rafmacalaba/datause-encoder-data huggingface dataset:
15 To explore the replacement effects of conflict-related deaths on fertility, we conduct crosssectional analyses with each of the post-genocide RDHS waves. The 2000 and 2005 RDHS provide insights into the short-term (five-year) and long-term (ten-year) effects of genocide on fertility, respectively. The dependent variable of interest is the number of children born alive in the aftermath of the genocide (while controlling for the number of children previously born). This estimation strategy builds on the idea that women may adjust their fertility in the post-conflict period, depending on the exact channel through which they were affected by the genocide. We exclude children conceived during the genocide (potentially through rape) from our analysis. Rather, the focus is on understanding how fertility decisions are made during the transition to peace. In the 2000 RDHS, the period of interest is May 1995 (when the first cohort of children conceived after the genocide is born) to June 2000 (the start of interviews for the 2000 RDHS). Similarly, in the 2005 RDHS, the dependent variable is the number of children born to a woman between May 1995 and February 2005 (when 2005 data collection began).

The determinants of fertility are estimated as a reduced-form equation, in line with the existing literature, as discussed above, with: where fertility _K_ of woman _i_ is a function of a constant α, a vector of the woman’s socioeconomic characteristics _Xi_ , district-level and province-level characteristics _D_ , region fixed effects _R_ , a proxy for conflict exposure _Conflicti_ (as discussed in Section 4.2), and a normally distributed error term _ui_ . The estimated coefficient _β4_ measures the impact of _actual_ conflict exposure during the genocide on fertility, relative to _all women after the genocide_ .

To explore the marriage market effects of conflict on fertility, we exploit the fact that the conflict variables are available for all data waves, including the 1992 RDHS collected before the genocide. We pool the three cross-sectional RDHS waves pair-wise, combining the 1992 and 2000 waves and the 1992 and 2005 waves. Pooling surveys allows us to compare postgenocide fertility trends relative to a pre-genocide baseline period. For instance, inferences can be made about fertility of a population subgroup (e.g. widows) after the genocide in comparison to a similar population subgroup before the genocide. A similar approach to pooling of cross-sectional survey waves is applied by Akresh and de Walque (2008).

To ensure that the dependent variables are comparable from before and after the genocide, the periods are now defined in relative terms. In the pooled 1992-2000 data, the period of interest
the extent that this listing gap has emerged only since 1999 (see Doidge, Karolyi, and Stulz [2017]), the early part of our sample period is immune to this. We also provide breakdowns of our results by high-technology and other industries.

We define star firms as firms that realize high returns for their investors. We begin by using a standard definition of Return on Invested Capital (ROIC) as our measure of returns, where ROIC for firm _i_ in year _t_ is defined as: where _EBIT_ is Earnings before Interest and Taxes (Compustat item EBIT) and _AM_ is Amortization of Intangible Assets (Compustat item AM). ROIC, as used in the Council of Economic Advisors [2016] report and Ben-David et al. [2013], amongst many others, computes the earnings that a corporation realizes over a period, as a fraction of capital that investors have invested into the corporation. The advantage of ROIC is that it measures investment capital as more than physical capital (fixed asset investment) which Doidge et al. [2018] show to be a declining portion of total assets over time in the US.

We adopt a relatively conservative definition for Invested Capital as the amount of net assets a company needs to run its business: where _PPENT_ is Net Property, Plant, and Equipment, _ACT_ is Current Assets, _INTAN_ is Total Intangible Assets, _LCT_ is Current Liabilities, _GDWL_ is Goodwill that represents the excess cost over equity of an acquired company, _CHE_ is Cash and Short-term Investments, and _SALE_ is net sales. All these variable labels are the corresponding items in Compustat.<sup>9</sup> The intangible assets as registered in Compustat, INTAN, include externally purchased assets like blueprints, copyrights, patents, licenses etc. and goodwill but does not include internal intangible assets like R&D and SG&A. We exclude Goodwill, which are the intangible assets arising out of M&A transactions when

> 9We replace missing values of AM and GDWL with 0.

