rafmacalaba/datause-encoder-data huggingface dataset:
2007a and 2007b).<sup>4</sup> But, surprisingly, there are very few studies on the cyclical behavior of gross capital flows. The literature has so far mostly focused on classifying episodes of abrupt reversals in capital inflows into those driven by foreign agents, or true sudden stops, and those driven by domestic agents, or episodes of capital flight (Faucette, Rothenberg, and Warnock, 2005, Cowan et al., 2008, Forbes and Warnock, 2011, and Rothenberg and Warnock, forthcoming). There are also a few studies that typically compare the behavior of some types of gross capital flows around specific events or in particular countries or assets (Frankel and Schmukler, 1996, Kim and Wei, 2002, Dvorak, 2003, Choe, Kho, and Stulz, 2005, and Milesi-Ferretti and Tille, 2010). Nevertheless, none of these studies provide a systematic cross-country analysis of the cyclical behavior of the different types of gross capital flows over the business cycle as well as during turbulent times.

Because of the limited research on gross capital flows, many important questions remain unanswered. For example, are periods in which foreign agents purchase domestic assets also periods in which domestic agents sell foreign assets? Is there a positive or negative correlation between capital flows by foreign and domestic agents? What is the behavior of gross capital flows over the business cycle and during financial crises? We know that crises are associated with reductions in net capital inflows. But are these reductions on average due to sales of domestic assets by foreign agents, purchases of foreign assets by domestic agents, or both? How large and how volatile are gross capital flows relative to net capital flows?

In this paper, we address the type of questions raised above by documenting a number of stylized facts about the dynamics of gross capital flows, which shed light on the behavior of domestic and foreign agents and the types of shocks underlying international capital movement.

> 4 These studies show that gross capital flows have on average been sizeable, which has resulted in large gross international investment positions.

3
sloping. This suggests that truckers set a lower price while going to a larger city and a higher price when coming back to the same origin but with a smaller market size.

**_Stylized Fact 3: Trucking freight rate per ton-km is negatively correlated with the economic density of a route._**

Is the freight rate lower if a route has higher densities at both ends? To check this, we add night light luminosity for origin and destination to define the thickness of each route, as two locations with higher GDP are expected to have higher trade flow as well. We then run the same regression as in column 1, this time with this measure of the thickness of a route replacing destination night light luminosity. The estimated coefficient of this economic density variable is negative and about 76 percent larger than that reported in column 1 (column 3). This correlation is robust to using the log of the number of trips between OD pairs as an indicator of the thickness of trade flow. Thus, the freight rate is lower not only when the destination is larger in terms of economic density but even more so when both origin and destination are large economically. 1 possible explanation for such a strong correlation is that larger economic density allows the adoption of better transport technology in terms of larger truck sizes which have a lower marginal cost of transporting goods. Another possibility is that thicker routes attract more competition reducing markups for truckers.

## **_Stylized Fact 4: Distance traveled is longer when the economic density of the origin is larger._**

Do large cities/origins have larger hinterlands with which they trade actively? To answer this question, we take the distance between OD pairs as the dependent variable and regress it on the log of night light density of origin while conditioning on destination fixed effects. The result reported in column 4 indicates a significant positive correlation implying a longer reach of origins with a larger size of the economy.

# 3.1 **Transport costs during the COVID-19 pandemic**

Our trips data set covers trips that took place during the COVID-19 national lockdown period (March 24May 30, 2021) and various localized lockdown periods that lasted till November 30, 2020. It also has information on trips during post-lockdown up to February 24, 2021. The pandemic period covered in our 10
households own a two-wheeler, while 8% own a car. These percentages are much lower than in Delhi, where 28% of households own two-wheelers and 13% own cars.

The lower rate of vehicle ownership in part reflects Mumbai’s extensive rail and bus systems. Mumbai is served by three rail lines, the Western, Central and Harbor, which carry 5 million passengers per day. All trains are electric, and therefore do not contribute to PM emissions in the Greater Mumbai Region (GMR). Mumbai’s municipal bus system, which carries 4.5 million passengers each day, is operated by the Brihanmumbai Electric Supply and Transport Undertaking (BEST). Of the 14,500 buses in Mumbai, approximately 3,400 are BEST buses. The remainder are school buses and buses that provide private commuting services. Taxis and auto-rickshaws (three-wheeled vehicles) comprise the remainder of Mumbai’s passenger transport system.

