rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5.  Direct Indicators of Degrees of Empowerment**

||**DOMAIN**||**INDICATOR OF FORMS OF EMP**|**OWERMENT**|
|---|---|---|---|---|
||**Sub-domain**|**National**|**Intermediary**|**Local**|
|**State**|**Justice**|•Number of court cases and the<br>time between submission and<br>conclusion of cases<br>•Percentage of positions in<br>justice system per<br>social/ethnic/religious group<br>•Number of national<br>newspapers/media<br>organizations independent of<br>government influence or<br>control|•Number of local court cases and<br>the time between submission and<br>conclusion of cases<br>•Percentage of positions in local<br>justice system per<br>social/ethnic/religious group|•Percentage awareness of listed (formal/informal)<br>justice systems (4.1)<sup>a</sup><br>•Number of times justice systems used (4.2-4.3)<br>•Score of effectiveness of justice systems (4.4)<br>•Score of fairness of justice systems (4.5-4.6)<br>•Score of gender equity in treatment by justice<br>systems (4.7)<br>•Score of equity by other stated social variable in<br>treatment by justice systems (4.8)<br>•Score of accessibility of justice systems (4.9)<br>•Score of ability to complain about justice<br>systems’ performance (4.10-4.11)<br>•Score of level of independence of police force<br>(4.12)<br>•Score of confidence in corrupt people facing<br>justice (4.13)|
||**Political**|•HH survey questions 4.14-<br>4.32 also apply at the national<br>level<br>•Percentage of elected<br>representatives in national<br>government per social/ ethnic/<br>religious group<br>•Number of people actively<br>voting in national elections<br>compared to those entitled to<br>vote|•HH survey questions 4.14-4.32<br>also apply at the regional level|•Percentage awareness of local electoral process<br>(4.14)<br>•Percentage interest in local electoral process<br>(4.15)<br>•Percentage entitled to vote in local elections<br>(4.16)<br>•Percentage voting in last local elections (4.17)<br>•Percentage wanting to vote in last local elections<br>(4.18)<br>•Percentage control over their voting choice (4.19)<br>•Frequency of, and impact of, discussion about<br>l<br>l l<br>i<br>did<br>(4 20 4 23)|

a Number in parentheses refers to the section in the national survey where questions corresponding to the indicator can be found. For example, questions about the respondent’s awareness of listed justice systems can be found in survey section 4.1 74
Figure 3. Cumulative Severity against Length of Outbreak and Population Size

- A. Length of Outbreak

- B.  Population Size

<!-- Start of picture text --> 40 BEL 40 BEL<br>ESP ESP<br>30 GBR 30<br>IRL GBR<br>IRL<br>20 ECUSWE ITA 20 SWE ITA<br>NLD USA FRA USA<br>LUX CHE<br>10 PER CAN IRN 10 MEX BRA<br>AUS KOR PHL ETH JPN RUS BGD NGA PAK IDN<br>0 IRQ THA JPN CHN 0<br>0 50 100 0 1 2 3 4<br>Days passed since first death Share in global population (percent)<br>Cumulative severity ratio (percent) Cumulative severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization

- Countries that exceed the second or third causes of death:

- United States, Luxembourg, Switzerland, Canada, Kuwait, Brazil + Sweden, Italy, France, Netherlands, United Kingdom, Ireland, Ecuador, Belgium and Spain: exceeding the second cause of death

- Peru, United Arab Emirates, Islamic Republic of Iran, Panama, Mexico + United States, Luxembourg, Switzerland, Canada, Kuwait, Brazil, Sweden, Italy, France, Netherlands, United Kingdom, Ireland, Ecuador, Belgium and Spain exceeding the third cause of death.

- Identification of causes of death:

- Virtually all countries are HICs, for which the typical top causes of deaths are: ischaemic heart disease, stroke and Alzheimer’s disease along with other dementias.

