rafmacalaba/datause-encoder-data huggingface dataset:
give up on preferred foods or cut back on quantity by reducing meal size or number of meals and does not run out of food, go to bed hungry or go a whole day or night without eating.

**Table 5**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|||Household<br>food insecurity<br>(access) scale<br>score (HFIAS)|Scale score (numeric, 0-27) based on HFIAS<br>questionnaire|Yes|No|
|||**Food**<br>**insecurity**<br>**status: Food**<br>**secure**|**A food secure household experiences none of the**<br>**food insecurity (access) conditions, or just**<br>**experiences worry, but rarely.**|**Yes**|**Yes**|
|Household<br>food<br>security|NNS Form<br>1.5|Food<br>insecurity<br>status: Mildly<br>food insecure<br>Food<br>insecurity<br>status:<br>Moderately<br>food insecure|A mildly food insecure (access) household worries<br>about not having enough food sometimes or often,<br>and/or is unable to eat preferred foods, and/or eats a<br>more monotonous diet than desired and/or some foods<br>considered undesirable, but only rarely. But it does not<br>cut back on quantity nor experience any of three most<br>severe conditions (running out of food, going to bed<br>hungry, or going a whole day and night without<br>eating).<br>A moderately food insecure household sacrifices<br>quality more frequently, by eating a monotonous diet<br>or undesirable foods sometimes or often, and/or has<br>started to cut back on quantity by reducing the size of<br>meals or number of meals, rarely or sometimes. But it<br>does not experience any of the three most severe<br>conditions.|Yes<br>Yes|No<br>No|
|||Food<br>insecurity<br>status:<br>Severely food<br>insecure|A severely food insecure household has graduated to<br>cutting back on meal size or number of meals often,<br>and/or experiences any of the three most severe<br>conditions (running out of food, going to bed hungry,<br>or going a whole day and night without eating), even<br>as infrequently as rarely. In other words, any<br>household that experiences one of these three<br>conditions even once in the last four weeks (30 days)<br>is considered severely food insecure.|Yes|No|

32
# **Section1. Introduction**

Forced displacement has been surging across the world over the past decade and reached 80 million as of 2019 (e.g., UNHCR 2020). Importantly, these populations are centered in and around countries in active and protracted conflict and represent a population with some of the largest welfare deprivations in the world (e.g., UNHCR 2016; etc.). In order to meet the Sustainable Development Goals, humanitarian and development agencies will need to make much greater progress on supporting the well-being of displaced populations during conflict and after peace has been restored (e.g., World Bank 2018).

Although the forcibly displaced exhibit a wide variety of deprivations on average (e.g., UNHCR 2016), there has been limited investigation into the immediate welfare consequences of forced displacement given data scarcity. A number of reports compare postdisplacement welfare outcomes of forcibly displaced households relative to hosts (e.g., WFP 2016; World Bank 2017a; OCHA 2018; OCHA 2019b; Sharma and Wai-Poi 2019; etc.), and others investigate long-term consequences of displacement using data collected years after conflict (e.g., Ibanez and Velez 2008; Verwimp and Maystadt 2015; etc.). Furthermore, a large literature investigates violence before and after displacement in origin and destination regions (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.). However, given the difficulty in collecting non-violence welfare data from displaced households leading up to, during, and immediately following displacement, it is difficult to precisely estimate how forced displacement impacts well-being and whether the impacts persist.

This paper investigates the impacts of forced displacement in the Republic of Yemen’s conflict using rare data collected during active conflict. The setting is one in which a large, forced displacement crisis began immediately after the conflict escalated in March 2015, where displacement might have been forced by either violence or the humanitarian situation caused by the conflict (e.g., TFPM 2016; OCHA 2018; etc.). Our data are from the World Food Programme, which has been implementing a monthly monitoring survey collecting food security and displacement information that includes both cross2
23

# BuLretme

- Brideau, R., B. Eggeratedt and S. van Wijnbergen, "Measuring Capital Flight in Mexici," simoo, World Bank, 1991.

- Maller, P., E. Livacich and P. Arrau (1984) "Una revisi6n del ailagro econ6mico chilenot 1976-1981" en Colleccion Estudios CIEPLAN 15, Santiago.

