rafmacalaba/datause-encoder-data huggingface dataset:
terms of the volume and variability of capital ‡ows, so that cross-sectional heteroskedasticity in particular is a concern. Below we use the HAC covariance estimator of Andrews and Monahan (1992) to estimate �<sup>^</sup> from the residuals of a ...rst-round estimation.

The above discussion assumes that the numbers of global and group factors are known a priori, which in practice is rarely the case. Following Choi and Jeong (2017), we determine the appropriate number of factors using the ICp2, BIC; and HQ criteria, as adapted to the multi-level setting by Choi et al (2017).<sup>8</sup>

# 3 Data

To study the global and group patterns of capital ‡ows, we assemble a large cross-country dataset drawing from the International Monetary Fund’s Balance of Payments Statistics (BoP). After dropping countries with incomplete data and very small economies, we end up with a balanced panel of 85 countries covering the years 1979-2015, with a total of 3,145 observations.<sup>9</sup> We classify the countries into three groups: advanced (19 countries), emerging (28) and developing (38). The list of countries and their grouping are given in Table A1 in the appendix.

Following Broner et al (2013), we construct two measures of capital ‡ows from the BoP data: i. Capital in‡ows by foreign agents (CIF): the sum of direct investment in the reporting economy, portfolio investment liabilities, and other investment liabilities.

ii. Capital out‡ows by domestic agents (COD): the sum of direct investment abroad, portfolio investment assets, other investment assets, and international reserve assets.

These measures of ‡ows relate to the assets and liabilities of the reporting country’s residents vis-a-vis non-residents. CIF is recorded as capital in‡ows to the reporting economy by foreign agents, with a positive entry indicating an increase in foreigners’ holdings of domestic assets. Similarly, COD records ‡ows from the reporting economy, with a positive value denoting an increase in the holdings of foreign assets by domestic agents. Hence a positive COD represents a capital out‡ow by domestic agents, while a negative COD means capital repatriation.

> 8We standardize the data prior to estimation subtracting the country-speci...c mean and dividing by the country-speci...c standard deviation, as recommended by Choi and Jeong (2017).

> 9 Speci...cally, we download capital ‡ows data from 1945 to 2015 for 196 countries. The data is heavily unbalanced, with some countries possessing very few observations. We construct a balanced panel comprising 98 countries with complete data from 1979 to 2015. We exclude from this sample 13 countries with population fewer than 500,000 in 2005. In addition to dropping very small countries, this also has the e¤ect of removing from the dataset several o¤shore ...nancial centers and tax havens that display an extremely high volume of ...nancial ‡ows. We are left with 85 countries.

8
**_Energy demand models for policy_** **_<u>formulation</u>_**

country transport policy context. This turns out to be the main issue with the econometric analysis of transport energy demand.

# **End-use approach**

While the econometric approach has focused on the elasticities of fuel demand and other variables that influence fuel demand, the end-use approach has focused on forecasting demand by capturing the diversity of transport modes, types of vehicles, efficiency and other drivers. The usual disaggregation of the transport sector is shown in table 4.

Table 4: Disaggregation of the transport sector in end-use studies

|Need|Modes|Vehicles|Fuel use|
|---|---|---|---|
|Public<br>passenger|Road|Taxis<br>Minibuses|Gasolene, Diesel, LPG, CNG<br>Diesel, CNG|
|transport||Urban buses<br>Intercity buses<br>Others|Diesel, CNG<br>Diesel<br>Gasolene, Diesel, LPG, CNG|
||Rail|Tramways, tube rails <br>light rails<br>Commuter trains<br>Intercity trains|electric<br>electric<br>coal, diesel, electric<br>coal, diesel, electric|
||Dom. Air<br>Dom water||Jet fuel<br>Fuel oil, gasolene,|
|Private<br>passenger<br>transport|Road|Motorcycles<br>Cars||
|Freight<br>transport|Road<br>Rail<br>Dom water|Pick-ups<br>Light trucks<br>Heavy trucks<br> Barges, ships|Diesel<br>Diesel<br>Diesel<br>coal, diesel, electric<br>Fuel oil, gasolene,|

Source: This study.

