e., students enrolled in public education, and the imputation of the direct investment benefits based on INEP’s latest estimates (i.e., relative to the year 2018). The analysis covers (i) preschool, (ii) primary, (iii) secondary, (iv) tertiary, and (v) youth and adult education. For each of these levels, INEP publishes the average public expenditure per student. The identification of public education beneficiaries in each level is undertaken using PNAD-C data, as it contains specific variables that allows us to identify those who reported to be attending public schools (which is applicable only for individual aged 5 or more). 27F<sup>28</sup> Once the beneficiaries of each level are

> 25 This was the case in PNAD-C 2017 (World Bank, 2020) and also in PNAD-C 2019.

> 26 Other technical details can be found in World Bank (2020).

> 27 Using PNAD-C data for 2015, the last year where explicit questions about worker and survivor’s pensions were included in the survey, World Bank (2020) finds that approximately 95% of the individuals receiving pensions with values equivalent to about 2 minimum wages were receiving both a worker and a survivor’s pension. By focusing on thresholds equivalent to
# **5. Robustness Checks**

## **5.1 What is the effect of government support on exit and non-response?**

Our analysis in Section 4 uses the sample of firms for whom government support is observed in all three rounds of the ES COVID-19 Follow-up Surveys, thus dropping any firms that closed between rounds or that did not respond to the later survey rounds for other reasons. This section asks whether using this sample of responding firms could bias our results, which may be the case if there is a relationship between government support and exit or non-response.

We start by pointing out that confirmed exit rates are very low in our sample. Table A.3 shows exit rates for the sample of 4,279 firms that reported government support in both Rounds 1 and 2 (i.e., firms that had not closed by Round 2). Only 1 percent of these firms were confirmed to have exited by Round 3. The exit rate is higher, 9 percent, if we include firms that are assumed to have closed since they could not be contacted in Round 3. We also lose firms in our balanced sample due to interview refusal in Round 3 or not reporting government support in Round 3. In total, 19 percent of firms drop out of the sample of 4,279 for any of the reasons mentioned above (confirmed exit, assumed exit, interview refusal, and not reporting government support), giving 3,647 firms in our balanced sample.

Tables A.4 and A.5 follow the same format as Tables 5 and 6, to examine whether receiving government support in Rounds 1 and 2 is associated with subsequent firm exit or non-response to Round 3. We find no relationship between government support and confirmed firm exit. The only variable that shows a statistically significant correlation with confirmed exit is firm age, where older firms were less likely to exit than younger firms (Table A.4 Column 1). When also including assumed exit as the outcome
br>40<br>8<br>20<br>4 0<br>0<br>World AEs EMDEs World AEs EMDEs<br>2003-07 2013-17 2003-07 2013-17 2003-07 2013-17<br>2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 1998-2002 1998-2002 1998-2002<br><!-- End of picture text --> Sources: Penn World Tables, World Bank. A. Investment-weighted averages. For a sample of 37 advanced economies and 145 EMDEs. B. Share of 139 countries in which investment growth is below the longer-term average (1998-2017).

23
place strictly in the central provinces, we form a “geographic” control group consisting of non-affected fishers located in other coastal provinces. To avoid the concerns related to contaminated counterfactuals, we restrict to only fishery workers living in the southern provinces distant from Formosa, specifically between Phu Yen to Ca Mau (see Figure 1). Second, the fact that _Formosa_ mainly affected the fishery industry allows us to form an “industry” control group consisting of individuals living in the affected zone, who worked in non-affected industries such as manufacturing, construction, and retail.<sup>8</sup> The first Panel (Panel I) in Table 1 presents descriptive statistics for the primary labormarket and observable characteristics of the saltwater fishery industry, using LFS 2015. The

> 7 This survey design allows us to additionally control for the individual-level unobservables with the inclusion of individual-specific fixed effects in one of our empirical specifications.

> 8 We subsequently check for the validity of both control groups in several empirical exercises.

6
However, while there has been extensive conceptual discussion, empirical data have not been analyzed in any detail to explore if proposed methodologies are sound and provide a basis for actionable policy advice. This paper aims to start filling this gap by using the case of Zambia where the SDG module, with supplemental questions on demand for title, was included in the country’s 2018 Labor Force Survey (LFS). Data generated in this way can also be compared to the Prindex global poll administered in Zambia during the same period. While our analysis focuses on the LFS, an evidence-based and coherent policy dialogue on land will only be possible if data provided by different initiatives are conceptually clear and complementary.

Data from the LFS suggest that in Zambia transferability of land is limited, few parcels have title, and tenure insecurity is widespread: only 42% of land owners can bequeath and 36% sell their land and 44% perceive a risk of losing it over the next 5 years. Less than 10% of households have title, 13% an informal document or incomplete title and 55% of those without title want to acquire it and are willing to pay a median of

> 1 SDG 1.4.2 aims to measure “the proportion of total adult population with secure tenure rights to land, with legally recognized documentation and who perceive their rights to land as secure, by sex and by type of tenure” whereas SDG 5.a.1 focuses on “the proportion of total agricultural population with ownership or secure rights over agricultural land, by gender” and “the share of women among owners or rights-bearers of agricultural land, by type of tenure” (FAO _et al_ , 2018).

