rafmacalaba/datause-encoder-data huggingface dataset:
the ownership of solar products that might have prevented children from using the lamps to study. By providing treated households with a clock, a detailed pictorial manual to help them maintain their solar products, and a bundle of solar lanterns, our experiment was designed to reduce these potential problems while still yielding robust evidence to support the finding: “no academic performance effects.” By contrast, Hassan and Lucchino (2016) found positive treatment and spillover effects on the educational achievements of 7<sup>_th_</sup> grade students in rural Kenya. The difference between their results and ours, especially in relation to spillover effects, may have arisen from the children’s different study habits in two different experimental settings. Hassan and Lucchino (2016) highlighted the fact that, after dark, students shared lights at school; this sort of group learning was less likely to happen in our study setting, because it is quite risky for young children to go out at night in unelectrified river islands. Our follow-up survey confirmed that only three students in our study shared solar lamps with non-household members.

The remainder of the paper is structured as follows. Section I explains the study setting, sampling framework, and detailed design of the RCT. Section II discusses the summary statistics of the sample households. Section III outlines the estimation strategy, followed by the estimation results and the associated discussions in Section IV, Section V, and Section VI. Section VII concludes the paper.

# **I Survey and experimental design**

This section explains our survey and experimental design.

## **Study setting**

Our study area is the river islands located in northern Bangladesh, which were formed by sediments and silt deposits. These islands lie a mere few inches above the normal functioning river-water level. As monsoon precipitation coupled with excessive glacier melting of the Himalayas usually overflows the major river channels of Bangladesh, the river islands are extremely vulnerable to flooding during the rainy season. Most years during the flood, the residents of _Chars_ are forced to evacuate to the mainland to seek shelter. The major mode of transportation from _Chars_ to the mainland is via boats, which are poorly managed, unreliable, and susceptible to weather conditions. Therefore, living in _Chars_ is highly precarious. The provision of electricity through the national grid is rare, and the REB of Bangladesh has hardly electrified any _Chars_ due to infrastructure challenges associated with these types of geographical locations.

During our experiment in 2013, the only available sources of lighting, other than biomass- or fuel-based ones, were small-scale SHSs that were financed and operated by some NGOs in the northern _Chars_ . However, the usages and 5
effect is doomed to be ignored, thus, our estimated trade gains tend to overestimate the actual gains. There are many factors to be consider if we wish to address aspects of benefit broader than on trade flows, say country’s or global welfare and resource costs for capacity building. Those aspects require far more information than are currently available, and therefore, we limit our scope to trade flows.

Figure 9: Trade Facilitation Indicators <!-- Start of picture text --> 1<br>0.8<br>Bangladesh<br>0.6 India<br>Pakistan<br>Sri_Lanka<br>0.4<br>East Asia Average<br>All sample countries' average<br>0.2<br>0<br>Ports (air and Customs Regulations Services<br>maritime)<br><!-- End of picture text --> Source: Wilson, Mann, Otsuki database 2004 Note: Data on regulatory environment and service-sector infrastructure are not available for Pakistan.

Table 1: The Estimated Total Gains in the Value of Trade Flow from the Capacity Building in Trade Facilitation <!-- Start of picture text --> Capacity building undertaken by<br>South Asia (SA) Rest of the world (ROW)<br>SA – SA 2.6 billion (A)<br>28.9 billion (B)<br>SA – ROW<br>4.8 billion (C)<br>Trade pattern Total 36.3 billion (D)<br><!-- End of picture text -->

# _Expanding Intra-regional Trade_

In this subsection, we estimate gains from regional integration with a focus on collective programs in capacity building within South Asia. We investigate the gains to regional 21
<!-- Start of picture text --> = #l<br>SS eonamny<br>OECD high income ese {=<br>—<br>Eastern Europe & Central Asia a=<br>East Asia<br>& Pacific et =<br>Latin America & Caribbean pra<br>Middle East & North Africa =<br>South Asia mS REE —OIC (118)<br>Sub-Saharan Africa aEES<br>—|<br>——<br>——= 185<br>Average ranking on<br>the ease of doing business<br>(1-183)<br><!-- End of picture text -->
In fact, our preferred model explains only about 25% of the out-of-sample variation in growth in mean welfare. The models using 1,000+ variables perform at about the same level or slightly better. In other words, most of the variation we are trying to predict cannot be predicted by any information we are utilizing.

