rafmacalaba/data-use-mentions-extended huggingface dataset:
--|---|---|---|
|Chemical|229.23|93.86|47.60|28.00|38.65|<br>21.12|
|Non‐metal|228.38|162.96|26.95|7.92|30.09|<br>0.45|
|Non‐ferrous metal|65.03|15.27|5.64|2.36|38.46|<br>3.31|
|Paper|28.47|15.72|0.89|0.23|6.58|<br>5.06|
|Textile|36.66|11.97|1.45|0.37|15.69|<br>7.18|
|Black metal|473.39|76.92|2.69|43.34|56.68|<br>8.44|
|Others|498.14|116.30|119.58|33.59|161.85|<br>27.13|
|Total|1559.30|493.00|204.80|115.80|348.00|<br>72.70|

Transport energy demands are derived based on vehicle type, fuel type, technical efficiency and fuel economy and different emission standards. In the base year, transport energy demands are as shown in Table B4.

**Table B4** Transportation sector data for the base year

|Road gasoline/Mt|99.47|
|---|---|
|Highway diesel/Mt|115.3|
|Railwaydiesel/Mt|9.79|

43
_Table 1: The aggregate impact of the oil shock_

||Percent change<br>with respect to<br>no shock|
|---|---|
|Consumption per capita|‐6.9|
|GDP per capita|‐1.1|
|_Labor demand_||
|Unskilled (Agriculture)|6.9|
|Unskilled (No‐Agriculture)|‐0.6|
|_Wages_||
|Unskilled (Agriculture)|‐3.9|
|Unskilled (No‐ Agriculture)|‐10.6|
|Skilled|‐11.5|
|Skill Premium|‐1.5|
|Urban Premium (for unskilled)|‐7.0|
|_Consumer Price Index_||
|Food|‐7.8|
|Othergoods|‐12.7|

Source: GTAP and ROSSTAT data, and CGE simulation results Following the reduction of demand, prices of non‐tradables decrease making tradables relatively more expensive. This real exchange rate depreciation induces shifts in economic activity and employment that are quite different across sectors. Table _2_ shows that, in terms of output contraction, besides the oil sector, the hardest hit sectors are non‐tradables, namely construction, transport and communication, other business services and public services. While import competing industries, and export oriented sectors, benefitting from the real depreciation, experience output expansions. The important caveat of the model operating as if the economy adjusts to its new long term equilibrium without frictions needs to be reiterated here. The gains of the expanding sectors are genuine opportunities, but they may not necessarily be realized quickly in the real economy. The analytical framework used here to model the oil shock is signaling what could happen if the markets were fully flexible and resources were allowed to respond to the new price signals; whereas, in reality, it may not be possible to reallocate resources as markets are just imperfectly flexible. For example, closure of firms in urban shrinking sectors such as
analysis on the errors, a missing value is inserted in place of the 0 for the error variables. Furthermore, a ‘Don’t Know’ binary indicator is created that takes the value 1 whenever a respondent does not know the answer to a question.

Our benchmark data is regarded as the best available in the Ethiopian context. One proxy of the quality of the benchmarking data is their stability over time. By assessing the extent to which indicators vary within districts over data collection rounds, we aim to understand whether the data reflect a noisy collection process. For example, if the ratio of pupils per school has a high variance from one year to the next within the same district, this could be seen as a warning sign as to the quality of measurement. Table A2 presents summary statistics and tests for the presence of a random walk for each of the indicators used in the analysis where panel data are available. Column 4 shows the estimated autoregressive parameter from a Dickey Fuller Regression, controlling for district fixed effects and column 5 shows the estimated autoregressive parameter from a Dickey Fuller Regression, controlling for district fixed effects and districtspecific linear time trends. The estimated coefficients are less than 1, which indicate stability in the trend, for all indicators except for three: the amount of land used for agricultural purposes; the amount of land used for pastoral purposes; and the number of business licenses issued. In addition, column 6 reports the z-statistics and column 7 the associated p-value from a unit-root test based on Harris and Tzavalis (1999) where there are sufficient data to do so. Column 8 reports the fixed-N critical value at the 5% level and column 9 the fixed-N test statistic from Im, Pesaran and Shin (2003), allowing for linear time trends. These tests are appropriate for panel data settings with short timeframes. The results of the tests provide significant evidence against the existence of a unit-root process, and therefore a random walk, for the tested series. These results provide support to the notion that
{.365}|-.111|
|**Pregnant girls allowed to sit exams in the nearest**<br>**secondary school**|.239|.319|{.270}|.132|
|**_C. Policy Responses_**|||||
|**Village was quarantined**|.060|.040|{.595}|-.065|
|**Village visited by contact tracing team**|.960|.933|{.455}|-.084|
|**Received Relief from NGO**|.780|.873|{.139}|.174|
|**Village received food aid**|.260|.213|{.504}|-.077|
|**Village received school supplies (excl. BRAC)**|.220|.207|{.818}|-.023|
|**Received relief from Government**|.800|.653|{.005}|-.234|