Data on vehicle kilometers traveled come from studies conducted by NEERI (2004) to construct a grid-wise emissions inventory for Mumbai, as do the emissions factors listed in Table 1. Estimates of VKTs for each grid cell and vehicle type were constructed from vehicle counts, obtained at different times of day, and estimates of gridwise road length. These, together with emissions factors, were used to estimate annual tons of PM10 emitted in each 2 km x 2 km grid in the city. The corresponding fraction of PM10 emissions accounted for by each vehicle class are obtained from the grid-wise emissions inventory. Table 1 indicates that, in 2002, diesel vehicles (buses and goods vehicles) contributed 76% of directly emitted PM10 from transport in Mumbai.<sup>7</sup> Private

> 7 According to Burningham (2005) BEST’s 3391 buses traveled approximately 240 million km in 20022003.  Applying NEERI’s  emission factor of 3 gm/km to this mileage yields 720 tons of PM10 emissions.

8
# **Table of Contents**

|**I.**<br>**Introduction**............................................................................................................... 3|
|---|
|**II.** **Literature Review and Empirical Strategy**............................................................. 6|
|**_An earnings model with an expanded measure of human capital_ ............................................... 6**|
|**_Expanding the measure of gender disadvantage_ ........................................................................ 15**|
|**_Gender earnings gaps, glass ceilings and sticky floors_ .............................................................. 18**|
|**III.** **Data Sources and Descriptive Statistics**................................................................ 22|
|**_Survey data on adults in middle-income countries_ ..................................................................... 22**|
|**_Country background and descriptive statistics_ ........................................................................... 23**|
|**IV.** **Results – Pooled Sample**......................................................................................... 33|
|**_Returns to schooling and skills_ .................................................................................................... 36**|
|**_Gender differences in returns to schooling and skills_ ................................................................ 37**|
|**_Gender differences in returns to schooling and skills along the earnings distribution_ ............ 38**|
|**V.**<br>**Country-Specific Results ................................................................................................ 42**|
|**_Gender differences in the returns to schooling_**........................................................................... 43|
|**_Gender differences in the returns to cognitive skills_**.................................................................. 45|
|**_Gender differences in the returns to noncognitive skills_**............................................................ 47|
|**VI.**<br>**Decomposing the Gender Earnings Gaps and the Role of Skills ................................ 51**|
|**_Estimating gender disadvantage: Constrained and unconstrained models_ ............................... 51**|
|**_The role of covariates, including skills, in closing the gender earnings gap_**............................. 53|
|**VII.** **Concluding Remarks**............................................................................................... 56|

2
intermediate manufactures (SITC 6) seems to provide more support for the models developed by Prussa (1988) and Staiger and Wolak (1990). For intermediat.es, prices only fell when investigations were followed by duties, while prices were maintained when the investigation failed to culminate in the imposition of a duty. As suggested by Prussa's (1988) theoretical model, it appears that importers which fail to curb price declines are "punished" with duties, while those which stabilize price levels are rewarded by zero duties. The types of products subject to LFV cases in SITC 6, such as metal and wood products, typically have a more concentrated market structure than those in SITC<sup>8</sup> (primarily garments). Since price collusion is likely to be easier in concentrated markets, it is not surprising that intermediates provide more support for the theoretical framework suggested in Prussa (1988), while garment exporters seem to follow more closely the noncooperative framework in this paper.

One of the implications of the model is that the coefficient on foreign costs (in dollar terms) should be equal to unity. When w/w* is excluded from the equation, the coefficient on log w* is significantly lower than unity, while if w/w* is included, the estimates on log w* range from 1.7 to 2.6. It is likely that inclusion of w/w* makes it difficult to estimate the coefficient on log w* precisely due to multicollinearity between the two variables. Although including w/w* or excluding it has no impact on the other variables of interest, it is disturbing that the estimate on log w* is so low when w/w* is excluded. One possible explanation for this low value is that we are not accounting for lagged effects. Exchange rate and cost changes may only slowly translate into changes in import prices, possibly due to contracts which make price adjustments sticky.

Table 6 re-estimates the import price equation under the assumption of a geometrically declining lag on all the ri&ht-hand side independent variables.

26
Research also frequently faces issues around motivation – not just for LGBTI respondents but for a useful comparison group. If respondents’ do not see the benefits of taking the time to respond, researchers need to apply different techniques to engage. Community involvement in all stages of the research can, therefore, be an important tool to reach the desired number of respondents.

