rafmacalaba/data-use-mentions-extended huggingface dataset:
service. At the same time, KE owes NTDC payment for the 600MW that it receives from the grid, and owes Sui Southern Gas Company and Pakistan State Oil for fuel supply.

**Table 14: Utility management index of utilities in Pakistan and comparators, 2015**<sup>**41**</sup>

||Odisha,|India|AP,I|ndia|Raj,|India|Pak|istan|International<br>|
|---|---|---|---|---|---|---|---|---|---|
||WESCO|CESU|APSPDCL|APEPDCL|JVVNL|JDDVNL|LESCO|KE|benchmark|
|**Utility Management**|**50%**|**40%**|**56%**|**56%**|**60%**|**60%**|**48%**|**86%**|**64%**|
|_Financial Discipline_|_43%_|_36%_|_53%_|_53%_|_64%_|_64%_|_50%_|_79%_|59%|
|_Human Resource_|_70%_|_40%_|_43%_|_43%_|_36%_|_36%_|_43%_|_86%_|62%|
|_Information and Technology_|_36%_|_43%_|_73%_|_73%_|_80%_|_79%_|_50%_|_93%_|71%|

_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._ The performance of the two utilities
in particular, are defined by Cislaghi and Heise (2020) as: Social norms<sup>2</sup> defining acceptable and appropriate actions for women and men in a given group or society. They are embedded in formal and informal institutions, nested in the mind, and produced and reproduced through social interaction. They play a role in shaping women and men’s (often

> 1 World Bank Gender Data Portal (national estimates). https://datatopics.worldbank.org/gender/. Accessed February 4, 2021.

> 2 Rule of behavior related to the differences in societal expectations for women and men. Individuals prefer to follow such rule if they believe that most people in their reference network conform to it and believe they should follow it (Bicchieri, 2005; Mackie et al., 2015).

7
53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **6 Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final
Development Indicators|
|Expected real growth|Expected real growth rate of GDP per capita, 2009|World Development Indicators|
|Foreign owned banks, share|Percentage of banking system assets in banks 50percent + owned by foreign|Barth, Caprio, and Levine|
|of assets|entities|(2004)|
|Fuels and mining/GDP|Exports of fuels and mining products (current USD) as percent of GDP, 2007|WTO|
|GDP growth|Gross Domestic Product per capita in current dollars of 2007|World Development Indicators|
|GDP growth avg|GDP growth 5 year average 2002‐2007|World Development Indicators|
|GNI per capita|Gross National Income per capita in current dollars of 2007|World Development Indicators|
|Inflation|Inflation, consumer prices (annual percentage) 2007|IFS|
|Interest spread|Interest rate spread (lending rate minus deposit rate, percent) 2007|IFS|
||Getting credit, Legal Rights Index, 2007. This index measures the degree to|World Bank Doing Business|
|Legal rights index|which collateral and bankruptcy laws protect the rights of borrowers and<br>lenders and thus facilitate lending.|Indicators (2008)|

29
Procuring Entity Officials were asked about the number of days taken for completing different administrative processes as per their estimation. Their estimates/perception of these times have been compared with the actuals times and tabulated in the table below.

TABLE 53: COMPARISON OF TENDER PROCESSING TIMES BASED ON ADMINISTRATIVE DATA AND ESTIMATION OF PROCURING ENTITY OFFICIALS

|Efficiency<br>Indicator|Model (<br>Manual|4a): CEM<br>across ye<br>e-proc|within PEs<br>ars<br>Treatment<br>vs Control|Model<br>ye<br>Manual|(4b): CE<br>ars acros<br>e-proc|M within<br>s PEs<br><sup>Treatment</sup><br>vs Control|S<br>Manual|tudent t-<br>e-proc|tests<br><sup>Treatment</sup><br>vs Control|
|---|---|---|---|---|---|---|---|---|---|
|Lead Time|84.1|65.0|-19.2***|80.9|65.3|-15.6***|64.9|50.0|-14.9***|
|Evaluation Period|9.0|19.2|10.2***|9.3|21.3|12.0***|15.3|10.5|-4.8***|
|Approval Period|11.0|3.5|-7.5***|13.4|3.4|-10.0***|19.8|14.9|-4.9***|

