rafmacalaba/datause-encoder-data huggingface dataset:
effect is generally negative. Since many current biofuel crops are especially suited for cultivation in tropical areas, biofuel expansion could convert natural ecosystems in tropical countries that are biodiversity hotspots into feedstock plantations (CBD, 2008). Ogg (2008) points out that European biofuel subsidies are major drivers of rainforest loss in Indonesia, the source of the lowest cost vegetable oil (palm) in the world (FAPRI, 2008). The expansion of oil palm plantations, for example, which do not require much fertilizer or pesticide, can trigger the loss of rainforests and the biodiversity in them. Koh and Wilcove (2008) estimate that half of oil palm plantations expanded in Malaysia and Indonesia replaced natural forests. Similarly, more than 60% of Brazil’s sugarcane cultivation is done in the Mata Atlantica region, one of the foremost biodiversity hotspots in the world, and sugarcane and soybean production are contributing to the clearing of the Cerrado, the world’s most biodiverse savannah (USAID, 2009). Nelson and Robertson (2008) find that areas rich in bird species diversity could be at risk from the expansion of agricultural lands in Brazil. Large scale expansion of biofuels could result in loss of agro-biodiversity due to the intensification of agriculture through mono-cropping since most biofuel feedstock plantations rely on a single species (FAO, 2008a). The Royal Society (2008) highlights the vulnerability of grasses used as biofuel feedstocks, such as sugar cane, to new pests and diseases. These pests and diseases have the potential to destroy the crop and spread into natural habitats (Kartha, 2008).

Second-generation biofuels present another set of problems: some of the promising feedstocks are classified as invasive species, which will require proper management in order to avoid unintended consequences (FAO, 2008a). In addition, many of the enzymes necessary to process the feedstock would also have to be carefully contained in industrial production processes as they have been genetically modified to improve their efficiency (CFC, 2007). However, there is also some evidence to suggest that biodiversity can be enhanced and ecosystem functioning restored by biofuel feedstock cultivation when it consists of the introduction of new perennial mixed species to degraded or marginal areas (CBD, 2008). Tilman et al. (2006) use experimental data from test plots on degraded and abandoned soils to demonstrate that, compared to maize-ethanol or soybean-biodiesel, low-input high diversity mixtures of native grassland perennials yield higher net energy gains, greater GHG emission reductions and less agrichemical pollution, and that this performance is positively correlated to the number of species.

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demonstrated an effect on children from indoor exposure to gas stoves, the primary source of indoor NO2. However, effects from outdoor NO2 on either children or adults have rarely been foundt This may be due to (1) large errors related to measuring outdoor NO2 (since animal experiments indicate that very short peaks may be responsible for the adverse health outcomes); (2) the occurrence of effects only at the high levels of NO2 associated with gas stoves; or (3) the possibility that chronic, not acute, effects of NO2 are importanL However, at least one recent epidemiologic study has found health effects related to ambient NO2 .

In a reanalysis of the Los Angeles student nurses data descrnbed earlier, Schwartz and Zegefl found an association between N%2 and the increased likelihood of phlegm production. Daily concentrations of NO2 averaged approximately 0.13 ppm. Specifically, the results indicated: logit (respiratory symptoms) = 0.843 * NO2 (ppm) The standard error of the estimated regression coefficient was 0.343 Taling the derivatives, substiuting the mean incidence rate of phlegm of 0.0345 and analyzing, the following functions are obtained for adults, based on a average annual change in the 1-hour daily maximu:n Upper change in respiratory symptoms per year = 14.42 * change in N% (ppm) Central change in respiratory symptoms per year = 10.22 * change in NO2 (ppm) Lower change in respiratory symptoms per year = 6.02 * change in NO2 (ppm)

# 6. Carbon Monoxide

Carbon monoxide (CO) affects health by inLtrring with the transport of oxygen to the heart and other muscles and to brain tissues. When CO enters the respiratory system, it forms carboxyhemoglobin (COHb) and reduces the oxygen carrying capacity of the bloodc Individuals with ischemic heart disease or coronary artery disease, which is the leading cause of disability and death in
discusses the results of this exercise and assesses the usefulness of Google Trends data in forecasting price series for Central American countries. Section 6 concludes, noting several paths for continuing research. While this section deals specifically with ideas for future research there are notes about ongoing research throughout the paper.