- Only five developing countries stand out. All are UMICs: Ecuador, Brazil, Peru, Islamic Republic of Iran, and Mexico. For Ecuador, the top three causes are ischaemic heart disease, diabetes and stroke; or Brazil: ischaemic heart disease, stroke and lower respiratory infections; for Peru: lower respiratory infections, ischaemic heart disease and stroke; for Islamic Republic of Iran: ischaemic heart disease, stroke and road injury; for Mexico: ischaemic heart disease, diabetes mellitus, stroke.

The severity of the mortality burden has so far been poorly correlated with the length of the outbreak (Figure 3.A). China, the Philippines and Japan started the period (over which the pandemic manifested itself locally) earlier than others, but their measures of cumulative severity were in fact lower than most countries that came later. Among second-wave countries, there is a large discrepancy between the few that top the chart and the vast majority of other countries with much lower cumulative severity ratios.

9
of each band is accounted for by including medians (column 4) and the interquartile range (25th and 75th percentile, column 5). Comparing specification (1) and (2) for enterprise, using only the band means as predictors ( _R_<sup>2</sup> = 0 _._ 416) yields already a better fit than NDBI and NDVI only ( _R_<sup>2</sup> = 0 _._ 340), indicating that other band values than those to calculate the indices provide valuable information. The LASSO regression picks all of the band means and the cross-validated R-squared is reduced only marginally compared to the full in-sample R-squared. Adding the standard deviations as predictors further increases both in-sample and out-of-sample R-squared measures ( _R_<sup>2</sup> = 0 _._ 492), while adding further information about the distribution yields only very marginal improvements in the fit.

For employment and per capita expenditure, the results are similar. Using band means and standard deviations yields higher prediction power than the Landsat indices, while adding further distribution characteristics beyond the first two moments yields only small improvements in the R-squared measure. Throughout the analysis, the LASSO technique tends to select almost all potential regressors as informative. This is the case despite the very high correlation between certain Landsat bands. The cross-validation exercise yields very little evidence of overfitting and only in the expenditure regression are there small differences between in-sample and out-of-sample fit. This suggests that in the cross-section, the parameters of the Landsat bands are highly stable throughout the whole country for predicting economic activity.<sup>10</sup>

# **3.2.5 Exploring Heterogeneity of the Prediction Power**

The analysis next explores sources of heterogeneity in the prediction power of both satellite products and address measurement errors that are likely correlated with the satellite

> 10We confirm an earlier result that SOL is an independent predictor of economic activity in unreported regressions. For each model specification, the LASSO approach picks the sum of lights measure as a predictor. Inclusion of this regressor can push the cross-validated R-squared measures of column (5) to 0.5499, 0.4538, and 0.4077 for enterprises, employment, and expenditure respectively.

17
**Figure 8: Currency Cyclicality Index and Monetary Policy Procyclicality**

<!-- Start of picture text --> URY<br>Monetary Policy Cyclicality=-0.28+0.35*CCI<br>(6.25) (2.25)<br>CRI MEX<br>IND<br>PERCHL<br>BGR ISR PRT<br>TUR<br>GRC<br>THA<br>CHNMAR<br>IRL CYP BRA PHL<br>ZAF FIN KOR<br>ESP<br>JPNFRAAUTNOR CZE NZL<br>DNKGBR COL SWE<br>AUS<br>MYS<br>CHENLD DEUUSABEL CANITA<br>-.5 0 .5 1<br>CCI<br>.4<br>.2<br>0<br>-.2<br>Monetary Policy Proyclicality<br>-.4<br>-.6<br><!-- End of picture text --> Note: CCI is the correlation between the HP filtered NEER and HP filtered real GDP. The cyclicality of monetary policy is calculated as the correlation between the cyclical components of real GDP and short-term interest rates. The t-statistics are reported in parentheses.