- Oks, Daniol F. (1991) "DevaJuation Expectations and Interest Rates in Mexico: **the** Role of Domestic Debt Nanagaoent", Discussion Paper, LAC Series, World Bank.
<!-- Start of picture text -->
1.75<br>15<br>1.25 a<br>1 | | Data Adjusted for<br>0.75 Final Month of<br>Exporting<br>0.250.5 | | J L @ RawCalendar Data BasedYear on<br>Yy Y Y<br>;oe NN \NN \ InitialExportingMonth of<br>-0.5<br>-0.75<br>1<br>Year 1to2 Year 2 to3 Year 3 to Year 4 YearT-3toT-2 Year T-2 toT-1 Year T-1toT<br><!-- End of picture text -->
this variable did not a¤ect the estimates of spatial disparity perceptibly. The estimates remained nearly unchanged when we instead included the standard deviation of rainfall over the 30 year period as a control (panel C). In panel D, we included variability of monsoon rain as a control. The variability of monsoon rainfall is measured by the coe¢ cient of variation of total rainfall for the monsoon season of June-September over the 30 year period. Higher variability of rainfall is signi...cantly and negatively correlated with LRPCE in rural areas (appendix table A.2). But its e¤ects on the sizes of spatial disparity indices are still small (panel D) in both survey years.

Next, we included a set of dummies to indicate the agro-ecological zones where those zones were determined by agricultural scientists on the basis of elevation, ‡ooding possibility, land quality, soil moisture and rainfall variability etc. The country has 17 distinct agro-ecological zones. Our data on agro-ecological zones come from the early 1990s. Since then, several new sub-districts have been created. For these newly created sub-districts, the agro-ecological zone information is missing and we capture this by introducing a dummy. The estimates of spatial disparity after controlling for agro-ecological zones (panel E) are considerably lower than the base line estimates (panel A). For instance, agro-ecological zones explain about 13 percent of intercommunity variations in LRPCE in 2000 and 11 percent in 2010. The corresponding estimates for rural areas are 12 percent in 2000 and 17 percent in 2010. In the ...nal panel (F), both agroecological dummies and variability of annual and monsoon rainfall are introduced as controls. As these two types of controls can explain considerable variations in LRPCE, the estimates of spatial disparity declined for all three samples in both years.

# 4.2 Access to Infrastructure and Services

In this sub-section, we focus on some basic infrastructure and services that households living in a community should have access to but may not have it yet. We analyze di¤erences in access to electricity, phone, sanitation, safe drinking water and to large urban markets across communities.

16
unpacking the effects of any given channel, parsing these mechanisms and understanding their interactions is a ripe area for future research, particularly for policy makers.

Additionally, future scholarship is needed to understand the heterogenous impacts that such programs can have on women from different caste, religious, and class backgrounds, which have been shown to mediate the impact that self-help groups and livelihoods training can have on women’s empowerment (Field et al., 2010). Tis study’s focus on the speech acts of women in the _gram sabhas_ limited data collection of those individual characteristics which could not be immediately visually ascertained. Collecting such information would have interfered with the natural functioning of the meeting. As a result, we cannot identify the extent to which PVP’s focus on including Scheduled Caste and Tribe households from the poorest of the poor may be relevant to understanding the null results on deliberative influence.

# 6 Conclusion

Motivated by the concern that inequalities among citizens may limit the ability of deliberative democratic institutions to produce more inclusive development outcomes, this paper opens the “black box” of the _gram sabhas_ at the core of India’s decentralization effort. We use _text-as-data_ methods on an original corpus of village assembly transcripts from rural Tamil Nadu to show that botom-up efforts to empower and induce women’s participation can be useful in mitigating the gaping gender gap in political participation. More specifically, we evaluate the impact of the a woman-centered poverty alleviation program, which explicitly aims to bring women into greater contact with village government and to provide them with greater agency in the administration of a government program. We find that PVP is able to significantly improve _gram sabha_ participation by women in terms of their atendance, their propensity to speak, and the floor time they enjoy. However, we also show that greater voice for women does not lead to greater agenda seting power or responsiveness from the state. Tis may result from the fact that project-facilitated participation encourages women to speak up primarily about activities related to PVP itself, in ways that potentially fail to engage the broader village community. Or it may simply reflect the 27
Panels A and B clearly illustrates the fact that Latin America is much more unequal than Europe.<sup>14</sup> In fact, the Latin American country with the lowest Gini coefficient in this sample (Chile) has inequality levels above those of the most unequal European country (Portugal).<sup>15</sup>