Why is poverty so hard to predict by any model? We hypothesize five possible answers. First, the available features may not be well-suited to predict growth in welfare. Though we rely on predictors from different databases including remote sensing data, it is possible that the most important features are contained in other data sources, such as mobile phone data or proprietary remote sensing data. Second, it may be that other algorithms or ensemble learning might perform better. Third, this analysis has only considered data at the country level, and it is possible that models specified at the subnational level may perform better.

Fourth, the reason for the seemingly poor performance of all methods may be due to measurement error and measurement differences in welfare aggregates across countries. Issues like whether a recall or diary is used, number of consumption items asked about, the treatment of rent and durables, and differential non-response to surveys vary between and within countries and can have significant bearings on poverty rates (Beegle _et al._ , 2012; Jolliffe, 2001). This affects both poverty rates in the cross-section as well as changes. Accounting for these issues would require a detailed database on the methodology used to construct welfare aggregates, which is not currently available. Finally, the fact that the distributions of many countries have a lot of mass around the international poverty line means that even small changes to the distribution of welfare can yield large changes to the poverty rate. This makes it difficult to predict the poverty rate with great precision.

Regardless of which, if any, of these hypotheses can explain why no model is able to explain a significant fraction of the variation in the growth in welfare, a very simple model utilizing growth in real GDP per capita and a prior welfare distribution remains an appealing method.

propriate caution.

28
In Ghana, women have a long history of participation in the public sphere and active engagement in economic activity (Darkwah, 2007). They own 70 percent of household enterprises, higher than the regional average in West Africa (Fox and Sohensen, 2012). In the Accra Metropolis, a slightly higher percent of men, 15 years or older, are economically active (72%) than women (68.5%) (Ghana Statistical Service, 2014: 34). Overall, there is wide acceptance of women working outside the home, and often a social expectation that they will engage in income generating activities to provide help or support for the household. This indepth research was designed to complement a quantitative impact evaluation of a program supporting women entrepreneurs.

As described below and summarized in Table 1, we conducted two rounds of data collection, resulting in a total of 78 interviews with 49 different respondents.

Table 1: Number of people interviewed

||Interview 1|Interview 2|Interview 3|Total # of<br>Interviews|Total # of<br>Individuals|
|---|---|---|---|---|---|
|First round:|30|5||35|30|
|Second round:|13|12|18|43|19|
||||Total|78|49|

# **_First round of data collection_**

The first-round of qualitative data was collected in January 2016, before the baseline survey for the impact evaluation and before the program had begun. It was designed to examine common household economic resource management strategies among urban women microentrepreneurs.

All participants for the first round were recruited and interviewed at their place of work. To be 9
8

