rafmacalaba/data-use-mentions-extended huggingface dataset:
information from millions of U.S. patents and validate this list by analyzing the frequency at which these technologies were mentioned in hundreds of thousands of earnings conference calls. Therefore, this strategy considers both the technology and the potential market components of disruptive technologies. As their exercise is based in the U.S., we consider these technologies a good proxy for the type of technologies that startups and innovative entrepreneurs might decide to incorporate into their products or services.

# **3 Data and methodology**

## **3.1 Data**

The main data source used in this paper is Crunchbase (www.crunchbase.com). Created in 2007, the use of Crunchbase as a primary source of business information and for academic research has gradually increased, with over 55 million users currently. As explained by Dalle et al. (2017), Crunchbase is updated on a daily basis, and the information is obtained from large investor networks, a community of contributors, global investment firms that submit their whole portfolio, as well as entrepreneurs and independent investors that provide their own information. All these data are validated and cross-linked with funders, firm staff, and management profiles. This self-reported information is complemented through Artificial Intelligence and Machine Learning algorithms that web scrape to enrich these profiles.

According to Dalle et al. (2017), comparing Crunchbase with the OECD Entrepreneurship Financing Database, the coverage of Crunchbase appears to be very comprehensive for young firms, especially in the case of the U.S. As this exercise was conducted in 2017, and given the exponential growth of this platform, we can expect that the coverage is even better now. Given the constraints on data availability in Africa, the increasing use of Crunchbase globally, the emergence of other platforms that concentrate information on Africa and the methodology (web scraping and self-reporting) that Crunchbase uses to gather and validate their information, the coverage for Africa appears to be relatively good for these types of exercises.

For this study, we use
number of workers is 2.44 per household.<sup>19</sup> The average number of years of education is 5.89, which is equal to the level of the primary school completion. The average land surface per household is 10.48 _mus_ ,<sup>20</sup> and the per capita level is only 2.92 _mus_ . In other words, land is a scarce resource in rural China, only 0.19 hectares per capita. Compared to the households that participate only in farm activity, the households that participate in non-farm activity have a larger number of workers, a higher education level, a lower level land surface, and live closer to urban centers / hubs of transport.

# **4 Methodologies**

Based on the LSMS data, we use two methods to study the impacts of non-farm income on rural inequality. First, we consider non-farm income as an “exogenous transfer”, which adds to pre-existing total household income, to decompose total household income and study the distribution of each income source and its contribution to total income inequality. Second, we consider non-farm income as a “potential substitute” for farm income, and compare the level and distribution of observed total household income with those without non-farm income.

# **4.1 Method I: Decomposition of the Gini index**

The method of the decomposition of the Gini index is often used to analyze income inequality (Pyatt et al _._ , 1980; Stark, 1991). Suppose that _y_ 1, _y_ 2 , L , _y K_ stand for the _K K_ components of household income and<sup>_y_</sup> 0<sup>the total income,</sup> _y_ 0 = ∑<sup>_y_</sup> _k_ . The Gini index of the _k_ = 1 total income, _G_ 0 , can be decomposed as follows: where _S k_ stands for the share of the component _k_ in the total income,
Antidumping, Retaliation Threats, and Export Prices<sup>1</sup> _Veysel Avsar_<sup>_2_</sup> 1 I am indebted to Marc Muendler for kindly inviting me to UC San Diego to work with the SECEX and RAIS data, sharing his data preparation codes, providing computational support and answering all of my related questions. I want to thank Lisandra Flach for helping me with the data and for many other useful suggestions. I also want to thank the UC San Diego Department of Economics, where part of this research was conducted, for their hospitality. I am grateful for the guidance of my advisor, Richard Chisik. This study also benefited from helpful suggestions from Jesse Bull, Sheng Guo, Alexis Habiyaremye, Cem Karayalcin, Peter Thompson, Mehmet Ulubasoglu, three anonymous referees, and Alain de Janvry, the editor. Financial support from Florida International University’s Doctoral Evidence Acquisition Fellowship and Dissertation Year Fellowship is gratefully acknowledged.