**Figure 1. Inequality of Disposable and Market income in Latin America and Europe** **<u>(Gini coefficients)</u>**

<!-- Start of picture text --> Disposable Income<br>Panel A. Latin America Panel B. Europe<br>0.65 0.65<br>0.55 0.55<br>0.45 0.45<br>0.35 0.35<br>0.25<br>0.25<br>0.15<br>0.15<br>Market Income<br>Panel C. Latin America Panel D. Europe<br>0.65 0.65<br>0.55 0.55<br>0.45 0.45<br>0.35 0.35<br>0.25 0.25<br>0.15<br>0.15<br>Brazil Chile Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Portugal Spain Sweden UK EURO 15<br>Argentina Colombia<br>Luxembourg Netherlands<br>Italy UK<br>Brazil Chile Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Portugal Spain Sweden EURO 15<br>Argentina Colombia<br>Luxembourg Netherlands<br><!-- End of picture text --> However, the situation is dramatically different when we look at Panels C and D, which depict the Gini coefficients of the distribution of market (i.e., pre-tax and transfer) incomes. In fact, whereas the average Gini coefficient of market income for the Latin America sample, at .52, is only 2 percentage points above that of disposable income, the drawn from Engel et al. (1999). The incidence of transfers is estimated on the basis of the information in Lindert et al. (2006). The European data come from EUROMOD, which is a source of harmonized microdata on the different income components before and after redistribution; see http://www.econ.cam.ac.uk/dae/mu/emodstats/index.htm 14 The graphs confirm the findings of De Ferranti et al. (2004) and World Bank (2006). For disposable income, they report an average Gini coefficient of about .5 for Latin America and an average Gini coefficient of .31 for European countries.

15 This result continues to hold in a broader sample of Latin American countries, because the lowest Gini in the region (Uruguay’s) is .42.

5
|**Adjusted**|||||||||
|---|---|---|---|---|---|---|---|---|
|9-months|1178|13.38 (5.45)|1221|13.40 (5.10)|-0.14 (-0.65,0.37)||||
|24-months|1006|14.21 (4.90)|1044|14.70 (5.10)|0.19 (-0.33,0.72)|0.122|0.04 (0.03,0.07)|0.40 (0.36,0.44)|
|**Adjusted for predi**|**ctors of miss**|**ingness**|||||||
|9-months|1141|13.39 (5.42)|1203|13.40 (5.10)|-0.02 (-0.54,0.51)||||
|24-months|971|14.24 (4.85)|1028|14.69 (4.58)|0.29 (-0.25,0.84)|0.160|0.05 (0.03,0.07)|0.37 (0.34,0.42)|

a Mean score and sd at follow-up b Pencil tap test was conducted at baseline and single digit code transmission task was conducted at 9 and 24 months follow-ups. c Double digit code transmission was conducted at baseline and both follow up visits.

d Mean difference (intervention-control) presented for continuous outcomes (scores on attention task) and are obtained from random effects regression analysis accounting for school-level clustering and repeated measures on children.

e p-value for the comparison of the intervention effect at 12 months to 24 months

**Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics.

**Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available. **Adjusted for predictors of missingness** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.  Additionally adjusted for parental education, SES and baseline educational level as measured by baseline spelling score (standardized by subtracting year-group baseline mean and scaled by year-group sd).

43
||IDP|Non-IDP|A|p(A)|
|---|---|---|---|---|
|Household head|||||
|Head female|0.35|0.33|0.02|0.26|
|Head widow|0.10|0.07|0.02**|0.02|
|Household composition|||||
|Household size|5.93|6.24|-0.31***|<0.01|
|No. of adults|2.65|3.06|-0.41***|<0.01|
|No. ofwomen|1.36|1.54|-0.19***|<0.01|
|No. of men|1.29|1.51|-0,22***|<0.01|
|No. of economically active adults|1.61|1.84|-0.23***|<0.01|
|No. of economically activewomen|0.68|0.77|-0.09***|<0.01|
|No. of economically active men|0.93|1.07|-0.14***|<0.01|
|No. of children|3.28|3.19|0.10|0.21|
|No. of girls|1.57|1.55|0.02|0.68|
|No. of boys|1.71|1.63|0.08|0.14|
|More females than males|0.39|0.40|-0.01|0.45|
|More women than men|0.28|0.26|0.02|0.16|
|More girls than boys|0.32|0.34|-0.02|0.28|
|No. offemales - No. of males|-0.07|-0.05|-0.02|0.79|
|No. ofworking females - No. ofworking males|-0.25|-0.30|0.05|0.18|
|No. ofwomen - No. of men|0.06|0.03|0.03|0.45|
|No.ofgirls-No.ofboys|-0.14|-0.08|-0.06|0.41|
Table II Official price indeces, 1990, quarterly data (to the same period of previous year)

