rafmacalaba/datause-encoder-data huggingface dataset:
We explored additional Twitter handles that focus on traffic and road safety in Kenya. These include twitter handles such as RoadAlertsKE, KenyanTraffic and ThikaTowntoday. The majority of tweets from these other handles are already tweeted out by Ma3Route; therefore, including these additional handles does not produce many new tweets to incorporate into the dataset. An additional source of data is including tweets that mention Ma3Route but are not necessarily posted by Ma3Route. While these tweets are not included in the current analysis, they can be easily incorporated to expand the data set that is used to generate additional crash reports. We have already done this for the data set of crashes that we are producing for the Government of Kenya.

# **Building a Truth Data set of Tweets**

We build a truth data set of Ma3Route tweets where tweets are labeled as to whether they refer to a specific traffic crash and, if they do, are geocoded. We code all potentially crash related tweets from July 2017 to July 2018. We define a tweet as potentially crash-related if one of the following words appeared in the tweet: _accident, accidents, ajali, axident, collision, crash, crashes, crashs, crush, crushed, damage, disaster, emergency, fatal, fatality, fender bender, fender-bender, hazard, hit, hit-and-run, incident, incidents, injuries, injury, magari zmegongana, mishap, overturn, overturned, ovrturn, ovrturned, pileup, rammed, read end, rear ended, roll, rolled, smash, smashed, wreck, wreckage, zilicrash, zimecrash_ To account for misspellings of select words, we also include tweets if they contained a word that had a Levenshtein distance of two or less to “accident” or “incident” or a Levenshtein distance of one to “crash” or “crashed”.

Six coders were trained to process the 9,480 tweets defined as potentially crash related. Coders were instructed to label a tweet as reporting a crash if the tweet referred to one or more specific crashes; general comments about crashes were labeled as not reporting a crash. If the coder labeled the tweet as reporting a crash, they were instructed to geocode the location of the crash based on the tweet text if they were able. Coders were instructed to record the street names and landmarks used to geocode the crash. In addition, they provided the approximate coordinates of the crashes.

S3
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variables. The solution to<sup>the first orderequationis:</sup> where co is a constant, the _p's_ are the persistent parameters in **g,**<sup>and a</sup><sup>**=[1,1, 1,1/ (1-**a),</sup> _a/ (a_ **1)]'.** Note that because<sup>ofconditionalheteroskedasticityofe,investmentisa</sup> linear function of lagged<sup>variancesof theshocks.We allowforthispossibilityinour</sup> estimations.

This completes the analysis<sup>ofinvestmentdecisionsofa singlesubsidiary.Assume</sup> now that the domestic<sup>economy is composed of manyidentical firms and thatevery period</sup> new firms enter into this<sup>market,overlappingwithfirmsalreadyinplace.Anaggregate</sup> version of **(5)** holds for<sup>theeconomyasawholeprovidedall foreignsubsidiariesface</sup> similar prices, productivity<sup>anddiscountfactors,whichweassume.</sup>

# **C Definition ofV ariables**

Here we provide ad etailed<sup>descriptionofthe data.Unlessnotedweuseanannual</sup> frequency.

Foreign Direct Investment/GDP:<sup>Ratioof GrossFDIinflows(incurrent</sup><sup>**US$)**</sup> toG DP (in current **US$).** IMF Balance of Payments and<sup>TheWorldBankGlobal</sup> Development Finance.

T-Bill: **US** 3-month Treasury<sup>Billrate takenfromBloomberg.Frequency:annual</sup> average (end of month<sup>price).</sup> Stock Market Return:<sup>Percentagechangeof Morgan StanleyWorldCapitalIndex</sup> from Bloomberg. Frequency:<sup>annualaverage(endof monthprice).</sup> Slope: Slope of the **US**<sup>TermStructurefromBloomberg,computedas the</sup><sup>**10**year</sup> Bond rate minus 3-month Treasury<sup>Bill rate.Frequency:annualaverage(end of month</sup> price).

Spread: **US** Credit Spread<sup>fromBloomberg,computedasMoody's</sup><sup>**AAA**bondrate</sup> minus Moody's **BAA** bond<sup>rate.Frequency:annualaverage(end of monthprice).</sup>

**U.S.** inflation rate: Geometric<sup>averageof currentand last-yearinflationrate.IMF</sup> International Financial Statistics.

