rafmacalaba/datause-encoder-data huggingface dataset:
**Table B2** Treatment-Control Balance at Baseline

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Outcome**|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**Supply-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|**A. Intimate Partner Violence**||||||
|Psychological abuse often|0.017|0.006<br>(0.008)|0.004<br>(0.008)|0.018|0.006<br>(0.006)|
|Physical abuse often|0.008|0.010<br>(0.007)|0.001<br>(0.006)|0.011|0.000<br>(0.004)|
|Forced sex often|0.012|0.004<br>(0.006)|-0.001<br>(0.006)|0.013|-0.001<br>(0.004)|
|Psychological abuse in last year|0.054|0.017<br>(0.017)|0.013<br>(0.019)|0.062|0.006<br>(0.010)|
|Physical abuse in last year|0.045|0.011<br>(0.016)|0.010<br>(0.017)|0.053|-0.007<br>(0.009)|
|Forced sex in last year|0.035|0.006<br>(0.013)|0.008<br>(0.014)|0.040|-0.007<br>(0.008)|
|**B. Sexual Activity**||||||
|Ever had sex|0.250|-0.001<br>(0.035)|0.047<br>(0.036)|0.261|0.006<br>(0.018)|
|Currently has partner|0.212|0.011<br>(0.034)|0.050<br>(0.034)|0.230|-0.006<br>(0.017)|
|Had partner past 2 years|0.266|0.010<br>(0.037)|0.043<br>(0.037)|0.279|0.003<br>(0.019)|
|Total sexual partners ever|0.312|0.018<br>(0.054)|0.073<br>(0.052)|0.334|0.001<br>(0.026)|
|Hours with boyfriend in the<br>past day|0.030|0.014<br>(0.013)|0.015<br>(0.016)|0.041|-0.002<br>(0.011)|
|Observations|1,074|3|,178|2,313|3,178|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

70
86 RENT CONTROL IN KMMASI, GHANA

**Table 5.7:** Summary Cost-Benefit Measures From Cross Country Model

||Actual<br>Rent<br>PcQc|PredLcted<br>Rent<br>P,Qm|Difference<br>PmQm-cQc|
|---|---|---|---|
|Tenement<br>Sample||||
|Mean|290|1179|887|
|Q3<br>Median|300<br>300|1334<br>1162|1043<br>868|
|Ql|300|999|711|
|N|358|338|338|
|Representative||||
|Consumer|300|t62|862|
|Indigenous<br>Sample||||
|Mean|244|1040|795|
|Q3|300|1186|955|
|Median|250|1025|766|
|Q1|200|829|584|
|N|322|312|312|
|Representative<br>Consumer|250|1025|775|

Figure **5.6;** Rents **and** Predictions from the **Cross** Country The implicit assumption is thatMoel the cross country demand results can be **1400** used to predict long run competitive 1200 equilibrium rents in Kumasi. While we _ have confidence in these cross country demand models, it is clear that in any 8oo0 event the difference between estimates of PmQm and actual PcQc is large enough bOO to dominate any likely imprecision of 400 the estimates. Table 5.7 and corresponding Figure 5.6 present 200 T summaries of the results. Note that rows O - of the table do not add up exactly, Pcoc PmOm PmOm-PcOc because the sum or difference of medians **Note Lostsscannot** be **distinguisned** (or of other statistics) is not generally the median of the sum or difference. The controlled rents are overwhelmintiy in the range of 200 to 00 cedis. The estimates of uncontrolled rents from the cross country model range .rom 800 to 1300. These results are so strong that they could be described as a "smoking gun."
# Unpaid work burdens and the value of time use data

As seen above, desire and availability for paid work, and hours worked, are not independent of unpaid responsibilities. Recent evidence from available time use surveys across countries shows that women’s total (paid and unpaid) work burdens are substantially greater than men’s (United Nations, 2015). Figure 5, using the 2013-14 Uganda LSMS-ISA, shows that time spent across paid and unpaid work is also higher for rural women, stemming from their engagement across paid and contributing family employment, own use production, and domestic activity. Figure 5 also indirectly highlights the role that unpaid care work plays in rural women’s work burdens; women’s time spent in unpaid work rises steeply in their childbearing years, whereas for men it falls, and women’s unpaid work burdens also fall much more slowly than time spent in paid work as men and women age.

