rafmacalaba/datause-encoder-data huggingface dataset:
representative survey on fraud victimization. By utilizing nationally representative data on fraud victimization and household financial circumstances, we can better understand the nationwide patterns of fraud victimization and make more representative inferences about the impact of credit constraints on fraud victimization.

The patterns of fraud approaching are in Table 1. In CHFS-III, each household head is asked whether the family members were approached by fraudsters last year, and if so, the intermedium through which the fraudsters approached. If a household was indeed approached by fraudsters last year, the household is considered as a fraud-approached household. Perhaps alarmingly, 67.4 percent of urban Chinese households were approached by fraudsters through various intermedium.<sup>10</sup> In the past decades, urban areas in China have expanded vastly with massive population inflow, among which are many fraudsters seeking more “opportunities.” The denser neighborhoods and the lifestyle in urban areas also involve more social interactions and easier leakage of private information. Since private information protection and fraudster arresting are costly, local provision of public safety services against fraud approaching is far from sufficient.

We classify the intermedium through which urban households were approached into telephone, texting (i.e., SMS, mail, and e-mail), and face-to-face contacts. Telephone is the most frequently used intermedium: 65.1 percent of urban households have been contacted by fraudsters over the telephone. While both texting and face-to-face contacts are less popular, texting is used more frequently than face-to-face contacts (14.1 percent vs. 8 percent).

Table 2 reports the differences in the probability of fraud victimization after being approached, using the subsample of approached urban households. We split the approached households into being credit constrained and not being credit constrained. Credit constraints happen when there are frictions in the supply of capital and individuals are unable to obtain sufficient loans from banks or other institutions (Hurst and Lusardi, 2004; Farre-Mensa and Ljungqvist, 2016). Following previous studies (Jappelli et al., 1998; Lee and Sawada, 2010; Rossi and Trucchi, 2016), we consider a household as being credit constrained if the household is unable to borrow money for any of the following reasons: (a) need loan, but being rejected by banks or other credit institutions; (b) unable to obtain

> 10 The average ratio of being approached by fraudsters in the rural areas is 40.3 percent.

12
# Figure 12: LAC: Investment needs

# A. Annual infrastructure investment needs

<!-- Start of picture text --> Percent of GDP<br>5<br>4<br>3<br>2<br>1<br>0<br>Transport Electricity Water Flood Irrigation<br> supply protection<br>and<br>sanitation<br><!-- End of picture text -->

# C. Fixed investment, and health and education spending

<!-- Start of picture text --> Percent of GDP<br>EMDEs excl. LAC<br>30<br>LAC<br>20<br>10<br>0<br>Capital Investment Health and education<br>expenditures<br><!-- End of picture text --> E. Selected health and education indicators <!-- Start of picture text --> Frequency EMDE region range<br>80 AEs<br>EMDEs<br>60<br>40<br>20<br>0<br>Pupil-teacher Physicians Safe sanitation<br>ratio, primary (per 10,000 (percent of<br>(ratio) people) population)<br><!-- End of picture text --> B. Projected urban population share in 2050 <!-- Start of picture text --> Percent of population<br>90<br>60<br>30<br>0<br>LAC ECA MNA EAP SSA SAR<br><!-- End of picture text --> D. Broadband and mobile connectivity <!-- Start of picture text --> Per 100 people Index<br>40 Broadband connections 90<br>Mobile connectivity (RHS)<br>30<br>60<br>20<br>30<br>10<br>0 0<br>LAC EMDEs excl. Advanced<br>LAC economies<br><!-- End of picture text --> F. Proportion of global commodity reserves in LAC <!-- Start of picture text --> Percent<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Lithium Copper Rare earths<br><!-- End of picture text --> Sources: GSMA Mobile Connectivity Index; Rozenberg and Fay (2019); UN Population Division; USGS (2021a-c); World Bank.