> 2 Prindex <u>(www.prindex.net) is implemented by private contractors, with oversight from the UK’s Overseas Development Institute (ODI) and</u> financial support mainly from DFID and the Omidyar network.

2
remote-learning systems almost immediately. In response to this disruption, education researchers are beginning to analyze the impact of these school closures on student learning progress or lack thereof. The term _learning loss_ is commonly used in the literature to describe declines in student knowledge and skills (Pier et al. 2021). Historic data provides researchers with information regarding where student learning should be year over year and is often measured through regular testing. Learning loss occurs when educational progress does not occur at the same rate at which it has historically compared to previous years.

School closures have a large, persistent, and unequal effect on learning. Long-term, nationwide school closures result in learning losses (see, for example, Belot and Webbink 2010). In turn, learning losses can lead to decreased future employment potential and lower earnings (see, for example, Chetty et al. 2014; Currie and Thomas 2001; Psacharopoulos et al. 2021). Online education is an imperfect substitute for in-person learning, particularly for children from low-income families. Peer effects also change: schools allow children from different socioeconomic backgrounds to mix, and this effect is lost when schools are closed. Another factor is the response of parents, some of whom compensate for the changed environment through their own efforts, while others are unable to do so. All this leads to the finding that school closures have a large, persistent, and unequal effect on human capital accumulation (Agostinelli et al. 2022). The channels operating through schools, peers, and parents all contribute to growing educational inequality during the pandemic.

As a result of the first wave of lockdowns and school closures beginning in March 2020, on average, a student has lost about one-third to a half a year’s worth of learning. The magnitude of our findings appears to validate scenarios projected by the World Bank (Azevedo et al. 2021). This has consequences in terms of further education as the lost learning may impact opportunities for higher levels of schooling. There are also long-term future earnings
|8939|32298|0.5|2.5|0.3|0.2|0.2|0.0|
|Paper andpaperboard in rolls or sheets|6415|31815|0.5|24.7|0.2|0.0|0.0||
|Synthetic organic dyestuffs|5311|30951|05|437|59|00|00||
|<br>Aluminium and aluminium alloys|6841|30042|.<br>0.4|.<br>79.6|.<br>0.4|.<br>0.0|.||
|Polyvinyl chloride|5834|29616|0.4|1.0|0.2|0.0|0.0||
|Tulle lace embroider ribbons|6560|29494|04|15|17|10|21||
|, , y,<br>_Source_: Calculated usin UN COMTRADE Databas|||.|.|.|.|.||
|g<br>_Notes_: A value of 0.0 represents less than US$100 in|**Sri La**<br> <br>exports;|**nka**<br>blanks repre|sent zero e|xports.|||||

||**Sri L**<br>|**anka**<br>|<br>||
|---|---|---|---|---|
|**Product Description**|**SITC**|**Total**<br>**~~I~~**|**Share of**<br>**~~I~~**<br>**Share of**<br>**~~I~~*
on vulnerable groups of the population (Bornukova, Chubrik, Shymanovich, 2017).

_Figure 6: Inflation rates, % yoy_ <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>CPI food non-food<br>utilities other services<br>2014m1 2014m4 2014m7 2014m10 2015m01 2015m04 2015m07 2015m10 2016m01 2016m04 2016m07 2016m10 2017m1 2017m4 2017m7 2017m10<br><!-- End of picture text --> Source: Own estimates based on Belstat data.

# 3. Cash Income and Poverty Dynamics

## 3.1. Cash income

As we noted in the previous section, the 2015‐2016 crisis was different from the previous 2009 and 2011 episodes. Inter alia, one of the key differences manifested itself in the fact that the latest recession affected the poorest deciles the most, while higher income households suffered welfare losses to a smaller degree, and later into the crisis. Figure 7 illustrates this point in more detail. It shows the evolution of real cash income (which includes all types of cash incomes like earnings, dividends, government transfers and other sources) 6
# **“Crowding In” Effect of Public Investment on Private Investment Revisited**<sup>*****</sup>

John Nana Francois<sup>†</sup> Maty Konte<sup>‡</sup> Franz U Ruch<sup>§</sup>

## : E22, E23, H11.

_Keywords_ : Capital markets distortions; Crowding-in; Public investment; Private investment; Instrumental variable.

> * <mark>This paper received funding from the KDI School of Public Policy and Management. We benefted from valuable comments, suggestions, and feedback from Maya Eden, Aart Kraay, Stephen A. O'Connell, James Sampi, Emilia Skrok, Kersten Stamm, and Fulbert Tchana Tchana. We are grateful to Gbenoukpo Robert Djidonou and Suhyun Lee for research assistance. We would like to thank Parul Agarwal for providing the oficial loan level data. We would also like to thank participants at the 2023 1st KDIS-World Bank Fiscal Policy for Growth Workshop in Seoul for their feedback, and the participants at the review meeting for their comments and suggestions. The fndings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the World Bank, IFC, their Executive Directors, or the countries they represent.</mark>

† Fiscal and Sustainable Growth Unit, Macroeconomic, Trade and Investment Department, World Bank. Email: jfrancois1@worldbank.org.

‡ Economic and Market Research, International Finance Corporation. Email: mkonte@ifc.org § Fiscal and Sustainable Growth Unit, Macroeconomic, Trade and Investment Department, World Bank. Email: fruch@worldbank.org.