# **2 Literature Review**

There is a growing literature that is using Internet search keyword data and Google Trends, in particular, for forecasting and nowcasting. Ettredge et al. [2005] were the first to use search engine keyword data to aid in forecasting. They found keywordbased searches to be helpful in predicting the number of unemployed workers in the United States. The use of Google Trends data, specifically, in forecasting yet to be released macroeconomic series goes back to Choi and Varian [2009, 2012]. They find that Google Trends data help to forecast initial unemployment claims, automobile sales, and consumer confidences in the United States. Since then, there are been numerous efforts to use Google Trends data in forecasting. Schmidt and Vosen [2012] uses search data related to the “cash for clunkers” program to improve forecasts for private consumption in France, Germany, Italy, and the United States. Guzman [2011] uses Google search data to estimate inflation expectations. Suhoy [2009] estimates accurate probabilities of downturn in early 2007 using Google search category data for Israel. The author also finds improvements in estimates of private consumption by employing the search data.

Early results on using Google Trends data as a proxy for consumer sentiment are promising. Traditionally, studies have made use of survey-based sentiment data to provide leading indicators of series of interest. However, this data is not always available, especially in developing countries. Vosen and Schmidt [2011] show that Google Trends outperforms The University of Michigan Consumer Sentiment Index and the Conference Board Consumer Confidence Index in predicting private consumption in the United States. One study which is very relevant to our present effort is that of Carri`ere-Swallow and Labb ́e [2013]. The authors look at the benefits of using Google Trends data in the context of a developing country, Chile. They develop an index of consumer interest in automobile purchaes and find that it outperforms benchmark specifications that take advantage of the IMACEC index of consumer activity. We will use a similar framework to the one employed in that study in what follows.

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close to the border (Tumen, 2016). Pre-treatment years include all years for which we have data and subscript _t_ is therefore defined as _t_ = (2009 _, ...,_ 2013). Two alternative treatment variables can be introduced. The binary treatment variable equals to 1 when a province is reported as having refugees by the UNHCR reports from late 2012 and 2013. The continuous treatment variable is simply the number of refugees reported. The latter is more likely to give stronger effects since the binary treatment is relatively imprecise when province level data are analyzed. Provinces like Malatya and Adiyaman have very few refugees compared to the other provinces hosting refugees, but still have value 1 for the treatment dummy variable.

One of the issues with the DD model is that the regions hosting refugees in southeastern Turkey are much less developed than the western portion of Turkey so that any choice with regard to the control group will be arbitrary. We complement the standard DD results by using the synthetic control method of Abadie et al. (2010), which explicitly recognizes uncertainty regarding the validity of the control group. We can therefore include all provinces outside of the treatment area in synthetic control estimations. The method then constructs a synthetic control unit from a combination of the provinces in the control group that most resemble the treatment provinces in the pre-treatment period. Rather than giving equal weights to all provinces as in the standard DD model, the synthetic control method gives more weight on provinces that have outcomes more similar to the treatment provinces. The basic estimation procedure is presented in 5 where _Y_ is the mean outcome in treated provinces. Since we cannot differentiate between provinces that receive refugees first in 2012 or in 2013, we define all provinces that have refugees in either year as the treatment group. The 71 provinces from the control group are weighted using the vector _W_ = _∥w_ 2 _, ..., w_ 1+71 _∥_ where _∥w_ 2 + _..._ + _w_ 1+71 = 1 _∥_ .<sup>5</sup> 10
# **_<u>The micro module: linking household surveys to the CGE model</u>_**

Poverty effects of trade reforms are estimated using a top-down approach. Initially the CGE model calculates the new equilibrium (i.e., new relative prices and quantities for factors and commodities) following a trade shock. Then prices are transferred to the micro module to estimate a new income distribution and poverty effects are calculated. No feedback from the micro module to the macro model is explicitly accounted for in this version. The following equation<sup>9</sup> represents the core of the micro module: where the relative gains or losses ( _W_ represents welfare) for each household ( _h_ ) depend on: 1) changes in prices for purchased goods ( _pg_ , where a dot represents percentage change) and the initial share of expenditure on each good ( θ _hc_ , _g_ ); 2) changes in factor returns ( _w_ stands for returns to skilled and unskilled labor, and _π_ is returns to capital) and the shares of total initial income by source ( θ _h_ l and θ _hkap_ ); 3) remittances and other transfers which depend on the wage rate and the government revenues. Income by source is calculated for each member of the household, and the above equation, to keep notation simple, shows results after aggregating incomes for each individual in the same household. Once the changes in welfare are calculated, a new distribution of income is generated and this counterfactual distribution is then compared to the initial distribution.