Note: AE = advanced economies; EMDEs = emerging and developing economies; LAC = Latin America and the Caribbean. A. Bars depict investment needs in LAC according to the preferred investment scenario (“ambitious goals, high efficiency”) from Rozenberg and Fay (2019).

B. Projections by the United Nations Population Division.
|(HAZ)|(9)|0.000<br>(0.003)<br>-0.001|(0.002)<br>-0.000|(0.002)<br>-0.005**<br>(0.002)|4,418<br>0.019<br>Yes|Yes<br>Yes<br>74<br>-2.056|ted estimates|e at the start<br>an indicator|
|---|---|---|---|---|---|---|---|---|
|for age sd|(8)|0.000<br>(0.003)<br>-0.001|(0.002)<br>0.001|(0.002)<br>-0.005**<br>(0.002)|4,418<br>0.007<br>Yes|Yes<br>74<br>-2.056|t on the trea|le refer to ag<br>ntrols include|
|Height|(7)|-0.000<br>(0.003)<br>-0.002|(0.002)<br>0.000|(0.002)<br>-0.005**<br>(0.002)|4,418<br>0.008|Yes<br>74<br>-2.056|evel. Treatmen|ages in the tab<br>test. Other co|
||(6)|-0.006<br>(0.018)<br>-0.007|(0.011)<br>0.001|(0.012)<br>-0.030**<br>(0.013)|4,421<br>0.019<br>Yes|Yes<br>Yes<br>74<br>151.4|(5%) (10%) l|of birth. All <br>as a placebo|
|eight in cm|(5)|-0.007<br>(0.019)<br>-0.008|(0.011)<br>0.004|(0.013)<br>-0.030**<br>(0.013)|4,421<br>0.008<br>Yes|Yes<br>74<br>151.4|ce at the 1%|the district <br>p that serves|
|H|(4)|-0.008<br>(0.019)<br>-0.010|(0.012)<br>0.001|(0.013)<br>-0.030**<br>(0.012)|4,421<br>0.008|Yes<br>74<br>151.4|cates signican|re clustered at<br>mparison grou|
||(3)|0.001<br>(0.001)<br>-0.001|(0.001)<br>-0.001|(0.001)<br>-0.001<br>(0.001)|4,421<br>0.123<br>Yes|Yes<br>Yes<br>74<br>0.253|(**) (*) indi|dard errors a<br>s a second co|
|Migration|(2)|0.001<br>(0.001)<br>-0.001|(0.001)<br>-0.001|(0.001)<br>-0.001<br>(0.001)|4,421<br>0.066<br>Yes|Yes<br>74<br>0.253|birth. ***|ation. Stan<br>rt 22 to 29 i|
||(1)|0.001<br>(0.001)<br>-0.001|(0.001)<br>-0.002|(0.001)<br>-0.001<br>(0.001)|4,421<br>0.064|Yes<br>74<br>0.253|. district of|nce specic<br>21 and coho<br>-eects.|
|||Age 22 to 29 _×_ Months of war<br>Age 9 to 15 _×_ Months of war|Age 4 to 8 _×_ Months of war|Age 0 to 3 _×_ Months of war|Observations<br>Adjusted R-squared<br>Birth year and month xed eects|Regional trends and other controls<br>District xed eects<br>Number of clusters<br>Control mean (Age 16 to 21|Note: Conict is dened at district level i.e|are reported based on a dierence-in-diere<br>of the war. Comparison cohort is age 16 to<br>for high caste and month of interview xed|

44
3 b1caus thgy cross 2 national frontier.<sup>4</sup> It also greatly reduces the utility of the data series since trade and output statistics are on a non-comparable base.

