rafmacalaba/data-use-mentions-extended huggingface dataset:
MoLSA budgets will determine how many new households can enter the program. Second, the degree of confidence of a successful referral is taken into account. Referring more people will increase the absolute number of households being determined eligible after MoLSA assessment but will also increase the absolute number of households being referred and not being determined eligible, potentially resulting in resentment and mismanaged expectations. Thus, thresholds were set for differing degrees of confidence that a given pseudo-PMT score would result in eligibility when the full MoLSA PMT is applied. To simplify the exposition, we present the results for targeting poorest 18 percent of the population under low (50 percent), medium (70 percent), and high (90 percent) referral confidence. The results for other targeting thresholds – 25 percent, 30 percent, and 35 percent – are presented in the Annex.

## 4.1 Likely Eligibility of Humanitarian Beneficiaries under MoLSA Targeting

Initial aggregate results highlight the potential for both significant referral numbers and a sequenced strategy. Table 7 presents a first scenario with a smaller MoLSA budget for varying level of referral confidence. For a MoLSA program targeting the poorest 18 percent of the population and requiring a 90 percent confidence in the referral based on the pseudo-PMT, 24.3 percent of the existing humanitarian database would be eligible for referral. For the same level of targeting, 44.2 and 54.1 percent of the

> 13 At the time of the analysis, the new MCNA models were yet to be implemented for the humanitarian assistance eligibility. Besides it being limited to the Norther governorates, the beneficiary database was entirely based on the 2016 VM targeting methodology. Hence, the analysis in this section focuses on the 2016 VM pseudo-PMT model for the North only. Similar analytical exercise using the database resulting from employing the SEVAT targeting criteria and for other regions using other CWG partner’s database remains part of the next steps.

14
total school funding, and ratio of computers connected to the web as a proxy for school resources. Student controls include gender, grade, socio-economic status and immigration status. **Prova Brasil controls** : School controls include student-teacher ratio, log of the number of students, dummies indicating the presence of an IT lab, science lab, and library as proxies for school resources. Given availability of principal characteristics, school controls also include a dummy for male principals, dummies for educational attainment, and dummies for experience as principal. Student controls include a dummy for male students, a dummy for white students, student households’ consumption index, dummies for mother educational attainment (grades 1-5, grades 6-9, secondary grades 10-12, and college). For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value. All panels use the same sample but have different subject outcome variables. Summary statistics for PISA dependent variables and controls are presented in Table B.1. 29
]|[0.031]|[0.031]||[0.037]|[0.037]|[0.037]|
|Married|||0.190***|0.191***|||-0.046***|-0.046***|
||||[0.015]|[0.015]|||[0.017]|[0.017]|
|Rural||||0.199||||-0.035|
|||||[0.217]||||[0.193]|
|Observations|23,107|21,900|21,886|21,886|23,383|22,526|22,514|22,514|
|R-squared|0.774|0.801|0.807|0.807|0.696|0.713|0.713|0.713|
|<br>Individual fixed effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed effect|YES|YES|YES|YES|YES|YES|YES|YES|
|Region dummies|NO|NO|NO|YES|NO|NO|NO|YES|
|<br>Dependent variable mean|0.777|0.775|0.775|0.775|0.327|0.325|0.325|0.325|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018. In columns (1) to (4)
household’s investment decisio to motivate the empirical analysis. In section four, I outline the identification strategy. Section five describes the dataset and household survey. Section six presents the results and Section seven concludes.

> 8See also Djankov, McLiesh and Shleifer (2007) who show that the protection of creditor rights has important effects on _ex-ante_ incentives for the provision of private credit. Visaria (2009) and von Lilienfeld-Toal, Mookherjee and Visaria (2012) provide empirical evidence on the effect of strengthening creditor rights using the introduction of debt recovery tribunals in India.

5
children. We provide a review of this evidence, and in the remainder of the paper we will develop economic models that can reproduce these findings. We also briefly discuss a number of studies that focus on other aspects of the link between female empowerment and development.