**Table 1.2.  Correlates for intimate partner violence**

|**Individual-level**<sup>**7**</sup>|**Relationship-level**<sup>**8**</sup>|**Community-level**<sup>**9**</sup>|**Societal level**<sup>**10**</sup>|
|---|---|---|---|
|**_Socialization and learning_**||||
|witnessing intimate partner<br>violence as a child (+)<sup>11</sup>||**neighborhood crime**<br>**rates (+)**|cultural norms that support<br>violence as an accepted way to<br>resolve conflicts or to punish<br>transgressions(+)|
|suffering abuse as a child (+)|association with gang,<br>delinquent or<br>patriarchalpeers(+)|absent or maladaptive<br>teaching of alternatives to<br>violence(+)||
|**_Power relations and patriarch_**|**_al gender norms_**|||
|absent or rejecting father (+)|male control of<br>household decision-<br>making and wealth (+)||norms that support male<br>dominance over women and<br>that require women’s obedience<br>and sexual availability (+)|
||Controlling behavior on<br>the part of the husband<br>(+)|||
||Multiple partners/wives<br>for the husband (+)||Policies and laws that<br>discriminate against women in<br>social, economic and political<br>spheres|
||Differences in spousal<br>age and education(+)|||
|**_Human capital and employmen_**<br>|**_t_**<br>|||
|female educational level (-)|economic hardship (+)|Lack of economic<br>opportunities for men(+)||
|male educational level (-)|||access and control over<br>economic resources for women<br>(+/-)|
|Women engaged in income<br>generation activities (+/-)<br>**_Life cycle_**||||
|age of woman (-)|length of relationship (-|||
||<br>)|||
|Triggers||||
|HIV status of man or woman<br>(+)|alcohol and substance<br>abuse bymale(+)|||

Sources: Gordon and Crehan (n/d); Heise (1998); Hindin and Adair (2002); Jewkes (2002a); Jewkes (2002b); Koenig (2003) Koss; Loi et al. (n/d); Martin (2002); WHO (2002).

> 7 Individual level: biological and personal history factors among both victims and perpetrators

> 8 Relationship level: proximal social relationships, including relations with friends, peers and family. 9 Community level: community context in which social relationships are embedded, including schools, workplaces and neighborhoods

> 10 Societal level:  larger societal factors that “create an acceptable climate for violence, reduce inhibitions against violence, create an sustain gaps between segments of society (WHO, 2002: 13).”

> 11 For boys, witnessing violence increases the risk of becoming an abuser, whereas for girls it increases the risk for future victimization.
**Y RtSi AR(Ai WORKING PAIPR** _1225_

# Summary findings

Trade aniong Sth-Saltrin Af\tricatn couintries is *crL litilited, This tact, plus other political and econoli, consideirations, has. been iisedl **to** rinotiv'ate I,rowig nimber ot regional integratriol sJchtites. AltrouIgh **man yv** authors bave show% n that ittrrl-SLih- **Saharani** Atricaii trade is limited, n onl hrls **vet r** asiied whether the le C%l of ilrItra-SuLb Sa rn 11 .'l :\tAiL'ar tradL. _I_ **is** highetr **ow** loweri tihan **One wold** expect, giveij a pilausitble mllodeSl ot theC dteICrmrinall.tioll ot tra de flows.

h I II L d thil e'!I tt 0 r111npare **Ac-rM,1l** trade !.1 i t iI.lt a trlditol,nal gra,vits iiotloe **\%onld** prevditr. T Irhe, filrri talt .gr erv itv ir ilmidl p(cJiCtr tle' I()o 1('C 0 ilrntr 1iil'S,Alir rl 5<sup>ir i tra</sup> IA\dri<sup>.1w thNn</sup><sup>_l_</sup> Saharan Aftricani counrtries **nr** t1ir santphl, the aclt 1tual. Stn SaIhairani Atlic ri share **0Im** Of oirts **plus expoirts was** 11 **a t.** acrlruc (r'il **L** iaIc ) **0'** 8.I I p rcerit (4. pc eIt ) whIIle I tilt **grvrrl% ls** lnlodcl peldicts .r sli _illl hoicerl., lot hligher,_ **1nLenil** (oiedrlit) otf - percett (4.5 percent).