7
income dispersion resulted in a increase in the concentration coefficient for this component income of 8 percent between 1994 and 2002, higher than for overall income and for any other component.

To conclude, an important source of the increase in inequality of consumption over the 19942002 period as discussed above can be found in the growth of inequality in original income. The main force behind this increase has been the growth of inequality in labor income, with is reflected in both a higher concentration coefficient for this type of income and the increased role played by this component in the overall measure of inequality.

**Table 5:  Income Inequality and Decomposition by Income Sources, 1994-2002**

|**Gini Coefficients:**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|---|
|**Original Income***<br>of which:|**0.379**|**0.377**|**0.386**|**0.395**|**0.366**|**0.377**|**0.395**|**0.395**|**0.407**|
|Labor income|70.5%|68.9%|70.8%|71.6%|72.1%|73.7%|74.1%|72.3%|69.9%|
|Old age pension|15.1%|16.5%|15.9%|16.0%|16.4%|16.4%|15.3%|17.8%|18.8%|
|Income from farm|10.1%|11.6%|9.3%|8.9%|7.6%|6.4%|6.2%|5.7%|6.6%|
|Other income|4.3%|3.0%|4.1%|3.5%|3.9%|3.5%|4.4%|4.3%|4.7%|
|**Disposable Income****|0.308|0.304|0.312|0.323|0.293|0.298|0.311|0.308|0.317|
|**Concentration Coeffici**|**ents***:**|||||||||
|Labor Income|0.389|0.382|0.396|0.402|0.377|0.398|0.420|0.414|0.421|
|Old age pension|0.394|0.408|0.404|0.410|0.380|0.385|0.372|0.407|0.423|
|Income from farm|0.332|0.358|0.326|0.371|0.290|0.273|0.292|0.287|0.338|
|Other income|0.307|0.254|0.320|0.289|0.293|0.265|0.304|0.280|0.295|

Method: Decomposition of the Gini coefficient into components, ranking by total income. * Before taxes and benefits. ** After taxes and benefits *** Household ranked by original income.

_Source:_ Authors’ calculations based on HBS.

# **The Role of Taxes and Social Benefits**

While increased inequality in original income is clearly an important component of the dispersion of consumption, it is not the full explanation. As the second part of Table 5 shows, the growth in the Gini coefficient for original income (7 percent) was just over half of the increase in the Gini coefficient for consumption (13 percent). Thus, other factors must be at play. Amongst these factors, of potential importance are changes in the tax and benefit system which, for any given distribution of original income, may affect the distribution of the income that households actually have to spend (i.e., their disposable income).

The top section of Table 6 shows that the concentration index for taxes increased by 32.8 percent in the period 1994-2002, which suggests that the tax burden has become more concentrated. At the same time, the social benefits system evolved toward the poor more strongly. This would suggest that since the mid-1990s changes in the tax and benefit system have counteracted the increased inequality in original income, leading to a much smaller growth of inequality in disposable income (only 3 percent, from 0.308 to 0.316).

11
# **Table S21. Relationship between the World Press Freedom Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.308***<br>(0.06)|-0.030<br>(0.03)|-0.027<br>(0.03)|||||
|Open Data Barometer<br>||||0.336***|0.000|0.000||
|Score||||||||
|||||(0.05)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.393|
||||||||(0.07)|
|Trade (% of GDP)|||0.041*|||0.000||
||||(0.02)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.108|||0.000||
||||(0.15)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||-0.185|||0.000||
||||(0.13)|||(0.00)||
|School Enrollment,|||0060|||0000||
|Primary (% gross)|||.|||.||
||||(0.04)|||(0.00)||
|Year 2014|||||0.000|0.000||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.000||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.000||
||||||(0.00)|(0.00)||
|Year 2017||-0.004|-0.225**||0.000|0.000||
|||(0.05)|(0.11)||(0.00)|(0.00)||
|Year 2018||0.139|-0.290|||||
|||(0.20)|(0.28)|||||
|Year 2019||-0.151|-0.669*|||||
|||(0.27)|(0.35)|||||
|Year 2020||0.015|0.095|||||
|||(0.44)|(0.42)|||||
|Year 2021||-0.314|-0.739|||||
|||(0.45)|(0.49)|||||
|Year 2022||-7.029***|-7.898***|||||
|||(0.77)|(0.86)|||||
|Constant|50.789***|||57.233***|||53.384***|
||(3.01)|||(2.51)|||(2.81)|
|Sigmau||12.98|12.12||12.78|12.03||
|_<br>Sigmae||4.19|4.08||0|0||
|_<br>R2|0.121|0.957|0.959|0.242|1.000|1.000|0.186|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|150|150|150|104|104|104|94|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1031<br>05, *** p<0.0<br>(WDI), Open|1031<br>1. Standard er<br>Data Watch (O|1031<br>rors are clustere<br>DIN), Global|362<br>d at the country<br>Data Barometer|362<br>level. Data fr<br>(GDB), and O|362<br>om the Wor<br>pen Data Ba|94<br>ld Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

