rafmacalaba/datause-encoder-data huggingface dataset:
indicating flows' ability to predict returns. Using high-frequency data aggregated across types of market participant, Froot et al. **(1998)** find evidence of all three, with the firstpositive feedback trading--being the most important for explaining quarterly correlation. Choe et al. **(1999)** and Kim and Wei **(1999)** use high-frequency data from Korea to examine positive feedback trading around the **1997** currency crisis. Choe et al. find that foreign investors as a group engage in positive feedback trading before the crisis, but during the crisis feedback trading mostly disappears. Kim and Wei examine foreign institutional investors separately and find that they engage in positive feedback trading at all times-before, during, and after the crisis.

Our analysis is related to, and borrows from, both the intemational-finance and asset-pricing lines of the literature. Like the work in intemational finance, we are more concerned about international flows and crisis transmission than portfolio performance. Like work in asset pricing, however, we maintain a direct link to investment strategy and its measurement. In particular, we focus on a specific class of international investormutual funds. **A** benefit of focusing on a specific investor class is that we can characterize the evolution of actual portfolios, and how that evolution relates to returns in various countries. Another benefit is that our data allow us to analyze jointly the behavior of fund managers and their underlying investors. On the cost side, focusing on funds as a specific investor class means that we lose resolution in terms of data frequency: our data are quarterly.

> which requires the buyer to pay compensation in the form of a higher price (so-called "inventory

> effects"). The third is imperfect substitutability-the buyer's purchase may be a large enough portfolio shift relative to the market as a whole that permanently higher price is required to clear the market (even if it is common knowledge that the buyer does not have superior information about the security's payoffs).

**_5_**
9

# **Projections**

The World Bank calculates an official set of GDP projections for in its annual Global Economic Prospects. No such projections are available for GDP composition (share<sup>in</sup> agriculture and in manufacturing) so we took take the simplistic and admittedly not<sup>so</sup> satisfactory approach of keeping them at 2000 values. For urbanization and population we have UN projections. Thus we have projections for up to 113 countries representing about 90% of world GDP.

Data on access to infrastructure are available for the year 2000 for another 34 other countries. We therefore expand our set of projections by using projected infrastructure growth rates for the region/income group to which an individual country belongs, and applying them to the 2000 actual infrastructure stock. As a result, we have projections for up to 147 countries (water, sanitation and rail have poorer coverage.) Looking at the results of our projections, it appears that the largest increases in coverage will occur in telecommunications, particularly mobile phones (table 5.) Fixed density<sup>is</sup> expected to more than double in low income countries and nearly triple in middle income countries. As to mobile density, it is projected to quadruple or quintuple in developing countries. Even high income countries should see a steady growth of 3 or 4% per annum in mobile and fixed lines.

Electricity generating capacity and road density should increase by similar orders<sup>of</sup> magnitude (2.3 to 2.5% per annum) in MICs. In low income countries, instead, electricity is expected to increase by about 3.2% p.a. while road density should only rise by about 1.4% p.a.. Water and sanitation should increase by about 2 to 2.5% p.a. in LICs and about 1.5% p.a. in MICs. For high income countries, increases are expected to be much smaller, except for roads density.

**Table 5: Infrastructure stocks, 2000-2010**

|||Electricity|Teleco|mmunications||||||
|---|---|---|---|---|---|---|---|---|---|
|||Generation|Fixed|Mobile|Roads|Rail|Water|Sanitation|Total|
|LIC|2000|504|26|9|1,001|142|139|147|1,968|
||2010|665|67|47|1,143|146|168|182|2,417|
|_Annua_|_l Increase_|_3.2%_|_15.9%_|_40.1%_|_1.4%_|_0.3%_|_2.1%_|_2.4%_|_2.3%_|
|MIC|2000|2,019|134|154|1,177|295|178|239|4,194|
||2010|2,528|350|562|1,450|298|204|280|5,673|
|_Annua_|_lIncrease_|_2.5%_|_16.2%_|_26.6%_|_2.3%_|_0.1%_|_1.5%_|_1.7%_|_3.5%_|
|HIC|2000|3,527|213|337|3,951|364|152|261|8,804|
||2010|3,920|290|437|4,587|343|157|271|10,005|
|_Annua_|_lIncrease_|_1.1%_|_3.6%_|_3.0%_|_1.6%_|_-0.6%_|_0.4%_|_0.4%_|_1.4%_|