The main advantage of this approach is that it takes into account important sources of heterogeneity across households given that the structure of income by type and the composition of consumption by commodity, the various _θ_ ’s in the above equation, are household specific. A large literature on trade and poverty<sup>10</sup> has shown that changes in the distribution of income (or consumption) might differ considerably across different

> 9 The formal derivation of this equation is presented in the Annex to this paper.

> 10 See Winters et al (2004) for an excellent survey.

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10

# **1.4 Health Consequences of Gender-Based Violence**

A growing body of epidemiological evidence documents the consequences of GBV for women’s health and well-being, ranging from fatal outcomes such as homicide, suicide and AIDS-related deaths to non-fatal outcomes such as physical injuries, chronic pain syndrome, gastrointestinal disorders, and unintended pregnancies and sexually-transmitted infections— the latter two largely as a result of being less able to able to negotiate family planning or condom use (Gazmararian et al., 1995; Heise, Ellsberg et al. 1999; Garcia Moreno, 2002).

Physical and sexual violence has consequences for women’s mental health, such as posttraumatic stress syndrome, depression, anxiety, and low self-esteem, as well as behavioral outcomes such as such as alcohol and drug abuse, and sexual risk-taking and a higher risk of subsequent victimization. It has become increasingly clear that injuries—previously considered the most common outcome of violence—represent only the tip of the iceberg in terms of negative health effects, and that violence is more appropriately conceptualized as a risk factor for health problems than as a health condition in itself. (See table 1.3 for a summary of the health consequences of intimate partner violence and sexual violence by any perpetrator.) .

# **Table 1.3. Health consequences of intimate partner violence and sexual violence by any perpetrator**

|**Fatal outcomes**||**Nonfatal outcomes**||
|---|---|---|---|
||Physical injuries and<br>chronic conditions|Sexual and reproductive<br>sequelae|Psychological and<br>behavioral outcomes|
|Femicide<br>Suicide<br>AIDS-related<br>mortality<br>Maternal<br>mortality|Fractures<br>Abdominal/thoracic<br>injuries<br>Chronic pain<br>syndromes<br>Fibromyalgia<br>Permanent disability<br>Gastrointestinal<br>disorders<br>Irritable bowel<br>syndrome<br>Lacerations and<br>abrasions<br>Ocular damage|Gynecological<br>disorders<br>Pelvic Inflammatory<br>disease<br>Sexually-transmitted<br>infections, including<br>HIV<br>Unwanted pregnancy<br>Pregnancy<br>complications<br>Miscarriage / low birth<br>weight<br>Sexual dysfunction<br>Unsafe abortion|Depression and<br>anxiety<br>Eating and sleep<br>disorders<br>Drug and alcohol<br>abuse<br>Phobias and panel<br>disorder<br>Poor self-esteem<br>Post-traumatic stress<br>disorder<br>Psychosomatic<br>disorders<br>Self harm<br>Unsafe sexual<br>behavior|

**Source:** Adapted from Heise, Ellsberg and Gottemoeller (1999).
# **2.2 FactSet data on firm-to-firm relationships**

We use data on firm relationships from FactSet, a private company specializing in market intelligence on firm networks, including inter-firm and intra-firm linkages. FactSet collates data from various sources, including annual reports, investor presentations, press releases, and company websites. Among FactSet’s offerings, the FactSet Fundamentals dataset provides consolidated financial statements and firm information for publicly traded companies, while FactSet Relationships dataset covers supply chain relationships, including suppliers, customers, and partnerships, as well as firm-to-affiliate relations.

The FactSet Revere Supply Chain Relationships database is recognized as one of the most comprehensive sources for global firm supply chain information (Huang et al., 2023). This dataset integrates information from official firm filings (10-K, 10-Q, and 8-K reports submitted to the SEC), investor presentations, press releases, and company websites (FactSet, 2021).<sup>4</sup> FactSet analysts normalize relationship types by cross-referencing a firm’s records with its partners’ (”reverse” links), providing a comprehensive view of supply chain linkages compared to other databases like Compustat Segment (Huang et al., 2023).