70
Sánchez-Martín, Escribano and de Arce, 2014.

“How regional integration and transnational energy networks have boosted FDI in Turkey”

|2-Aug-10|Aves - S-OIL JV|75%|NuStar|USA|Oil and<br>gas|
|---|---|---|---|---|---|
|23-Oct-10|Petrol Ofisi|54%|OMV|Austria|Oil and<br>gas|
|1-Dec-10|Entek Elektrik|50%|AES|USA|Utilities|
|10-Feb-11|Thrace Basin Natural Gas|100%|TransAtlantic (35%), Valeura Energy<br>(40%),Pinnacle(25%)|USA,<br>Bahamas,<br>Canada|Oil and<br>gas|
|21-Apr-11|Arar Petrol and Gas|5%|Niche|UK|Oil and<br>gas|
|24-Jul-11|Yeni Elektrik Uretim|40%|Ansaldo Energia|Italy|Utilities|
|28-Oct-11|Aksa Enerji|15%|Goldman Sachs|USA|Utilities|
|23-Dec-11|Enda Enerji|100%|Hot Rock|Germany|Utilities|
|28-Dec-11|SOCAR & Turcas Enerji|25%|SOCAR|Azerbaijan|Oil and<br>gas|
|12-Mar-12|Avrasya Gaz|60%|Prima EnergyTrading|Russia|Oil and<br>gas|
|9-May-12|Petkim|10%|SOCAR|Azerbaijan|Oil and<br>gas|
|29-Jun-12|AkCez|23%|CEZ|Czech<br>Republic|Utilities|
|17-Jul-12|Trakya Elektrik Üretim|90%|Inter RAO|Russia|Utilities|
|10-Sep-12|Karasular Enerji|40%|Aquila Hydropower INVEST|Germany|Utilities|
|27-Nov-12|Petgaz & wholesale LPG|100%|Rus Oteko|Russia|Oil and<br>gas|
|4-Dec-12|EnerjiSA|50%|E.ON AG|Germany|Utilities|
|12-Dec-12|Petrol Ofisi Petrol Arama|100%|TiwayOil|Norway|Oil and<br>gas|
|12-Feb-13|Aksa Enerji|3%|Goldman Sachs|USA|Utilities|
|14-Mar-13|Karasular Enerji Uretimi|60%|Aquila Capital Wasserkraft|Germany|Utilities|
|12-Jun-13|Benal Danismanlik|100%|Aswar National|Kuwait|Utilities|
|16-Aug-13|Bagistas II Hydro Plant|100%|Globtec|Germany|Utilities|
|25-Sep-13|AlpayEnerji|75%|San Leon Energy|U.K.|Utilities|
|18-Dec-13|Global enerji Elektrik<br>Uretim|15%|Emkaan In+D2:D53vestment|U.A.E.|Utilities|

Sources: Pricewaterhousecoopers. Turkey Energy Deals (2008-2014).