It is a conventional practice for the transport sector’s energy demand analysis to divide the sector into passenger and freight transports. The determinants of energy demand and the units of measurement of outputs are different in these two types of transport activities. On a macro or national level, energy consumption for passenger transport depends on the number of passengers traveling, the frequency and average length of trips, the distribution of trips among various modes of transport (i.e. air, sea, rail, road) and the technical 66
5

# Table 1. Foreign Commercial Banks in DeveloRing Country Markets

|Country and date|Number of<br>foreign banks|Percentage share in<br>total bank assets|
|---|---|---|
|Argentina- 1987|32|20|
|Bangladesh- 1986|7|6|
|Bolivia-<br>1987|3|3|
|Botswana- 1986/1987|3|100|
|Brazil-<br>1987|17|6|
|Chile-<br>1987|21|17|
|Colombia|8|6|
|Indonesia- 1986/1987|10|6|
|Kenya- 1986/1987|11|37|
|Malaysia-<br>1988|16|25|
|Nepal- 1988*|3|6|
|Nigeria- 1987/1988*|15|74|
|Pakistan- 1987/1988|20|12|
|Peru - 1987|5|2|
|Philippines- 1986/1987|4|19|
|Senegal- 1986/1987|2|4|
|Thailand- 1988|14|4|
|Tunisia-<br>1985|7|1|
|Turkey-<br>1986/1987|18|4|
|Uruguay- 1987|8|10|
|Venezuela-<br>1987|3|1|
|Median|8|6|

* Joint Venture Foreign Banks Source: World Bank Reports.
**Figure 6.9: Average Private Sector Participation Index by country grouping as of 2015**

|Percentage of countries<br>with/Private Sector|No PSP|Generation<br>PSP Index|Distribution PSP<br>Index|Aggregate PSP<br>Index|
|---|---|---|---|---|
|Participation Index|||||
|**By incomegroup**<br>|||||
|LIC|37*|32|16**|24*|
|LMIC|21*|34|35**|34*|
|UMIC|0*|50|43**|47*|
|**By power system size**|||||
|<1GW|45*|29|17*|23|
|1‐5GW|20*|41|22*|31|
|5‐10GW|20*|47|33*|40|
|10‐20GW|11*|32|19*|26|
|>20GW|15*|31|49*|40|
|**By political economy**<br>|||||
|Dominant|26|37*|18|27|
|Intermediate|24|33*|26|30|
|Competitive|22|35*|32|34|
|**By rule of law**|||||
|High rule of law|31|39|38|38|
|Low rule of law|30|32|21|27|
|**By fragility status**|||||
|FCS|30|39*|24|31|
|Non‐FCS|29|27*|23|25|
|**By oil exporting status**<br>|||||
|Importer|25|33|27|35|
|Exporter|15|44|37|35|
|**Total**|**31**|**33**|**24**|**19**|

Source: Own elaboration based on RISE 2015, Platts and PPI 1990‐2015 databases

****** Differences are statistically significant at the 5% level

***** Differences are statistically significant at the 10% level

In nearly half of all 20 countries that introduced private sector participation, at least 50 percent of their generation and distribution segments spent less than 5 years on the process (Figure 6.10). Another 40 percent of countries took between 5‐10 years while only 15 percent took longer than 10 years. The time lapse involved in fully implementing private sector participation seems to be considerably shorter than that found for market competition.

37
addressed the possibilities of widening the tax base and rationalizing tax rates. These papers favor the GST as relatively easier to collect and more amenable to tax reforms than changes to direct taxation. Among such studies are Refaqat, S. (2003), Ahmed, V. and O’Donoque, C. (2007), and Ahmed, S. et al. (2010).

# **III. Modeling Tax Evasion**

Pakistan has severe problems with tax evasion. In particular, apart from having a poor tax collection effort, Pakistan has had a steadily declining tax to GDP ratio. In recent years, tax collection has come down from 10.9 percent of GDP in 2003 to around 9 percent of GDP in 2012. Widespread evasion is reported; for example, only 2 percent of the working age population pays personal income taxes (Kleven and Waseem, 2013). Additionally, a study in 2011 found that of the 46,000 registered firms, only around 24,000 filed corporate income tax returns.<sup>5</sup> Thus Pakistan provides a case study of a country in which there are both high degrees of tax evasion, as well as a steadily declining tax effort.<sup>6</sup> We confine our analysis of tax evasion to business’s evasion of the corporate income tax. There are, of course, many other forms of tax evasion. There might be evasion of the personal income tax, as well as under-invoicing of the sales tax. We will emphasize the corporate income tax since it is generally the most problematic source of tax revenue. The personal income tax is typically withheld at the source, making it relatively difficult to evade. Sales taxes are also not simple to evade, since doing so requires falsification of receipts, as well as corresponding

> 5 This study was carried out by the International Center for Public Policy, Andrew Young School of Policy Studies in 2011 to develop a micro-simulation model for the Federal Board of Revenue, Pakistan.

