rafmacalaba/datause-encoder-data huggingface dataset:
14 Table 5. Shares of Different Government Levels in Consolidated Itemized Expenditures, %

|**Budget Expenditures / Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|**Total Expenditures**|**100**|**100**|**100**|**100**|**100**|
|- federal|73.0|54.4|52.5|51.1|54.1|
|- regional|13.2|22.8|25.4|25.2|23.0|
|- local|13.9|22.8|22.1|23.7|22.9|
|**Total Expenditures Net of**<br>||||||
|**Interbudgetary Transfers**<br>|**100**<br>|**100**|**100**|**100**|**100**|
|- federal|76.0|N/A|52.8|52.3|55.4|
|- regional|9.1|N/A|21.1|20.9|18.4|
|- local|14.9|N/A|26.1|26.8|26.2|
|**Transfers to OtherLevels of**||||||
|**Government**|**100**|**100**|**100**|**100**|**100**|
|- federal|37.6|N/A|51.2|43.9|45.7|
|- regional|60.5|N/A|47.5|52.6|_51.5_|
|- local|1.8|N/A|1.3|3.5|2.9|
|**National Economy**|**100**|**100**|**100**|**100**|**100**|
|- federal|80.8|48.6|30.4|26.3|27.5|
|- regional<br>|8.7<br>|24.6<br>|34.8<br>|35.4<br>|32.4|
|- local|10.5|26.8|34.9|38.2|40.1|
|**Education**|**100**|**100**|**100**|**100**|**100**|
|- federal|33.8|19.5|19.9|15.9|14.5|
|- regional|14.5|18.9|19.1|19.7|18.0|
|- local|51.8|61.6|61.0|64.4|67.5|
|**Health and Sport**|**100**|**100**|**100**|**100**|**100**|
|**-** federal<br>|11.3<br>|10.5|11.8|9.9|10.2|
|- regional|28.5|30.2|33.4|33.4|33.1|
|- local|60.2|59.3|54.8|56.7|56.8|
|**Social Protection**|**100**|**100**|**100**|**100**|**100**|
|**-**federal|71.8|52.0|50.3|18.5|31.2|
|- regional|20.4|34.9|34.9|31.1|26.8|
|- local|7.8|13.2|14.8|50.3|42.0|
|**Administration And Justice**|**100**|**100**|**100**|**100**|**100**|
|- federal|80.5|79.6|81.4|63.1|63.7|
|- regional<br>|_5.5_|6.1|6.2|N/A|19.0|
|- local|14.0|14.3|12.4|N/A|17.3|
|**Culture and Mass Media**|**100**|**100**|**100**|**100**|**100**|
|- federal|51.2|34.8|39.6|31.6|34.7|
|- regional|21.2|30.2|28.9|30.8|29.3|
|- local|27.6|35.0|31.5|37.6|36.0|
|**Defence**|**100**|**100**|**100**|**100**|**100**|
|**-** federal|100|100|100|100|100|
|- regional|0|0|0|0|0|
|-local|0|0|0|0|0|
|Loans|100|100|100|100|100|
|- federal|91.5|N/A|84.6|58.9|30.3|
|- regional|4.0|N/A|12.5|34.6|N/A|
|- local|4.5|N/A|3.0|6.5|N/A|
|Other Expenditures|100|100|100|100|100|
|- federal|77.5|N/A|69.2|89|94|
|- regional|8.5|N/A|18.0|N/A|N/A|
|<br>- local|14.0|N/A|12.8|N/A|N/A|
Table 4: Summary Statistics of Estimated Jones Act Tariff Equivalent - 2016