World Growth: **GDP** weighted<sup>averageof</sup><sup>**GDP**growthrate(sampleof countries</sup> with complete data). World Development<sup>Indicators.</sup>

**33**
40 deficit his is furter subsnatd by the fact that profitability, measured as the rate of return after tax on assets of de public entrprises (PEs) in the edmtive industries, recovered substandally during this period. Becase ith group of PEs is doninated by PETRONAS, the state-owned oil company whose profits to a lre etent are also dependent on domestc petroleum prices, dhanges in domestic oil product prices would have contributed to better financial performance of this enterprise.' Using 1983 as ihe base year, GDP growth peaked at 7.8 percent during 1984, ihe firstyear of the energy price reforn. It was primarily th'e demand stimulus of the expansionary fiscal policy and the property boom, which strted in the early 1980s, that sustained this growth. The negative side of the expansionary fiscal policies and construction boom was a tight labor market with rising real wages and appreciai of real echag rate, which led to a loss of intntional compettiveness in the mufacturng sector. The recession in the following year was parily a result of these negative development and fiscal polic es whih involved, for example, cutbacks in public investment resultng in a wfihdrawal of the demand stiuus ofthe eary 1990s.

I overnm **rweauso fimn** pdolaum **dividekda, icrse** n **1985.** from **MS** 500 **n in 1983** to over **MM30 mili**
driven through two key channels. Firstly, due to the increasing carbon price in the EU (from US$39/tCO2 under the NDC target to US$213/tCO2 under the EU Green Deal target), there is lower demand for fossil fuels within the bloc. This has an adverse impact on regions like MENA and Central Asia, including the Russian Federation, as the EU is one of their primary destinations for fossil fuel exports. Secondly, due to the increasing price of carbon in the EU, production becomes more expensive, which negatively impacts key destinations of EU energy-intensive exports, such as neighboring European countries. Unlike the EU carbon tax, which mainly impacts demand and prices for fossil fuels, CBAM puts more pressure on energyintensive goods, such as metals, chemical products, non-metallic minerals (cement, lime, etc.), and electricity. As a result, main exporters of these commodities to the EU (ECA – chemicals, metals, electricity; China and MENA– chemicals) are among the regions that experience lower exports following CBAM implementation.

The impact of climate policies on other countries will depend on the degree of their carbon intensity and reliance on related exports (Figure 21). Separating macro impacts of the CBAM on EU trading partners from domestic EU mitigation policies (within the EU Green Deal), we can observe that the impacts correlate with the carbon intensity (in kg per US$1 of exports) and magnitude (share of exports to the EU in a country’s GDP) of exports to the EU. For estimating both carbon intensity and the share of exports in a country’s GDP we focus on commodities that correspond to the EU ETS sectors and thus face the CBAM in our simulations. The top three regions most impacted by the CBAM – Europe and Central Asia, Middle East and North Africa, and Sub-Saharan Africa – have high carbon intensity of exports per US$1 and at least 1 percent of the EU-designated export share in country GDP. At the same time, high-income countries, including the United States, having the lowest carbon intensity of exports to the EU, experience negligible impacts from the CBAM.

Figure 21. Impacts of the Carbon Border Adjustment Mechanism on total exports by EU trading partners and carbon intensity of exports to EU, % change in 2030 relative to the EU Green Deal implementation scenario <!-- Start of picture text --> 0.2 0.1 3.5<br>0.0<br>3.1<br>0.0 3.0<br>-0.2 -0.1 2.5<br>-0.1 -0.2 -0.2 -0.1<br>-0.4 2.0<br>-0.6 -0.4 1.5<br>1.4<br>-0.8-1.0 0.70.4 0.60.9 1.10.6 0.5 0.8 0.40.5 0.60.5 0.7 0.5 1.00.5<br>0.2 0.3<br>-1.2 -1.0 0.0<br>Change in total exports due to CBAM,% Carbon intensity of exports to EU, kg per USD<br>Share of exports to EU in country's GDP, %<br>Change in total exports, %<br><!-- End of picture text --> Source: ENVISAGE simulations.

34
# **1. Introduction**

As noted by the World Economic Council (1999), and in Pachauri et al (2004), in the past, energy was not usually considered as a part of basic human needs. However, in recent years there has been a consensus that access to modern energy forms is an important mechanism for the provision of healthcare, sanitation, water, and so forth, and at the same time it benefits development through more efficient provision of lighting, cooking, power, and transport, etc. (IEA, 2010).

There has been an increasing focus in research on analyzing the importance of energy access and use and their links with income and poverty. For example, in some countries, such as Mexico, the availability of some forms of energy at the household level has been taken into account when measuring poverty from a multidimensional perspective; one such measure is “access to basic services in the household” (this involves mixing items together in the same indicator, for example, electricity, fuel for cooking, water, sewage, garbage collection, etc.).