**Notes:** Data sources are the Village Census (Panel A), and the Village Leader Surveys (distance measures in Panel A, all outcomes in Panels B and C). Column 3 reports p-values from a test of equality of means carried out by OLS regression of each characteristic on a dummy for assignment to treatment. All regressions include strata (district) dummies and calculate robust standard errors. Normalized differences in Column 4 are computed following Imbens and Wooldridge [2009]. The Poverty Score (PPI) is calculated through scorecards and its value, ranging from 0 to 100, represents the likelihood of a household being below the poverty line. The number of pre-existing NGOs includes all organizations apart from BRAC. Distance from Freetown and Kailahun are computed from GPS data.
.783***|11.796***|12.171***|7.432***|7.124***|5.410***|3.989***|6.771***|6.438***|8.995***|
||(178.574)|(164.356)|(87.859)|(53.230)|(24.620)|(22.630)|(16.838)|(15.997)|(9.123)|(8.376)|(5.914)|(4.200)|(7.561)|(5.320)|(8.685)|
|Observations|1,404|1,313|1,262|1,255|1,058|1,021|935|654|651|650|575|572|650|445|491|
|Number of country_code_|109|103|102|101|94|92|84|64|64|64|63|62|64|50|51|

Note: Annual information between 2005 and 2017 for 51 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _Policy rate_ is the Central Bank or Monetary Authority’s benchmark interest rate for monetary policy. In case that policy rate is not available, money market rate is used. Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included. t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 42
# **Figure 2.1 Evolution of uncertainty in EMDEs**

_While financial market uncertainty, defined in terms of stock market volatility, has declined in most EMDEs, policy and political uncertainty increased from the pre‐crisis to the post‐crisis period for most EMDEs. Generally low post‐crisis financial market volatility was interrupted by several bouts of global financial market volatility._ **A. Share of EMDEs with higher uncertainty in post‐ B. Evolution of uncertainty crisis than pre‐crisis.** <!-- Start of picture text --> Percent Index, 2002Q1=0<br>100 Financial market uncertainty<br>6 Political Risk<br>80 EPU<br>VIX<br>60 3<br>40<br>0<br>20<br>-3<br>0<br>Financial market Political risk EPU<br>uncertainty 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Sources: Baker, Bloom, and Davis (2016); Bloomberg; Haver Analytics; International Country Risk Guide; World Bank estimates.

Notes: 33 countries for measure based on standard deviation of daily stock market changes; 102 countries for ICRG political risk score; and 4 countries (Brazil, China, India, and Russia) for the EPU measure. Financial market uncertainty refers to realized standard deviation of daily changes in stock price changes. Political risk refers to the ICRG political risk index (adjusted such that higher index denotes higher risk).

A. Pre‐crisis and post‐crisis refer to 2003‐08 and 2010‐15, respectively. To exclude data for the spike in global financial market uncertainty in the wake of the bankruptcy of Lehman Brothers, pre‐crisis average for financial market uncertainty excludes 2008. Last observation is for Q1 2016.

B. All series are normalized to standard deviation of 1.

# **Figure 2.2 Financial market uncertainty
purchases. Uncertainty regarding the true ownership of the land is rarely a serious concern in the case of intra-village transactions. However, many sale-purchase transactions go unrecorded in land records, since the process of mutation (voluntary registration of a sale deed and acquisition of title) is complex, lengthy and expensive. Officially sanctioned transaction costs amount to at least 17 percent of the value of the land transacted, and the 'informal' transaction costs required to expedite the process may amount to as much again, even discounting the opportunity costs of repeated visits to registrar and tehsildar's offices over a period of several years. The computerization of land records may contribute to a reduction in these transaction costs, but only if coordinated with computerized land registration.