# **2.1  Correlations between Female Income and Child Outcomes**

There exists a sizeable literature that uses household data to document correlations between gender-specific resources and outcomes, specifically with regard to spending on children and children's outcomes. For an early survey of this literature see Haddad, Hoddinot, and Alderman (1997). Since these studies are based on observational data, one cannot rule out that the results are driven by unobserved heterogeneity, i.e., there may be unmeasured ways in which households with larger female income shares are special. Nevertheless, the findings in this literature are suggestive, and they motivate much of the subsequent research in the area.

Hoddinott and Haddad (1995) uses cross sectional data from the 1986/87 Living Standards Survey of the Cote d'Ivoire. The authors find that an increase in the wife's share of income is associated with an increase in the share of expenditures on food, and a decrease in the share of expenditures on alcohol and cigarettes.<sup>2</sup> Based on the same data, Haddad and Hoddinott (1994)

> 2The study also finds a decrease in meals eaten out, children's clothing and adult clothing. But these findings are not

4
**Figure A2: The Relationship Between Public Expenditure on Health per Capita and GDP per Capita Across Prefectures Within Provinces** (Trend lines for each selected province across prefectures), for the most recent available year (2007 or 2008)

<!-- Start of picture text --> Xinjiang<br>500<br>450<br>400 Inner Mongolia<br>350 Tianjin<br>Hebei<br>300 Jiangsu<br>250<br>200 Sichuan Jilin Guangxi Liaoning<br>Chongqing Shandong<br>150<br>Anhui<br>100 Sichuan* Henan<br>50<br>0<br>5000 50000<br>Prefecture GDP per capita (RMB, log scale)<br>Prefecture health spendign per capita (RMB)<br><!-- End of picture text -->

* This trend line excludes the two prefectures, Aba 阿坝藏族羌族自治州 (population 894,077) and Ganzi 甘孜藏族自治州 (population 975,207).  Each prefecture attributes to 1% of the total population of 81,331,778.

_Source: China Statistical Yearbooks and Provincial Statistical Yearbooks_ 33
079)|||
|treatment x post x goal|0.072<br>(0.115)||||
|IPV often index|||||
|treatment x post|0.015|-0.060|0.000|1,100|
||(0.133)|(0.122)|||
|treatment x post x goal|-0.169||||
||(0.171)||||
|IPV in last year index|||||
|treatment x post|0.109|-0.010|-0.000|1,100|
||(0.201)|(0.204)|||
|treatment x post x goal|-0.001<br>(0.270)||||
|Girls have a right to ask to use a condom|||||
|treatment x post|0.106|-0.002|0.881|1,100|
||(0.091)|(0.081)|||
|treatment x post x goal|-0.026<br>(0.098)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. See notes from Table 3 for detail on table structure, outcome definitions, and control variables. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Male baseline and endline data, balanced panel, but restricted to males who had ever had sex at baseline.

64
# **1. Introduction**

_“One by one, the factories shuttered and left our shores, with not even a thought about the millions upon millions of American workers left behind”_

- President Trump, Inaugural Address, 2016

In 2018, after many decades of focus on lowering barriers to international trade, the US embarked on a wave of protectionism, unilaterally raising tariffs on imports from many of its major trade partners. Tariffs were levied in several rounds, covering approximately 12 percent of total US imports by the end of 2018. One of the central justifications offered for this unprecedented shift in trade policy was boosting the competitiveness of the US manufacturing sector and protecting US manufacturing jobs. This was a common theme during the Trump Presidency, as is reflected in statements such as that listed above and the promise that ‘we will bring back our jobs.’<sup>1</sup> In response to the US tariffs, from April 2018 onward, the US’s main import partners, starting with China, then followed by Mexico, Turkey, the European Union, Canada and Russia, all levied retaliatory tariffs on US exports. By the end of 2018, these had covered approximately 8 percent of the US’s total exports.<sup>2</sup> While many of the impacts of this trade war, such as on trade flows, prices and consumption, have now been well documented, research on the labor market consequences of the trade war remains more scarce. Did these tariffs really achieve the goal of improving job opportunities for US workers? In this paper, we aim to shed light on this question by studying how the trade war affected the posting of online job adverts during the immediate aftermath of the tariff hikes in 2018. In contrast to official employment statistics, which capture both labor demand and supply-side factors and provide only total employment counts, online job adverts provide an almost real-time proxy for firms’ intentions to hire and include comprehensive and rich information on the
the total FDI flows into resource-related flows, which include flows to hydrocarbons, minerals, and agriculture, and non-resource-related FDI flows, which include flows to manufacturing, construction, distribution, and commercial services. FDI flows are measured in millions of US dollars, and because the distribution of these flows is skewed, they are log-transformed, using the logarithm of the inverse hyperbolic sine: ��ln ��� <u>√�</u><sup>�</sup> �1 ).