*I'his paper -- a product _of thIe_ Frade Polioy D)ivision. Pol'ic% Researci Department - is part ot a larger etfort in tht cdepartmentto study trade patterns in Srih-Sflharan Africa. _(i_ pies **t** the **paper** are asaihable trce fr **ito** trhe ld rik, Fi 18 18 H Street NW, Washington, [)C' 20433. Pleascomntactr shi !a 1all(o, roonM N I (t **-1)** I ' exte nsior; **80))9** (3 2 **pages).** Novembniecr 1993

**_/ihe P_** _diy Restwaa,l VAn r4'lrn '/ s u s_ **_mv;;_** h'zuiat's _t_ **_'.i ._** _-rk_ **4** **_:t ;' "_** ' _:_ **_o.i_ U .** _;._ .'.: _1.'_ **_2/J d'revh ptmnt rissu,s._** _An_ **_ch;'_** _jtnttv ,,_ **_., th'_ ;'** . r **_S_** _,fir'_ **_I_** _iPj:h_ **_nd 'l ' !_** _a_ **_q_** _:. k_ **_'_** _, . * 0.: 1_ ' _'r.;r:, .;_ p' _-_ **_ti';' :._** _-l's .I ;_ **_.7'','rs C-r71_** 'th, **_names -f_** _ti_ . 4r **_i.' slu._** _bu,cl ,;'z,l_ **_1.1.L._** _',rjol.'.s I_ i' _nlet .'._ **_'t '': .. :':_ ;** _. , autt-rs_ **_oun_** _W_ **_a 'r_** _s/il,) nt t Ib ao V ti_ **_Ar_** _J ._ P _/ J',,_ :' _Iv, o' :tt1 t_ **_lip._** _1,;_ **_I' .;_** _,_ ' **_.t '''I, .0p._**

Prodnctdi iby thIe PolIcv Reseac arch IIIIc rnrr.i.ri I f ( te r
Once direct taxes and all social contributions are subtracted and direct transfers are added, the extreme poverty headcount for disposable income declined further, to 5.5 percent. However the legal poverty headcount increases somewhat to 8.6 percent, on account of the relatively large burden of direct taxes and social contributions for all but those under the extreme poverty line.<sup>13</sup> Once indirect taxes are incorporated into the analysis, all of the improvement in poverty that takes place through direct transfers is canceled out. In fact, indirect taxes result in a 3.5 percentage point increase in extreme poverty when compared to disposable income and a 2.9 percentage point increase when compared to market income plus pensions. Similarly, both the poverty gap and the severity of poverty decline with pensions and social transfers, but increase once indirect taxes are taken into account. Similar results hold when the international US$5-a-day (2005 PPP) and the US$2.5-a-day (2005 PPP) poverty lines are used.

How does this look for different types of households? As shown in Table 5, there is quite a bit of heterogeneity in terms of the impact of taxes and transfers on poverty across different types of households. Although poverty increases with indirect taxes for all types of households, households with children are especially hard hit, with extreme poverty being higher for consumable income than for market income for households with more than one child and the at-risk-of poverty rate being higher for single parents and for couples with children.

Table 5. Main Scenario: Changes in Poverty on account of taxes and transfers by household type

||Market<br>Income|Disposable<br>Income|Consumable<br>Income|Total impact on<br>extreme<br>poverty|
|---|---|---|---|---|
||(1)|(2) =<br>(1) + pensions +<br>direct transfers -|<br>(3) =<br>(2) - indirect<br>taxes|<br>(4) = (3) - (1)|
|||direct taxes -<br>contributions<br>Extrem<br>|ePoverty<br>||
|Overall|27.2%|5.5%|8.9%|-18.3%|
|Single adult|24.4%|6.0%|9.4%|-15.1%|
|Single parent|22.6%|7.0%|10.6%|-12.0%|
|Couple without children|27.4%|3.9%|6.3%|-21.1%|
|Couple with 1 child|6.6%|4.1%|6.1%|-0.5%|
|Couple with 2 children|6.1%|4.7%|8.0%|1.9%|
|Couple with 3+ children|15.9%|9.9%|17.1%|1.3%|
|Single retiree|91.4%|0.5%|1.3%|-90.1%|
|Couple of retirees|93.6%|0.4%|0.8%|-92.8%|
|Mixed|24.0%|6.8%|11.2%|-12.9%|

> 13 Results for the alternative scenario where old age pensions are considered as deferred savings are presented in Appendix 2.