_Landfragmentation:_ the fragmentation of land holdings into tiny, scattered plots is a consequence of the custom of partible inheritance, in which each individual plot is subdivided among various heirs. There is thus a lifecycle effect, in which newly formed households acquire very small holdings on the subdivision of formerly joint family holdings. Land fragmentation is widely perceived to operate as a brake on agricultural productivity, and the Government of Orissa has responded by implementing a land consolidation program since 1974. Land consolidation does not contribute directly to improving access to land for the rural poor, since it aims to leave land distribution unchanged. But as in the case of survey and settlement operations, there is some evidence that land consolidation operations result in a certain amount of discrimination against the rural poor and other socially excluded groups. In spite of continuing demographic pressure, the rate of fragmentation actually declined from an average of 6.4 to 5.0 parcels per holding between 1961-62 and 198182. Much of this decline took place before the impact of the land consolidation program could be observed, which suggests that a certain amount of individually initiated land consolidation takes place through the voluntary exchange of land plots in the market.

Evidence from the field confirms that land fragmentation persists for two main reasons: the need to spread risk,
|Computer (basic)|
|**Project Management**|39|33|12|2,10|Project management and<br>business|
|**Occupational Health and**<br>**Safety**|498|227|13|8,03|People management|
|**Environmental**<br>**Management**|963|369|14|12,06|Project management and<br>business|
|**Writing**|15|18|15|1,07|Writing|
|**Engineering**|33|22|16|1,52|Cognitive (analytical)|
|**Budgeting**|18|37|17|1,06|Financial|
|**Fundraising**|935|478|18|9,77|Customer|
|**Policy Research**|3684||19|51,22|Cognitive (analytical)|
|**Compliance Management**|1244|267|20|11,26|Project management and<br>business|

_Source_ : authors using OV data.

# Application #3: Gender and education differences in employment

Access to green jobs is not even across groups of workers, with women being less likely to hold a green job independently of the definition applied. Females hold 9 percent of green jobs (Figure 4.4 panel a). This is significantly lower when compared to zero GTI (non-green) jobs, for which female represent 41 percent of workers. Furthermore, green jobs employ more educated workers (although not necessarily at the university level) (Figure 4.4 panel b). Two-thirds of green workers have at least a high-school or vocational degree as compared to half zero GTI workers. When using the narrow definition, our analysis also suggests that workers at green jobs are younger (43 percent of non-green workers are under the age of 35 while 54
In this paper, we report the results of an innovative systematic review identifying the most effective interventions to improve girls’ access to education and learning outcomes within an evidence base that includes both girl‐targeted and general education interventions. We pose three research questions: (1) Are girl‐targeted interventions more effective for girls’ outcomes than general interventions? (2) For general, non‐targeted interventions, do impacts on girls tend to be larger? and (3) In absolute terms, what are the most effective interventions for girls?

To answer these questions, we collected and reexamined a large database of education studies with access or learning outcomes for students. We categorized the studies as either evaluating girl‐targeted or non‐targeted (i.e., general) interventions and identified all studies that reported gender‐differentiated impacts. Only one in three studies of interventions not targeted to girls report disaggregated impacts by gender, so a first implication of this work is that in order to understand how best to improve girls' education, studies should consistently report impacts for girls. For those studies that did not report gender differentiated impacts, we contacted their authors asking them either to run the additional gender differentiated analysis or to share the data with us. We then standardized the effects of different programs to ensure comparability of effect sizes across studies. Ultimately, we synthesized the effects for girls from 177 studies. (The full list of studies is available in Appendix A.) We find that general, non‐targeted interventions perform similarly to girl‐targeted interventions on average to increase both girls’ access to school and their learning in school. General interventions tend to have similar effects for girls and for boys. (The evidence suggests that if anything, girls benefit more from general interventions, although the differences are not statistically significant.) In examining the most and least effective interventions for girls’ education, we find that girls’ access to school is more responsive to changes in costs, distance, and health
the multiplier, as the eect of these years is mostly osetting (standard errors are slightly larger, and signicance is now at the 5% level). For temporary transfers, the scatter plots in Figure 2 suggest that Mississippi (MS) might be inuential. In the right two columns of row D1, I drop MS and show that it has little eect on estimated multipliers, which are still signicant at 5%. No other states are inuential either when dropped one-by-one (see Appendix Figure 3.2).<sup>22</sup> 3.3.4. Instrumental Variable Estimates. As discussed in Section 2.3, the specication (Equation 2) is a reduced form as the exogenous changes in transfers are essentially the variable of interest. However, it is worth estimating an instrumental variables specication (row D2) in case the constructed transfer variables are measured with error (which could attenuate multiplier estimates), or there are other correlated transfer payments that I am not capturing (leading to upward bias). For permanent transfers in the rst column, I instrument all Social Security transferswhich are potentially endogenoususing my sample of exogenous Social Security increases. The multiplier is around 1.9, signicant at the 1% level (the rst-stage F-stat is above 500).<sup>23</sup> For one-o transfers in the second and third columns, I instrument changes in the potentially endogenous BEA category All other personal current transfer receipts (as a share of labor income or GDP) using the pooled 2008 ESP