We focus on greenfield investment because it consists of a relatively homogeneous group of investments in new facilities and excludes investments resulting from fire sales (Krugman, 2000). This focus eliminates concerns that heterogeneity of FDI is driving the results and the possibility that investment reflects repairs of facilities associated with prior investments rather than new projects. In developing countries, the inflow of greenfield investments is also considerably greater than the inflow of brownfield investments (Markusen & Stähler, 2011). A comparison of the number of greenfield investments in our data set to all mergers and acquisitions (M&As) registered by the Thomson One data service in the same period reveals that 81.6% were greenfield investments. Moreover, many policy makers are particularly interested in attracting greenfield FDI (UNCTAD, 2013). Finally, data on greenfield investments are more detailed than data on M&As. Although the Thomson One data service includes information on M&As, the size of the investment is missing for approximately 50% of the observations in developing countries for the period under study.<sup>4</sup> Following the political science literature, we measure political conflict using the number of battle-related deaths ( _BRD_ ) per year in a country. The data are obtained from the UCDP/PRIO Battle Related Death database (Pettersson & Wallensteen, 2015) and are gathered using information taken from a selection of publicly available sources, including journals, news agencies, NGO reports and statements of governments. The battle-related deaths variable is
Of course, none of the above results controls for other explanatory variables that can influence the school‟s performance measures. The regression analysis presented below will analyze whether these preliminary findings are confirmed once one controls for such confounding factors.

# _b. Are there selection effects?_

Before we can study the determinants of the test scores, we need to discuss a number of sample selection issues as sample selection has the potential to bias our regression estimates.

We have data on 12,075 schools participating in the 2010 EIT<sup>29</sup> . In the MOE database, however, we only have 11,950 schools which have students in the 11<sup>th</sup> and/or 12<sup>th</sup> grade in 2010. For 11683 schools we have information in both databases, implying we have data for at least 94.5 percent of the Ukrainian schools with graduating students (11,683/(11,683+(12,07511,683)+(11,950-11,683))). Another implication is that almost all schools which have students in the last year of secondary education have students who participate in the EIT. Hence, the „selection‟ introduced by the fact that we use EIT results, which is only taken by students aiming to go to the university, rather than some general test which would be taken by all graduating students , is likely to be minor. Of course, there is also the issue of selection among the students, an issue we will discuss next.

Only those students interested in continuing their studies at a university within Ukraine have an interest in taking the Independent External Test, hence the school average we have are based on a selected group of students rather than all students graduating in a given year. Moreover, while the students take the decision to participate or not themselves, schools do have a possibility to try to influence this choice. For example, if a school would like
Dewailly E, Dombrowski C, Rousseau F, Sanchez VD & Bailey JL (2006) Reduced seminal parameters associated with environmental DDT exposure and _p_ , _p_ ′-DDE concentrations in men in Chiapas, Mexico: a cross-sectional study. J Androl, **27** : 16–27.

Dobson, J. E., E. A. Bright, P. R. Coleman, R. C. Durfee, and B. A. Worley (2000). LandScan: a global population database for estimating populations at risk, Photogram. Eng. Remote Sens. 66, 849–857.

Eriksson P, Talts U. (2000). Neonatal exposure to neurotoxic pesticides increases adult susceptibility: a review of current findings. Neurotoxicology, **21:** 37-47.

Eskenazi B, Marks AR, Bradman A, Fenster L, Johnson C, Barr DB & Jewell NP (2006) In utero exposure to dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethylene (DDE) and neurodevelopment among young Mexican American children. Pediatrics, **118** : 233–241.