> 6 We should be clear that we cannot prove that the low tax effort in Pakistan is the result of evasion, or of an inefficient collection mechanism.  However, in this paper, we are implicitly assuming that evasion is responsible for a major part of the revenue shortfall.

6
# **Table 2: Financial Institution Probability of Default and Exposure to the Sovereign in EMDEs**

|Dependent variable: Median Probability|of Default Fin|ancial Instituti|ons in 60 mo|nths (monthly|data at count|ry level)||
|---|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Claims on Total Gov. and PNFEs (% GDP)|0.161***|0.161***|||0.0718|0.0817|‐0.0233|
||(0.0439)|(0.0440)|||(0.0533)|(0.0511)|(0.164)|
|Sovereign EMBI spread (bps)|||0.125**|0.0989*|0.124**|0.0951|0.0854|
||||(0.0556)|(0.0560)|(0.0590)|(0.0589)|(0.0915)|
|Claims on Tot. Gov.*Sov.EMBI spread|||||||0.0161<br>(0.0284)|
|US VIX Index||0.264***||0.228***||0.231***||
|||(0.0214)||(0.0368)||(0.0383)||
|Constant|0.410***|‐0.224|0.151|‐0.251|‐0.00133|‐0.421|0.225|
||(0.120)|(0.135)|(0.302)|(0.245)|(0.314)|(0.264)|(0.502)|
|Observations|7,812|7,812|5,398|5,398|5,295|5,295|5,295|
|Countries|52|52|36|36|35|35|35|
|R‐squared|0.680|0.693|0.710|0.719|0.714|0.723|0.714|
|All variables in logs. "Sovereign EMBI spre<br>Total Government includes central and lo<br>Monthly data January 2000 to December<br>Robust standard errors in parentheses. *|ad" is the m<br>cal governme<br>2017. Countr<br>** p<0.01, **|aximum value<br>nts. PNFEs: pu<br>y and year fixe<br>p<0.05, * p<0|of Sovereign<br>blic non‐fina<br>d effects incl<br>.1|EMBI spread o<br>ncial entities.<br>uded.|bserved in eac|h month.||
|Source: National University of Singapore‐|Credit Resear|ch Initiative, I|MF‐Internatio|nal Financial S|tatistics, Bloo|mberg.||

50
**Figure 17: Increase in economics research by developing country authors**

<!-- Start of picture text --> Economics articles, by author's region<br>1200<br>1000<br>271 Latin America & Caribbean<br>800<br>Europe & Central Asia<br>600 249<br>Middle East & North Africa<br>21<br>400<br>East Asia & Pacific<br>344<br>200 66107 Sub-Saharan Africa<br>42<br>59 72<br>0 65 58 S outh Asia<br>1985-90 2005-10<br>Source: Web of Science<br><!-- End of picture text --> Source: Reproduced from Adam Wagstaff‘s contribution to the _Let’s Talk Development_ blog of the World Bank. Posted January 14, 2011 under the title ―The (gradual) democratization of development economics.‖ As a result, the thinking that may have direct relevance for developing countries‘ development is likely to come increasingly from the developing countries themselves. As such, a key phrase that would dominate the agenda is to ―democratize development economics.‖<sup>42</sup> As World Bank President Robert Zoellick has stated: _―With the end of the outdated concept of a Third World, the First World must open itself to competition in ideas and experience._ _The flow of knowledge is no longer North to South, West to East, rich to poor._ _Rising economies bring new approaches and solutions. We see that as India advises Africa on dairy farming; as China learns from Africa about effective community-driven development approaches in Ghana and Nigeria; as the United States learns from China about high-speed railways;......._ _This is no longer about the Washington Consensus. One cannot have a consensus about political economy from one city applying to all. This is about experience regarding what is working ---- in New Delhi, in São Paulo, in Beijing, in Cairo, and Accra. Out of experience may come consensus. But only if it is firmly grounded -- and broadly owned.‖_<sup>_43_</sup>

> 42 See World Bank President Robert Zoellick‘s speech, ―Democratizing Development Economics,‖ delivered at Georgetown University on September 29, 2010.

> 43 Ibid.

37
satisfaction. Below, we describe each treatment arm.

**Extended Training Treatment.** We randomly assigned one clinic in each matched pair to receive more intensive pre-study RDT training. This “extended training” was designed to increase providers’ trust in the diagnostic accuracy of RDTs and adherence to test results. Another aim was to empower providers with evidence in case patients pushed to get unnecessary medication. The treatment was administered as part of the pre-study training described above, following the core “basic training” which every clinic received. The content covered the high sensitivity (and specificity) of rapid diagnostic tests available at clinics, both in WHO quality assurance testing and in field studies conducted in Mali.<sup>14</sup> The training also reviewed the dangers of unnecessary treatment with antimalarials, from drug interactions to missed diagnoses to parasite resistance. The objective was to improve provider reliance on RDT results and to heighten awareness of the problem of overtreatment.