18
Asset Price Effects of Peer Benchmarking: Evidence from a Natural Experiment<sup>_∗_</sup> Sushant Acharya<sup>_†_</sup> Alvaro Pedraza<sup>_‡_</sup> FRB New York The World Bank

# **JEL Classification:** G12, G14, G23

# **Keywords:** Herding, Institutional Investors, Asset Prices, Comovement.

> _∗_ We are grateful to the World Bank Research Committee for funding this research. This study has benefited from the comments of Pablo Cuba, Mark Jensen, Pete Kyle, John Shea, Paula Tkac and Russ Wermers. We also thank seminar participants at the Inter-American Development Bank and Federal Reserve Bank of Atlanta for comments and suggestions. We are grateful to Miguel Lea ̃no for excellent research assistance. The views expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the Federal Reserve Bank of New York or the Federal Reserve System.

> _†_ sushant.acharya@ny.frb.org

> _‡_ apedrazamorales@worldbank.org
**Figure** 5.2. **Deposits in Dollars and Interest Rate Differential**

<!-- Start of picture text --> 25 - 75<br>- 20 -70<br>'U C~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~A<br>15- 65<br>0~~~~~~~~~~~~~~~~~~~~~~~~<br>*~~~~~~~~~~~~~~~~~~<br>- ~~~~~~~~~Share of Deposits In US Dollars i<br>oCO 10 - S|<br>°5- /^ 55<br>Currency Premium<br>0 50<br>co co 0 0) 0 0 0 CD a0 a, a ,<br>0 0 0 0 0 0 a a a o a a<br>-= X o E -= 0 o E -n X o E n to<br><!-- End of picture text -->

**Source: Deposits from** Ministeno de Economia (http/www.mecon.org); 30 **day** deposit interest rates in pesos and dollars from Bloomberg. Currency premia is defined as the difference of domestic interest rates in pesos and **US** dollars.

However, with the benefit of hindsight we can now identify at least three crucial vulnerabilities in the financial sector and weaknesses in the regulatory framnework, even in the hypothesis that the Currency Board was to be permanent. The shortcomnings relate to:

- the link between debtor capacity to pay and the required deflationary adjustment to a more depreciated equilibrium real exchange rate;

- the growing exposure of the banking system to government default; and

- the insufficient realization that _general_ liquidity buffers, even if high, do not adequately protect the payments system from a run.

The first vulnerability had to do with credit risk-the latent non-performing loans (NPLs) in the context of an overvaluation of the _real_ exchange rate (RER) relative to its _equilibrium_ level. As mentioned, it is estimated that, by the year 2001, the Argentine RER was overvalued by more than 50 percent. Under convertibility, the adjustment of the RER towards. equilibrium was bound to imply a protracted and painful deflation and recession which would have certairly eroded the capacity to pay of debtors whose earnings came from the non tradable sector.<sup>4 5</sup> Hence, the first prudential shortcoming was the failure to recognize the special risk of loans to debtors in the non tradable sector-a credit risk that would materialize in the event of significant adverse shocks that

> _45_ By contrast, in a country with a flexible exchange rate (i.e., where a fixed parity is _not_ part of the social contract) and _without_ a liability dollarization problem, the adjustment to a more depreciated equilibrium RER would come through a nominal depreciation, which would be associated with an _improvement_ (through debt dilution) in the capacity to pay of debtors in the nontradable sector.

46
Table 7 shows the OLS estimators of the yield functions for the rich and poor households. IMT appears to have an impact on rice yield for both the rich households, Column (1), and for poor households, Column (2). A 9 percent boost in rice yield for poor households is associated with the presence of IMT whereas the increase in rice yield for rich households is 4 percent. The 5 percent difference in the gain in rice yield for the poor as compared with the rich is statistically significant at 5 percent level. Thus, we find no evidence of elite capture in IMT IAs. The poor households in IMT IAs tend to gain more from IMT as compared with the rich households there.