Access to and use of energy are also related to inequality, since the share of payments for energy at different income levels differs substantially. Moreover, to the extent that energy subsidies are provided by public sources to all households, or that a social tariff may be designed for improving access to energy for certain social groups, the structure of the correlations between energy and income levels has to be studied to better target such public mechanisms, and to avoid regressive subsidies.

The effect of energy use on household poverty may run through several mechanisms. Kanagawa and Nakata (2007) aggregated four main lines where energy affects welfare: health, education, income, and environment. Barnes et al (2011) and Khandker et al (2012) analyzed the relation between income poverty and energy poverty in India and Bangladesh. Using different surveys the authors estimate a demand for energy at the household level, including income deciles, to determine at which decile there is an increase in the demand for energy. They find that households 2
FIGURE 11

# BRAZIL SAVING RATES

## NRR=1 BY 2010

<!-- Start of picture text --> 0.09 -<br>0.08<br>0.07-<br>0.06 -<br>0.05 -<br>0.04<br>0.03<br>0.02<br>0.01<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text --> O LP GROWTH=0% 0 L P GROWTH=--6 + LP GROWrH=;3%
# 3.2 Summary Statistics

Table 1 shows the means and standard deviations of the main outcome and control variables, as well as key demographic, labor market and other characteristics of respondents and their households.

**Table 1: Summary statistics for key variables**

||**Mean**|**SD**|**N**|
|---|---|---|---|
|**Panel A: Outcome variables**||||
|Job ofered abroad: Same as current job (=same skill level)||||
|Will accept job abroad for current nominal earnings|0.35|0.48|2400|
|Migration-inducing wage premium (%)|387.65|496.92|2400|
|Respondent will not migrate regardless of earningspremium ofered(%)|0.23|0.42|2400|
|**Panel B: Control variables**||||
|Daily survey success rate|0.27|0.08|2398|
|After 11 February 2024|0.77|0.42|2400|
|Respondent is male|0.39|0.49|2400|
|Age of respondent (years)|30.88|5.83|2400|
|Respondent is married|0.38|0.49|2400|
|Respondent has children<br>|0.29|0.45|2400|
|Perceived cost of livingdiference at destination(1 = same as Myanmar)|2.15|2.28|2281|
|**Panel C: Other covariates**||||
|Highest level of education is TVET diploma (GTI, GTC etc.)|0.03|0.16|2400|
|Highest level of education is undergraduate diploma|0.01|0.12|2400|
|Highest level of education is bachelor graduate|0.94|0.24|2400|
|Highest level of education is postgraduate diploma|0.01|0.08|2400|
|Highest level of education is master’s degree|0.01|0.11|2400|
|Highest level of education is PhD|0.00|0.02|2400|
|Employment type is employee|0.66|0.47|2400|
|Employment type is paid apprentice/intern|0.02|0.13|2400|
|Employment type is employer (that hires workers)|0.12|0.32|2400|
|Employment type is self-employed (no hired workers)|0.21|0.41|2400|
|Individual income (USD)|117.89|73.19|2391|
|Household income per capita (USD)|75.77|85.48|2394|
|Perceived ratio of individual income to average income for similar work|0.95|1.40|2337|
|Respondent has a high skilled occupation (ISCO codes 1-2)|0.29|0.46|2394|
|Willing to migrate|0.52|0.50|2400|
|Want to migrate alone|0.52|0.50|1245|
|Want to migrate with family|0.48|0.50|1245|
|No. of contacts abroad who can help fnd a job|1.42|3.02|2400|
|No. of contacts in Myanmar who can help fnd a job abroad|1.47|3.41|2400|
|No. of contacts abroad who may be able to host short term|1.03|2.39|2400|
|Has savings or liquid assets if needed for move abroad|0.63|0.48|2400|
|Has a valid passport|0.19|0.40|2400|

11
# Measuring Human Capital in Europe and Central Asia

Asli Demirgüç-Kunt and Iván Torre

**Keywords:** human capital, education, health, Europe and Central Asia.

**JEL:** I1, I2, O1, O4

> *The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Aart Kraay for his guidance and advice, and Tania Dmytraczenko, Roberta Gatti, Harry Patrinos, Fadia Saadah, Gil Shapira and Christel Vermeersch for useful comments. Sharanya Venu Pillai provided excellent assistance.
positive total asset position only after recovering from the Gulf War. The total foreign assets of these countries rose sharply during the late 1990s; that growth slowed, particularly in the past five years, in what seems to be a response to the volatility of and decline in oil prices and the uncertainty caused by the Arab Spring.