**Patient Information Treatment.** The “patient information” intervention was randomized within clinic and consisted of a seven-minute video shown to patients individually while they were waiting to consult a health provider. The video depicted dialogue between a doctor and a mother visiting a clinic with her child to explain the symptoms of malaria, the use of an RDT, the importance of testing for malaria before treating, and the risks (and unnecessary costs) associated with treatment for severe malaria in cases where the patient is not at high risk. The main message was that antimalarials should only be prescribed to individuals with positive tests. The video also demonstrated how to interpret RDT results. The video’s objective was to teach patients about Mali’s malaria diagnosis and treatment policies while empowering them to ask questions about treatments they do not understand. The empowerment component was designed to help patients in case a health provider prescribes unnecessary medication.

We have records from surveyors whether the video was watched as well as administrative notes on the length of time that was watched. 89 percent of patients for whom we have administrative data watched the full video. Combining this with the surveyor records, we estimate that 79.5% of patients watched at least some of the video (Appendix Table A2). While this one-on-one intervention took some time, we see no statistically significant difference in self-reported waiting time prior to seeing the health care provider among patients in the patient information arm versus the control, suggesting intervention officers were successful at administering the treatment during normal wait times.<sup>15</sup>

**Voucher Treatments.** The patient information treatment was cross-cut with two other

> 14The training specifically addressed the performance of SD Bioline tests, the brand purchased and distributed by the government for free distribution in the public health system (Djimde et al., 2016). 15The average waiting time before consultation is 17.328 minutes in the control group and 17.994 minutes in the patient information group.

9
_For example:_ The water system of the country N provides water services 12 hours a day supplying 100,000 cubic meters of water a day to its consumers. The fixed asset value is $100 million (approximately $100 per capita). The reported cost of water services is $0.10 per cubic meter.

ACRP is calculated as follows: C = US$ 0.10 per m<sup>3</sup> T = 0.25 US$ 0.10 per m<sup>3</sup> (12/24) = US$ 0.013 per m<sup>3</sup> I = 0.04 US$ 100,000,000 / (365 x 100,000 m<sup>3</sup> per day) = US$ 0.109 per m<sup>3</sup> And ACRP _=_ US$ 0.10 per m<sup>3</sup> + US$ 0.013 per m<sup>3</sup> + US$ 0.109 per m<sup>3</sup> = US$ 0.22 per m<sup>3</sup>

# **_Thresholds Used for the Average Cost-recovery Price in the Water Sector_**

Where data are not readily available to calculate the Average Cost Recovery Price the following thresholds could be applied:

||**Developing countries**||**Industrialized countries**||
|---|---|---|---|---|
|<US$0.20/m|Tariff_insufficient_to cover basic operating<br>and maintenance costs|Tariff<br>operati|_insufficient_<br>to<br>cover<br>ng and maintenance costs.|basic|
|US$0.20-0.40/m<sup>3</sup>|Tariff_sufficient_to cover operating and<br>some maintenance costs|Tariff<br>operati|_insufficient_<br>to<br>cover<br>ng and maintenance costs.|basic|
|US$0.40-1.00/m<sup>3</sup>|Tariff_sufficient_to cover operating,<br>maintenance and most investment needs|Tariff<br>mainte|_sufficient_to cover operatin<br>nance costs|g and|
|>US$1.00/m<sup>3</sup>|Tariff_sufficient_to cover operating,<br>maintenance and most investment needs<br>in the face of extreme supply shortages|Tariff<br>moder<br>income|_sufficient_to cover full c<br>n water systems in most<br>cities|ost of<br>high-|

Source: Foster and Yepes, 2005

# **_Normative Loss Rates_**

In the event that country specific data are unavailable for the normative loss rate “ln”, we would suggest the use of 0.2 in the water sector.

16
- Barrot, D. and L. Serv ́en. (2018) “ Capital flows, common factors, and the global financial cycle.” _World Bank Policy Research Working Paper 8354_ .

- Bayoumi, T., G. Fazio, M. Kumar and R. MacDonald (2007) “ Fatal attraction: using distance to measure contagion is good times as well as bad.” _Review of Financial Economics_ , 16, 259–273.

- Bekaert, G., E. Engstrom and N. Xu (2017). “The Time Variation in Risk Appetite and Uncertainty.” _Columbia Business School Research Paper No._ 17–108.

