rafmacalaba/data-use-mentions-extended huggingface dataset:
Defining skilled workers as those with some college (complete or incomplete) and the unskilled as those without any college education, the corresponding skilled-unskilled wage premium is given by: where _βit_ is the coefficient associated to educational level _i_ in the Mincer equation at time _t_ , and γ _ik_ = _Ei_ / _Ek_ is the share of employment of the individuals with the educational level _i_ in the employment of input _k_ in a base period.<sup>21</sup> Analogously, the high school-dropout wage premium or differential can be obtained as: To sum up, the remuneration for each type of work (skilled and unskilled) is obtained as a weighted average of the returns to education of each educational level, where the weights are given by the proportion of individuals with the corresponding educational level, and the wage premiums are obtained as the ratio of these remunerations (in logs). These two variables are based on six educational levels included in the Mincer regression: complete college ( _c.coll_ ), incomplete college ( _i.coll_ ), complete secondary ( _c.sec_ ), incomplete secondary ( _i.sec_ ), complete primary ( _c.pri_ ) and incomplete primary, the excluded category.

# **_B3.Construction of relative supplies_**

The relative supplies are also computed from individual microdata from the sixteen countries in our sample. We use the working age population as a basis for the relative labor supplies, although the trends described in this paper are qualitatively the same if instead of using the population we construct our measures of relative supply, as in Manacorda et al. (2010), based on the labor force, those in employment or hours of work.

The relative supplies represent a weighted sum of individuals in each of the three skill categories defined above. Following Katz and Murphy (1992) and Goldin and Katz (2007), we first compute _efficiency units_ for each country dividing the population into 24 groups (cells) according to their gender, educational level and potential experience.
# **1. Introduction**

While women are beginning to get ahead of men in selective countries in a few areas, such as college admission, in both high-paying and leadership jobs, women continue to lag significantly behind (Goldin et al. 2006; Blau and Kahn 2017). For instance, women remain severely under-represented both among corporate directors (Bilimoria and Piderit 1994, Westphal and Stern 2006, 2007) and conductors of symphony orchestras (Goldin and Rouse 2000). Among the largest U.S. firms (i.e., the Standard & Poor’s 1,500 firms) in the 1990s, only 2.5 percent of top executives were women, and these female executives tended to serve in the smaller of these large firms (Bertrand and Hallock 2001). Things have improved only slightly since then: in a large sample of privately held and publicly listed firms, about 9.4 percent were found to have female CEOs (Faccio et al. 2014). Women’s share in businesses ranges from 20 to 40 percent in the United States and European countries, and much lower in many developing countries (Klapper and Parker 2010). Based on the World Bank Enterprise Survey data, which we use in this paper, in some countries the share of firms led by women approaches zero.

Why do women not fare as well in labor markets as men in general and in top-level jobs in particular? Some attribute the gender disparity to differences in human capital and discrimination, which results in lower pay and responsibility (Polachek 1981, Goldin and Rouse 2000; Black and Strahan 2001). A new literature considers the roles played by psychological and non-cognitive traits such as self-confidence, risk aversion, acceptance of competition, and inter-personal skills, which are extensively surveyed in Gneezy et al. (2009), Bertrand (2011), and Blau and Kahn (2017). This new literature comes largely from laboratory and field experiments (Gneezy et al. 2009), with subjects
repeat it, or drop out of their level. A share of labor-force-age students exiting from the school system enters the labor force in the segment that matches their educational background. Similarly, a share of the non-school population (perhaps drop outs at an earlier age) that each year reaches labor force age enters the labor force, typically in its least educated segment.

# _Database_

A typical MAMS application requires an extensive data set for the application base-year – a social accounting matrix (SAM); stocks for production factors (including different types of labor and capital), population, and school enrollment; indicators for selected MDGs and the educational system – as well as a set of elasticities (for production, consumption, trade, and human development relationships), and projections into the future (for growth in GDP at factor cost and the evolution of disaggregated MDG and education indicators and determinants), to which the MAMS baseline simulation is calibrated. (See Appendix 1 for more details.) The database for the current application was designed in light of data availability and the analytical objective of shedding light on the impact of changes in world food prices on MDGs and other major social and economic indicators in LICs. Two database variants were constructed: a LIC net food importer with trade shares that characterize a LIC that is relatively dependent on imports for its food consumption; and a LIC net food exporter that represents a country that has a relatively strong reliance on food exports. In other respects, including initial macro structure and HD, the starting points for the two archetypes are identical, facilitating comparisons between the two.