2 Assistant Professor, Department of Economics, Antalya International University, Antalya, Turkey. E-mail:veysel.avsar@antalya.edu.tr
# The Anatomy of Index Rebalancings: Evidence from Transaction Data<sup>_∗_</sup>

## Mariana Escobar<sup>_♥_</sup> , Lorenzo Pandolfi<sup>_♠_</sup> , Alvaro Pedraza<sup>_♦_</sup> and Tomas Williams<sup>_♣_</sup>

_♥_ Banco de la Republica de Colombia

> _♠_ University of Naples Federico II and CSEF

> _♦_ The World Bank

> _♣_ George Washington University

### **JEL Classification:** F32, G11, G15

**Keywords:** index rebalancings; institutional investors; stocks demand elasticity; passive funds; arbitrage

> _~~∗~~_ This paper greatly benefited from helpful comments by seminar participants at the International Monetary Fund and Bank of Italy.
public upper secondary education teachers who hold a tertiary degree increased from 82% to 93% in more than 20 years, mainly due to _licenciatura_ degrees (Figure 2). However, there are still teachers without higher education or with a pedagogy degree which is not focused on secondary education, whose share corresponds to 7.6%. Also, states and municipalities selected teachers with distinct profiles, since there was not a full compliance with the national standards for teacher admission. All these teachers were trained in different types of institutions that are practically autonomous to decide about the instructional design of their curriculum.

Louzano et al. (2010) investigated the profile and preferences of secondary school graduates who declared themselves interested in a teaching career in 2005.<sup>4</sup> Only 10% of this group belonged to the top

> 2 _Pedagogical supplementation_ was instituted in 1997 as a provisional act and originally required a workload of 540 hours. Its provisional nature is reversed 2017, and the certification is now recognized in LDB as a valid requisite for teaching. 3 The authors find that Latin American countries are trapped in a low-level equilibrium with low standards for entry into teacher training programs, which are not prepared to produce the set of competences found in high performance educational systems. 4 Based on socioeconomic questionnaire data of the National Examination of High School (ENEM).

3
_CHAPTER 8. CHALLENGES AND POTENTIAL RESEARCH TOPICS_ 64 3. _Estimation of error variances in the FH model and associated MSE estimates:_ Error variances in the FH model are assumed to be known, because they cannot be estimated with area-level data. Typically, these error variances are replaced with the estimated sampling variances of the direct estimators acting as model response variables. However, these variance estimators are also based in the area-specific survey data, making them inefficient in small areas. Moreover, the usual estimators of the MSE for FH estimators do not account for the uncertainty due to the estimation of these error variances, which might be substantial. More research is needed to obtain efficient error variances and derive MSE estimators that account for the uncertainty due to the estimation of these variances.

4. _The Alpha model in unit-level models:_ The traditional ELL method includes heteroscedastic idiosyncratic errors, and EB or Census EB under the nested error model allows for the inclusion of known heteroscedasticity factors _kdi_<sup>2,althoughthispossibilityisnotimple-</sup> mented in the `sae` R package. In the ELL method, the error variances _σdci_<sup>2are firstly predicted through the so-called “Alpha model”,</sup> which uses a regression model in logistic form for the squared residuals _e_<sup>2in termsof possibly different covariates than those included</sup> _dci_<sup>,</sup> later in the regression of the welfare variable. Heteroscedasticity is likely to be needed in applications where the statistical units are households instead of individuals, due to the different households sizes. In general, before applying the Alpha model, it would be recommendable to check for evidences of heteroscedasticity in the particular data set using a formal test such as Wald test (Greene 2000, Section 12.5.3), and consider the Alpha model only when necessary. In any case, further research is needed on proper variable selection methods in models for heteroscedasticity within the SAE context.

5. _Machine learning techniques:_
are clustered at the school level. All figures are rounded to three decimal places.

_Source:_ Own survey data, school year 2015/2016, own calculations.