This paper will assess the various definitions of manufactures that are employed with the objoctive of determining the principal ways in which they differ. Underlying conceptual issues are also examined in order to determine the justification for the differences. In addition, these alternative defrnitions are applied to actual developing countries' exports to quantitatively assess the importance of their differences for measuring the level, share and trends in manufactures trade. These results are also used to evaluate the validity of international organizations country groupings like "fast growing exporters of manufactures" or "newly industrialized countries." This study closes with an overall assessment of the importance of the definitional problem for measuring manufactures production and trade and also identifies specific products responsible for major discrepancies in statistical series. Proposals are also made for reconciling differences between the various definitions.

# XT. Q a

Conceptually the distinction between broad classes of goods like "commodities and "manufactures" appears relatively straightforward. Several international organization publications define a manufacture broadly as "an item that experiences considerable change in shape, value, production 4 For example, in GATT (1987) trad in manufactures is compiled on the basis of all products falling in SITC groups 5 through 8 less 68. See the notes or definitions presented on page 139 of the GAIT report as well as appendix tables on pages 155 to 227. This same source defines (p. 139) manufactures otlut as Section 3 of the ISIC classification which includes a broad range of goods falling in SITC 1, 2, 3, 4 and 68 as well as tne more limited grouping used for trade. As a result, the statistical series on manufactures trade and production presented by GATT (see Appendix II) are on a noncomparable base, a point that greatly limits this information's utility. The IBRD has followed similar procedures. See World Bank (1987).
For each “family” of outcomes, cognitive skills, socioemotional outcomes, and wellbeing, we present the results from a test that the treatment coefficients are jointly zero (using seemingly unrelated regressions, SUR). Within these sets of outcomes, we also use SUR to test whether the treatment coefficient for the poverty subsample is equal to that for the merit subsample.

The data support the fact that our experimental design is valid. First, we find that both sub-samples are balanced on observables. This holds true for the full set of respondents at baseline, as discussed by Barrera-Osorio and Filmer (2016), and for this paper’s estimation samples (see Tables A1 and A2, in the Appendix). Second, overall attrition is 31 percent for either sub-sample, and we managed to track 88 percent of respondents who were included in the three-year follow-up study Belotti et al. 2015). Results do not lead to substantial changes and are available upon request.

Our sampling frame consists of 5,964 fourth-grade students (in the program’s 207 schools), who participated in the baseline eligibility assessment, in December 2008 and January 2009. Of those, 2,996 respondents were randomly selected for the first three-year follow-up survey, in 2011. For this first follow-up, an additional 658 “replacement” students were randomly selected, in case students from the target group could not to be found. In the 2016 follow-up, we tracked all students who had participated in the 2011 study, a random subset of 140 respondents who had previously been found to be attritors, and all replacement students who were interviewed in 2011. Our 2016 sample includes 2,252 respondents, of which 2,024 had been interviewed in 2011, 86 had not been reached previously, and 142 had served as replacements, in 2011. Table 1 provides the control group means for key demographic characteristics, for the “merit” and “poverty” sub-samples (for the control group, these refer to respondents who would have qualified if their school had been assigned to one or the other scholarship approach). Our analysis sample consists of 890 and 825 respondents for the merit-based and poverty-based sub-samples, respectively. Among those, about half (48% and 51%, respectively) are female. On average, respondents live with an additional six household members. Almost all the respondents were already working at the time of the three-year follow-up survey. The data support the fact that our experimental design is valid. First, we find that both sub-samples are balanced on observables. This holds true for the full set of respondents at baseline, as discussed by Barrera-Osorio and Filmer (2016), 12
<!-- Start of picture text --> Overarching Content Delivery Perceptions<br>Focus?<br>Who What did<br>implemented? (Content, Core activities? teachers like?<br>pedagogy, etc.)<br>Professional What do you<br>Subject area? Cascade?<br>implications? think mattered?<br>Proportion in<br>Based on a<br>lecture?<br>diagnostic?<br>Practice?<br><!-- End of picture text --> _Figure 2._ Summary of the in-service teacher training survey instrument (ITTSI) 38
**Coefficient Plot: Distrust of Instutitions by Date**