Units: Electricity Generation: kilowatts per hab; Telephone Mainlines: lines per 1000 hab. Paved<sup>Road</sup> Length lineal km by square km of surface Rail Road Length: km per 1000 hab. Mobile: subscribers per 1000 hab Water; % Households with access Sanitation % Households with access
crash is likely reported on Twitter). Therefore, these data can be used to target areas for design solutions where we are seeing high numbers of crashes consistently over time. In settings where there are limited or non-existent administrative records and, therefore, lack of any geolocated data, this tool can produce information in real-time for one of the most pressing challenges in developing countries.

Furthermore, by developing tools that generate time-stamped geolocated data and statistics from crowdsourcing on different “events” that are reported on social media, we can hope to expand data availability across other contexts and across issues beyond RTCs. For example, real-time traffic applications like RIDLR in India can be used to expand data on road safety. These improved tools can also help geolocate victims during a natural disaster or alert disaster management teams to the location of unsafe buildings or areas needing immediate attention. They can support law enforcement or communities to locate and respond to crimes, cases of violence against women, or police violence. Improved identification of the time and location of events can help to automate and accelerate policy response across a wide set of issues, potentially leading to better policy outcomes.

# **References**

1. Serajuddin U, Uematsu H, Wieser C, Yoshida N, Dabalen A. Data deprivation: Another deprivation to end. The World Bank. 2015;.

2. Notzon F, Nichols EK. Global Program for Civil Registration and Vital Statistics (CRVS) Improvement; 2015.

3. WHO. Global status report on road safety 2018. World Health Organization. 2018;.

4. IEAG. A World that Counts–Mobilising the Data Revolution for Sustainable Development. Independent Expert Advisory Group on a Data Revolution for Sustainable Development. 2014;.

5. GSMA Intelligence. The Mobile Economy 2020. London: GSM Association. 2020;.

6. Kemp S. Digital 2020: Global Digital Overview. Retrieved from Datareportal: https://datareportalcom/reports/digital-2020-global-digital-overview. 2020;.

7. Batty M. Big data, smart cities and city planning. Dialogues in human geography. 2013;3(3):274–279.

8. Miller G. Social scientists wade into the tweet stream. Science. 2011;333(6051):1814–1815.

9. Kitchin R. The real-time city? Big data and smart urbanism. GeoJournal. 2014;79(1):1–14.

10. Einav L, Levin J. Economics in the age of big data. Science. 2014;346(6210).

11
## **2.2 Political equilibrium**

While the capacity to tax is a necessary condition for collecting revenue, it cannot fully explain the differences in tax collection across countries. Ultimately, the stock of administrative capacity in a country can be thought of as the result of past decisions to invest in that capacity. As Di John (2006) describes, “the institutional capacity of states to mobilise resources had to be created.” These decisions are the result of the governance environment, or political equilibrium, which depends on the balance of power between different actors with different interests and incentives (World Bank, 2017). This can help to explain why even countries with similar economic structures have very different rates of tax collection (Mkandawire, 2010).

The actors with more bargaining power to directly influence policy decisions tend to be actors with higher income levels (as the distribution of wealth and resources is an important source of _de facto_ power). Thus, it would be unsurprising to find that in places with low contestability of power (high concentration of political influence) fiscal policy is also less progressive and tax laws are more easily evaded by the wealthy (Besley and Persson, 2014). Moreover, as C ́ardenas (2010) proposes, if those who are currently in power may benefit from future tax avoidance, they are likely to under-invest in fiscal capacity –particularly so if there is a high likelihood that a more progressive opposition government may be elected in the next term. However, more research is needed to understand the specific institutional channels which facilitate this process –for example, Ardanaz and Scartascini, 2011 look at how legislative malapportionment may have enabled this outcome in Latin America.