Note 1: +
640|8.578|-0.062<br>(0.241)|-0.167<br>(0.266)|-0.234<br>(0.309)|
|±0.03|43|8.700|8.708|0.008<br>(0.204)|0.032<br>(0.213)|-0.058<br>(0.232)|
|±0.04|48|8.714|8.693|-0.022<br>(0.203)|-0.068<br>(0.206)|-0.094<br>(0.207)|
|_Controls_|||||||
|Socio-ec.<br>background||||No|Yes|Yes|
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional
The robust and cluster choices can make a difference to inference about the Gini, especially if the sample is small and the number of countries and years is reduced. Different choices do not have an impact on the sign or size of the coefficients but can have an impact on standard errors and significance levels. In Table 3 we see that when the robust and cluster estimations are introduced (eq. 2 and 3) the z-statistics can visibly change. This is particularly true for the Gini WIDER and for the smaller sample that considers a restricted number of countries and years (sample ‘2’).

More importantly, introducing or removing country and year fixed effects can alter inference on inequality remarkably. When country fixed effects or both country and year fixed effects are introduced, all Gini are negative and significant (eq. 4, 7, 8, 10, 12, 13, 15, 16). This is what we found in Tables 1 and 2 where we used country and year fixed effects for all equations. When country and year fixed effects are removed or year fixed effects are used alone, the Gini turns positive and significant or non-significant (eq. 1, 2, 3, 5, 6, 9, 11 and 14). In particular, it would seem that country fixed effects have an important influence on multicollinearity and significance levels while year fixed effects have a relevant role in changing the sign of the Gini. Indeed, the VIF values for the Gini are small only when country fixed effects are removed and the Gini turns positive only when year fixed effects are used alone. This phenomenon applies equally to both Gini and both samples considered indicating that this is not a phenomenon dependent on the choice of these factors.

The sensitivity of the Gini coefficient to country and year fixed effects may relate to various factors. It may be for example that there is moderate within countries variation of the Gini over time or, if there is variation, time trends are similar across countries. The World Values Survey is
population study<sup>2</sup> (Brown et al, 2019) revealed that 78 percent of the population of registered refugees lived below the Jordanian poverty line, and between 2017 and 2019, there was only a 2- percentage point reduction of highly vulnerable cases.<sup>3</sup> UNHCR (2018a) also reports that close to 37% of the registered Syrian refugees in Jordan are separated from a family member. Household structures have often been fluid. Many Syrian families sent members ahead to Jordan to settle and sometimes, after the reunification of the rest of the family in Jordan, male family members traveled on to <mark>Türkiye</mark> or to Europe (UNHCR, 2018a). These separations likely affected families emotionally and economically. However, little is known about the changes in economic well-being over time or how gender inequality has shaped poverty and expenditure outcomes.

We use two waves of data from a unique household survey, the United Nations High Commissioner for Refugees (UNHCR) Home-Visits survey for 2013-14 and 2017-18. First, we create comparable measures of expenditure per capita over time. Then, we assess whether certain types of households are more likely to be below the median of the per capita expenditure distribution in each time period. The UNHCR registers households as having a male or female principal applicant (PA) and we use this classification as equivalent to male or female headship used in the poverty literature. We find that there is no significant difference in the share of female versus male- PA households below the median in either 2013-14 or 2017-18.

However, there are important differences between male and female PA households that impacts poverty risk. As is evidenced in other countries (see for example Brown et al.’s study of 43 African countries (2020)), we find that Syrian refugee female PA households are smaller in size than malePA households. Once economies of scale in consumption are considered we find female-PA households are more likely to be poor than male
in order to carry out a more comprehensive and accurate assessment of local impacts of aggregate growth in tourism expenditures—survey data at the level of tourism destinations. No data are available to help determine which specific types of employment and specific subsectors of the economy the incremental income from the tourism sector accrues to. For example, it is not possible to distinguish how much of the increased demand for agricultural products and services is met through local providers, and how much is imported from outside the region. Availability of such data would greatly enhance the possibility of accurate monitoring of local impacts of tourism development.

17
2|0|2|1|2|2|5|
|Cash acceptance|15|4|2|3|2|2|1|1|

Source: World Bank COVID-19 Financial Sector Policy Response Database.

Note: World Bank regional classification. High Inc. = High income; EAP = East Asia Pacific; ECA = Europe & Central Asia; LAC = Latin America & Caribbean; SAR = South Asia; MENA = Middle East & North Africa; and SSA = Sub-Saharan Africa. WBG regions exclude high income countries.