## Figure 5.  Uganda National Panel Survey, 2013-14: hours spent across paid and unpaid non-market activities in the last week

<!-- Start of picture text --> Rural (own-reports)<br><!-- End of picture text --> <!-- Start of picture text --> Urban (own-reports)<br><!-- End of picture text --> Few socioeconomic surveys have detailed data on time spent across different areas of unpaid work (among the LSMS surveys, for example, the Uganda survey has the most detailed breakdown of time spent across multiple unpaid activities; see Figure 6). Well-designed time use surveys are critical in highlighting total work burdens, but are not without significant costs and measurement issues (Buvinic and King, 2018), and may be best as standalone surveys. Among socioeconomic surveys, narrowing the set of activity categories to those that are most contextually and policy-relevant is a priority measurement initiative under the EDGE2 project with UNSD and ILO (ILO, 2018d).

Figure 6 shows, for example, that despite asking about time spent over multiple non-market labor activities, reported hours in the Uganda survey were concentrated in only a few areas (catch-all categories for agriculture and domestic work, in addition to water and fuel collection). Rather than a broad range of activities, therefore, surveys could break out specific activities within agriculture that are relevant for policy targeting. One such activity is time spent marketing/selling output — Koolwal (2018) shows that across the ILO LFS pilot countries, for example, around a third of women who were involved entirely in own use production in agriculture (and thus classified as not employed) nevertheless reported regularly selling a share of their agricultural production. Within domestic work, as well, breaking out time spent in childcare 13
from -100% to +50%. More generally, our analysis of the BPS data suggests that within countrysize-subsector variation of change in sales is larger than the between-groups variation (left panel, Figure 2). Limiting the comparison to firms within South Asia only does not change this conclusion, implying that the shock has a heterogeneous effect on firms within the same size and sector groups (World Bank, 2020c).

<!-- Start of picture text --> Figure 1: Impact of COVID-19 on firms’ operating status and sales<br>Survival: Average probability of business being<br>Average change in sales<br>open or partially open<br>0<br>100<br>94<br>80 82 -20<br>80 -24<br>72<br>66<br>-40<br>60 -46 -44<br>-52<br>-60<br>40 -63<br>-80<br>20<br>-100<br>0 SAR SSA Non-high income EAP High income<br>SAR SSA Non-high income EAP High income (excl. SAR)<br>(excl. SAR)<br>Panel (a): Regional/by income-group comparison<br>100 0<br>88 88<br>84<br>80 80 80 79<br>69 71 -20<br>60<br>-34<br>-40<br>-40<br>40 -47 -45<br>-54<br>-60<br>20 - 63<br>- 66 - 66<br>0 -80<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+) Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SAR Rest of the world SAR Rest of the world<br>Change in sales (%)<br>Probability of being open (%)<br>Change in sales<br>Predicted probability of being open (%)<br><!-- End of picture text --> Panel (b): Comparison by firm size The observed variation within size-sector groupings can be associated with the transmission channel of COVID-19, that is, the type of shocks suffered by firms. Unreported analysis suggests that COVIDinduced demand shocks have the largest effect on South Asian firms relative to shocks pertaining to disruption in production chains and finance. This is in contrast to the rest of the world where disruptions in supply chains and finance shocks had a large and comparable impact relative to demand shocks. Globally, two-thirds of the within-country change in sales can be attributed to the nature of shock firms experienced, while size and sector explain only 20% (right panel, Figure 2).<sup>6</sup> This implies that firm size and sector are imprecise targets for designing firm support programs.

6Shapley values are computed to assess the relative power of size, sector, exporter status, and shocks suffered in explaining the change in sales for firms in SAR.