# **6. The potential of big data in building LGBTI knowledge**

Surveys are only one tool to collect data on LGBTI people, in recent years so-called ‘big data’ has become an important source for data. The information that every user is providing by surfing the internet and using digital services can be a powerful tool to better understand any given population.

Social media platforms like Facebook, for example, give users the opportunity self-identify as LGBTI. According to the companies own <u>research from 2015</u> more than 6 million American’s have come out on Facebook. While this estimation is based on self-identification Facebook, like many other companies, collects much more data about their users. Peoples likes, the places they frequently visit and the post they write could all be used to develop a much more accurate estimation of how many LGBTI users the social network has.

According to an article in the New York Times from December 2013, 5% of all Google porn <u>searches</u> in the US is for gay porn. Looking at these (gay) porn searches around the world, Google data shows they range from 6% of all porn searches in Central America, South America, Europe, Australia, Pacific Islands and the Caribbean to 2.5% in Africa, Eastern Europe, and the Middle East. This data is useful, for instance in showing the simple but often contested fact that LGBT people exist around the world. Yet it does not allow much more substantial conclusions to be drawn, as for example, women search for gay porn as well.

Definitional issues remain a problem for big data like for any other source of data. Is Facebook’s self-identification enough or should other user data be used to identify those who do not publicly state they are LGBTI? Is someone LGBTI just because they search for gay or lesbian porn? While the underlying issues are similar, ‘big data’ does face its own unique challenges when used to better understand the LGBTI population. Nevertheless, it provides important additional sources of data, especially as Internet access becomes more widely available.<sup>63</sup>

> 63 One innovative new technique is random domain intercept technology whereby Internet users who incorrectly type in a domain name, are directed to a survey. In 2015, this technique was used by ILGA Global <u>for the first global online survey</u> of attitudes toward LGBTI people, which attracted over 96,000 responses and will be repeated each year.

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Table 11: Heterogeneous Effects - By Gender

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Dependent Variable:|Time-use<br>Index|Efort<br>Index|Self-discipline<br>Index|Confdence<br>Index|Aspiration<br>Index|Math<br>Z-score|English<br>Z-score|
||||Panel|A: Male Studen|ts|||
|Goal-Setting|0.079*|0.089|0.053|0.051|0.010|0.010|0.045|
||(0.047)|(0.062)|(0.049)|(0.047)|(0.047)|(0.087)|(0.069)|
|Goal-Setting + Recognition|0.071|0.055|0.020|0.031|-0.028|0.009|0.037|
||(0.055)|(0.067)|(0.052)|(0.050)|(0.052)|(0.096)|(0.067)|
|Observations|5,132|4,854|5,315|5,305|4,930|5,454|5,454|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|
||||Panel B|: Female Stude|nts|||
|Goal-Setting|0.130***|0.107**|0.115**|-0.010|0.020|0.102|0.089|
||(0.049)|(0.054)|(0.051)|(0.038)|(0.047)|(0.070)|(0.070)|
|Goal-Setting + Recognition|0.121**|0.046|0.106**|-0.041|-0.025|0.041|0.085|
||(0.051)|(0.059)|(0.048)|(0.049)|(0.045)|(0.053)|(0.059)|
|Observations|7,583|7,054|7,734|7,676|7,215|7,972|7,972|
|Baseline Outcome Controlled|Yes|No|No|No|Yes|Yes|Yes|

_Notes_ : This table reports the heterogeneity (by gender) of the impact of interventions on main outcomes of interest: Time-use Index, Effort Index, Self-Discipline Index, Confidence Index, Aspiration Index, Math test score, and English test score. Construction of these indices is discussed in Section 2. Panel A has the sample of only male students while Panel B reports the results for the sample of female students. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.
of time are available with cross-sectional surveys. Secondly, an important issue with the examination of income dynamics with genuine panel data is the extent to which non-random attrition biases the results. In particular, one may be concerned that individuals whose incomes suffer large falls or large increases may be much more likely to move houses and leave the sample. While there are structural approaches one can take to attempt to account for attrition (e.g. see Lokshin and Ravallion (2004)), the use of pseudo-panel data is not subject to these same attrition issues. Thirdly, the use of different individuals each period allows for very general forms of serial correlation in the individual measurement errors. The main disadvantage of the use of pseudo-panels instead of genuine panels for the purpose of identifying poverty traps is that while we can still allow for heterogeneity of the intercept at the individual level, we can only evaluate the condition in equation (4) for each cohort at the crossing point(s) determined by the cohort average intercept, rather than at a separate intercept for each individual. This enables us to determine whether the income dynamics of the average individual within a particular cohort give rise to a poverty trap.