Table 4a shows that our intervention had a significant, positive impact on our S/E skills index on average (Column 1), with stronger impacts in the teacher-delivery treatment (about 13% SD) compared to student self-learning (about 6% SD). Disaggregating the index into the different sets of skills that are included, in particular deliberate practice beliefs and grit _versus_ all others, we find that, while all other skills do play a role in the teacher-delivery treatment (Column 2), deliberate practice beliefs and grit are clearly the driving forces, being twice as important in terms of effect size as all other skills (which turn out to be entirely insignificant when it comes to student self-learning (compare Columns 2 and 3).

Next, we turn to our main analysis in Table 4b. Panel A shows the impact of our intervention on our S/E skills index, on average (Column 1) and for different sub-groups defined by gender (Columns 2 and 3), ethnicity (Columns 4 to 7), grade (Columns 8 and 9), and tercile in the pre-treatment S/E skills distribution, whereby the first tercile is the lower and the third tercile the upper end (Columns 10 to 12).

When it comes to heterogeneous treatment effects by gender, the impact of both treatments is positive for girls and for boys, but they are larger for girls. Moreover, treatment effects are slightly larger for students of Macedonian ethnicity (the largest ethnic group in the country) than for students of Albanian ethnicity (the second largest group). They turn out especially large for Roma students (the most disadvantaged group), for whom the impact of the teacher-delivered treatment is about 42% SD (Column 6). The finding for Roma students should be taken with caution, though: in this heterogeneity analysis,
4 The 1992/93 was based on the sample design of 1987 which had 22 states. In each state a total of 120 EAs, 48 urban, 12 semi-urban, and 60 rural, were studied. These 120 EAs are allocated over the 12 survey months of the year covering 10 EAs in the ratio of 5:4:1 for rural, urban and semi-urban respectively in each state. Otherwise the sample was similar in design to the 1985/86 survey.

The Sample was weighted on the basis of the probability of selecting an EA within a state and choosing a household within an EA. A diary was given to each household to record flows of income to and expenditure from the household within the reference period or reference month2/. Field staff usually made daily visits to each household to ensure quality of entry and also to help those households who have no one literate to fill in the diary. This information was then transferred on a daily basis to the enumerator's memo book. The enumerator's book was then deposited in the field office where it was checked for quality and consistency by the supervisor.

The survey collected information on the following items on each household:

- (a) Household Demographic Statistics: age, gender, location and type of housing of the household.

- (b) Household Expenditure: Expenditure on all goods and services incurred by the household during the survey period. It also includes all monetary transactions (savings, donations and Esusu3/ contributions)

- (c) Imputed Rent on owner-occupied and rent-free dwelling

- (d) Cash Income: Income from wages, overtime, bonuses, professional fees that is received by the household.

# **3.** THE MEASUREMENT OF WELFARE AND POVERTY

# **3.1 Household Expenditure**

The measurement of household welfare or standard of living is a question which has not been resolved completely (Sen, 1987). There are many ways one could go about addressing this issue depending on the context, need and
NALO, respectively.

Next, multilevel linear regression analysis was used to explore the different factors that are associated with boys’ underperformance in Saudi Arabia as well as all students’ achievement in mathematics and science in grades 4 and 8 more generally. The multilevel linear regression models draw on the TIMSS 2019 data, with NALO 2018 data used to supplement the analysis by describing data that are not available in TIMSS. Four separate families of models were constructed: grade 4 mathematics, grade 4 science, grade 8 mathematics, and grade 8 science. All plausible values of achievement and sampling weights were used in all the analyses as per the relevant guidelines by von Davier et al. (2009) and Rutkowski et al. (2010), respectively. Assumptions necessary for conducting the multilevel linear regression analysis (i.e., linearity, homogeneity of variance, normality of errors) were checked and met, and parameters for the models were estimated using the maximum likelihood estimation with robust standard errors ( _SEs_ ) (Muthén and Muthén 2017). Multilevel linear regression analysis was performed using Mplus 8 software.