|Control of PR’s Imports DA|# Obs.|Simple<br>Average|Trade<br>Weighted<br>Average|Median|Maximum|
|---|---|---|---|---|---|
|||Ta|ble3estimat|es||
|FE: YEAR - All controls|609|30.6%|53.6%|29.5%|114.3%|
|FE: YEAR - No_τ_ + No FTA’s<br>|609|35.5%|61.9%|34.8%|130.8%|
|FE: YEAR - No_τ_ + No FTA’s + No Dist_×_<sup>_−→_</sup><br>_Z_|609|6.4%|14.2%|5.4%|32.2%|
||||Robustness|||
|FE: YEAR - All controls - BEC|1,099|11.3%|11.2%|14.0%|24.0%|
|FE: YEAR_×_product (HS6) - All controls|609|11.8%|25.2%|7.4%|80.7%|
|FE: YEAR_×_sector (HS2) - All controls|609|33.2%|57.2%|32.1%|128.5%|
|FE: YEAR - All controls + Soderbery_σ_|609|310.0%|555.3%|335.8%|917.9%|
|FE: YEAR - All controls + Common_σ_|609|49.2%|76.0%|43.6%|226.7%|

Note: These statistics are calculated as the product of the predicted JA-tariff equivalent for 2016 and the vessel share of 2016 U.S.-PR shipments in the corresponding product. Estimates are reported for numerous specifications of the structural regression (in equation (14)). All estimates rely on the _σ_ estimates of Fontagné et al. (2022), except the row labeled “Soderbery _σ_ ” (which uses _σ_ �<sup>_k_</sup> from Soderbery (2015)) and the “common _σ_ ” case (which uses the MFN tariff coefficient associated with Column 5 of Table 2). The UN BEC classification has more observations because it defines a broader set of products.

Rather than report all of the regression estimates, we focus our reporting on the distributions of _<u>τ</u>_<sup>_~~k~~_</sup> _JA_<sup>linked to each regression.These values are reported in the bot-</sup> tom half of Table 4. All of the results in Table 4 apply to estimates from a set of structural regressions among a sample of final goods. We provide a fuller discussion of our estimates and inferences in Appendix B. The general lessons are that a) the qualitative predictions of our hypothesis are robust among final goods, especially with respect to interactions involving products’ vessel share and container share of shipments, b) the econometric specification matters for the estimated sizes of _<u>τ</u>_<sup>_~~k~~_</sup> _JA_<sup>, and c) the values of</sup><sup>_σk_imposed in the regression are even more important</sup> for predictions of _<u>τ</u>_<sup>_~~k~~_</sup> _JA_<sup>than the set of controls entering the specification.</sup> 33
<mark>Table 3: Transition Matrices - Synthetic vs. Actual Panel Data Using Different Panel Length</mark>