6
**Figure 2: Impact of a 50% increase in oil prices on GDP in 2020 (% change from the baseline)**

<!-- Start of picture text --> Rest of SSA<br>South Africa<br>MENA<br>Rest of ECA<br>Russia<br>Rest of LAC<br>Brazil<br>Argentina<br>Rest of South Asia<br>India<br>Rest of EAP<br>Thailand<br>Malaysia<br>Indonesia<br>China<br>Rest of EU & EFTA<br>UK<br>Spain<br>Italy<br>Germany<br>France<br>United States<br>Canada<br>Japan<br>Australia & NZ<br>-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0<br><!-- End of picture text --> It would be interesting to compare the results with those of comparable studies (i.e., CGE studies) although studies are not strictly comparable due to differences in assumptions, data, time horizon and so on. Nevertheless, a rough comparison is possible. Using a CGE model, Sanchez (2011) estimates annual GDP loss due to oil price change between 2002 and 2006 for six countries (Bangladesh, El Salvador, Kenya, Nicaragua, Tanzania and Thailand). If we calculate average annual GDP elasticity with respect to (w.r.t.) oil price between 2002 and 2006 based on their results (Table 3, p.334), we find that their GDP elasticity would vary between -0.007 (Tanzania) to -0.195 (Nicaragua). Similarly, using a CGE model, Aydın and Acar (2011) show that a doubling of oil prices (i.e., 100% change) could cause a 14% loss of economic outputs in Turkey in 10 years. This implies -0.014 annual average GDP elasticity w.r.t. oil price. Our study 8
8 Sub-Saharan Africa’s share of global cotton exports from 12 to 17 percent, and the share of all developing countries from 52 to 72 percent.

Also striking is a comparison of the welfare result from cotton reform with that from removing _all_ merchandise tariffs and agricultural subsidies. While the latter gain is nearly 300 times as great as the former globally, for Sub-Saharan Africa cotton reform is crucial: its potential contribution to the region’s welfare of $147 million per year is onefifth of the estimated $733 million gain for the region from the freeing of _all_ goods markets globally, according to our GTAP model results. It is therefore not surprising that some African trade negotiators have threatened to walk out of the WTO’s Doha round of talks if substantial reforms to cotton policies are not included in the final Doha agreement – in which case the global cost of not reforming cotton would be many times greater than implied in Table 1.

If the distortions to cotton markets were removed, the final row of Table 2 shows that the United States’ policy reform would be responsible for more than half of the global gain. Perhaps more surprising is the result that the EU25 is responsible for nearly all of the rest, but that is mainly because the cost of the EU’s policies to its own economy is so high. Even so, the estimated cost to Sub-Saharan Africa is only half due to US policies and only one-quarter to developing countries’ policies, with most of the rest due to EU cotton policies. The reason the latter are so much more important to Sub-Saharan Africa has to do with the pattern of bilateral trade in cotton. As shown in Appendix Table A5, Sub-Saharan African cotton is sold in direct competition with EU cotton in EU and East Asian markets.
||PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|---|
|WPS24|World Bank Investments<br>In Vocational<br>Education and Training|John Mlddleton<br>Terri Demsky|July 1988|W. Ketema<br>33651|
|WPS25|A Comparison<br>of Alternative<br>Training<br>Modes for Youth In Israel: Results<br>from Longitudinal<br>Data|Adrian Ziderman|July 1988|W. Ketema<br>33651|
|WPS26|Changing Patterns in Vocational<br>Education|John Middleton|July 1988|W. Ketema<br>33651|
|WPS27|Family Background and Student<br>Achievement|Marlaine E. Lockheed<br>Bruce Fuller<br>Ronald Nyirongo|<br>July 1988|R. Rinaldi<br>33278|
|WPS28|Temporary Windfalls and Comxpensation<br>Arrangements|Bela Balassa|June 1988|N. Campbell<br>33769|
|WPS29|The Relative Effectiveness<br>of<br>Single-Sex<br>and Coeducational<br>Schools<br>In Thailand|Emmanuel Jimenez<br>Marlaine E. Lockheed|August 1988<br>|T. Hawkins<br>33678|
|WPS30|The Adding Up Problem|Bela Balassa|July 1988|N. Campbell<br>33769|
|WPS31|Public Finance and Economic Development|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS32|Municipal Development<br>Funds and<br>Intermediaries|Kenneth Davey|July 1988|R. Blade-Charest<br>33754|
|WPS33|Fiscal Policy In Commodity-<br>Exporting LDCs|John Cuddington|July 1988|R. Blade-Charest<br>33754|
|WPS34|Fiscal Issues in Macroeconomic<br>Stabilization|Lance Taylrw|September 1988|R. Blade-Charest<br>33754|
|WPS35|<br>Improving<br>the Allocation and Manage-<br>ment of Public Spending|Stephen Lister|August 1988|R. Blade-Charest<br>33754|
<!-- Start of picture text --> Peak inflation and dollarization<br><!-- End of picture text --> <!-- Start of picture text --> 100000<br>10000<br>UKR<br>ARG<br>KAZ<br>1000 RUS<br>POL<br>LTU<br>100 URY<br>PRY KGZ<br>MDV BOL<br>HKG<br>10<br>VUT<br>1<br>0 20 40 60 80 100<br>Deposit dollarization average 2000-4<br>Highest annual inflation 1990-2005<br><!-- End of picture text --> Figure 5: _Peak inflation and dollarization_ See note to Figure 3 The proposition that a history of high inflation is a key causal factor for dollarization is clearly shown in Figure 5. _No country_<sup>_5_</sup> _that has kept inflation below about 35 percent per annum has high dollarization_ . More generally, the upper curved (green) line suggests the minimum inflation trigger for high dollarization ratios. Bolivia, Paraguay and the Kyrgyz Republic aside, the only countries below this line are offshore financial centers. (Bolivia has a much higher dollarization ratio than predicted by the line, but the chart is drawn for the highest inflation recorded 1990-2005 and Bolivia’s hyperinflation was in the mid-1980s; Paraguay also recorded rather higher inflation rates in the 1980s. The case of the Kyrgyz Republic is simpler: the IFS data source for this country starts only in 1996 and so omits the hyperinflation of 1992-93.) Conversely, _no country with_ _hyperinflation has managed to escape high dollarization_ except by regulation. The lower curved (blue) line suggests the threshold. The three or four countries to the left of the line and with peak inflation rates of more than 500 percent per annum all had higher dollarization ratios (above 40 percent in each case) in the 1990s before falling back to the average 2000-4 ratios shown.