Everett CJ, Frithsen IL, Diaz VA, Koopman RJ, Simpson JWM, & Mainous AG (2007) Association of a polychlorinated dibenzo-p-dioxin, a polychlorinated biphenyl, and DDT with diabetes in the 1999-2002 National Health and Nutrition examination survey. Environmental Research, **103** : 413-418.

Garabrant DH, Held J, Langholz B, Peters JM & Mack TM (1992) DDT and related compounds and risk of pancreatic cancer. J Natl Cancer Inst, **84** : 764–771.

17
countries for which we have city data, do we find the value of Zipf’s coefficient to be less than 1. This implies that cities in our sample are more evenly distributed relative to predictions by the rank-size rule. For urban agglomerations however, we find that for 20 of the 39 countries which report their populations in urban agglomerations, we observe a Zipf’s coefficient lower than 1, implying that urban agglomerations present greater concentration in economic activity of a country relative to that predicted by the Zipf’s law.

# _Do urban agglomerations represent higher spatial concentration?_

In the last few years, administratively defined cities have become less meaningful as most of the growth is occurring through expanding suburbs, especially in developed countries. Table A.3 presents a comparison of the estimated Zipf’s coefficient from regression in equation (3) for a subset of countries across administratively defined cities with that in their urban agglomerations. Gabaix and Ioannides (2004) note that the Zipf’s coefficient for administratively defined city is higher than urban agglomerations. This point was made first by Rosen and Resnick (1980) and has been revisited by Soo (2005) who finds that the mean value of the Pareto exponent is lower for agglomerations than for cities. Our results suggest that this pattern continues to hold for most countries in our sample. Since urban agglomerations tend to be larger relative to the core city, they tend to show greater statistical dispersion in size distribution, that is, in economic terms have a greater concentration in economic activity.

# _Participation in GVCs and Size Distribution: Some Motivating Correlations_

Although we use four indicators to measure participation in GVCs, here we show an association between participation in GVCs and spatial dispersion using the “Re-imported domestic value added (DVA)”. Figures 2a and 2b show an added variable (AV) plot of this index with the city and UA level Zipf’s coefficient, respectively, after controlling for population density, GDP per capita (linear and square terms) and an indicator
the heterogeneity in welfare patterns within the 10 districts.

**<u>Table 8.</u>** <u>Results when training models on sample data from UBR districts instead of all  districts</u>

||<br>**All districts-poorest 5**|<br>**0% HH**<br>**UBR di**|<br>**stricts-poorest 50% HH**|
|---|---|---|---|
|||**Rank correlation**<br>|**s**<br>|
|Partial registry (10% of the census sample)|0.753||0.399|
|PMT scores|(0.02)||0.15|
|IHS training sample|0.13||0.04|
|RWI|0.20||0.11|
|||**AUC**||
|Partial registry (10% of the census sample)|0.89||0.64|
|PMT scores|0.50||0.53|
|IHS training sample|0.53||0.50|
|RWI|0.60||0.53|
|||**R-squared**||
|Partial registry (10% of the census sample)|0.57||0.17|
|PMT scores|0.00||0.01|
|IHS training sample|0.01||0.00|
|RWI|0.04||0.01|

22
The third factor mixes different types of skills, e.g., communication skills, cognitive skills, and computer skills. The high loading skills in the third factor include ― _Written communication_ ‖, ― _Design & conduct experiments, and analyze and interpret data_ ‖, ― _Reading_ ‖, and ― _Communication in English_ ‖. This factor includes skills which may not be directly relevant to communication, such as ― _Design & conduct experiments, and analyze and interpret data_ ‖. However, Table 3 and Table A2-2 show that all communication skills fall in the third factor with relatively higher loadings. Therefore, the third factor is named _Communication Skill_ .

The three names of the factors do not necessarily represent all skills in respective factors, but these three names do represent the majority of skills with high loadings. It should also be noted that naming factors is a mere poetic, theoretical, and inductive leap (Pett, Lackey, Sullivan, 2003). Therefore, it is important to look into the composition of these three factors and understand actual skills explaining each factor.

