rafmacalaba/datause-encoder-data huggingface dataset:
## _2. Y_<sup>_∗_</sup> _is complete for Z._

Completeness is common in the literature on nonparametric identification with instrumental variables (as in Newey and Powell, 2003), and is a weak regularity condition. Formally, completeness of _Y_<sup>_d_</sup> for _Y_<sup>_∗_</sup> is guaranteed if _E_ [ _h_ ( _Y_<sup>_d_</sup> ) _|Y_<sup>_∗_</sup> = _y_<sup>_∗_</sup> ] = 0 for all _y_<sup>_∗_</sup> only when _h_ ( _Y_<sup>_d_</sup> ) = 0. Intuitively, we require sufficient variability in the conditional distribution of _Y_<sup>_d_</sup> at different values of _Y_<sup>_∗_</sup> and, similarly, for the conditional distribution of _Y_<sup>_∗_</sup> given _Z_ . The latter requirement is a generalization of the rank condition for the instrument _Z_ to the non-parametric setting.

# **4.2 Empirical Specifications**

Assumptions 1-5 imply: and that there exists only one set of unobservable distributions on the right-hand side yielding the observable distribution on the left-hand side. In other words, under the assumptions stated, data on ( _Y_<sup>_d_</sup> _, Y_<sup>_r_</sup> _, Z_ ) are sufficient to retrieve the distribution of usual consumption: and the distributions of diary survey errors ( _fY d|Y ∗_ [ _y_<sup>_d_</sup> _|y_<sup>_∗_</sup> ]) and recall survey errors ( _fY r|Y ∗_ [ _y_<sup>_r_</sup> _|y_<sup>_∗_</sup> ]). This identification result does not hinge on any parametric assumption, and follows from adapting results in Hu and Schennach (2008) to the case of multiple (diary and recall) survey measurements.

We constructed _Z_ by matching each household to the _set_ of all other households in the _same_ income decile, filling out the diary in the _same_ survey wave and receiving visits from a _different_ team of interviewers. We defined _Z_ as average diary acquisition in this set, which is also an estimate of the average of _Y_<sup>_∗_</sup> in this set because of Assumption 1. The precision of this estimate depends on the sample size used to compute _Z_ for each household, which in our data is about 100 households. The idea then is to use the correlation between a household’s own consumption, _Y_<sup>_∗_</sup> , and the average consumption of similar peers, _Z_ , to learn about the properties of survey measurements. Considering peers in the same income decile and interviewed in the same wave strengthens the case for this correlation. The key exclusion restriction here is that variability in the average consumption of peers in different EAs (and thus visited by different interviewers) cannot explain a household’s diary and recall reports other than through her _Y_<sup>_∗_</sup> . Violations of this assumption due to interviewer effects are ruled out by defining _Z_ from averages of diary acquisitions supervised by non-overlapping survey teams.

We estimated the conditional densities on the right hand side of equation (4) using a flexible class of exponential families (Barron and Sheu, 1991). We let the density of log _Y_<sup>_r_</sup> given log _Y_<sup>_∗_</sup> depend on a vector of parameters **_β_**<sup>_r_</sup> as follows: 17
Do the parties rely on legal mechanisms to enforce their contracts or on alternative enforcement devices? If a contract dispute arises, does the contract stipulate an arbitration mechanism? Does the contract require "hostages" for deterring opportunistic behavior, such as collateral, and down payments? What devices do the parties involved in the exchange employ to economize on transaction costs? Are organizational forms and the scope of the firm affected by transaction costs considerations-for instance, because of contract enforcement problem, are family firms over-represented? Does the firm become more vertically-integrated to reduce contracting costs?

Bank surveys have collected a significant amount of data on these issues, especially in recent years. Four major surveys-RPED, ICS, Bosnia, and _Survey of Russian Enterprises-all_ gather detailed information on the type of relationships firms had and the devices used to regulate transactions. Together, these surveys yield a rich picture of the enviromnent in which firms operate. The following extracts from existing bank surveys may serve as nice recipes for future surveys on transaction environments:

- The nature of contract: Do contract specifications cover price, methods of adjusting price to rate of inflation, quantity, quality, delivery date, method of payment, warranty, penalty for late payment, penalties for breach of contract, and the method of resolution if a dispute arises? (RPED Survey and the survey of Russian Enterprises).