24
# **6.1 Bloom Van Reenen Sample: US vs. France, Germany, UK**

Table 7 first explores Bloom and Van Reenen’s sample, decomposing the gap between the US, the UK, France, and Germany. Lines A, B and C correspond to the raw _z_ score gap from the simple quantile comparison, the Machado-Mata and Melly estimates, and then the RIF estimates respectively. The results are broadly consistent across measures and with our previous findings: the gap in all cases is largest at the top, in the case of the RIF .278, .266, .321, at the 10<sup>_th_</sup> 50<sup>_th_</sup> and 90<sup>_th_</sup> respectively.

The next lines in panels B and C are the amount of this gap that can be explained by firm characteristics. Overall, the MM method suggest statistically explanatory power only at the 10<sup>_th_</sup> , of roughly 80% of the gap.<sup>15</sup> . Again, this method does not allow us to identify which covariates are most responsible. The RIF methodology does, and suggests more significant explanatory power across the distribution, ranging from 41% at the 10<sup>_th_</sup> to 31% at the 90<sup>_th_</sup> . Looking at the covariates individually, whether the manager has a degree accounts for 21% of the gap with the US at the 10<sup>_th_</sup> , 12% at the median, and 9.0% at the 90<sup>_th_</sup> . The greater explanatory power in the lower quantiles despite the higher coefficient in the higher quantiles in table 5 reflects the larger gap in this variable among the worst firms. At the median, the share of managers with a degree accounts for 18.4% but is not significant at the other quantiles. The share of employees with degrees accounts for 9.8% at the 50<sup>_th_</sup> and 12% at the 90<sup>_th_</sup> . The contribution of human capital variables to explaining the gap is high across the distribution. As found earlier by Bloom and Van Reenen and Bandiera et al. (2013), diffuse ownership appears important, accounting for 11% at the 10<sup>_th_</sup> and 7.9% and the 50<sup>_th_</sup> . In these decompositions, the degree of competitiveness does not enter importantly or significantly.

The next panel explores the overall explanatory power of differing “returns” to each of these covariates and suggest that the contribution is higher than the differences in endowments. However, first, this includes differences in the constant term which picks up unspecified residual factors. Further, the only significant entries are on size and being a

> 15The O-B decomposition is applied on the RIF dependent variable which permits the typical standard error from that composition can be used

18
- 14 -

dramatic improvements in a relatively short period...<sup>Fourth, to</sup> maintain human development during recessions and natural disasters, targetted interventions nay be necessary...<sup>Fifth,</sup> growth is crucial for sustaining progress in human development<sup>in</sup> the long run, otherwise human progress may be disrupted. . .Sixth, despite rapid periods of GNP growth, humani development<sup>may not</sup> improve significantly if the distribution of income<sup>is bad and if</sup> social expenditures are low.

These (somewhat overlapping) conclusions find<sup>expression in the WDR also. The</sup> differences are more on emphasis than substance.<sup>Both reports agree that what</sup> is really crucial to outcome is policy. This<sup>consensus, on broad based growth.</sup> and effective social expenditures, itself takes<sup>us back to some of the studies</sup> by the World Bank and the ILO in the 1970s.

# 4.3 Public Expenditures. Targeting and Poverty Alleviation

The question of the targeting of public expenditures<sup>runs through both</sup> reports. This is important not only for ensuring<sup>that social expenditures on</sup> basic health and education reach the poor, but also<sup>for the safety nets that</sup> are advocated by both reports to ensure that those<sup>(such as the aged or the</sup> disabled) who cannot benefit from broad based growth<sup>and/or better education</sup> are supported.

Coming first to social sector expenditure, both<sup>reports are agreed that</sup> there must be concentration on basic health and<sup>education. Analysis shows</sup> that the composition of public expenditure<sup>in the health and education sectors</sup> is disproportionately skewed towards expensive<sup>curative facilities and higher</sup> education, which benefit the urban elite (see<sup>Ferroni and Kanbur, 1990). Thus</sup> there must be systematic restructuring of public<sup>expenditure towards primary</sup> health care and basic education.
the effect of the transfers on the resilience deprivation headcount ratio (R0) using median consumption and the 10<sup>th</sup> percentile of the consumption distribution as thresholds. Columns (6) and (7) display the resilience deprivation gap (R1). Consistent with findings above using the matched-DID specification, results suggest that cash transfers strengthen households’ resilience. We find that beneficiary households have a higher probability to be above both the median and the 10<sup>th</sup> percentile of the consumption distribution. Cash transfers also reduce the resilience deprivation gap. Although this methodology has limitations given we do not have multiple postintervention survey rounds, these results suggest that transfers led to an improvement in factors that predict higher future per capita consumption (which include current per capita consumption, for which an increase was documented in section 4.1).