<!-- Start of picture text --> racionalizaumentgobiernurl_linkpropancuandeliminestanhuelgentretambcocinrecibformfmlnhastestajajajtantquedecmalcadverfuntusmeleñnore dollar_amountracionalizaumentgobiernurl_linkgasolinpetrolepropanmayorinuestrfamoscuandeliminminecgrandporquestanahorrrecibitambestarcocintiendvendgracipuedrecibformgentverdporqusarmanesteestovienestacomjajajtantmarpaisquedebunodandecmalcadesellegesaseaversuslicutodrobtusmequivalleñnomihadijtu distribuidortambienpreguntaumentcontrarigobierngasolinpetrolepropanformatpreparcuandcaminporquestabestanhuelgahorrtambtiendvendgracirecibdesdformgentgustmanestoestacomalbajajajmarpaisquitquedebunodandeceseseaverlicutodfunserrobtusquivalleñasinomitu dollar_amountdistribuidorgobiernurl_linkpaquetagricolfamosaunquporquestanahorrestarrecibgustporqviencomtanttemquedebdabmalesellegseatodrobmequileñasinoha beneficiarigobierngasolincuandeliminminecestanquiercocinpuedgentgustesteestacomcarrquitquedebmalesellegseatodmeleñasinomiha dollar_amountpos_emoticongobierngasolineliminestanahorrtambrecibverdjajajquedebunovertodmevalnomidij<br>January 2011 April 2011 May 2011 August 2011 May 2012 August 2012<br><!-- End of picture text --> Figure 12: The top 100 or fewer coefficients associated with the “distrust of institutions” over time. The blue indicates positive coefficients and the red negative. This graph demonstrates topic drift during the sample period. The scales of each graph are not constant over time. Source: Author’s calculations.

## interactions, or an indicator for a retweet.

Finally, we might take a deeper look in the “other” category and in the August 2011 and 2012 tweets to see if it would be beneficial to further refine our taxonomy and search the Twitter firehose archive again. Given even the small number of “Irrelevant” tweets here, this is unlikely and may simply reflect a secular drop in twitter use during August in El Salvador. Collecting tweets over the entire timeline of interest rather than restricting the tweets to the time of the _La Prensa Grafica_ surveys may also help smooth out these statistical aberrations.

# **8 Conclusion**

In this study, we were able to confirm that Twitter can be a valuable complement to existing household survey data. We found that the decrease in negative sentiment tweets concerning several issues surrounding the propane gas subsidy reform in El Salvador coincided with the increase in positive sentiment found by household surveys conducted by 35
Table 4: Analysis of structure of selection into treatment group and attrition and the role of weighting in balancing these variables.

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|Treatment|Attrition|Treatment with<br>Weights|
|age|-0.00393|-0.000957|-0.00397|
||(0.00333)|(0.00269)|(0.00377)|
|gender|-0.00418|0.0881|0.0704|
||(0.0926)|(0.0748)|(0.0914)|
|married|0.00462|-0.0656**|-0.00411|
||(0.0411)|(0.0332)|(0.0447)|
|employment|-0.300*|0.0610|-0.260|
||(0.171)|(0.138)|(0.166)|
|education|-0.0349*|0.0417**|-0.0399|
||(0.0209)|(0.0169)|(0.0250)|
|host|0.130***|-0.00160|0.00596|
||(0.0312)|(0.0252)|(0.0367)|
|finish|0.0170|-0.00285|0.0230|
||(0.0150)|(0.0121)|(0.0177)|
|discourage|0.0149|-0.00176|0.0217|
||(0.0187)|(0.0151)|(0.0205)|
|diligent|0.0133|-0.00674|0.00378|
||(0.0249)|(0.0201)|(0.0262)|
|hard_worker|0.000241|0.00902|0.0133|
||(0.0274)|(0.0221)|(0.0321)|
|life_optimism|0.000427|-0.00781|-0.0110|
||(0.0215)|(0.0174)|(0.0230)|
|econ_optimism|0.00299|0.00678|0.0283|
||(0.0217)|(0.0175)|(0.0258)|
|risk|-0.0135**|0.00758|-0.0107*|
||(0.00584)|(0.00472)|(0.00633)|
|vulnerability|-0.293|-0.0236|-0.241|
||(0.251)|(0.202)|(0.241)|
|Constant|1.172***|0.0416|0.996**|
||(0.415)|(0.335)|(0.424)|
|Observations|927|927|927|
|R-squared|0.051|0.027|0.031|