> 5 The offshore center Vanuatu aside.

7
across eleven developing economies between 2002 and 2013.

Numerous studies identify notable cost _pass-through_ from firms to the consumer indicating firms only bear part of the carbon costs (Lin and Jiang, 2011; Arlinghaus, 2015; Joltreau and Sommerfeld, 2019). However, firm-level evidence on this transmission channel remains relatively scarce for emerging economies. Rentschler and Kornejew (2018) confirm that higher prices for all energy types are associated with higher long-run sales in Indonesia. Cal`ı et al. (2023) do only find significant evidence for an aggregated cost pass-through effect for firms experiencing power outages.

# **3 Institutional Setting**

In 1998, Chile suffered a severe drought, which had a notable impact on the energy generation of the Central Interconnected System (SIC) network.<sup>7</sup> This event, in conjunction with disruptions in three natural gas plants, caused blackouts between November 1998 and April 1999, forcing the government to ration supply (Serra, 2022). The subsequent period of the late-1990s to mid-2000s was characterized by heavy use of natural gas in electricity generation and massive natural gas imports from Argentina (Cansino et al., 2018). However, the gas-heavy energy production episode abruptly stopped in 2005 when Argentina began restricting and taxing natural gas exports due to domestic deficits leading to a notable price surge in Chile.

In response, Chile saw the construction of new coal plants to close the wedge between reduced energy supply and growing demand. Coal plants played a crucial role in this endeavor given their comparably lower cost of electricity generation (Serra, 2022). The country also diversified its energy mix in terms of energy type and destination. For example, in 2009/2010, Chile imported natural gas from Algeria, Equatorial Guinea, the Arab Republic of Egypt and Indonesia and constructed largescale liquefied natural gas terminals (Mundaca et al., 2015), leading to an overall reduction in the relative weight of hydropower (Cansino et al., 2018).

Technological advancements and growing environmental concerns have led to a stronger focus on green and renewable energy in recent years. By the 2010s, increasing public opposition and stricter environmental requirements halted the construction of additional coal-fired plants (Serra, 2022), and new policy measures promoting non-conventional renewable energies were put into practice (MEFR, 2008; Cansino

> 7Throughout the analyzed period, Chile had four main electricity grids: the Northern Interconnected System (SING), the Central Interconnected System (SIC), the Aysen System (SEA), and the Magallanes System (SEM), with the former being integrated into the National Electricity System (SEN). Within these grids, numerous state-owned and private energy producers have been in charge of energy generation (Serra, 2022).