Finally, as an alternative, Table A7 shows that transfers reduce the likelihood that households suffer from consumption drops between 2012 and 2015 by 7.7 percentage points (column (1)). For households whose consumption levels are predicted to be lower in 2015 than in 2012 (based on their baseline characteristics, see section 3.3), transfers decrease the likelihood of an actual consumption drop by 4.6 percentage points. Overall, this is another way to show that cash transfers protect households from welfare losses for the subset of households that could be considered vulnerable based on their baseline characteristics.

In this section, we have shown that cash transfers have substantial welfare effects among eligible households exposed to shocks. Findings consistently point to households becoming more resilient in the sense that they can protect themselves against the welfare effects of shocks. The use of recently suggested resilience indicators confirms findings obtained from heterogeneity analysis based on exogenous rainfall data.

# _4.3 Mechanisms: Savings, income-generating activities, and assets_

We now analyze mechanisms that contribute to improve welfare and resilience among households eligible for cash transfers. One key question is whether households’ improved ability to mitigate the welfare effects of shocks is solely due to the direct effect of the transfers, or if instead it is due to the way households use the transfers to save, accumulate assets or generate income from economic activities when shocks occur. Section 4.2 has shown that the welfare 23
||Colombia|Peru|Philippines|Vietnam|<sup>International</sup><br>benchmark|
|---|---|---|---|---|---|
|**Regulatory Governance**|**45%**|**83%**|**48%**|**32%**|**59%**|
|**Accountability**|**75%**|**85%**|**95%**|**64%**|**83%**|
|_Regulatory Oversight_|67%|67%|100%|67%|81%|
|_Legal Appeals_|100%|100%|100%|100%|100%|
|_Transparency_|57%|89%|85%|25%|67%|
|**Autonomy**|**60%**|**98%**|**51%**|**50%**|**71%**|
|_Decision-Making Autonomy_|64%|92%|79%|36%|79%|
|_Budgetary Autonomy_|88%|100%|50%|50%|80%|
|_Leadership Autonomy_|88%|100%|75%|14%|66%|
|_Managerial Autonomy_|0%|100%|0%|100%|59%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform In general, ERCs performance on Accountability is very good (95 percent), as the average of its de jure scores in Regulatory Oversight—100 percent, Legal Appeals—100 percent, and Transparency—85 percent. The main limitations for transparency include the fact that regulator recommendations are not required to be publicly available.

# _3.4.2.2 Autonomy_

ERC is an independent commission in charge of granting licenses for electricity generation and supply; of setting transmission and distribution wheeling rates and retail rates for the captive market of distribution utilities; of regulating quality of supply and service; and of regulated utilities oversight. ERC is also responsible for regulating prices or terms of Power Purchase Agreements (PPA), for market design, and for promoting renewable energies and energy efficiency.

ERC’s decisions are binding regarding all matters but utility oversight, in which area its decisions are advisory. Also, according to the EPIRA Act, the ERC shall promulgate and enforce the National Grid Code and a Distribution Code, including performance standards for TRANSCO O & M Concessionaire, distribution utilities and suppliers, and financial capability standards for the generating companies, the TRANSCO, distribution utilities, and suppliers. These standards are set to ensure that electric power industry participants meet the minimum financial standards to protect the public interest.

ERC also enforces the rules and regulations governing the operations of the electricity spot market, as well as the activities of the operator and other participants in this market, for the purpose of ensuring a greater supply and rational pricing of electricity.

It is reported that there are no formal provisions under which a ministry or other government body can overturn ERC’s decisions. However, according to the survey, members’ appointment process is not transparent. The Regulator’s leadership does in fact have known connections with the government, the current Chairman being a former employee from the Department of Justice.

47