Note: OLS regression of selection into treatment group (Column 1) and attrition (Column 2) on a range of key control variables. Column 3 shows the determinants of selection into the treatment group following the application of inverse probability weights on the demographic variables (marriage and host status) that determine selection into treatment or into attrition. Standard errors in parentheses. ***, ** and * = p<0.01, p<0.05 and p<0.10 respectively.

32
||(1)<br>_indexjt_|(2)<br>_FDIsharejt_|
|---|---|---|
|ln(_FDIjt_)|0.156***|0.013**|
||(0.041)|(0.005)|
|Constant|0.466|-0.133*|
||(0.534)|(0.063)|
|N|32|32|
|_R_<sup>2</sup>|.32|.23|
|* p<0.05, **|p<0.01, ***|p<0.001|

Table 10: IV. First stage

# **6.2 IV first stage**

We regress our services sub-sector specific indicators of liberalization _indexjt_ and _FDIsharejt_ on the log of services sub-sector specific ourward FDI from the EU towards the rest of the world, ln( _FDIjt_ ). The results are presented in Table 10.

We further construct _serv libit_ =<sup>�</sup> _j_<sup>_aijt·_</sup> _indexjt_ and _serv lib_ ( _FDI_ ) _it_ =<sup>�</sup> _j_<sup>_aijt·_</sup> _FDIsharejt_ , where _indexjt_ and _FDIsharejt_ are linear predictions taken from the first stage regressions.

# **6.3 Mapping EBRD indices to services sub-sectors**

We have constructed two indices of services liberalization: one that includes utilities and retail trade and another that does not. All results in the paper are reported for the index that includes transportation, telecom, financial services, and business services. The results for the index that includes utilities are very similar.

## **Index with utilities and retail trade**

– For eight services sub-sectors Electricity, Gas, Water and water waste, Retail trade and repair, Transport, Telecom, Finance, and Other business-related services (hotels and restaurants, real estate, rent, informatization, R&D, agencies) – we map the sub-sector with EBRD indices of reforms as follows: E: Electricity - (electric) 39
# **1. Introduction**

A great many people in the world still do not have electricity. In much of the developing world, households continue to rely on traditional sources of fuel for lighting, heating and cooking. Indeed, it is estimated that 1.3 billion people in 2009 were still without electricity.<sup>2</sup> Rural households in the developing world can spend a lot of time collecting and preparing fuel for domestic use. Many continue to cook with wood and biomass (mainly dung) with deleterious effects on the health of family members. Across the world, these time and health burdens are thought to be higher for women and the children under their care. Over recent decades, governments and donors have made a concerted effort to bring more efficient sources of energy, particularly electricity, to rural households.

It is often simply assumed that electrification will result in significant welfare gains for households and particularly women within then. Referring to policy discussions in the 1970s and 1980s, Barnes and Binswanger (1986, p.26) note the “blind faith placed in rural electrification.” Still, agreement on this faith is not universal, with some observers expressing skepticism on the claimed benefits of electrification over other energy sources.<sup>3</sup> There are concerns about both internal and external validity of past evidence. In an extensive review of the literature, the World Bank’s Independent Evaluation Group (2008, p. xvii) concluded that “... the evidence base remains weak for many of the claimed benefits of rural electrification.” Similarly, Bernard (2010, p. 41) writes that “While funding for rural electrification programs often rests on their supposed impacts on such outcomes as health, education, or poverty level, there is still very little empirical evidence to substantiate them.” A number of issues make identification of the household welfare impacts of rural electrification difficult. Three stand out. First, there is the potential for electricity acquisition to be jointly determined with outcomes or correlated with omitted variables. Second, non-random placement is likely to entail sensitivity to the choice of regression controls. Third, there are likely to be external effects of electrification in the village, which could well bring benefits to an individual household even if it did not have electricity itself. Indeed, some part of the externality