As shown in Table 2.2, the database is disaggregated into 14 production activities, each of which produces one commodity (good or service), as well as one commodity without domestic production (refined petroleum). Among the sectors, government production is represented by 8 services, covering human development, infrastructure and other areas. The factors of production are split into land (for the agricultural activities) and different types
districts, and 249 of its 322 Divisional Secretariat Divisions (DSDs).<sup>21</sup> The DSD identi...er in the HIES (2002) allows us to examine the behavior of wages at a more disaggregated geographical level. From the 16,924 households in the survey, about 25,886 individuals participated in the labor force. Our sample consists of adults (age 21 to 65 years) who are labor force participants in the rural subsample consisting of 243 DSDs. The HIES 2002 has complete employment, wage and other information for 22,323 individuals in this age range. Our estimation is based on the rural sample consisting of 12363 individuals. In addition to employment and wages, the survey collected information on education, age, gender, ethnicity and religion. The HIES 2002, however, has only limited information on farming (farm size and income only).

A key piece of information for our analysis is the amount of land under LDO restrictions in a DSD. We draw this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The geographic information including travel time from surveyed DSDs to major urban centers with population of 100 thousand or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing di¤erent travel speed on di¤erent types of roads.

A critical variable for our instrumental variables analysis is the historical district level malaria prevalence rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district.

Sri Lankan provinces di¤er considerably in
household head irrespective of their residency status at the time of the survey.<sup>13</sup> This is especially important in a comparative study such as ours, because cross-country comparisons based on coresident samples can lead to wrong conclusions (see Emran, Greene, and Shilpi (2018) on Bangladesh and contrast, all of the papers on intergenerational educational mobility we are aware of use a linear-in-levels estimating equation. This is partly motivated by the fact that, in many developing countries, 20-40 percent of fathers have zero schooling. However, we are not aware of any published work on developing countries that derives the estimating equation from a theoretical model.

> 11This deserves especial attention because the focus in much of the existing literature has been on the slope-based measures of mobility such as the intergenerational regression coefficient (IGRC) and intergenerational correlation (IGC).

> 12For example, widely used surveys such as LSMS and DHS collect information only on the coresident children.

> 13 Although some surveys collect limited information on the non-resident parents of the household head and spouse, we are aware of only a few surveys that include all children of the household head.

3
Table 13 Summary statistics for selected comparable variables used to impute consumption, by visit of the 2018/19 GHS

||2018/19 GHS post-<br>planting (%)|2018/19 GHS post-<br>harvesting (%)|Difference<br>(percentage<br>points)|
|---|---|---|---|
|North-Central|14.08|14.07|-0.01|
|North-East|8.29|8.27|-0.02|
|North-West|19.50|19.53|0.03|
|South-East|13.20|13.07|-0.13|
|South-South|17.91|17.88|-0.03|
|Gender|80.43|79.90|-0.53|
|Dependency Ratio|42.88|43.30|0.42|
|Employed: Non-farm|42.64|36.90|-5.74|
|Employed: Waged|16.24|18.17|1.93|
|Main cooking fuel: Wood|60.52|60.62|0.10|
|Main flooring: Cement|71.37|71.42|0.05|
|Fresh Fish|15.07|16.34|1.27|
|Imported Rice|46.92|47.34|0.42|
|Beef|42.37|45.44|3.07|
|No Toilet|29.09|28.97|-0.12|
|Recharge Cards|84.24|85.88|1.64|
|Air Conditioner|1.69|1.66|-0.03|
|Washing Machine|1.98|1.95|-0.03|
|Cars and other vehicles|10
alike. As beginning a new job requires learning processes, technology and culture of the new workplace, a new employee will not reach peak productivity in a position immediately upon being hired. An employer may be less likely to consider hiring a worker who is close to mandatory retirement age simply because there is not sufficient time to earn a return on initial training and start-up costs relative to a younger worker. In the US, where many workers start to leave the workforce upon eligibility for social security benefits, difficulties finding new employment among older displaced workers are well-documented. Research using the US Health and Retirement Study (US-HRS), for example, has demonstrated that of workers who lose jobs after age 55, only 60 and 55 percent of men and women are employed again within two years, while 80 percent of non-displaced workers are employed (Chan and Stevens, 2001).

In China, and to some extent Indonesia and Korea, a mandatory retirement age for some occupations and types of employers is even more binding than social security eligibility in the US. Differences in mandatory retirement ages of men and women in China may have a significant impact on how employers view the relative returns to hiring male and female employees who are in their 40s and older.<sup>12</sup> In Korea and Indonesia, retirement ages are not mandated by the law, but employment laws allow for firms to set mandatory ages, and government employees and civil servants face mandatory retirement. Explicit rationales for mandatory retirement provisions and legal support for them in Korea is based recognition that firms have long promoted worker allegiance by paying employees below their marginal product early in their careers and rewarding them with a premium after significant tenure. As workers later in their careers earn significantly more than their reservation wages and thus have a disincentive to retire, mandatory retirement provisions implemented by firms and the government civil service provide a means of guaranteeing that less productive older workers will eventually leave the firm, and facilitate promotions for younger employees.<sup>13</
Asian Development Bank.