Non-oil-producing countries (the Arab Republic of Egypt, Jordan, Morocco, and Tunisia) maintained positive net total liabilities positions over the past two decades. Total liabilities in these countries rose sharply over this period, with total assets falling, widening the gap between total liabilities and total assets to 3: 1 over the past 10 years.

**Figure 3 Net foreign assets as percent of GDP, by country, 2007 and 2015**

<!-- Start of picture text --> by country<br>2007 2015<br>AREBHRCOM DJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text --> Source: Authors, based on data from Lane and Feretti 2015.

**Figure 4 Current account as percent of GDP, by Figure 5 Capital account as percent of GDP, by country, 2007 and 2015 country, 2007 and 2015**

<!-- Start of picture text --> by country by country<br>2007 2015<br>2007 2015<br>AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>.5 .15<br>.1<br>0 .05<br>0<br>-.5 -.05<br><!-- End of picture text --> _Source_ : Authors, based on data from Lane and Feretti 2015.

_Source_ : Authors, based on data from Lane and Feretti 2015.

In order to understand the dynamics of the foreign sector in the oil versus the non-oil sector, we also look at changes in NFA and its key components over the period 2007–15. With the exception of Libya, which has been in severe conflict, oil-exporting countries increased the share of NFA in GDP. The surplus in the current account as a percent of GDP plummeted, except in Iraq. Only Bahrain and Qatar achieved growth in the capital account balance as a percent of GDP; all other oil-exporting countries suffered declines.

This simple elevator analysis confirms that the NFA positions of oil-exporting countries are under extraordinary pressure. The change in NFA to GDP was negative in most non-oilexporting countries, with the exception of Jordan and the Comoros, where it soared, driven mainly 7
- <mark>Viollaz, M. (2018). “Enforcement of Labor Market Regulations: Heterogenous Compliance and Adjustment Across Gender.”</mark> _<mark>IZA Journal of Labor Policy</mark>_ <mark>, 7:2.</mark>

- World Bank. (2010). _Women’s Economic Opportunities in the Formal Private Sector in Latin America and the Caribbean: A Focus on Entrepreneurship_ . The World Bank.

- World Bank. (2011). _World Development Report 2012: Gender Equality and Development_ . World Bank.

27
<!-- Start of picture text --> 2<br>=<br>©<br>3<br>|<br>a<br>eeS9<br>Qn<br>3%<br>&<br>2<br>><br>A<br>cs}<br>gr<br>=<br>2<br>a<br>2<br>£G<br>® oe<br>Name Adjacent to Rich Students Name Adjacent to a Poor Student<br>Note: 95% confidence intervals around mean amount given.<br><!-- End of picture text -->
# Table <u>5: Legal Gender Disparities and Proportion of Women Top Managers</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|||**Pro**|**portion of wo**|**men top mana**|**gers**|||
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.266**<br>(0.131)||||||||
|Legal disparities: Accessing<br>Institutions||-0.614**<br>(0.288)|||||||
|Legal disparities: Using Property|||-2.262***<br>(0.633)||||||
|Legal disparities: Getting a Job||||-0.228<br>(0.240)|||||
|Legal disparities: Going to Court|||||-4.280***<br>(0.753)||||
|Legal disparities: Providing<br>Incentives to Work||||||0.741<br>(1.091)|||
|Legal disparities: Building Credit|||||||-0.104<br>(0.667)||
|Legal disparities: Violence Protection||||||||-0.143|
|||||||||(0.537)|
|Log of GDP per capita (constant 2005<br>US$)|-0.989|-0.867|-1.387|-1.060|-1.201|-1.062|-1.040|-0.990|
||(1.028)|(1.026)|(1.040)|(1.037)|(1.034)|(1.055)|(1.058)|(1.091)|
|Rule of law|0.978|1.543|1.694|1.421|1.609|1.898|1.827|1.740|
||(1.764)|(1.620)|(1.538)|(1.836)|(1.620)|(1.682)|(1.670)|(1.757)|
|region==Europe & Central Asia|-8.318**|-8.300**|-9.085***|-7.881**|-7.424**|-7.090*|-7.636**|-7.640**|
||(3.316)|(3.489)|(3.472)|(3.499)|(3.451)|(3.655)|(3.416)|(3.445)|
|region==Latin America & Caribbean|-5.495*|-6.065*|-6.178*|-5.240|-5.095|-4.719|-5.189|-5.327|
||(3.288)|(3.508)|(3.406)|(3.465)|(3.428)|(3.671)|(3.449)|(3.437)|
|region==Middle East & North Africa|-17.287***|-17.834***|-14.353***|-20.397***|-18.195***|-20.682***|-20.702***|-20.571***|
||(3.512)|(3.591)|(3.556)|(3.352)|(3.240)|(3.337)|(3.384)|(3.412)|
|region==South Asia|-14.430***|-15.068***|-12.318***|-16.021***|-14.081***|-15.790***|-16.212***|-16.285***|
||(4.019)|(4.119)|(4.036)|(4.149)|(4.071)|(4.250)|(4.142)|(4.094)|
|region==Sub-Saharan Africa|-10.794***|-10.863***|-10.685***|-11.999***|-11.423***|-11.458***|-11.670***|-11.612***|
||(3.078)|(3.277)|(3.128)|(3.258)|(3.189)|(3.341)|(3.342)|(3.377)|
|Constant|39.009***|35.288***|39.838***|37.805***|36.595***|35.089***|35.634***|35.185***|
||(8.258)|(8.535)|(8.690)|(8.111)|(8.599)|(8.739)|(8.922)|(8.940)|
|Number of observations|108|108|108|108|108|108|108|108|
|Adjusted R2|0.322|0.317|0.358|0.297|0.342|0.292|0.290|0.291|