- The contractual relations with suppliers, with subcontractors, and with clients: ownership, payment method, frequency of purchase, relationship with the firm, whether the other party comes from the same ethnic group, whether there is a trade discount, whether there is subcontracting, the amount of credit purchase, interest, guarantee to supplier (none, physical collateral, third-party checks, witnesses, signed invoices, group guarantees); whether using written contracts more frequently with new trading partners than old trading partners; whether using written contracts more frequently with new private enterprises than other new trading partners? (The survey of Russian Enterprises).

- Informal lending: the amount, maturity, the existence and amount of collateral, and provisions in case of default? (RPED survey)

The data gathered through these surveys has allowed researchers to better understand the role of contracts and legal institutions on firm's performance. In particular, Hendley, Murrell, and Ryterman (1999) focus on the importance of laws and legal institutions for Russian enterprises 15
<!-- Start of picture text --> Crop and animal production, hunting and related...<br>Land transport and transport via pipelines<br>Wholesale trade, except of motor vehicles and...<br>Retail trade, except of motor vehicles and motorcycles<br>Electricity, gas, steam and air conditioning supply<br>Specialised construction activities<br>Manufacture of other non-metallic mineral products<br>Construction of buildings<br>Manufacture of basic metals<br>Manufacture of chemicals and chemical products<br>Food and beverage service activities<br>Wholesale and retail trade and repair of motor...<br>Human health activities<br>Manufacture of food products<br>Civil engineering<br>0 0.001 0.002 0.003 0.004<br><!-- End of picture text --> Source: Authors based on AECM survey; Eurostat.

**There are large differences in individual transition risk scores between CGSs.** For each individual CGS we calculate the sum of their transition risk scores for each sector _i_ . We find that within our NACE2 sample, the mean transition risk score is 0.03 with a standard deviation of 0.04. The highest scoring CGS has a transition risk score of 0.17 compared to 0.01 for the lowest scoring CGS. The highest scoring CGSs tend to be highly exposed to, and specialized in, crop and animal production. The lowest scoring CGSs have their highest exposures in retail and wholesale trade and financial service activities.

# _4.2 Physical risk vulnerability_

**Using the vulnerability data by Think Hazard, CGSs in Europe are exposed to a broad range of climate-related risks.** Our main results are provided in Figure 4. We find the main high-risk exposures to be for wildfire (56 percent of aggregate CGSs’ portfolios in our sample), coastal flood (41 percent), and river flood (6 percent). There are also some exposures to high landslide risk (1 percent). The high risk for wildfires is mainly driven by regions in Greece, Portugal, Spain and the United Kingdom, while the high risk for coastal flood is primarily driven by the United Kingdom. High risk for river flood is driven by regions in Germany, Hungary, Poland the United Kingdom.

Figure 4 – Exposures to climate-related physical risks in the CGSs’ portfolios (NUTS 2 sample, 2020) 13
# <u>Table A2: Descriptive Statistics</u>

||<br>Me<br>|<br>an<br>|Difference|Annual|
|---|---|---|---|---|
||1995|2010||Growth|
|Household Characteristics|||||
|Number of adults|3.82*|3.53**|-0.06||
||(2.07)|(1.73)|(0.10)||
|Number of workers|3.17*|2.80*|-0.77***||
||(1.77)|(1.45)|(0.08)||
|Share of adults|0.55***|0.61***|0.03***||
||(0.19)|(0.21)|(0.01)||
|Share of workers|0.47**|0.49**|-0.10***||
||(0.19)|(0.21)|$0.00||
|Number of agriculture workers|2.61<br>(1.85)|2.06<br>(1.62)|-0.97***<br>(0.08)||
|Share of agriculture workers|0.80***|0.70*|-0.13***||
||(0.31)|(0.40)|(0.01)||
|Number of non-agriculture workers|0.56|0.75|0.19***||
||(0.90)|(1.12)|(0.03)||
|Share of non-agriculture workers|0.2|0.3|0.13***||
||(0.31)|(0.40)|(0.01)||
|Share in total income (in percent)|||||
|Labor income|71.10***|69.93**|-5.36***||
||(25.02)|(28.98)|(0.72)||
|Non-labor income|28.9|30.07|5.36***||
||(25.02)|(28.98)|(0.72)||
|Capital income|23.23<br>(21.40)|13.69<br>(15.77)|0.87<br>(0.58)||
|Remittances|5.67|16.38|4.49***||
||(15.65)|(26.33)|(0.52)||
|Agriculture income|<br>48.35|<br>35.8|<br>-10.59***||
||(30.34)|(31.35)|(0.82)||
|Non-agriculture income|22.75|34.12|5.23***||
||(29.30)|(33.32)|(0.84)||