# 3 Data

The data come from Mexico’s Encuesta Nacional de Empleo Urbano (ENEU), a quarterly urban employment survey collected by Mexico’s Instituto Nacional de Estadística, Geografía e Informática (INEGI) over the period from the first quarter of 1987 to the second quarter of 2001.<sup>9</sup> Over this period the ENEU expanded coverage from 16 cities in 1987 to 34 cities by the end of 1992 and 44 cities by the second quarter of 2001. We include all 39 cities present by the end of 1994, although our results are robust to restricting the sample to just the 16 cities present in all years. The survey is designed as a rotating panel, with households followed for five consecutive quarters. In addition to information on household demographics and education of all members, each individual aged 12 and above in the household is asked detailed questions about employment and

> 9 The survey was changed after this date, preventing the use of more recent data.

14
**Figure 7: Share of Workers Applying for Jobs in Only One Occupational Category by Main Occupation**

**Choice**

Source: Authors’ calculations using Babajob data.

Note: The analysis was conducted by excluding job seekers who applied for multiple jobs. Of 90,258 job seekers who submitted at least one job applications between January and December 2015, 88,098 (99%) submitted multiple applications.

The analysis also examined the relationship between the behaviors of job posting and application. Yamauchi et al. (forthcoming) applied time-series analysis to Babajob data to understand the relationship between the demand for and supply of workforce skills. The numbers of advertisements per firm and applications per job seeker were used to represent the demand and supply sides, respectively, and both reflect trends on the intensive margin. Focusing on business-process outsourcing (BPO) jobs advertised on a daily basis between 2012 and 2015 in Bangalore, the analysis found that job seekers responded quickly to job advertisements, suggesting that the supply of required skills, at least in terms of quantity, was not a binding constraint, and that the quantitative skills gap between the demand and supply for BPO jobs in Bangalore appears to be relatively small. Moreover, an increase in the number of job advertisements was found to have a surprisingly fast and significantly positive impact on the number of job applications received, with most job seekers applying for a posted position within one to two days.<sup>12</sup> The above results suggest that (i) job advertisement information circulates very quickly through the Babajob job portal and (ii) the supply of required skills is not a constraint for BPO jobs.<sup>13</sup> Such job seekers’ responsive behavior may reflect the success rate of their job search activity. The analysis also examined employer preferences regarding the timeframe for completing the hiring process. From employers’ point of view, while they wish to recruit the candidate whose qualifications and skill set best matches their selection criteria, they cannot search for this candidate indefinitely. As a result, employers

> 12 The demand for skills is not sensitive to application behavior primarily because of Babajob’s sequential system structure. Both series have serial correlations.

> 13 Other job categories show different patterns, and by comparing different job categories Yamauchi et al. (forthcoming) provide deeper insight into the supply and demand for workforce skills.

18
The paper incorporates foreign intervention in a model a la Esteban and Ray (2011). In the model external intervention affects the probability of winning of warring groups<sup>12</sup> and the resources available to them and therefore modifies the horizontal distributional measures. External interventions modify the effect of the distributional measures on the risk of conflict as they alter the balance of power among potential warring groups and therefore the incentives of groups to raise war-related resources. In other words, the revised model tells us that the equilibrium level of conflict depends on the distributional measures of inequality, fractionalization and polarization, modified to reflect the effect of military intervention. The theoretical specification does not indicate the direction and strength of the modifying effect– depending on the type of intervention and the presence of other interventions, it may increase or decrease the risk of conflict or it may have no effect on it.

The theory informs the format of the empirical model, which allows us to estimate empirically the direction and strength of the intervention and its modifying effect. We rely on the global International Military Intervention (IMI) data set for data on different types of external military interventions, the Peace Research Institute of Oslo (PRIO) data set for civil wars, and the databases on ethnic and religious fractionalization used by Montalvo and Reynal-Querol (2005). Our findings are consistent with the results in the literature (Montalvo and Reynal-Querol, 2005; Esteban, Mayoral and Ray, 2012) that ethnic polarization is a robust predictor of civil wars. In addition, we find robust evidence that religious polarization is positively and significantly associated with civil conflict in the presence of non-humanitarian and non-neutral foreign military interventions. Such external interventions exacerbate religious polarization, leading to high-

> 12 The extent to which probabilities shift remains unknown to opponents due to asymmetric information and incentives to dissemble, creating conditions for violence (Fearon 1995).

9