||<br>L|<br>Panel SurveyI<br>Th|<br> <br>U|<br> <br>L|<br>Panel SurveyII<br>Th|<br>U|
|---|---|---|---|---|---|---|
|Status in Year[1],<br>Year[2]|ower<br>Bound<br>Estimates<br>[1]|rut<br>Confidence<br>Interval<br>[2]|pper<br>Bound<br>Estimates<br>[3]|ower<br>Bound<br>Estimates<br>[4]|rut<br>Confidence<br>Interval<br>[5]|pper<br>Bound<br>Estimates<br>[6]|
|_Panel A: Chile; Years i_|_n Panel Surv_|_ey I: 1996-2001_<br>|_; Years in_<br>|_Panel Surv_|_ey II: 2001-2006_<br>||
|Poor,Poor|10.43|(5.44,8.17)|5.19|8.48|(3.18,6.00)|3.49|
|Poor,Non-poor|7.37|(9.59,13.02)|21.74|13.94|(8.21,12.29)|25.46|
|Non-poor,Poor|2.95|(5.23,7.91)|8.19|0.48|(2.99,5.73)|5.47|
|Non-poor,Non-poor|79.24|(72.96,77.63)|64.88|77.10|(78.13,83.43)|65.58|
|<br>_Panel B: Nicaragua; Ye_|<br>_ars in Panel_<br>|<br>_Survey I: 1998_<br>|<br> <br>_-2001; Yea_<br>|<br> <br>_rs in Panel_<br>|<br>_Survey II: 1998-_<br>|<br>_2005_<br>|
|Poor,Poor|32.69|(24.44, 31.27)|21.23|26.57|(23.43, 30.68)|21.16|
|Poor, Non-poor|4.23|(4.95, 8.81)|14.54|1.27|(1.72,4.58)|6.91|
|Non-poor, Poor|9.70|(11.84, 17.21)|21.16|32.91|(28.59, 36.24)|38.32|
|Non-poor, Non-poor|53.38|(46.91, 54.53)|43.08|39.25|(33.41, 41.31)|33.61|
|<br>_Panel C: Peru; Years in_|<br>_Panel Surv_|<br>_ey I: 2004-2005_|<br> <br> _; Years in_|<br> <br>_Panel Surv_|<br>_ey II: 2004-2006_||
|Poor,Poor|34.64|(28.68, 33.30)|23.24|34.35|(26.11, 30.73)|22.08|
|Poor, Non-poor|9.31|(9.34,12.46)|21.21|10.64|(12.21,15.76)|21.93|
|Non-poor, Poor|5.59|(7.79, 10.68)|17.00|4.07|(8.45, 11.52)|16.33|
|Non-poor, Non-poor|50.45|(46.36, 51.35)|38.56|50.94|(45.03, 50.14)|39.66|
|Obs.Panel A|1,313|1,313|1,313|852|852|852|
|Obs. Panel B|664|664|664|577|577|577|
|Obs.PanelC|1,543|1,543|1,543|1,468|1,468|1,468|
|Data source: Chile CA<br>Peru ENAHO 2004,20<br>Note: R-squared is calc|SEN, 1996,<br>05,and 2006<br>ulated for op|2001, and 200<br>.<br>posite halves o|6; Nicarag<br>f the total|ua EMNV,<br>Year[1] sam|1998, 2001, and<br>ple. Results are|2005; and<br>constrained|

<mark>Note: R-squared is calculated for opposite halves of the total Year[1] sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Controls are the same as in columns [3] and [7] of table 2c. Results in column [2], and [5] show actual panel mobility. 95 percent confidence interval between parentheses.</mark> 26
**Policy, Research, and External Affairs**

# **International Trade**

WPS 642 Tlhis palpcr- -I prtodLC (oIl Ch Iiternaliional 'l'rae (iel)iv isbn, Iote,ail i **jional** tEcoioniiiics Departmenltli is parl of a larger Cf'orit in PRl to understaInd the impact ol changes in CoulintriCs' trade policies on or1id commodilt miarkc:s. Copies are availablefree Ironii the Worl(d Bantik, 1 8 I 8 11 SLtreel Nct \NW ashinWlolt. n)( 20(433. Please colitlact Audrey Kirsonl-Wa;liers, rooln] S7-053, exteisionl 33712 (3) pa-cs).

Alihoughi Brar/iI is the world's largest sugarcane producer, oinly one-third o0 'thc Caine it grows is used to pro(luce sugar; **thc** rest is used to produce etlhanol as fuel lor automobiles. Still, Bratzil is the world's fburth largest sugar producer. What would it mean Ior Brazil and lfo the world sugar market if Brazil werc to shift largely a\A l'rom ethanol to sugaIr pro(ductioni ?

This questionl is of kcen initerest or thc world sugar maliket because suchi a shil' - alllhoughi politically di 'licuIll -- is possiblc. Brazil's system ol' coitrollinig the sugarcane and sugar industries to ensuic cnoughl ethanol lor domestic lucl needs is costly. With the border price ol'petrolcum **at** $24 a barrel, forexamplc, the shadow price of ethanol as a fuel substitute is about 4 to 5 cenis a pound in sugar equivalent. (The world pricc ol' sugar is low 9 centis a pound.) Borrell uscs a nine-rcgion trade mod(el of thc world sugar industiy to study this qluestion under both dynamic and stochastic simulations. Simu-

> lations were run1 on suslalillCnd inlCICISCS ill Brazilian suglar pr-oductioll ol .5 mlillioni ton1s, 2 Iflillionl tols, anid 6i millioni toils. **To** exalmlile ilic sensitivity of' Brazil's inilucice on pricc a1t dilfferent phases ol' tlie world sugar price cycic. these sustainei( increalses were simulate(l 'ront two difl'erent stalt dales. Moreover, the mo(lcl was ruIn i60 timiecs over the period 1985-2004, with di 'ffrctt shocks repr-escingit random clemenit **s** uch as weather.