|[l_______|_____<br>I|__<br>II|I<br>III|IV|Average|
|---|---|---|---|---|---|
|CPI|3.4|3.9|14.1|18.4|10.0|
|of which|._|.||||
|foodstuff|0.0|0.2|20.0|24.2|11.1|
|durables|6.8|7.4|11.0|16.7|10.5|
|catering|0.2|1.3|15.9|116.2|_8.5_|
|services|3.3|3.7|9.9|12.7|7.4<br>|||
|PPI<br>industry|1.3|1.4|1.5|17.8<br>|5.5|
|GDP deflator|1.07|1.09|1.14|I1.20|1.13|

Source: Federal Statistical Office

> * 1.1.1984= 100
# **Figure 17: Top 20 six‐digit products with highest EXPRELY of overall GVC Exports, 2014**

<!-- Start of picture text -->
1.6 1.2<br>1.4<br>1<br>1.2<br>0.8<br>1<br>0.8 0.6<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0 0<br>852031 902150 851921 540320 900930 847040 852039 910119 902111 910199 570252 910221 902129 850613 600241 640191 900620 852210 901600 551432<br>Six‐digit products, H0 Nomenclature<br>EXPRELY of GVC Exports HHI_X of GVC Exports<br>Notes:  Values expressed in ratios computed from original variables expressed in thousand current USD, and calculated on the total number of 254 countries/geographies left<br>after excluding two small countries (Bermuda and Luxembourg) with normalized 2001 GDP >1 from the original dataset with 256 countries/geographies with available data on<br>at least some years along the 27‐year period spanning 1988‐2014, even though some may have missing data in some years (HS 1988/92 nomenclature, mirror data, six‐digit<br>products). All trade data is drawn from Comtrade on WITS, and countries' GDP data comes from WB WDI.<br>HHI of GVC Exports<br>EXPRELY of GVC Exports<br>textured yarn, nprs apparatus of man‐mad cartridges sensitivity of 5 cg o<br>incorporating sou stimulating heart mu without loudspeaker, recorders incorporati battery powered and other watches with automatic windin knit, of wool or fine covering the kn used for recordin thread twill, <85%<br>primary cells&batterie<br>ELECTRONICS FINAL: Magnetic tape rec ELECTRONICS FINAL: Pacemakers for ELECTRONICS FINAL: Record‐players APPAREL INTERMEDIATE: Artificial ELECTRONICS FINAL: Thermo‐copying ELECTRONICS FINAL: Accounting machines ELECTRONICS FINAL: Magnetic tape ELECTRONICS FINAL: Wrist‐watches, ELECTRONICS FINAL: Artificial joints ELECTRONICS FINAL: Pocket‐watches & TEXTILES FINAL: Non‐pile floor coverings ELECTRONICS FINAL: Wrist‐watches with ELECTRONICS FINAL: Dental fittings, nes ELECTRONICS INTERMEDIATE: Silver oxide APPAREL INTERMEDIATEFabrics, warp FOOTWEAR FINAL: Waterproof footwear ELECTRONICS FINAL: Cameras of a kind ELECTRONICS INTERMEDIATE: Pick‐up ELECTRONICS FINAL: Balances of a APPAREL INTERMEDIATE: Coloured 3 or 4‐<br><!-- End of picture text -->

<u>Sources:</u> Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator). World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators).