31
# **<mark>A1. Appendix 1 - Variable descriptions</mark>**

|Variable Name|Description|Number of observations|Source|
|---|---|---|---|
|GDP pc|Real GDP per capita<br>constant prices (2011 US<br>dollars)|Unbalanced panel from<br>1800 to 2018 (219 years) with<br>a minimum of 20 countries to<br>a maximum of 169|Maddison Project<br>Database, version 2020|
|Sovereign default|Binary variable taking<br>the value 1 if the country is in<br>sovereign default and 0<br>otherwise|Panel from 1800 to 2020<br>(221 years) of 195 countries|Farah Yacoub et al.<br>(2021)|
|Infant mortality|Number of children who<br>die before reaching their first<br>birthday per 1000 live births.<br>Some of the data points are<br>extrapolated using uses the<br>past trend in a country and the<br>global trend.|Unbalanced panel from<br>1960 to 2020 (61 years) with<br>a minimum of 79 countries to<br>a maximum of 199|United Nations Inter-<br>Agency Group for Child<br>Mortality Estimation|
|Population|Number of inhabitants<br>measured mid-year in<br>thousands|Unbalanced panel from<br>1900 to 2018 (119 years) with<br>a minimum of 53 countries to<br>a maximum of 169|Maddison Project<br>Database, version 2020|
|Life expectancy|Life expectancy at birth<br>in years for the total<br>population|Panel from 1960 to 2020<br>(61 years) of 208 countries|World Development<br>Indicators|
|Energy supply|Basic supply and demand<br>data for all fuels are required<br>to compare the contribution<br>that each fuel makes to the<br>economy and their<br>interrelationships through the<br>conversion of one fuel into<br>another.|Panel from 1971 to 2019<br>of 146 countries|OECD Economic,<br>Environmental and Social<br>Statistics, IEA World Energy<br>Statistics and Balances|

41
customs clearance in South Asia requires dedicated improvements administrative rules applied at the border and associated reform.<sup>20</sup> Figure 5 shows that it takes more than 8 days on average to clear customs by sea in South Asia, while it takes less than six days in East Asia (Figure 5).

Figure 5: Average Days Required for Customs Clearance by Sea <!-- Start of picture text --> Developed(16)<br>East Asia(9)<br>South Asia(4)<br>0 2 4 6 8 10<br>days<br><!-- End of picture text --> Source: World Bank (2004a) Note: Calculated from International Exhibition Logistics Associates data. Developing countries include Australia, Australia, Belgium, Denmark, Germany, Finland, France, Great Britain, Italy, Netherlands, Norway, Portugal, Sweden, U.S.A., Canada, and Japan. East Asia includes Hong Kong, Indonesia, Malaysia, China, Philippines, Singapore, Taiwan, Thailand, and Vietnam. South Asia includes Bangladesh, India, Pakistan, and Sri Lanka.

Countries in the South Asia region have moved over the past decade, however, to improve customs. For example, India has launched a modernization project in customs which includes leveraging Electronic Data Interchange (EDI) technology, which allows exchanging documents and forms electronically, to streamline clearances. With assistance from the World Bank, Pakistan has started reforms in the Central Board of Revenue including customs offices and is expected to have a revenue increase by Rs.65 billion of in the fiscal year 2004-5 (Rizvi, 2004). Pakistan has also introduced electronic filing of a single shipping document at Port Qasim as part of an effort by its customs service to streamline clearance and reduce transaction costs (World Bank, 2004a). In Bangladesh, the steps required for import and export clearance of fiber, fabric, and garments have been reduced by 75 percent (World Bank, 2004a). Afghanistan is working on customs modernization in a new $31 million World Bank project. Nepal is currently undertaking reforms under a Three Year Customs Reform and Modernization Action

> 20 Policy programs and action to reduce corruption in the region is also an issue which must be addressed within this context. In some countries, exporters have to pay informal payments not only to customs officers but also to police at check points along the routes and to cargo handlers at ports. In the South Asia region, there are examples of payments of 30 percent of invoice value for a consignment on the Bangladesh-Phulbari corridor through Kakarvita and of US$150 per consignment on imports and exports via Haldia (Subramanian and Arnold, 2001).