# _2.3 Financial Policy Response Activity Index_

This section introduces the _Financial Policy Response Activity Index_ (FPRAI), a simple, transparent proxy that can be used to compare financial sector policy activity across countries _._ FPRAI is computed by summing up all the policy measures implemented up to time ( _t_ ) in country ( _c_ ) across the four categories: _Banking Sector_ , _Financial Markets and NBFIs_ , _Liquidity and Funding_ , and _Payments Systems_ .<sup>9</sup> As such, it is important to keep in mind that the FPRAI is silent on the _scale_ and _effectiveness_ of the financial policy response. Figure 2 displays the FPRAI and shows that most financial sector policy makers around the world have been active, although to a lesser extent in Sub-Saharan Africa (see Table A3 in Appendix A for the descriptive statistics of FPRAI by country groupings).

> 9 Alternative, less transparent approaches, such as min-max transformations and averaging across categories produce similar results.

13
# ABSTRACT

Representative information on household energy is essential for organizations and individuals seeking to: (a) identify residential energy proMlems and trends; (b) prepare supply and demand projections; (c) engage in energy sector planning, (d) conduct market research; (e) develop effective policies and programs as part of household energy strategies; and (f) evaluate ongoing and completed projects. Without comprehensive knowledge of how households acquire and use energy, attempts to deveiop activities and policies which purportedly seek to improve domestic welfare and/or protect the environment can be futile and even counterproductive. In fact, recent efforts to develop household energy databases have led to the overturning of inaccurate preconceptions and false paradigms upon which past actions and policies had been based.

Typically, information relating to residential energy consumption has been dealt with through country energy balances, national budget surveys and micro-studies. Edch c' lhese has certain advantages which, however, are outweighed by their drawbacks. Instead, a dedicated, nationwide household energy survey can be the most effective means of generating relevant data for plaraiers, policymakers and evaluators. This database, combined with the results of more specialized studies, can then be used to understand and improve on the complex systems that are part of household energy consumption.

For household energy surveyin%, this paper offers some lessons and caveats from recent experience concerning:

- overall survey Jesign;

- sample design;

- questionnaire preparation;

- survey fieldwork and logistics;

- data processing and analysis; and

- costs and timing.

Finally, a quick checklist is provided to assist practitioners in the preparation of household energy survey work.
Policy Research Working Paper 9168

# **Abstract**

Intimate partner violence is the most common form of violence against women in conflict and non-conflict settings, but in conflict settings it often receives less attention than other forms of gender-based violence, such as conflict-related sexual violence. Using data from the 2008 and 2013 Domestic Violence module of the Nigerian Demographic and Health Survey spatially linked to the Boko Haram actor file of the Armed Conflict Location and Events Database, this paper employs a kernel-based difference-in-difference model to examine the effect of the Boko Haram insurgency on women’s experience of physical and sexual intimate partner violence. It also examines the effect of the Boko Haram insurgency on women’s experience of controlling behavior from a husband or partner, women’s autonomy in household decision making, and their control over their own earnings. The paper finds that the Boko Haram insurgency is associated with slower progress toward preventing and eliminating women’s experiences of physical and sexual intimate partner violence. Controlling behaviors from husbands/partners and reductions in women’s autonomy in household decision making are heightened in locations that are impacted by the Boko Haram insurgency, indicating that the Boko Haram insurgency adversely affects women’s agency and exacerbates behaviors that are often precursors to intimate partner violence.

This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at uekhator@worldbank. org, lhanmer@worldbank.org, erubiano@worldbank.org and darango@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of
nancial hedges are complements rather than substitutes. If that is the case, sectors with natural hedge would be the ones better able to use nancial instruments and would be stronger in countries where these instruments are available. Disentangling these competing explanations is an interesting topic for future research.

# 5 Conclusion

This paper has shown evidence of an indirect causal eect of exchange rate volatility on trade by looking at the dierential eect of this volatility on the export share of sectors producing commodities whose price correlation with the home country's exchange rate oers dierent degrees of natural hedge against exchange rate uctuations. The results indicate that sectors that oer natural hedge (i.e. those whose international price comoves negatively with the country's nominal exchange rate) are indeed larger relative to others, even after controlling for standard determinants of trade composition, in countries with more volatile exchange rates. Thus, exchange rate volatility tilts the structure of trade towards sectors that produce goods that oer a natural hedge against this volatility. This dierential eect comes mainly from countries with a oating exchange rate regime. Contrary to expectations, there is only weak evidence that the relation between natural hedge, exchange rate volatility, and export composition is stronger in countries where rms have no access to nancial instruments to hedge currency risk, such as exchange rate derivatives.