note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: Eastern Europe and Central Asia (sample consists of only developing economies) 29
<!-- Start of picture text --> Percentage points<br>0.2<br>-0.4<br>0.6<br>-1.0<br>4 4<br>16<br>18<br>-2.0<br>1-year 2-years 1-year 2-years<br>Government Monetary policy<br>spending uncertainty uncertainty<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>1.6<br>1.0<br>0.4<br>0.2<br>-0.8<br>-1.4<br>-2.0<br>-2.6<br>-3.2<br>EMDEs AEs EMDEs AEs<br>Government Monetary policy<br>spending uncertainty uncertainty<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>;<br>-4<br>6<br>-8<br>10 _ 0 - 0<br>§ 5 § S<br>> Q > o<br>~ ~ ~ ~<br>Government spending; Monetary policy<br>uncertainty uncertainty<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>05<br>0.0<br>-0.5<br>EMDEs AEs EMDEs AEs<br>Government Monetary policy<br>spending uncertainty uncertainty<br><!-- End of picture text -->
2005, 2006b). In several decentralized countries in Africa, South and East Asia, more than 90 percent of local government expenditures are financed by grants from higher tiers (Choudhuri, 2006; World Bank, various).<sup>4</sup> Additionally, studies in a range of countries as diverse as Albania, Brazil, India, Mexico, Peru, the Philippines, Sweden, and the United States, find evidence that higher tiers of governments strategically distribute grants across local jurisdictions to favor their political supporters (reviews in Boex and MartinezVazquez, 2005; and Khemani, 2006).

There is some available evidence, mostly anecdotal, from a range of countries, including Brazil, India, Nigeria, Pakistan, and the Philippines, on the salience of votebuying, patronage, and violence in local elections (Bardhan et al, 2008; Cheema and Mohmand, 2008). In the political capture model presented here, and in this anecdotal evidence, the capture of public resources for private benefits need not be restricted to the elite; even poor and traditionally disadvantaged voters may demand private goods from local politicians (such as jobs, subsidies, cash and in-kind transfers) instead of broad public goods (such as quality health and education).

Indeed, the critical assumption required to derive the result of political capture of decentralization for greater clientelism at the expense of broad public goods, is that the pivotal swing voters in local jurisdictions provide strictly greater vote returns when allocated private transfers instead of broad public goods. Lizzeri and Persico (2004) use completely opposite assumptions, of the increasing value of the public good to pivotal voters, to explain the extension of suffrage by English elites in the mid-late 1800s. They argue and provide some evidence that in England, the increase in the value of urban public goods following the industrial revolution (public health infrastructure such as sewerage, waterworks and paved roads), led to a majority of the franchised elite pushing for reforms to extend the suffrage so that political parties would have stronger incentives to deliver these public goods. They show that following suffrage reforms, spending by municipal corporations on public health infrastructure increased substantially, while spending on welfare transfers to the poor was reduced. That is, a diametrically different

> 4 Even this number, 90 percent, masks the true extent of grants dependence of rural local governments which is closer to 99 percent when some cities that do raise significant resources from own revenues are excluded from the calculation.

5