> 22The second last column of Appendix Table 3 adds back in previously dropped extreme outliers, where |growth|>20% (annualized). For permanent transfers that reduces signi- cance to a p-value of 8%, and it also increases the estimated multiplier to 2. For temporary transfers, there is little eect on the labor income specication
pension funds may be targeted by authorities to finance increasing levels of public debt. Policy makers in countries with little fiscal space might promote home bias to support increased public assistance spending resulting from the COVID-19 lockdowns (Stewart, Davis & Knaack 2021 forthcoming).

In line with arguments in favor of international diversification, foreign exposure of pension funds has increased over the last two decades, from 16% of assets in 2002, to 36% by the end of 2019 (data and details in Section 3). However, such systematic increase in international exposure has been uneven, with pension funds in many countries displaying little or no change. Taken as a whole, whether through regulatory imperative or behavioral tendencies to avoid international investment, many pension funds around the world are delivering less than their full potential and exhibiting much higher volatility than necessary given their lack of international diversification. This is occurring despite what is generally a building crisis in adequate pension provision in many countries –mainly due to the rapid aging happening across the world, coupled with what have been some unsustainable parametric design features for existing schemes. Given the magnitude of this unfolding dilemma, it will be important for funds to consider better incorporating the benefits of international diversification and regulators to reconsider restrictions that have shortchanged many systems at the likely future cost to their members.

Most of the financial literature investigating home bias takes the perspective of a US investor. Research on home bias outside of the US is scarce and, if available, does not explain the dynamics of home bias in mature markets. Because the gains from international diversification vary considerably across markets, there is room to study home bias in other markets. In this sense, the pension industry offers an invaluable opportunity to further explore the determinants of home bias. The availability of multi-asset data in many pension jurisdictions with a sufficiently long horizon provides the opportunity to study the cross-sectional variation in domestic bias in the
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index (Diener et al., 2009) and ultimately reflects the analysts’ and policy makers’ subjective opinions on the value of different domains for experienced welfare (Diener et al., 2009).

Second, objective measures capture well the quantifiable part of a concept but often do not include all its relevant and/or qualitative aspects. Income does not always reflect people’s standard of living or whether people are able to make ends meet (Ravallion, 2012). People may have to work two or three jobs to ensure that they have sufficient income, or they may have to commute long distances on a daily basis to access well-paying jobs. Similarly, objective crime levels are not always accurate in capturing the safety in an area (Lorenc et al., 2012) and objective inequality can differ considerably from perceived inequality (Gimpelson & Treisman, 2018). Standards of living are affected by environmental and climate issues, quality of public services, governance, social cohesion, and other difficult to measure quality dimensions of development. SWB measures factor in the effects of these issues on experienced welfare by gauging people’s evaluations, experiences, and expectations (Diener et al. 2009; Jahedi & Mendez, 2014).

Third and particularly related to this research, SWB data can be used to uncover the objective and subjective conditions that underpin experienced welfare using regression analyses and identify the domains that are most important in explaining changes in experienced welfare for specific groups. The main objective and subjective factors underpinning individuals’ SWB have been welldocumented (Helliwell, 2006; Frey, 2010; Layard, 2011; Clark, 2018), but the regressions used in these analyses pose challenges. Systematic differences have been found in how people value different domains of importance to experienced welfare across and within countries (Deaton, 2008; Tiefenbach & Kohlbacher, 2016). In addition, people recalibrate their SWB based on the ‘ideal’ they have for their personal life (‘reference point’). Thus, people’s values
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