It would be apt to add here that, even though our analysis primarily looks at correlations, the positive correlation between currency and monetary policy procyclicality seems to have a “causal flavor.” The typical reverse causality argument that the monetary policy stance affects the cyclical behavior of a currency would indicate a relationship quite opposite from the one we are suggesting. A procyclical monetary policy would reduce the procyclicality of capital flows (by increasing their remuneration in bad times and decreasing it in good ones) and thus reduce the degree of procyclicality of a currency. This implies that, if anything, our estimated positive correlation between currency and monetary policy cyclicality is likely to be biased downward.

Finally, even though there are no clear a priori theoretical reasons to expect a link between the currency and fiscal policy cyclicality, we examine if the two are correlated in the real world. We compute the cyclicality of fiscal policy as the correlation between the cyclical components of real GDP and real total central government expenditure, as in Vegh and Vuletin (2012) and, perhaps unsurprisingly, we do not find it to be correlated significantly with the degree of cyclicality of the currency (not reported).

# **4 Currency Cyclicality over Time**

Moving from the cross sectional dimension of the correlates of the cyclicality of currencies and their implications for economic policy, in this section, we focus also on the time series dimension of the CCI. In particular, we are interested in knowing whether currency cyclicality has evolved over time; whether its relationship with the policy variables and other correlates has changed over time; and how do the changes we observe reconcile with the monetary policy graduation phenomenon observed in Vegh and Vuletin (2012).

13
accepted, resisted, navigated and interpreted differently depending upon the sense of efficacy, self esteem, and social support of the individuals receiving it.

# **“If people only cared”**

A second general way of answering the question of why so few people respond to climate change is that people do not care about this problem. According to this perspective, few people are engaging because people are either too greedy or too selfish to bother about future generations, or don’t care about climate change because they don’t think it will impact them (note: the economic corollary that people cannot afford to care will be discussed below). At least in the U.S. public, this assessment is not uncommon. We look around, we see that smart people continue to carry on highly consumptive behaviors, even in the face of knowledge of their consequences, and assume that people are too self-interested to be motivated for change. My environmental studies students express this sentiment on a regular basis. Similarly, there is evidence for this perspective from the focus groups conducted by the American Geophysical Union on climate change: “Many respondents in our focus groups were convinced that the underlying cause of environmental problems (such as pollution and toxic waste) is a pervasive climate of rampant selfishness and greed, and since they see this moral deterioration to be irreversible, they feel that environmental problems are unsolvable. As a result, convincing people of the seriousness of the problems is at best only part of the solution, and may, in fact, be counterproductive.” As discussed earlier, public opinion data on concern are varied however. Again we refer to the earlier mentioned study by Paul Kellstedt on relationships between efficacy, sense of responsibility and levels of concern regarding climate change, as well as parallel work by Krosnic. And while concern could be higher, the trend towards concern is worldwide. And that concern is increasing. As discussed earlier, Dunlap’s Gallup survey conducted in Canada, the U.S., Mexico, Brazil, Portugal, and Russia on public perceptions of global warming found that most respondents saw global warming as a problem even as far back as 1992, even though most respondents did not understand climate science well. Since that time concern has increased in the United States and around the world (see earlier studies on concern). Thus, the notion that people do not respond because they do not care about climate change is inadequate.