Along with mathematics and science achievement scores, variables included in the multilevel models are drawn from the student and home questionnaires (level 1 of the analysis) as well as the teacher and school principal questionnaires (level 2 of the analysis). Variables were selected for inclusion in the models _a priori_ , based on the previous literature on student achievement and the gender gap in education in particular, as discussed in section 1 of this paper, in addition to the expected theoretical or policy relevance of these variables to the question of gender differences in performance in the Saudi context. As far as possible, each model was constructed using the same set of variables, notwithstanding some slight differences arising from the selection of variables related specifically to mathematics or science instruction and some differences between the grade 4 and 8 questionnaires.

A hierarchical approach was followed, whereby conceptually similar variables were entered into each step of each model in blocks, as shown in
) it leads to inefficient entry.

An interesting result of the Edmond et al. (2018) framework calibrated using U.S. census data is that while competition reduces the markup of incumbents, it also reallocates market share toward the largest incumbent firms, leaving the net effect of markups unchanged.

We recast Edmond et al. (2015) in the domestic market as two sectors, ICT and nonICT and three types of firms: final good producers, traditional intermediate goods firms and ICT intermediate goods firms.

## **3.1 Final Goods Producers**

A single consumption good is produced in a perfectly competitive industry using inputs _y_ ( _s_ ) from a continuum of sectors: 6
temperature metrics perform the best. Based on R<sup>2</sup> and _p_ -values, mean and median daily temperature appear to be the best predictors of outcomes across many different cases. However, neither do particularly well in being consistently positively or negatively correlated with outcomes. Coefficients on both variables are as likely to be associated with better outcomes as worse outcomes. The different signs do not appear to be more or less likely in one country over another, suggesting the reason for the inconsistency is measurement error and not aggregation over regions, countries, or crops. While not as strong as mean or median in explaining variance in outcomes or correlating with outcomes, coefficients on variance of daily temperature are consistently negative, making it among our preferred measure for temperature. Somewhat surprisingly, given its frequency of use in the economics literature, GDD and deviations from GDD are poor metrics for temperature as measured by our heuristics. While GDD may be appropriate for predicting crop yields in temperate climates such as the United States, it appears to be a poor choice for use in the tropical regions of Sub-Saharan Africa.

Based on this evidence, we proceed to analyze measurement error in remote sensing products focusing on four rainfall metrics and three temperature metrics. These are mean rainfall, total rainfall, number of rainy days, share of rainy days, mean temperature, median temperature, and variance of temperature. Without additional evidence, these appear to be the best metrics for measuring precipitation and temperature. When researchers are considering how to include precipitation or temperature in their economic analysis (at least in Sub-Saharan Africa), we recommend they use one or more of the above seven metrics.<sup>22</sup> The choice of a different weather metric should be accompanied by a theoretical or agronomic justification and likely warrant robustness checks to ensure results are not driven by the choice of any one specific weather metric.

# **4.3 Remote Sensing Products**

By narrowing in on a single extraction method and a
selected among the high-income countries with a GNP per capita above $25,000 were the USA, Germany, Japan, France and Italy. The middle-income countries selected, with a GNP per capita of $5000 - $25,000, were Botswana, Brazil, South Korea, Poland, the Russian Federation and South Africa. From low-income countries with a GNP per capita below $5,000, we selected Bolivia, China, India, Indonesia, Kenya, Morocco and the Philippines. In making this selection we also sought to represent a diverse range of geographies in order to avoid focusing on a single region. While data on licenses and royalties received was available for all countries, it was not possible to get comprehensive data relating to all stocks of patents and trademarks in all of the selected countries. Despite this limitation, our findings suggest that global licensing revenues continue to grow. However, revenues remain unequally distributed across nations. Interestingly, the gap in licensing revenues is stronger than the gap in patent ownership. Though the growth in licensing revenues can be seen as an indicator of the greater efficiency of technology utilization, we still observe strong differences in the global distribution of licensing revenue.