Boricil coniclu(des thiati altihougih Bria/zil conIl( inifluence world sugar pricCs si'rlnigicantly in thec shlior run. it could not inilucice thcimi to its sliortor long-term advantage by restirictingr plro0dUcioln. Indeed, to the exteni that B3raZil COUld mia;kC world prices more stable by) allowing its produccrs incrcalsc( llcxibility in prOdUCtiOll, rCellOViilu existing productioni conltiols coul(d pl)vide not oinly subst:rntial econoiliic ,gains (in leiuns ()1 increased exports to Brauzil) but also moie stable world prices. FoI otlihCr producers, thCIC could bc a tradeolf inl eCtIIS lHowr but tMore StIaC income.

> The PRE Working **Paper** Series **disscminaite the findings** of' **%ork** un(ler A ay in tlt Bank's lPolic! Re'oarclh. arid E\tkial **Affatirs** Conililx. An ohjective ofhlie scries ij to get these findings **0ou** (luickly. c en if prewiination'. **arc** **_leI_** titan lullI- **1 ld** idid **The** Fin(lings. nk'rpr.tp.rios. and (olnl fusions ill 1liew p.iller **(1do** **_not 1CCC'srrill\_** TellrC.111ot **0 il** 13.nk policy1

> **PlOdUKlCI** hs _tlI_ PRIE l)D,'enmn.ntion (Cnk!c
due to attrition, along with bias for non-response to be discussed later, by calculating Lee (2009) bounds and Kling-Liebman bounds (Kling & Liebman, 2004).

# 6. Results

The 85 villages participating in the RMG program are among Lao PDR’s poorest and offer few job opportunities. According to the baseline data, only one-fifth of working-age individuals in RMG-eligible households had paid work outside the household. Over 70% of RMG-eligible women worked on the household farm or business in unpaid roles. When compared against rural households from a nationally representative Laos Expenditure and Consumption Survey (LECS) V, used for calculation of poverty rates in Lao PDR, the RMG-eligible households were poorer than the average Laotian rural households across a range of wealth indicators, including housing composition, ownership of durables, and nutrition consumption; wait-listed and RMG women are also less educated on average and less likely to be in the labor force (Baseline Report, East Asia & Pacific Gender Innovation Lab 2020). A supplementary survey confirmed that the program women are among the poorest in these already poor villages (Dervisevic et al. 2020).

## 6.1. Impact on Women’s Economic Situation

The RMG program was meant to directly change labor market activities by giving participants a regularly paying job for 18 months. We first confirm that the intervention has worked as intended before testing how GBV was impacted. To do so, we estimate the impact of treatment on whether the women were listed among the household members that regularly contributed wage income, and on household average monthly wage income contributed over the 12 months preceding the endline survey. We find increases in regular work for RMG members. RMG members were 77.4% more likely than the waitlist group to be regular income earners (Table 2). Monthly income (winsorized at the 99<sup>th</sup> percentile to minimize the effects of outliers) increases by 173,558.24 kip ($23.56) for RMG households, over 40% greater than waitlist household income. We note that the value of the increase is only about 20% less than the amount of the payment to the RMG groups. This is notable within the PWP literature because other income generation is often displaced (Bertrand et al., 2017). Lee’s bounds estimates adjusting for attrition confirm positive outcomes (results not shown).