As of March 2024, FactSet actively monitored over 54,000 ”source” companies, resulting in a dataset of more than 2 million relationship links involving approximately 360,000 firms. Each link includes information on start and end dates, with historical data available from 2003 and global coverage starting in 2014, expanding to Latin America in 2016.<sup>5</sup> The database specifies the nature of each relationship. For our study, we focus on buyer, supplier, and innovation partner relationships, supplemented with information on ownership ties and other firm characteristics. At any given time, we observe the parent and ultimate parent firm of a company, and any subsidiaries, if applicable.<sup>6</sup> The dataset also includes firm name, industry affiliation, headquarters country, and entity type, which we use in our analysis. Annual sales data, converted to constant 2020 U.S. dollars, are available but not used in our study.

We combine the two aforementioned FactSet data modules to construct a final dataset of 169,420 firms with bilateral relationship information from 2012 to 2022. These are defined as de jure corporate institutions, with 27% (46,000 firms) having available information from FactSet Fundamentals for at least one year within the sample period.

4In financial reporting, a 10-Q is a company’s quarterly report with unaudited financial statements, while the 10-K is an annual report with audited financials. An 8-K updates shareholders on significant unscheduled events. U.S. listed firms must detail any customer accounting for more than 10% of revenue (Gofman and Wu, 2022), with some firms voluntarily disclosing additional customers (Huang et al., 2023).

> 5Many Latin American records from 2014 suggest backward updating for these firms.

> 6The parent or ultimate parent is identical to the company itself for self-owned firms.

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half of the questionnaires are filled out in the first and second communities, respectively. The supervisor is in charge of filling out the community questionnaire, while the anthropometrist weighs and measures the height of all household members and fills out the price questionnaire. After the first week's work is finished in the first community, the team takes the halfcompleted household questionnaires to the data entry operator, who is located in the regional capital. The data from those half-completed questionnaires are then entered on diskettes using personal computers (PC's) which are programmed to detect inconsistencies and ceding errors in the data. Before the team returns to the first community at the beginning of the third week, they pick up computer printouts from the data entry operator and use them to correct any inconsistencies or errors from the first week's work by going over the questions again with the households during the second visit. This greatly increases data accuracy. After a four week period has ended, the teams have a one week break, during which they often return to the capital city and bring the computer diskettes with the "fresh" data.

The quality of LSMS survey data is further enhanced by heavy supervision at all levels, most of the work of the supervisor is to check the work of the other team members. Every household questionnaire is checked by the supervisor both before and after data entry, and the work by the data entry operator and the anthropometrist is also constantly being checked by him. The supervisor also visits some of the households after interviewers have left to see if they performed their work correctLy and were polite to the respondents. In addition, higher level officials make unannounced visits to the teams in the field to inspect their work, including that of the supervisor. Team members whose work is deficient are replaced by standbys who
# **Appendix Table 2b.  Decision-making: Person who usually decides how to spend husband’s earnings**