32
**Table 15: Distribution of Tea Markets (%)**

||**India**|**Nepal**|**India**|**Nepal**|**India**|**Nepal**|**India**|**Nepal**|
|---|---|---|---|---|---|---|---|---|
|Quantity (kg)|Farmg|ate|On own pr|emises|Local|market|District|market|
|<1500|14.3|29.4|33.3|28.6|0|18.2|0|0|
|1500 to3000|28.6|29.4|44.4|0|0|18.2|14.3|0|
|3001 to 4500|14.3|5.9|11.1|28.6|0|9.1|28.6|0|
|4501 to6000|14.3|17.6|11.1|0|0|9.1|42.9|0|
|6001 to 7500|0|0|0|0|0|0|14.3|0|
|7501 to 9000|0|0|0|0|0|27.3|0|0|
|9001 to 10500<br>10501 to|0|11.8|0|0|0|9.1|0|0|
|12000<br>12001 to|0|0|0|14.3|0|0|0|0|
|13500<br>13501 to|0|0|0|0|0|0|0|0|
|15000<br>15001 to|28.6|0|0|0|0|0|0|0|
|16500|0|0|0|0|0|0|0|0|
|>16500|0|5.9|0|28.6|0|9.1|0|0|

_Source: Field Survey_ In Siliguri, tea auctions are conducted by auctioneers who include brokers, sellers and buyers. In auction, sampling needs to be done three weeks ahead. Money is received within 45 days. It is mandatory that only buyers registered with the Siliguri Tea board can buy tea from the auction. Darjeeling tea is auctioned in Kolkata and all the other tea are auctioned in Siliguri. There are 7 brokers, and about 300 sellers and buyers each in Siliguri. The sellers include plantation owners as well as factories unattached to plantations that source in all tea.

There are no auctions in Nepal and Bangladesh. Private sales are the main sales option for tea in Nepal, with most going to processors. There are 2-4 large producers who export directly. There is a feasibility study underway in Nepal on establishing an auction system for tea. The opinion of the officials interviewed in Nepal is that allowing auction system can facilitate tea trade in Nepal.

Tea being sold in the Siliguri auction goes immediately into warehouses, which are organized through the Tea Board (with strict specifications). There are 20 warehouses in Siliguri registered with the Tea Board, and an additional 10 that are rented to cope with high demand during the on-season (June to December). In Nepal, after the blending of CTC and orthodox tea, it is taken by small buyers. There is only one packaging unit for CTC, and no major warehousing in Nepal. Generally, across all three case study countries most farmers (two thirds) do not own any storage facilities. They have to rely on state or third party facilities. Specially built storage facilities can be optimally located to exploit economies of scale taking into consideration hinterland and downstream market connectivity.

Brokers arrange for the transportation of tea in Darjeeling. The commission is paid to the middlemen in the market, who are the commission agents. The transporters contact the brokers and the brokers contact the vehicle owners. There are two types of payment system for the transporters: paid, and Freight on Delivery (FOD). In the first case, the seller pays the transporter directly. In the second system, the margins are decided by the customers and thus there is a sales agreement with the buyer. In Nepal, for the transport, 5 to 6 farmers get together and organize transport, the cost of 23
regulatory practice is more readily established and maintained.” (Cubbin and Stern, 2000a) The basic empirical implications of these “theoretical” hypotheses is that, thanks to the structuring and the practice of regulation it entails (e.g., as an independent regulator that makes transparent regulatory decisions), better regulatory governance increases capacity and enhances productive and allocative efficiency. In the case of telecommunications, which is the sector concerned by our study, these implications are typically tested in data collected on a set of developing countries observed during a given time period. Regulatory performance is measured by mainline penetration rates and mainlines per employee, and regulatory governance is captured in an index (see Gutierrez, 2003a) that aggregates a set of aspects related to the structuration and internal organization of regulation. The methodology applied to both telecommunications (Gutierrez, 2003b) and electricity (Cubbin and Stern, 2000a) yields a positive impact of regulatory governance on output.<sup>6</sup> A typical contribution to this line of research starts from the global conceptual view that the “..institutional quality is the dominant determinant of variations in long-term growth performance.”<sup>7</sup> However, in its implementation part often it only accounts for micro dimensions of institutional quality embodied in what is referred to as the quality of regulatory governance. Our view is that this approach should substantially gain in richness by drawing lessons from the literature on the design of institutions discussed in the beginning of this section. Our goal then is to take a step towards a unified approach that, when evaluating regulatory performance, in addition to specifying variables of regulatory governance, explicitly incorporates variables that link political and regulatory structures and processes. Hence, our study can be viewed as a first exploration of the relative merits of such an integrated empirical approach.

In our empirical analysis, the variables through which the interface between political and regulatory structures and processes is going to materialize

> 6For a survey of empirical studies on regulatory performance and regulatory governance in developing countries, see Cubbin and Stern (2005b).

> 7See Cubbin and Stern (2005a) and the citations thereof.

7