## 6.2. GBV

We examine four types of IPV (controlling behavior, emotional violence, physical violence, and sexual violence), two types of non-IPV domestic violence (physical and sexual violence by household members other than intimate partner) and two types of GBV outside the home (physical and sexual). The single deviation from our pre-analysis plan regarding these outcomes is that we had initially grouped together the 10
# **3 This study**

## **3.1 Study design**

The study is a randomized control trial of short clips of edutainment campaigns designed to reshape gender norms, roles and VAW attitudes. The study, a partnership with the Population Foundation of India and the World Bank, sought consent from all study participants and received ethical clearance from Solutions IRB (IORG0007116).

We recruited 18-24-years-olds residing in New Delhi and six other large cities in northern India.<sup>10</sup> Study participants were recruited on Facebook and Instagram through a 1-week geo-targeted advertising campaign.<sup>11</sup> As participation incentives, individuals who completed the baseline and at least one follow-up survey were eligible for a lottery to win Samsung Galaxy smartphones or a “selfie” picture with a Bollywood celebrity.<sup>12</sup> Individuals who clicked on the ad banner were redirected to Facebook Messenger, where both the intervention and data collection surveys were delivered through Virtual Lab, a newly-developed open-source automated chatbot described by Rao et al. (2020).<sup>13</sup> This platform also allowed the research team to directly measure treatment adherence (i.e., viewership rates, an interesting outcome for edutainment campaigns) and therefore allowed for more reliable estimation of the treatment effect on the treated.

Of 33,000 users who clicked our Facebook ad, only 5,299 individuals filled the baseline survey. They were then randomized into treatment and control conditions. In both conditions, participants were shown between three and seven short edutainment video clips totaling up to 25 minutes. We measured program impacts one week and four months after the end of the intervention, what we refer to in the rest of the paper as short-term (n=606) and medium-term impacts (n=619), respectively. The timeline and structure of the study are described in Figure 1.

> 10 We focused on younger people due to several reasons: (i) their larger presence on social media; (ii) their higher risks to be exposed to violence, for instance due to their lack of experience in dealing with intimate partner relationships (e.g., Borker et al. 2021); (iii) the tested interventions explicitly targeted youths.

> 11Figure B1 in the Appendix shows the geographic targeting and the ad content used in the recruiting campaign.

> 12The celebrity was Farhan Akhtar, a popular Indian actor, director, screenwriter and producer.

> 13Here is the website of the platform, while here is the GitHub repository of its code. Moreover, Appendix Figure B2 shows an example of its functioning.

7
12

# **Figure 5 Civil Service Employment as a Share of General Government Employment in Selected OECD Countries, 1989**

<!-- Start of picture text --> 4L-1<br>41.3X<br>18.2% 173%<br>IZJ1%3<br>1aL6%<br>&8%~~.2<br>France Sweden United lingdom Australa<br>United States Japan Canada Germany<br><!-- End of picture text --> Note: General government employment encompasses personnel at the central, regional and local levels. With the exception of Australia, public enterprise enployees have been excluded. Share for Canada refers to 1988. Source: CECPS and OECD
businesses. Therefore, we define the job loss shock as the loss of employment of previously salaried household members. Only 2.5 percent of households experience this kind of job loss.

On average, 43 percent of households report unusually high food prices. Approximately 26 percent of households did so in IHS 2 and IHS 3 and 71 percent in IHS 4, most likely reflecting the adverse weather conditions observed in 2016. The correlation between drought and self-reported inflation for food is 12.3 percent. Delays in passing the cost on to consumers and potential changes in importations or transportation costs all matter to this weak correlation.

About 18 percent of households suffer the death of a member. The deceased was a breadwinner in around 20 percent of cases. Parts 4 to 7 of Table 1 present household characteristics. The average family is headed by a male with low education and has half of its working-age members employed in agriculture. Moreover, around 22 percent of households receive food aid. We remove food aid from consumption per capita when constructing our dependent variable. However, we do not include it as a control in our regression as it is responsive to weather conditions and would therefore bias our coefficients on weather shocks.<sup>6</sup> Instead, we control for receiving aid unrelated to weather conditions (Malawi Social Action Fund [MASAF] program and educational aid). Finally, note that 84.2 percent of the households reside in rural areas.