Overall, the results provide evidence that exchange rate volatility has real consequences for trade and that the structure of trade responds not only to standard factors, such as endowments, but also to other more institutionally driven constraints. If what you export matters, as suggested by Hausmann et al. (2007), addressing these sources of ineciencies would be a better way of reaching a trade pattern that is not driven by risk hedging than engaging in some sort of industrial policy.

The analysis of this paper can be extended along several dimensions. First, using more disaggregated data may permit obtaining better proxies of international price uctuations and
to account for any potential unobserved bias.

The last concern is the likelihood that the accession impacts within the treated group also affect the control group; for example, through intra-districts trade or cross-border interactions – the violation of the classical stable unit treatment value assumption (SUTVA).<sup>19</sup> Indeed, we expect the treated and control districts to have been trading even before China’s WTO accession. Nonetheless, we do not have district-level trade data with which to squarely address this concern. As a way to control for this violation, we rely on both district and country-year fixed effects controls. We argue that these fixed effects implicitly capture the unobserved intra-trade or crossborder interactions between the treated and control districts.

# 4. RESULTS

# _A. Descriptives_

Table 3a reports the descriptive statistics for annual lights growth, population density, spatial Gini, and the spatial Sen Index from 1997 to 2007. The table shows that, on average, annual light growth was around 0.03 percent. Population density averaged 288 people per square kilometer. Spatial Gini and Sen Index averaged 0.254 and 4.003, respectively. Except for spatial Gini, the rest of the outcome variables show considerable variations around their mean.

> 19 We rule out the second SUTVA assumption, variations in treatment, since the treatment does not vary across the treated group: all districts are subject to the same post-WTO accession dummy for the treatment period 2002–2007.

14
73

# **Table A2a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Innovation Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A2a.1 **|**Reg A2a.2 **|**Reg A2a.3 **|**Reg A2a.4 **|**Reg A2a.5 **|**Reg A2a.6 **|**Reg A2a.7 **|**Reg A2a.8 **|**Reg A2a.9 **|**Reg A2a.10 **|**Reg A2a.11 **|**Reg A2a.12 **|**Reg A2a.13**|**Reg A2a.14**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**Per Worker**|**0.2117*****|**0.2119*****|**0.2084*****|**0.1973*****|**0.2147*****|**0.2126*****|**0.2133*****|**0.2251*****|**0.2031*****|**0.2111*****|**0.2118*****|**0.2020*****|**0.2027****
**101.4**|55.9|
|Paving ratio|Percentage of primary network paved|82.1|**39.4**|71.6|
|GIS Rural accessibility|Percentage of rural population within 2 km of<br>an all-season road|19.7|**23.7**|34.1|
|Paved road traffic|Average annual daily traffic (vehicles per day)|1,408.2|**573.9**|1,287.7|
|Unpaved road traffic|Average annual daily traffic (vehicles per day)|54.2|**17.1**|38.5|
|Classified network condition|Percentage in good or fair condition|79.9|**60.0**|86.2|
|Overengineering|Percentage of main road network over-paved relative<br>to traffic flows|15.0|**32.2**|29.6|

Source: Gwilliam and others 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data

## **Challenges**

Current spending on roads still falls short of what is needed, despite recent efforts to increase it _._ Based on the physical configuration and condition of Liberia’s infrastructure network, it is possible to estimate the resources that would be needed to clear the current rehabilitation backlog and maintain the network in good condition. Comparing recent spending against these norms indicates that Liberia still has a substantial shortfall of around 40 percent both in capital spending for rehabilitation and in network maintenance spending (figure 4). This situation is not unusual in West Africa. Nonetheless, some countries—such as Ghana and post-conflict Rwanda—have succeeded in securing adequate resources for road network preservation.

8
|(0.000)|
|Female|-0.002|-0.000|-0.002|-0.002|0.000|-0.002|
||(0.005)|(0.005)|(0.001)|(0.005)|(0.005)|(0.001)|
|arcsinh(income)||||-0.001*|-0.001|-0.000***|
|||||(0.001)|(0.001)|(0.000)|
|Wage worker mean|0.171|0.159|0.014|0.171|0.159|0.014|
|Sample size|22,830|22,802|19,930|22,727|22,699|19,827|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the KHDS 1991/94 and 2010, PSID 2001 and 2017, ELMPS 1998 and 2012, IFLS 2000 and 2014/15, MxFLS 2002 and 2009/12, and LSMS-ISA 2010/11 and 2015/16. We drop respondents who are not self-employed or paid workers in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country fixed effects. Columns (4)(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave.

41