In table 1 below (ranked according to license fees received), high-income countries are marked in violet, middle-income countries are marked in red and low-income countries are marked in black. The IMF data used in this classification is not ideal because it reflects licensing and royalty revenues received from a range of forms of intellectual property and intellectual capital (IC), such as patents, trademarks, business franchises and copyright. The figures do not indicate the proportion of revenue and payments attributable to patents alone. The share of patents in the licensing and royalty figures is unlikely to be homogeneous across all countries. This complicates cross-country comparison of licensing fees and patent application data. Balance of payment data for license fees can also be misleading because it captures many intra-firm transactions that are not specifically tied to knowledge creation and patent monetization. For example,
precisely the ages at which children are expected to be enrolled in primary in the country. It is therefore possible to identify the proportion actually in school to compute the net coverage of education services at this level. Six years later, in the year 2001, the same cohort can be identified again at ages 13-17, which is the age group expected to be attending secondary. Within the cohort, it is also possible to verify whether those ages 13-15 are actually enrolled in LS as expected, and if the older half of the cohort who are ages 16-17 are enrolled in US. With this information, net enrollment in both segments can be determined, as well as the share that is enrolled at a lower level (and therefore overage) and the share that is out of school.

The following sub-sections implement this approach in order to identify patterns of secondary, LS, and US dropout over time in Central America, with comparisons to the rest of the region.

# **1.3 The general context: Progress in education in Central America over six decades**

Taking a long-term regional view of the extent and speed of education progress helps to place the current dropout challenge in perspective. This is illustrated in Figure 2, which plots the average years of schooling of individuals belonging to different cohorts across all of Latin America, reported in the latest household survey available for each country (circa 2013). According to these estimations, individuals belonging to the cohort born in 1935-40, and who were ages 78-82 in the year of observation (that is, 2013), attained on average 4.7 years of schooling.<sup>6</sup> When comparing them to the average of 11.5 years of schooling attained by the cohort born 60 years later in 19901995, and who were ages 18-23 in 2013, we conclude that that there has been an increase of 6.8 years of average educational attainment across generations over the period.<sup>7</sup> This way of
# **Table A3: Correlation between Exports and other variables used** **<u>in the regressions as controls</u>**

|Variable|Exports (%, cell<br>average)|
|---|---|
|Exports (%, cell average)|1|
|No. of Workers (logs)|0.169|
|Herfindahl-Hirschman Index (HHI)|-0.105|
|Age of Firm (logs)|0.015|
|Foreign Ownership (proportion)|0.091|
|Female Ownership Y:1 N:0|0.067|
|Skills Obstacle (0-4)|0.029|
|How Much Of An Obstacle: Transport?|-0.040|
|Firm Purchased Fixed Assets Y:1 N:0|0.055|
|Power Outages (days)|-0.075|
|Courts Obstacle (cell average)|0.010|
|Crime Losses Y:1 N:0|-0.008|
|Firm Inspected Y:1 N:0|-0.038|
|How Much Of An Obstacle: Tax Rates|0.051|
|How Much Of An Obstacle: Labor|0.060|
|Regulations?||
|GDP per capita (logs)|0.176|
|Population (logs)|-0.184|
|Urbanization (%)|0.041|
|WBL Index|0.181|
|Women can work the same night hours as|0.067|
|men||
|Women can work in the same industries as|-0.016|
|men||
|Ratio of Female to Male Population|0.072|
|Fertility Rate|-0.217|
|Starting a Business (DTF)|0.058|
|Inflation(%)|-0.104|

55
-- Start of picture text --> Employment to Population Ratio comparisons<br>Avereage relative deviation of RIWI: 30.03%<br>90<br>45 deg line<br>80 Brazil<br>Indonesia<br>70<br>Kenya<br>Sri Lanka<br>60<br>Turkey<br>50<br>40<br>30<br>30 40 50 60 70 80 90<br>Comparative Data<br>RIWI<br><!-- End of picture text --> 24 The results for each country are presented as bar graphs in appendix figures A.4.6 to A.4.11. These figures also include comparative summary data from other nationally representative sources as line graphs whenever available. 25 We do not have enough information from the comparative data to test the statistical significance of employment measures between our online surveys and the comparative data.

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