||||Respond|Share of re<br>ent alone|spondents (|R) and h<br>Jo|usband/partners<br>int|(H) reporti<br>H|ng:<br>usband/ p|artner alone|
|---|---|---|---|---|---|---|---|---|---|---|
|||R|H|Diff|R|H|Diff|R|H|Diff|
|Benin|2011|0.16|0.03|0.13***|0.25|0.09|0.16***|0.59|0.88|-0.29***|
|Burkina Faso|2010|0.02|0.04|-0.02***|0.05|0.12|-0.07***|0.93|0.83|0.10***|
|Burundi|2010|0.01|0.02|-0.01**|0.64|0.55|0.09***|0.34|0.42|-0.08***|
|Cameroon|2011|0.08|0.05|0.03***|0.35|0.35|0.00|0.56|0.60|-0.04***|
|Comoros|2012|0.16|0.13|0.03**|0.31|0.39|-0.08***|0.51|0.48|0.03|
|Cote d’Ivoire|2011|0.06|0.02|0.04***|0.18|0.25|-0.07***|0.76|0.72|0.04**|
|Congo, Dem. Rep.|2013|0.07|0.07|0.00|0.49|0.49|0.00|0.43|0.44|0.01|
|Ethiopia|2011|0.05|0.03|0.02***|0.67|0.73|-0.06***|0.27|0.23|0.04***|
|Gambia|2013|0.05|0.01|0.04***|0.16|0.25|-0.09***|0.77|0.73|0.04**|
|Liberia|2013|0.08|0.12|-0.04***|0.66|0.68|-0.02|0.23|0.20|0.02|
|Mali|2012|0.09|0.04|0.05***|0.04|0.03|0.01**|0.87|0.92|-0.05***|
|Mozambique|2011|0.10|0.05|0.05***|0.42|0.42|0.00|0.45|0.52|-0.07***|
|Namibia|2013|0.13|0.11|0.02|0.57|0.65|-0.08***|0.24|0.24|0.00|
|Nigeria|2013|0.03|0.09|-0.06***|0.24|0.16|0.08***|0.71|0.74|-0.03***|
|Rwanda|2010|0.03|0.03|0.00|0.68|0.75|-0.07***|0.29|0.23|0.07***|
|Senegal|2014|0.02|0.001|0.019***|0.14|0.14|0.00|0.82|0.85|-0.04***|
|Sierra Leone|2013|0.05|0.13|-0.08***|0.38|0.34|0.04***|0.54|0.53|0.01|
|Uganda|2011|0.06|0.05|0.01|0.41|0.57|-0.16***|0.52|0.39|0.13***|
|<br>Zambia|2013|0.08|0.07|0.01***|0.58|0.65|-0.07***|0.31|0.28|0.03***|
|Zimbabwe|2011|0.11|0.06|0.05***|0.74|0.84|-0.10***|0.11|0.10|0.01**|

Notes: (1) Source: Demographic and Health Surveys (Waves 5 and 6) for Sub-Saharan Africa. Diff = T-tests for whether differences in respondent’s (R) and husband’s (H) responses were statistically significant. *** p<0.01, ** p<0.05, * p<0.1 58
- (iv) The investment data only exist up to 1998 which is before the major recent downturn. An obvious issue is whether countries with better regulatory frameworks have had a smaller fall in private investment  following the post1999 downturns.

Pargal reports estimates from two fixed effect regressions to explain the determinants of private investment levels. The equations are pooled across all 5 sectors and all 9 countries in her sample. The first set of estimates covers all years and the relevant regulatory variable is a simple 0/1 dummy for each sector, country or year on whether or not a regulator existed (in legal terms).

The second set of estimates reports the effect of a variety of 5 major regulatory governance features:

- 1) Regulator inside or Outside the Ministry 2) Appointment of Regulatory Commissioners by Executive alone or with involvement of Legislature

- 3) Number of regulatory commissioners (Note not regulatory _staff_ numbers.) 4) Regulator funded by central government 5) Rate or return or price cap regulation.

The equations are estimated on the pooled sample but only for the years after the regulatory body for the country and sector was (legally) established.

Finally, regressions for the 9 countries with the single regulatory variable were estimated separately for private investment in each of the five sectors.

The main result is that the variable for legislation to open up the sector consistently dominated the regulatory variables. This is true both for the pooled regressions and the individual sector (including the electricity industry) regressions. Indeed, the simple regulatory variable never had a t-value greater than 1. The question is whether or not this is a robust finding.

We have pointed out above that the ascription of the relative effects of regulation and liberalisation is very difficult – particularly if they are both measured by legislative indicators and the change was in the same law. This is confirmed empirically. Pargal notes in a footnote that the coefficient on the regulatory variable had higher t-values when the liberalisation variable was omitted<sup>48</sup> . Unfortunately, we are not told the results of that regression nor is there any discussion of this issue in the regressions by sector. In addition, we note that the estimates of the regulatory variables may be downwards biased because of the errors-in-variables from the (unavoidable) absence of any data on regulatory practice.

The results for the individual regulatory variables in the pooled regressions are also strange and, in some cases counter-intuitive. Only the coefficients on 1), 2) and 4) are significant at the 10% level or above. The coefficient on funding is significant at the 5% level and the right sign (ie licence fee funding is associated with higher private investment, presumably because this increases the effectiveness of the regulatory

> 48 See Pargal, op cit, footnote 7, p.17.

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