23
# **Appendix: Variable Definitions and Sources**

|**Variable Name**|**Description**|**Source**|
|---|---|---|
|Narrow Interest Rate Margin|Difference between interest income received on loans<br>(divided by total loans) and interest paid on deposits<br>(divided by total deposits)|<br>Central Bank of Nigeria (CBN)<br>and author's calculations|
|Wide Interest Rate Margin|Difference between total interest income over total<br>earning assets and total interest expenses over total<br>interest bearing liabilities.|CBN and author's calculations|
|Overhead|Overhead costs over total assets|CBN and author's calculations|
|Liquidity|Liquid assets over deposits|CBN and author's calculations|
|Equity|Total equity over total assets|CBN and author's calculations|
|Liquidity*2004, Liquidity*2005|Interaction of liquidity variable with year dummy|CBN and author's calculations|
|Equity*2004, Equity*2005|Interaction of equity variable with year dummy|CBN and author's calculations|
|Loan Loss Provisions|Provisions for bad debt relative to recoveries over<br>total loans|CBN and author's calculations|
|Market share deposits|Bank's market share in the deposit market segment|CBN and author's calculations|
|Interest Income Share|Total interest income over total operating income|CBN and author's calculations|
|Intermediation|Total loans over total liabilities|CBN and author's calculations|
|Herfindahl deposits/ loans|Sum of squared market shares of banks in the deposit<br>or the loan market segment, scaled by 10,000. The<br>Herfindahl ranges from 0 to 1.|CBN and author's calculations|
|Inflation|Annualized quarterly change of the CPI index|IMF International Financial<br>Statistics Database|
|Real T-Bill Rate|The nominal treasury bill rate adjusted for the<br>inflation rate|IMF International Financial<br>Statistics Database|
|Industrial Production Growth|Quartely growth rate of industrial production index|IMF International Financial<br>Statistics Database|
|Exchange Rate Depreciation|Quarterly growth rate of Nigerian Naira (NN) per<br>US$|IMF International Financial<br>Statistics Database|

34
**Table 2.3:    Sectoral Trends in Bahia’s Economy, 1992-2000**

||**Percentage of total,**<br>**2000**|**Change 1992-2000**<br> **(avg. annual percent)**|
|---|---|---|
|Agriculture|11|3|
|Mining|1|-3|
|Manufacturing|26|3|
|Public utilities|4|1|
|Construction|10|4|
|Tourism|2|5|
|Commerce|8|3|
|Transport|2|6|
|Communication|2|15|
|Finance|3|3|
|Rent|11|3|
|Publicadministration|14|1|
|Otherservices|6|2|
|**Total**|**100**|**3**|

_Source:_ SEI (www.sei.ba.gov.br/PIB/pib.asp)

# **LOCATION STRUCTURE OF ECONOMIC AND POPULATION GROWTH**

Economic growth experienced in the last decades in Bahia is far from homogeneous throughout the state. The large regional differences within the state are associated with the expansion of economic activities in a few of the areas away from the Sertão. Of the 15 zones in Bahia, only four, the East, the Extreme South, Salvador, and San Francisco, experienced an above-average expansion of output during 1980–2000 (see map above). In the past two decades, these four fast-growing zones also experienced the largest population inflow of the 15 zones (Table 2.4). The largest real GDP growth, above 2.7 percent annually during 1980–2000, took place in the capital region (Metropolitan Salvador) where the industry and service sectors are particularly strong. The other three regions, the East, the Extreme South, and San Francisco, experienced around 1.9 percent annual GDP growth. In these three areas, agricultural related crops have expanded rapidly in some municipalities and created opportunities for the population, in particular, soy in the East, cellulose in the South, and irrigated tropical agriculture around Rio San Francisco.

8
( _Population_ ). Collier and Hoeffler (2004b) report a negative effect of country size as measured by population on military budgets, arguing that large countries deter external threats. We control for regime type ( _Polity_ ) using the POLITY IV dataset’s polity2 indicator, which uses a weighting scheme to treat periods of transition (www.cidcm.umd.edu/inscr/polity/). We expect autocracies to have higher military spending than democracies (Collier and Hoeffler 2002; Goldsmith 2003). Many have argued that autocracies are dependent on military force to sustain their rule against attempts to overthrow their government, whereas democracies command a greater degree of legitimacy and are thus less in need of a strong military (Kimenyi and Mbaku 1995; Maizels and Nissanke 1986). We additionally control for overall government spending ( _Government expenditures_ ), since high government consumption generally will have the same causes as high military spending.