> 2  See World Energy Outlook (WEO) 2011, published by the International Energy Agency. The WEO estimates that in 2009 25% of the population of the developing world was still without electricity, and the proportion rises to 37% in rural areas. In South Asia, the corresponding proportions are 32% and 40%, while in Sub-Saharan Africa they were 69% and 86%.

> 3  See, for example, the discussion in Mathur and Mathur (2005) with regard to India’s “Electricity for All” program. Also see the discussion in Barnes and Binswanger (1986).

2
<!-- Start of picture text --> Figure 2. Sex ratio at birth by mother's HepB status<br>and birth order of child, Taiwan (China) 1988-99<br>116<br>114<br>Sex ratio at birth (m/f)<br>112<br>110<br>108<br>106<br>104<br>102<br>100<br>1 2 3<br>Birth Order of Child<br>HepB- HepB+<br>Source: Derived from Lin and Luoh (2007: Table 2)<br><!-- End of picture text --> 12
**Table 2: Program expenditures per capita across states**

||Expenditure p<br>2009/10|er capita (Rs)<br>2010/11|
|---|---|---|
|Andhra Pradesh|749<br>|896<br>|
|Assam|406|358|
|Bihar|214|309|
|Chhattisgarh|723|884|
|Gujarat|214|226|
|Haryana|87|128|
|Himachal Pradesh|936|837|
|Jammu and Kashmir|221|445|
|Jharkhand|586|539|
|Karnataka|742|683|
|Kerala|186|276|
|Madhya Pradesh|734|707|
|Maharashtra|54|60|
|Orissa|281|455|
|Punjab|89|98|
|Rajasthan|1133|647|
|Tamil Nadu|555|744|
|Uttar Pradesh|389|365|
|Uttarakhand|406|539|
|West Bengal|335|399|
|All India|464|477|

_Notes and sources_ : Cumulative expenditures (including wage and non-wage spending) in current prices during the 2009/10 and 2010/11 FY were obtained from the Ministry of Rural Development website (http:\\nrega.nic.in). To calculate expenditure per capita the authors used the population projections for 2009 and 2010 done by Registrar General of India.

**Table 3: Coverage of MGNREGS across consumption quintiles of the rural population of India, 2009/10**

|Quintiles|Participation<br>rate|Demand<br>rate|Rationing<br>rate|Excess<br>demand<br>rate|Share of<br>HHs with<br>jobcard|Mean person<br>days amongst<br>participating<br>HHs|<br>Mean<br>person days<br>among all<br>rural HHs|
|---|---|---|---|---|---|---|---|
|Q1 (Poorest)|0.335|0.609|0.450|0.274|0.465|33.7|11.3|
|Q2|0.297|0.540|0.450|0.243|0.414|36.2|10.7|
|Q3|0.273|0.507|0.462|0.234|0.385|38.3|10.4|
|Q4|0.226|0.434|0.479|0.208|0.329|40.0|9.0|
|Q5 (Richest)|0.138|0.309|0.553|0.171|0.218|40.0|5.5|
|All|0.242|0.462|0.476|0.220|0.347|37.4|9.0|

_Notes:_ The participation rate is the share of rural households working on MGNREGS. The demand rate is the share of rural households who want work on the program. The rationing rate is the share of those who wanted work who did not get it. _Source_ : Authors‘ estimates from NSS (2009/10).

26