The three factors obtained from factor analysis are similar to other studies using factor analysis. For instance, as presented in Table 1 from Section 2, Paranto and Kelker (1999) grouped skills into four factors, Specific, Core, Personal Characteristics, and Communication Skills. The factor, _Core Employability Skills,_ corresponds to Core and Personal skills, and _Professional Skills_ to Specific skills, and _Communication Skills_ to Communication Skills. This similarity with empirical findings from other employer surveys increases our confidence of the above categorization of skills.

The three skills group identified by the above factor analysis partly corroborates one of the most used learning classifications, the Bloom‘s taxonomy, (Bloom 1956). Bloom‘s taxonomy suggests the existence of three domains of learning. The term ―learning‖ is synonymous to the term ―skill‖ as used in this paper. The three domains are:

- <u>Cognitive skills involve knowledge and the development of intellectual skills,
5), which includes all three CLASS domains at the same time, show that _Classroom Organization_ is most strongly associated with test scores. In contrast, the association between _Emotional Support_ and achievement is not significant once other CLASS domains are included as controls.

To further explore the associations between the CLASS and achievement, we next make use of data from another experiment, carried out in a different sample of 200 schools in Ecuador (see Araujo et al. 2016; Campos et al. 2020). In that experiment, children were randomly assigned to classrooms within schools in kindergarten and were then reassigned to different classrooms in every grade between 1<sup>st</sup> and 6<sup>th</sup> grades. Much as in the coaching pilot, teachers were filmed, and the video was used to calculate teacher CLASS scores. At the end of each grade, children were given a large battery of age-appropriate tests in math and language. In 1<sup>st</sup> grade, these tests included the same math and language tests as were applied in the coaching pilot (as well as other tests). For the calculations we report below, we limit the sample to teachers and children in 1<sup>st</sup> grade and use only those tests that were also applied in the coaching pilot, scored and aggregated in the same way.

9
**

The nationally-representative sample used for the 2003 surveys, which our current study uses as a base, covered both urban and rural areas across the 19 most populous states of India, except Delhi. This represented over 95 percent of the country’s population. The 2010 sample covered only _rural_ India. The sampling strategy in 2010 aimed to maintain representativeness of the current landscape of schools in rural India and to maximize the size of the panel. We met these twin objectives (representativeness and panel) by retaining the villages in the original sample to the extent possible, while re-sampling schools from the full universe of schools in these villages in 2010, and by conducting the panel analysis at the

> 7Such misallocation in education spending is also seen in other low-income countries. An even more striking example is provided by de Ree et al. (2015) who experimentally study the intensive-margin impacts of an Indonesian policy reform that doubled teacher pay across the board (at a similar cost of $5 billion/year) and find that the teacher pay increase had no impact on student learning.

5
**Figure 1A: All Respondents' Population Estimates and Actual Population**

<!-- Start of picture text --> Population and claims across all respondents<br>20<br>25<br>20 15<br>15<br>10<br>10<br>5<br>5 Actual<br>Estimated<br>0 Median population in sample<br>0<br>0 100000 200000 300000 400000 500000 -400000 -200000 0 200000 400000<br>Population Error<br>Only claims below 500,000 shown Only errors for claims below 500,000 shown<br>Percent<br><!-- End of picture text -->

**Notes:** Figure 1A shows the histograms of actual populations (black) and population claims (blue dashed) for all claims on the left-hand panel;  the right-hand panel show histograms of actual errors for all claims.  Actual errors are the claim of the respondent minus the actual population value.  The black dashed line represents the median actual population in the sample of districts.  All claims refers to claims over the 75th percentile, 50th percentile, and 25th percentile district in terms of population within the (chosen) region for the federal and regional respondents; and claims over own districts for district level respondents.  The sample includes all employees and managers in the Ethiopian Civil Servants Survey that were asked the information module.  In both panels, observations corresponding to populaton claims over 500,000 are not included for presentation purposes.

# **Figure 1B: Federal and Regional Respondent Population Estimates and Actual Population**

<!-- Start of picture text --> Population error for 25th percentile district Population claim for 25th percentile district<br>25 as a percentage of the actual population<br>25<br>20<br>20<br>15 15<br>10 10<br>5 5<br>0 0<br>-