- Bekaert, G., Harvey, C., Kiguel, A., Wang, K. (2016). “Globalization and Asset Returns.” _Annual Review of Financial Economics_ , 8, 221–288.

- Bekaert, G., Hodrick, R., and Zhang, X. (2009). “International stock return comovements.” _The Journal of Finance_ , LXIV, 2591–2626.

- Bekaert, G., and A. Mehl (2017). “On the global financial integration ’Swoosh’ and the Trilemma.” _Unpublished manuscript_ .

- Bolton, P. and O. Jeanne. (2011). “Sovereign Default Risk and Bank Fragility in Financially Integrated Economies.” _IMF Economic Review_ , 59, 162–194.

- Broner, F., G. Gelos and C. Reinhart (2006). “When in peril, retrench. Testing the portfolio channel of contagion.” _Journal of International Economics_ , 69, 203–230.

- Bruno, V. and H.S. Shin. (2015). “Capital Flows and the Risk-Taking Channel of Monetary Policy.” _Journal of Monetary Economics_ , 71, 119–132.

- Cerutti, E., Claessens,S. and Rose, A.K. (2017). “How Important is the Global Financial Cycle? Evidence from Capital Flows.” _IMF Working Paper_ , WP/17/193.

- Chen, N., Roll, R., and Ross, S. (1986) “Economic Forces and the Stock Market.” _The Journal of Business_ , 59, 383–403.

- Chinn, M.D., Forbes, K.J., (2004) “A decomposition of global linkages in financial markets over time.” _The Review of Economics and Statistics_ , 86, 705–722.

- Choi, I., and H. Jeong (2018) “Model selection for factor analysis: Some new criteria and performance comparisons.” _Econometric Reviews_ , forthcoming.

- Chong, T., W. Wong and J. Zhang (2011) “A gravity analysis of international stock market linkages.” _Applied Economics Letters_ , 18, 1315–1319.

- Christofferse, P., V. Eerrunza, K. Jacobs and H. Langlois (2012) “Is the potential for international diversification disappearing? A dynamic copula approach.” _Review of Financial Studies_ , 25, 3711–3751.

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30
closures reduced infection rates, then it is a high cost to pay, especially after it was deemed secure to open schools safely.

**<u>Table 2: Determinants of Learning Loss</u>**

|Variable|1|2|
|---|---|---|
|Weeks|0.006***|0.007***|
||(0.001)|(0.002)|
|Death rate||0.000|
|||(0.000)|
|Private||-0.0025|
|||(0.002)|
|Internet||0.0012|
|||(0.003)|
|School quality||-0.001|
|||(0.001)|
|Vaccination||0.001|
|||(0.001)|
|Stringency||0.000|
|||(0.003)|
|Union||0.002<br>(0.002)|
|Democracy||0.005|
|||(0.006)|
|Log GDP p/c||-0.0499|
|||(0.048)|
|Constant|0.110*|0.568*|
||(0.029)|(0.295)|
|Observations|41|41|
|R-squared|0.415|0.542|
|Standard errors in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||

# **3. The Determinants of Duration of School Closures**

While closures were exogenous, the decision to remain closed longer was a conscious choice, especially after the American Academy of Pediatrics (2020) released, on 24 June 2020, the first school guidance for safe in-person learning and the European Centre for Disease Prevention and Control (2020) released its recommendation on 6 August 2020. By the time of the 2020-21 school year, it had become clear that it was possible to safely open schools (de Hoyos and Saavedra 2021). By August 2020 evidence supported a marginal role of children as drivers of the first wave of infections (Munday 2021). School reopening did not play a causal role in the increased number of new cases and hospitalizations in Italy during the period of September–October 2020 (Gandini et al. 2021), and it was not the main driver of the increased spreading of the COVID-19 UK variant in England at the beginning of 2021 (Davies et al. 2021). Moreover, school closures have no detectable effect on the contact patterns of adults (Cauchemez et al. 2008). Other studies suggested that school closure during the 2009 H1N1 influenza epidemic in Pennsylvania would not have 5
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.7. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable<br>Tercile 2<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.04<br>.01<br>.005 .02<br>0 0<br> (95% CI) (95% CI)<br>β β<br>-.005 -.02<br>-.01 -.04<br>.04<br>.06<br>.02<br>.04<br>β (95% CI) .020 β (95% CI) 0-.02<br>-.02 -.04<br>-.04<br>-.06<br>.06<br>.04<br>.02<br> (95% CI)<br>β<br>0<br>-.02<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercil 2 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

81