Conversely, as contestability of power increases –and elites face greater social and economic pressures from below– the elites’ tax contributions tend to increase (Boix, 2003). Moreover, these dynamics can be self-reinforcing, with greater economic equality giving way to more democratic systems. As Boix (2003) argues, “since the tax [that the holders of the most productive assets] will pay in a democratic regime finally becomes smaller than the costs of repression that they would have to bear to exclude the majority of the citizens, they accept the introduction of a system of universal suffrage” (Boix, 2003, p.10).

# **3 Arithmetics and politics of redistribution rules: A framework**

To develop a method to measure the potential DRM constraints related to taxable capacity, this paper looks at the _arithmetics_ of DRM from the perspective of the income of the potential tax base. It seeks to measure if that income would be sufficient to eliminate poverty using simple income redistribution rules and proposes a Poverty Eradication Capacity Index. It builds in particular on three recent efforts to quantity this capacity.

5
**Figure 4: Change in share of emissions by type of gas between 1990 and 2005.**

<!-- Start of picture text --> CO2 CH4 N2O<br>Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia & Herzegovina<br>Bulgaria<br>Croatia<br>Czech Republic<br>Estonia<br>Georgia<br>Hungary<br>Kazakhstan<br>Kyrgyzstan<br>Latvia<br>Lithuania Other gas<br>Macedonia, FYR<br>Moldova<br>Poland<br>Romania<br>Russian Federation<br>Serbia & Montenegro<br>Slovakia<br>Slovenia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>-100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> Source: WRI (2011) and authors‘ calculations.

60
20 Also, as discussed in the previous section, working women’s personal beliefs are strongly negative about the risks of working in the private sector (primarily from public sector workers) as shown in Column 9.

For male respondents, social empirical expectations are correlated for the working women and gender role themes (Columns 2 and 7), showing that men also know more working women, and other households who deviate from expected gender roles. Their expectations of female counterpart views are also positively significantly correlated with the gender role theme as shown in Column 7. This suggests that men may in some cases pay attention to the female counterpart’s views as well, alongside their own views.

The results taken together suggest that women who work and their counterparts tend to have somewhat different views and expectations across these thematic areas. When it comes to women working, in general, it appears that working women are more aware of others who work, but are also more likely to think that others will speak badly of working women. When topic areas such as gender roles (and the private sector as discussed above) it appears that along with what others do, individual perceptions and expectations of counterpart are different for working women, i.e. both societal expectations, as well as individual and household perceptions (or cultural beliefs in the household) are different. While these are derived from correlations, it still suggests that individuals and their counterparts in households with working women were amenable to changing these perceptions in the ways outlined above.

# **3.2.3 Job Seeker Effort: Persistence and Channels to Finding Work**

Job search intensity, including both persistence in looking for work, “intention-action” gaps (i.e. what people say they want to do, and what they actually do) and exploring additional channels to finding work are further barriers to finding work for women in KRI. We find evidence of these barriers through responses in the social norms sample, as well as an additional sample of job seekers registered at a job agency in KRI called Rwanga Foundation.

In an initial diagnostic exercise with 100 randomly selected job seekers with women who were registered with Rwanga, women who are currently working, had to on average look for a job for 7.5 months before they were able to successfully find a job. However, non-working women had stopped looking for work after 2.76 months. Working women respondents (N=175) in the social norm sample reported a similar number of an average of almost 9 months looking for jobs before they found their current job (the median was 3 months, and 62% had found jobs within 6 months).