14
- United Nations Economic Commission for Europe. (UNECE). (2014). Global Assessment Report: National Statistical System of Mongolia. <u>https://www.unece.org/fileadmin/DAM/stats/documents/technical_coop/GA_Mongolia_EN. pdf.</u>

- United States Census Bureau. Tool for Assessing Statistical Capacity (TASC)—Version 2.0. -

- <u>https://www2.census.gov/software/tasc/tasc booklet.pdf</u>

World Bank. (2020a). Statistical Capacity Indicators <u>https://datatopics.worldbank.org/statisticalcapacity/</u>

- ---. (2020b). “ _The Human Capital Index 2020 Update: Human Capital in the Time of Covid-19_ .” -

- <u>https://www.worldbank.org/en/publication/human capital</u>

23
# **Figure 16. Methodological quality from youth and adolescents studies, by country. Andean Region; 2009**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Limited<br>50%<br>Intermediate<br>40% High<br>30%<br>20%<br>10%<br>0%<br>Peru n= 7 Ecuador n= 11 Bolivia n= 5<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** Figures 15 and 16 are very similar, which implies that in each of these countries the methodological quality is similar for studies done on the general population and for studies done on youth and adolescents.  In all three countries, the proportion of high methodological quality studies done on youth and adolescents is above the average. This is most likely due to the same reason why it is true for studies done on the general population: it is easier to access youth and adolescents than most-at-risk groups such as MSM, injection drug users, and female sex workers.

# **Comparison of methodological quality scores among population groups**

In order to analyze whether the difference in quality observed among the studies of the different population groups was statistically significant, ASAP divided the studies into two groups: (i) one group comprised of the studies done on the general population and youth; and (ii) a second group comprised of the studies done on MSM, female sex workers and people living with HIV. To analyze if the difference was statistically significant, ASAP applied the non32
Table 6. Placebo effects of COVID-19 exposure on educational outcomes

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Dropout_|||||
|Warning index x 2019|0.000|0.001|0.001|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
||[0.670]|[0.626]|[0.626]|[0.626]|
|Warning index x 2020|0.008|0.008|0.007|0.007|
||(0.002)***|(0.002)***|(0.002)***|(0.002)***|
||[0.003]|[0.002]|[0.001]|[0.002]|
|Pr(2019=2020)|0.000|0.000|0.000|0.000|
|Mean outcome|0.086|0.086|0.086|0.086|
|Adjusted R<sup>2</sup>|0.006|0.079|0.097|0.136|
|Observations|12,342,065|12,294,413|12,341,959|12,294,343|
|_B. Effective promotion_|||||
|Warning index x 2019|0.005|0.005|0.005|0.004|
||(0.002)**|(0.002)**|(0.002)**|(0.002)**|
||[0.043]|[0.046]|[0.039]|[0.046]|
|Warning index x 2020|-0.025|-0.025|-0.025|-0.025|
||(0.005)***|(0.005)***|(0.004)***|(0.004)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2019=2020)|0.000|0.000|0.000|0.000|
|Mean outcome|0.802|0.802|0.802|0.802|
|Adjusted R<sup>2</sup>|0.021|0.066|0.096|0.120|
|Observations|10,014,671|9,988,171|10,014,417|9,987,940|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

(Continues on next page) 27
areas with a high incidence of lightning strikes, and vice versa. In spite of the seemingly strong correlation between lightning intensity and mobile network penetration, the destructive effect of lightning activities is not limited to mobile infrastructure. Indeed they affect the diffusion of general technologies such as electrification and computers (Andersen et al., 2012), thereby constraining their direct use as instrumental variables in studying the effect of mobile penetration on economic growth, as the exclusion restriction assumption is likely to be violated. However, the negative association between lightning and mobile penetration provides a sense check to our main instrument: mobile network operators are more likely to extend coverage to places with low lightning intensity due to the relatively low cost of deployment compared to places with high lightning intensity. This suggests that the role of policy instruments in improving connectivity is expected to be more salient in places where (private) mobile network operators are less motivated to expand coverage due to the lightning-induced cost of network expansion.