Table 8 shows the stochastic frontier estimators of the yield functions for the rich and poor households. IMT is a significant negative factor determining the heteroskedasticity of the technical inefficiency in the estimations for both the rich and poor households. The average yield increase attributable to IMT for rich households range between 1.7 percent to 2.1 percent and that for the poor households ranges from 3.4 percent to 5.1 percent. As expected the stochastic frontier estimates are lower than the OLS estimates. The gain in yield attributable to IMT for the poor households is double or more as compared with the gain for the rich households. The relative gain in yield for the poor as compared with the rich are of similar magnitude (double or more) irrespective of the type of estimator (OLS or stochastic frontier).

In order to understand why the poor appear to gain from IMT, we examine some additional questions asked the survey. Table 9 provides information on the how the poor view irrigation water delivery and offers some insights into why the poor may be slightly 32
<!-- Start of picture text --> 1<br>0.8<br>_€0.6(<br>Zt 04<br>0.2 |<br>0<br>0 10 20 30 40 °° *<br>x [m]<br>27... vegetatedarea<br>a 1.5<br>£<br>N<br>1<br>0.5<br>0 10 20 30 “0 °° a<br>x [m]<br><!-- End of picture text -->
in the regression model and a set of instruments.<sup>Giventhatwehave twosuspectedendogenous</sup> variables (education, and assets), we also identify two<sup>sets</sup> of<sup>instruments.FollowingCard(2001),</sup> we instrumented education using viUage level average<sup>distanceto nearest school,distance to school</sup> interacted with ethnicity dummies and with inherited<sup>agriculturalland.28For assetownership by</sup> the household, inherited agricultural land and its squared term<sup>are used as identifying instruments.</sup> The details of the first stage regression results are presented<sup>in the appendixTable A.2.The first</sup> stage regressions explain a considerable amount of the<sup>variationsin educationand assetswhile</sup> avoiding over-fitting. The adjusted R<sup>2</sup> is estimated to<sup>be 0.47(fordaughter's)and 0.49(forson's)</sup> education equations respectively, 0.58 (for daughter's)<sup>and0.56(forson's), regressionsforassets.</sup> Consistent with the findings of Card(1995), Conneely and<sup>Uusitalo(1997)andMaluccio(1998),</sup> both son's and daughter's education vary inversely with distance<sup>to nearestschool (p-value=0.0).</sup> While distance to school interacted with inherited land<sup>appearstohaveno significantinfluence</sup> on the level of education of either sons or daughters, distance<sup>to schoolinteractedwith ethnicity</sup> is statistically significant in explaining son's education<sup>level.Consistentwiththe expectation,</sup> inherited land and its squared terms are highly statistically<sup>significantin explaining household's</sup> current level of assets in both son's and daughter's<sup>samples(p-value=0.0inbothcases).The</sup> estimated coefficients imply a concave relationship<sup>betweenassetownershipandinherited land</sup> for both sons and daughters. The two sets of instruments<sup>areseparatelyandjointlyhighly</sup> statistically significant in the relevant regressions. The parental<sup>employmentstatusvariables</sup> are not statistically significant in education (for both<sup>sons anddaughters)orasset equationsfor</sup> daughters. Parent's non-farm participation appears to<sup>havenegativeinfluenceonthelevelof</sup> assets owned by the household only in the son's sample.

For the second stage regression, the set of explanatory<sup>variables(includingthe suspecteden-</sup> dogenous variables) is augmented with the residuals<sup>fromthe first stage and then probit regression</sup> of non-farm participation is run on this augmented<sup>setofexplanatoryvariables.Anicefeature</sup> of the TSCMLE is that the t-statistic on the residual<sup>is</sup> a<sup>validtest of the nullhypothesisthat</sup> the suspected endogenous variable is in fact exogenous.<sup>Theestimatedcoefficientsofthefirst</sup>

> 28While ethnicity dummies are already in the structural equation, if educational attainment varies with ethnicity, then ethnicity interacted with distance to school still qualify<sup>as stronginstruments.Fordetail,pleaseseeCard</sup> (1995).

17