We also observe “intention-action” gaps for non-working women who want to work in the social norms sample as well. For example, while 68% of non-working women said they want to work, only 20% of these women (who want to work) had either spoken to a friend or relative about work or looked for work in the past 6 months. Even fewer, 13.8%, had contacted a job
income, expenditures and employment. A community questionnaire was administered for the rural PSUs. The initial survey did not collect information on the precise geographic location of each cluster which would allow integration of survey data with other spatially referenced information. Instead, we took advantage of a highly detailed map of enumeration areas in Bangladesh to identify the location of the centroid of each survey PSU.<sup>16</sup> While the survey provided a large number of explanatory variables at individual, household and community levels, it lacked information on the agricultural endowment of survey locations. We derived this information from a spatial database of crop suitability that was constructed by UN-FAO and the Bangladesh Agricultural Research Council as part of a project to develop a database of agroecological zones (AEZs) in Bangladesh (FAO/BARC, 1998). The crop suitability index was developed by agricultural scientists by taking into account individual crop characteristics, input/management levels, the soil’s physical characteristics, hydrologic and climatic conditions, and seasonal variability.<sup>17</sup> In developing this index, attributes of each of Bangladesh’s 486 Thanas or districts are rated on a scale of 1 to 5 and the index is de fi ned for 48 di ff erent crops. In this analysis we use an average suitability of nine major cash crops.<sup>18</sup> We also check robustness of our result using an index of all crop suitability.<sup>19</sup> Out of about 39,000 individuals in HIES 2000, about 40 percent are children (less than 15 years of age). Another 32 percent reside in urban areas. This leaves a sample of 6,828 individuals residing in 248 PSUs who reported their primary occupation. The summary statistics for the variables that are included in the empirical analysis are reported in appendix Table A.1. Nonfarm activities account for about 48 percent of total employment in rural areas. Among all employed

> 16 This task was implemented by the Center for Environmental and Geographic Information Services in Dhaka, Bangladesh.

> 17 The crop suitability index is the result of the interactions of as many as eleven important soil factors together with the moisture and thermal regimes of the climatic factor. See BARC website for details.

> 18 These include jute, tea, sugarcane, cotton, potato, yam, nuts, hemp and berry.

> 19 This index rates the suitability of each location for cereal, pulses, vegetables, fruits and cash crop production.

13
# **Online Appendix**

## **Contents**

|**A NO**|2**: Context and Characteristics**|**S1**|
|---|---|---|
|A.1|OMI Details and Alternative Sources . . . . . . . . . . . . . . . . . . . . . .|. .<br>S1|
|A.2|US Downward Trend and Cycles . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>S1|
|A.3|World Geographic Heterogeneity. . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>S1|
|A.4|COVID-19 Lockdown Stringency and NO2 . . . . . . . . . . . . . . . . . . .|. .<br>S3|
|A.5|US Political Landscape and Collective Inaction<br>. . . . . . . . . . . . . . . .|. .<br>S4|
|**B Oth**|**er Results**|**S6**|
|B.1|Night Lights Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>S6|
|B.2|Results without Country Time Trend . . . . . . . . . . . . . . . . . . . . . .|. . S10|
|B.3|Alternative Country Grades . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . S12|
|B.4|2020 Misstatement<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . S12|
|**Refere**<br>**List **|**nces**<br> **of Figures**|**S15**|
|S1|NO2: United States.<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>S2|
|S2|NO2: World Concentrations of NO2 as of Year-End 2019. . . . . . . . . . . .|. .<br>S3|
|S3|NO2: World Diference Between First Half of 2019 and 2020. . . . . . . . . .|. .<br>S4|
|S4|COVID-19 Shutdowns and NO2 in Hubei Province and Northern Italy.<br>. . .|. .<br>S5|
|S5|COVID-19 Lockdown Stringency Index and NO2, by-Country. . . . . . . . .|. .<br>S6|
|S6|U.S. County-Level COVID-19 Change in NO2 and 2016 Election Results. . .|. .<br>S7|
|S7|Data Quality Grades Correspondence . . . . . . . . . . . . . . . . . . . . . .|. . S14|
|S8|Efect on Composite Estimate for 2020 when Normalized to C-Grade Weight|. . S18|
|**List **<br>S1|**of Tables**<br>Updated Data and Assumptions in Henderson et al. (2012) Baseline.<br>. . . .|. .<br>S9|
|S2|Efect of Extending DMSP-OLS Baseline with More Recent VIIRS Data. . .|. . S10|
|S3|Proxy: No Country Time Trend.. . . . . . . . . . . . . . . . . . . . . . . . .|. . S11|
|S4|Proxy: By-Country Data Quality Grade, No Country Time Trend. . . . . . .|. . S12|
|S5|Data Manipulation: Role of Country Time Trend. . . . . . . . . . . . . . . .|. . S13|
|S6|Growth in 2020 (%): Understatement.<br>. . . . . . . . . . . . . . . . . . . . .|. . S16|
|S7|Growth in 2020 (%): Overstatement. . . . . . . . . . . . . . . . . . . . . . .|. . S17|