Therefore, I construct an additional instrument, by interacting the main instrument (predicted regional mobile coverage rate) with quintiles of lightning intensity at the district level. The intuition is that the effect of regional policy harmonization on mobile penetration at the local level should be high in places where mobile operators would otherwise not extend mobile coverage due to the effect of lightning strikes, relative to places with low lightning strikes and hence lower cost of mobile network extension. Thus, even if individually, lightning intensity and possibly the predicted regional mobile penetration rate may not satisfy the requirements of a good instrument, their interactions may plausibly suffice. The corresponding first-stage equation can be expressed as: where 1( _Lightning q_ ) is an indicator variable equal to 1 if the lightning intensity in district _j_ is in the _q_<sup>_th_</sup> quintile and 0 otherwise. Again, to isolate the direct effect of lightning on economic development from confounding the relationship between the instrument and the outcome variable, I include the district-level lightning intensity interacted with time trends.<sup>30</sup> In this set-up, the main identifying assumption is that the differential effects of regional expansion in mobile network coverage across places with varying lightning intensity affect local economic development only through its effect on expansion in (local) mobile network coverage. Estimates are weighted by population. Robust standard errors are also clustered at the district level.

30The interaction with time trends is necessitated by the fact that the lightning intensity data is cross-sectional.

15
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# **Table A.4.6 Marginal Effects for Colombian Model.**

||**Product**<br>**innovation**|**Process**<br>**innovation**|**IPR**|
|---|---|---|---|
|**Firms characteristics**||||
|Firmsize (ln #employees)|**0.08**|**0.17**|**0.06**|
|Capital location|**0.06**|0.02|0.01|
|R&Ddepartment|**0.18**|**0.27**|**0.40**|
|**Human capital**||||
|Share ofemployee withtertiary|**0.35**|**0.21**|0.08|
|Share ofemployee withsecondary|**0.14**|0.07|0.07|
|**Collaboration on innovation**||||
|Universities|**0.44**|0.17|**0.51**|
|Researchcenters (private and public)|**0.58**|**0.41**|**0.57**|
|Suppliers|**0.50**|**0.69**|0.23|
|Internal|0.39|0.30|0.40|
|Other firms|0.20|0.09|0.12|
|**Training and Re-education**||||
|Innovationand development|**0.48**|**0.55**|**0.14**|
|Organizational|**0.26**|**0.22**|0.13|
|Management|-0.07|**_-0.13_**|-0.04|
|Observations|6212|6212|6212|

34
6 where  ≥0 is a coefficient. The closer  is to 0, the closer _I_<sup>3</sup> comes to capturing just the number of publications. (In this case, we have to add an arbitrarily small amount to _ci_ in order for uncited papers to count toward the publication count.) The closer  is to 1, the closer _I_<sup>3</sup> comes to capturing the total number of citations. With 0<  <1, _I_<sup>3</sup> is concave in _ci_ . With intermediate values of  , _I_<sup>3</sup> captures the idea that citations are associated with diminishing returns in producing scholarly influence but that additional citations always add something to influence (something that does not occur with the h-index). But we are also able to capture the idea that the number of publications of itself matters (something that _does_ happen with the h-index).

All three measures we have considered thus far speak to the _total_ influence of a publication portfolio. Particularly in the case of journals—but also perhaps with institutions and individual researchers—one might reasonably want to get a sense of _average_ influence. After all, there is often large variation between journals in the number of articles they publish per year, and in the number of years they have been publishing. Ravallion and Wagstaff (2011) introduced the _normalized h-index_ : a value of 20 means that 20% of articles have received at least 20 citations.<sup>4</sup> This measure adjusts for the scale of the publication portfolio. We report this for journals as well as for edited volumes.

# III. **DATA**

In this section we introduce our data, explaining our choice of database, how ―health economics‖ was defined, how we cleaned the ―metadata‖, and how we supplemented our metadata with citation and topic data.

# **Database choice**

A number of online bibliographic databases include articles (and in some cases other types of publication) on what could be construed as ―health economics‖. These include EconLit<sup>5</sup> ,

> 4  This should not be confused with the _h_ -index per publication, which is also called the ―normalized _h_ -index‖ in some of the literature (Alonso et al., 2009).

> 5 Details of EconLit coverage are to be found at http://www.aeaweb.org/econlit/doctypes.php.