6
3. Origin of Value-Added in Exports of B&R Economies A focus on intra-B&R trade reveals varying degree of importance of sectors from region to region (Figure 13). In particular, the dominance of Computer, Electronics and Optical Equipment is only an EAP phenomenon.

FIGURE 13: SHARE OF EXPORTS OF B&R SUB-REGIONS TO B&R ECONOMIES, TOP 5 SECTORS <!-- Start of picture text --> EAP ECA<br>Coke, refined petroleum Coke, refined petroleum<br>products and nuclear fuel products and nuclear fuel<br>Chemicals and chemical<br>Transport and storage<br>products<br>Transport and storage Basic metals<br>Wholesale and retail Wholesale and retail trade;<br>trade; repairs repairs<br>Computer, Electronic and<br>Mining and quarrying<br>optical equipment<br>0 5 10 15 20 25 0 5 10 15 20<br>MENA SAR<br>Wholesale and retail<br>Basic metals<br>trade; repairs<br>Transport and storage Mining and quarrying<br>Coke, refined petroleum Coke, refined petroleum<br>products and nuclear fuel products and nuclear fuel<br>Chemicals and chemical Wholesale and retail<br>products trade; repairs<br>Mining and quarrying Transport and storage<br>0 20 40 60 80 0 10 20 30<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the TiVA database and the Kummritz and Quast (2017) algorithm. The x-axis represents the share of a sector’s exports to B&R Economies.

# **3. Origin of Value-Added in Exports of B&R Economies**

# **_3.1. Concept and Methodology_**

Since gross trade data do not allow a full understanding of the importance of production linkages, this section turns to trade in value-added. We focus on the origin and destination of exports of value-added, effectively identifying the shares of foreign tasks needed to produce exports.

To assess the amount of value-added that B&R Economies source from each other, we separate the sample in three groups: B&R, Non-B&R and the Rest of the World (ROW). Once again, please notice that there are some countries in the ROW aggregate that are part of the B&R initiative but for whom it is not possible to separate the effect because of data issues. Although of high quality, the TiVA database only 13
and welfare monitoring. While accuracy is better for rural areas, the sample design is also accurate for most urban areas.

**Table 14**

# **Alternative approaches for estimating RSE of annual per capita consumption at the Division level (Rural/Urban)**

|**Division**|**Ru**<br>|**ral**<br>|**Ur**<br>|**ban**<br>|
|---|---|---|---|---|
||**top-down**|**bottom-up**|**top-down**|**bottom-up**|
|Barisal|4.0%|1.8%|8.5%|3.5%|
|Chittagong|3.6%|2.2%|12.6%|7.4%|
|Dhaka|2.6%|1.3%|7.2%|4.0%|
|Mymensingh|3.2%|2.2%|10.2%|5.4%|
|Khulna|3.6%|1.0%|7.4%|2.3%|
|Rajshahi|3.1%|1.8%|6.3%|2.6%|
|Rangpur|3.3%|1.4%|9.6%|3.4%|
|Sylhet|4.6%|2.3%|11.1%|4.1%|

Note: Urban estimates for the four main divisions of Chittagong, Dhaka, Khulna, and Rajshahi are population weighted averages for Other Urban and City Corporations estimates.

**Table 15 Alternative approaches for estimating RSE for annual total expenditure at the Division level (Rural/Urban)**

|**Division**|**Ru**<br>|**ral**<br>|**Ur**<br>|**ban**<br>|
|---|---|---|---|---|
||**top-down**|**bottom-up**|**top-down**|**bottom-up**|
|Barisal|4.3%|1.9%|8.0%|3.2%|
|Chittagong|3.5%|2.0%|13.8%|8.3%|
|Dhaka|3.0%|1.5%|8.2%|4.5%|
|Mymensingh|3.3%|1.8%|11.4%|5.3%|
|Khulna|3.5%|1.2%|6.1%|1.9%|
|Rajshahi|3.1%|1.8%|7.0%|3.0%|
|Rangpur|3.6%|1.4%|9.3%|3.3%|
|Sylhet|5.4%|2.4%|9.8%|3.8%|

Note: Urban estimates for the four main divisions of Chittagong, Dhaka, Khulna, and Rajshahi are population weighted averages for Other Urban and City Corporations estimates.

31