- Anderson, Mary Ann, James E. Austin, Joe D.Wray, and Marian F. Zeitlin. 1981. _Nutrition Interventions in Developing Countries, Study 1: Supplementary Feeding_ . Cambridge MA: Oelgeschlager, Gunn and Hain.

- Beaton, George, and Hossein Ghassemi. 1982. Supplementary feeding programs for young children in developing countries, _American Journal of Clinical Nutrition_ 34 (supplement):864-916.

- Behrman, Jere R. and John Hoddinott. 2001. Program Evaluation with Unobserved Heterogeneity, Selective Implementation and Imperfectly Targeted Beneficiaries: the Mexican _Progresa_ impact on child nutrition. Paper presented at the Northeast Universities Development Consortium (NEUDC) Conference, September 28-30, 2001, Boston.

- Behrman, Jere R., Harold Alderman, and John Hoddinott. 2004. Hunger and Malnutrition, Copenhagen Consensus Challenge Paper.

- (http://www.copenhagenconsensus.com/Files/Filer/CC/Papers/Hunger_and_Malnutrition_07 0504.pdf)

- Berggren G. Hebert J. Waternaux C. 1985. “Comparison of Haitian children in a nutrition intervention program with children in the Haitian National Nutrition Survey. “ _Bulletin of the World Health Organization_ 63:1141- 1150.

15
aggregated emissions (across types of vehicles) from the road transportation subsector (while preserving the breakdowns in types of fuels and GHG emissions). The emissions are calculated in general as a multiplication of fuel consumption as available from EPE’s Brazilian Energy Balance (BEN) with adjustments in vehicle usage and emission factors as published by the MCTI. The emission factors are fuel specific and those for GHG gases other than CO2 take process/activity and technology into additional consideration. SEEG allocates national emissions to subnational levels including to all 27 FUs based on actual fuel consumption for the transportation sector. Our collection starts from 2006 when data is almost complete for all FUs and fuel/vehicle types till 2021. To our knowledge, this is the most comprehensive dataset on Brazil’s GHG emissions in terms of time periods and level of disaggregation.

# V. Empirical Strategy

We focus on the relationship between fuel prices and emission intensity. Fuel prices are subject to policy intervention, such as through fuel/carbon taxes and state-owned fuel producers. To remove the effect of inflation, we will analyze deflated/real fuel prices. Emission Intensity, defined here as aggregate emissions in CO2 equivalent from road transportation divided by aggregate real income of an administrative region, could be a target of the government that represents a balanced view of economic development and climate mitigation. This emission intensity measure also serves to scale the gross emission values so that it is more comparable across our units of analysis and the heteroskedasticity of the residuals would be reduced. Based on literature and our assumptions, the emission intensity should respond to fuel prices, such as through reducing fuel consumption and substitution effects across fuels due to price differences. Given the complexity of Brazil’s fuel markets, the primary question this paper will answer is how effective fuel prices are in reducing emission intensity in Brazil. We will analyze both the effect of the general fuel price level and price differentials between fuels on emission intensity. Additionally, we will also understand the evolution and heterogeneity of emission intensity in
_CHAPTER 4. AREA LEVEL MODELS_ 16 regression and _xd_ 2 _, . . . , xdp_ are the population means of _p −_ 1 auxiliary variables, obtained from census data.

Note that the true indicators _δd_ are not available and hence model (4.1), as it is, cannot be fit. However, direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> , _d_ = 1 _, . . . , D_ , are assumed to be available from the unit-level data in a (current) survey, where the areas may be identified. Traditional indicators of interest are the area means _δd_ = _Y_<sup> ̄</sup> _d_ , for which basic direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> and corresponding design variances var _π_ ( _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> ), are described in Chapter 2. Note that the direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> are subject to sampling error, which might be substantial, since they are based on the _nd_ observations from area _d_ , which is supposed to be too small for certain areas. Fay and Herriot (1979) proposed to account for the (important) sampling errors of the direct estimators _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> of _δd_ , by considering the following model, known as _sampling model_ , in the second stage: where _ed_ , _d_ = 1 _, . . . , D_ , are supposed to be independent, and independent of the area effects _ud_ , following _ed ∼ N_ (0 _, ψd_ ), with variances _ψd_ = var _π_ ( _δ_<sup>ˆ</sup> _d_<sup>_DIR_</sup> ), _d_ = 1 _, . . . ,
9