Next, we control for internal and external security threats as militarization variables will be driven by such factors (Collier and Hoeffler 2002). We enter a term for _Civil war_ , which is a dummy variable for years in which a country experiences armed conflict with over 25 battlerelated deaths (Gleditsch et al. 2002). Following Goldsmith (2003), the international war variable is a dummy for years in which a country engages in conflict between states with at least 1000 deaths ( _International conflict_ ). These data are taken from (Gleditsch et al. 2002). We also compute a count of civil and international peace years ( _Peace years (civil war)_ and _Peace years (int. conflict)_ ), or the number of years since the last civil and international war since 1946 in order to gauge the proximity of previous conflict (Collier and Hoeffler 2002). It is well established that, for civil wars at least, there is a high risk of revival even after a civil war has formally ended, which suggests that militarization after the end of civil war is likely to diminish only slowly over time (Collier and Hoeffler 2004b). Civil wars could also be endogenous to militarization. High military expenditures can deter international conflicts, but can also provoke them if foreign countries go for preventive action as Kant (1795) already pointed out more than 200 years ago (Fordham and Walker 2005). High military expenditures can signal to rebels that the initiation of a civil war is likely to end in defeat, but particularly in fragile post-conflict societies high expenditures can also increase the risk of renewed conflict if the former rebels take such expenditures as a signal for the government’s willingness to renege on the peace terms (Collier and Hoeffler 2004c). For these reasons, we run tests with and without the civil war variables (incidence and peace years) included.

9
**Table 1: Key Data and Sources**

|**Data Requirement**|**Source**|
|---|---|
|Energy Balances of Romania|<br>National Institute of Statistics, Romania; International<br>EnergyAgency (IEA)|
|Resource Potential,<br>including imports/exports|<br>Various sources published by Romanian Regulatory<br>Authority for Energy (ANRE), Ministry of Economic<br>Affairs|
|Installed capacity and<br>characterization of new<br>technologies for electricity<br>generation, heating and CHP<br>plants|<br>ANRE, Electricity Utilities (Electrica, Hidroelectrica,<br>Nuclearelectrica)<br><br>IEA Clean Coal Centre Database, Energy Information<br>Administration of USDOE<br><br>World Bank: Private Participation in Renewable Energy<br>Database|
|Load Profile|<br>European Network of Transmission System Operators for<br>Electricity|
|Fuel prices projections|<br>IEA World EnergyOutlook(2013)|

**Table 2. Definition of scenarios considered in the study**

|**Scenario**<br>**Name**|**Scenario Definition**|
|---|---|
|Baseline|It is an extrapolation of the current state of the energy sector including already<br>planned or implemented mitigation measures, in particular ongoing implementation<br>of the current EU 2020 climate and energy package, which sets an EU-wide target to<br>reduce GHG emissions by 21 percent in energy-intensive sectors, which participate<br>in EU emissions trading, compared to 2005. However, it does not include broader<br>reforms that the energy supply system needs to implement in line with the EU’s long-<br>term plan to reduce carbon emissions.|
|Green|It assumes the implementation of the proposed EU 2030 framework for climate and<br>energy policies, which sets overall GHG mitigation at 40% compared to 1990 levels.<br>For the power sector and other ETS participants, EU 2030 target corresponds to the<br>reduction of GHG emissions of 43% for the EU as a whole compared to 2005. This<br>scenario basically assumesfull implementationof EUINDC.|
|Super Green|This scenario is driven by the EU’s prospective 2050 Roadmap which aims for the<br>EU to reduce GHG emissions by at least 80% below 1990 levels by 2050, to a large<br>extent by almost total decarbonisation of the power sector.|

Each of these three scenarios was implemented under two cases: ‘low demand’ case and ‘normal demand’ case. The low demand case considers large-scale energy efficiency improvements in the demand side or energy end-use sectors (e.g., residential and commercial sectors). Major energy efficiency improvement measures include use of more efficient lighting and electric appliances, retrofitting buildings with wall, window and roof insulation, heating system improvement in the residential, commercial and public buildings, and use of efficient 7