# **2.2 Impacts of Weather and Idiosyncratic Shocks on Consumption**

As a first step in the analysis of vulnerability to poverty following a household’s exposure to common and idiosyncratic shocks, we assess the impact of these shocks on per capita consumption. To do so, the following consumption model is estimated: where _Cict_ is per capita consumption without aid of household _i_ in TA _c_ at time _t_ .<sup>7</sup> _Sct_ is a vector of weather shock indicators and their historical geographic distribution: drought and excess rainfall in the growing season preceding the survey time in TA _c_ at time _t_ . We also include controls of the historical trends in droughts (median Palmer index for the TA) and excess rainfalls (98th percentile of rainfall in the TA) for exogeneity of the shocks. _Sict_ are selfreported idiosyncratic shocks: job loss, death of a household member, or food price inflation. The additional _k_ control variables, _x_<sup>_k_</sup> _ic_ , are household-specific controls:

> 6 Angrist and Pischke (2008) refer to theses variables as “bad controls”.

> 7 Adding one to the consumption per capita to account for households that have zero consumption when food aid and cash transfers are excluded from the consumption basket.

11
15

# **Table 4. Motor vehicle share of OECD pollutant emissions, 1980 ('000 tons)**

|Pollutant|Total Emissions|Motor Vehicle Share|
|---|---|---|
|HC|33,869|13,239 (39%)|
|NOx|36,019|17,012 (47%)|
|CO|119,148|78,227 (66%)|

# **India**

The motor vehicle related air pollution in India is already serious and worsening. While the problem of diesel smoke and particulates is the most apparent, carbon monoxide, nitrogen oxide, hydrocarbon, lead, and ozone levels exceed internationally accepted levels in several cities. The lead content in gasoline is very high and is the major source of lead contaminants in the air. The problem continues to worsen as the vehicle population, particularly motorcycles and three-wheeled autorickshaws, continues to grow and age; vehicles once introduced into use remain active much longer than in industrialized countries. The adverse consequences of air pollution are especially severe because the life style and climate are such that public exposure to high pollution levels is very high.

## **Thailand**

Urban air pollution in Thailand, especially with regard to diesel particulates, is already quite acute and is reaching chronic levels, with the overall vehicle population increasing at a rate of 10 % per annum. Carbon monoxide and ambient lead levels periodically exceed internationally accepted limits, although they are apparently stabilizing. Because of meteorological conditions, the ozone problem is not yet serious.

The emission characteristics of the gasoline-powered vehicle fleet is gradually improving as there is a tendency, when serious engine problems occur, to replace the old engine with a used one imported from Japan. The used engine is less polluting and costs about US$300, considerably less than the cost of a typical engine overhaul. In addition, taxicabs and tricycles have been largely converted to run on Liquified Petroleum Gas (LPG) which emits less CO (and no lead) compared to gasoline. As diesel fuel is about 25% cheaper than gasoline, its use is encouraged. So it is not surprising that diesel vehicles continue to increase rapidly, although the diesel fuel quality is believed to be poor.

The adverse consequences of air pollution are particularly severe in Bangkok because public exposure to elevated pollution levels is quite high. Smoke from diesel vehicles is palpable
Policy Research Working Paper 7754

# **Abstract**

The paper takes stock of eight country studies and a cross-country analysis to review the experience with subsidy reforms in the Middle East and North Africa region between 2010 and 2014. This unprecedented period of subsidy reforms occurred during a period of extraordinary political changes, which makes this particular experience unique. The paper reviews the facts and the different paths to reforms taken by different countries faced with different challenges, and discusses the pros and cons of alternative policy options. It concludes with an assessment of the experiences and a discussion on future prospects.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The author may be contacted at pverme@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 the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team