# **2.2.1 Key sources of NTMdata**

Data on non-tariff measures come from three types of sources. First, there are efforts by international organizations, as well as other non-government institutions, to directly collect data on NTMs. As discussed above, a widely used data source is TRAINS, which has information on non-tariff measures by country and sector for HS6 product lines (i.e. a six digit sub-heading in the Harmonized System (HS) classification) and for selected years.<sup>5</sup> These data are the most complete source of comparable information on NTMs across countries. However, they do have some well recognized limitations. For instance, these data do not provide an assessment of stringency or distinguish measures that are discriminatory from those that are not. Moreover, the coverage is partial and focuses only on a subset of the chapters comprising the classification presented in Table 1, mostly chapters A to F. Another widely used institutional source is the Temporary Trade Barriers (TTB) database by the World Bank, which contains information on a number of contingent trade measures for approximately 30 countries that can date back to 1980 and be at the product level.<sup>6</sup> The obvious limitation is that the database only contains information on a narrow set of NTMs.<sup>7</sup> A second source of information is through various forms of government notifications, which are mostly collected by the WTO. These notifications can be direct or indirect. First of all, WTO members are subject to several notification requirements of trade-related measures under the GATT and the TBT and SPS Agreements (direct notifications). In addition, governments can raise Specific Trade Concerns (STC) on TBT and SPS measures that are adopted by other WTO members (indirect notifications). Both sources of information have pros and cons. Direct and indirect notifications are more consistently available for a longer time period relative to data collected by international organizations. However, direct notifications
quality assurance exercises performed. For example, the ethnic-name database can be applied to foreign patents registered with the USPTO. The ethnic-name database assigns ethnicities to 98% of foreign records. Moreover, estimated inventor compositions are quite reasonable– for example, approximately 90% of inventors filing from Chinese countries and regions are classified as ethnically Chinese.

The developed approach, in combination with the interactions embedded in model Mit<sup>kout-</sup> lined in the main text, has the subtler advantage of allowing the construction of instruments for many more countries and their ancestry groups than what one can directly identify through inventor names, either due to lack of names for some countries (e.g., Ethiopia, Oman) or due to extensive name overlap among countries within an ethnic group (e.g., most of Latin America sharing Hispanic names). The instrument also has only non-zero predicted changes for country-industries due to its summation over cities and the connection to Anglo-Saxon technology development in cities.

The Costinot et al. (2012) model dictates the inclusion of importer-industry-year fixed effects δ<sup>k</sup> jt<sup>inthefirst-differencedspecificationforstructuralreasonssuchascountrypreferences.Two</sup> other rationales exist for having at least industry-year fixed effects due to the data development depicted. First, the US technology frontier is taken as exogenous in the IV model, and the identification of the θ parameter should thus be independent of the pace of US technology expansion in different industries. A second methodological rationale stems from the US patent process. US patent grants have increased dramatically since the early 1980s. While several factors lie behind this increase, it is clear that USPTO grant rates grew faster than the underlying growth of US scientific personnel and innovation can explain. Moreover, differences in grant rates exist across industries. The fixed effects account for these secular changes in the underlying patenting productivity.<sup>1</sup>

# IV Extended OLS Empirical Results

Table S3 considers several extensions of Table 1 to characterize heterogeneity in the
2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable.

# **Challenges**

Liberia is not currently connected to the SAT3 submarine cable that skirts the coast of West Africa or to the backbone networks of neighboring countries with submarine cable access. As a result, internet access is only available through small scale VSAT systems. Service costs $140 per month, which is about double the average for other African countries lacking access to a submarine cable. Compared to other developing regions, the cost is about ten times as high.

Given the imminent arrival of several new submarine cables along the West African coast, Liberia is likely to develop a connection in the near future and is considering various options. The experience of other African countries suggests that achieving cable access can substantially reduce the costs of internet and international connectivity. Nevertheless, the extent to which these cost savings are passed on to consumers depends critically on the regulatory environment. In other words, countries that preserve a monopoly on access to the international gateway will experience a more modest price reduction than those that ensure competition between multiple international gateway providers.

**Table 11. High international call charges driven both by technology and market power**

|US$|**Percent of**<br>**cases**|**Call within**<br>**region**|**Call to U.S.**|**Internet**<br>**dial-up**|**Internet**<br>**ADSL**|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0
where the fetus did not survive to birth.

> 62 If anything, according to mothers’ statements in DHS surveys, the bias would slightly favour girls. For instance, the reported share of unintended pregnancies is 23.02% when the baby is a girl, and 23.4% when it is a boy.

> 63 This is around the 18th-20th week (second trimester), and prenatal ultrasounds are common and widely accessed in Colombia.

19