i
Table 3. Explaining relative concentration between high-skill and low-skill and change in relative concentration <!-- Start of picture text --> Rel conc Rel conc ∆RC<br><!-- End of picture text -->

||Rel conc|Rel conc|∆RC|
|---|---|---|---|
||(OLS)|(Heck)|(Heck)|
|Lagged diasp|-0.502|-0.514|-0.008|
||(5.87)***|(9.67)***|(16.05)***|
|Col. links|-4.635|-4.619|-0.040|
||(4.68)***|(10.69)***|(9.93)***|
|Language|0.338|0.321|-0.004|
||(0.84)|(1.09)|(1.58)|
|Log(dist)|0.266|0.269|0.006|
||(1.24)|(1.69)*|(3.78)***|
|Schengen|-0.193|-0.180|0.002|
||(0.50)|(0.36)|(0.49)|
|Constant|5.607|-3.240|-0.037|
||(0.29)|(1.19)|(1.60)|
|Mills||-0.405|-0.873|
|||(1.07)|(2.44)**|
|Observations|3920|5730|5730|

<!-- Start of picture text --> (OLS) (Heck) (Heck)<br>Lagged diasp -0.502 -0.514 -0.008<br>(5.87)*** (9.67)*** (16.05)***<br>Col. links -4.635 -4.619 -0.040<br>(4.68)*** (10.69)*** (9.93)***<br>Language 0.338 0.321 -0.004<br>(0.84) (1.09) (1.58)<br>Log(dist) 0.266 0.269 0.006<br>(1.24) (1.69)* (3.78)***<br>Schengen -0.193 -0.180 0.002<br>(0.50) (0.36) (0.49)<br>Constant 5.607 -3.240 -0.037<br><!-- End of picture text --> <!-- Start of picture text --> (0.29) (1.19) (1.60)<br>Mills -0.405 -0.873<br>(1.07) (2.44)**<br>Observations 3920 5730 5730<br><!-- End of picture text --> Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 18
61 in the future, this argument is not watertight.<sup>125</sup> After all, the pension system chosen now should be designed for individuals who will be retirees many decades later.

Moreover, the long-term financial viability of the new pension system may be questioned. One reason is the combination of expected future changes in demography (the “graying” of the population) and the low pension age (formally 60 years for males and 55 for females, with an effective retirement age of only 55 for the former). This problem could, in principle, be solved through a higher effective retirement age, provided there is also a reasonably well functioning labor market for the elderly (which requires flexible wages and working hours).

A more specific Chinese problem is that contributions originally paid into the funded part of the system have, in fact, been used to finance deficits in the paygo part of the system – resulting in the “problem of empty individual accounts”. As a consequence, the funded part of the mandatory pension system so far looks more like a defined-contribution paygo system with “notional” rather than real accounts.<sup>126</sup> The Chinese authorities would have basically two alternative strategies to deal with the issue of “the empty accounts”. One would be to abandon the idea of funded individual accounts altogether and be satisfied with a paygo system (possibly with notional individual accounts based on previously paid contributions). The other alternative would be to “recapitalize” the funded accounts. This could, be done in different ways. One possibility would be tax-financed capital injections to the individual accounts. Another possibility, suggested by Pieter Bottelier (2002), could be to let the National Council of Social Security Fund take over the shares in a number of state firms, and instruct the Fund to sell the shares – at appropriate intervals to avoid strong negative effects on share prices. The sales revenues would then be deposited on the individual accounts in proportion to what each individual has paid (directly or indirectly through his/her employer). Two birds would then be killed in one shot: the funded system would be saved and the privatization of state-owned firms and assets would be speeded

> 125 Some of the reasons to expect that the household saving rate will fall in the future are changes in demography (fewer individuals in working age), slower per capita income growth and the build-up of mandatory social insurance – as predicted by the life-cycle saving model; see, for instance, Modigliani and Cao (2004). Another reason is that the liquidity constraints on individual households are likely to become weaker as domestic credit markets develop.

> 126 According to OECD (2005a, p.188), the return to individuals on these accounts has been no more than 2.4 percent per year. Some observers have hypothesized that the meager return in this system is an important explanation for the low participation as well as the evasion of paying contributions.