rafmacalaba/datause-encoder-data huggingface dataset:
exchange of the crypto currency for cash and in person. Offers by sellers - who as market makers pay the 1% fees, proposing to share the amount saved by avoiding the fees are indeed common on the platform.

**The sending party is a market maker in one of the two trade-legs**

Being the market maker in a trade, in our application, means fees will be deducted from the nominal Bitcoin amount, and a division of the total nominal and multiple smaller trades is more likely.

# **Section IV: Event Study: República Bolivariana de Venezuela**

To underscore that our crypto cross-country currency vehicle estimates are lower bounds, it is interesting to consider the spillover effects to global LocalBitcoins trade of a massive unanticipated three-day power outage that took place in the Venezuela, starting March 7<sup>th</sup> , 2019.

If a significant share of the trades are indeed paired to implement cross-border transfers, an exogenous shock that constrains the ability to trade Bitcoins in one currency should impact Bitcoin trade in other currencies that are a major destination for/origin for cross-border-flows from the affected country.

A major power-cut in the Venezuela that began on March 7, 2019, provides an interesting natural experiment.<sup>31</sup> The power-cut, caused by an incident at the country’s major hydro-electric power plant at Guri Dam, left more than 30 million Venezuelans without electricity for more than 72 hours. Many Bitcoin trades were obviously halted. A question worth asking is how this affected Venezuela’s main trading partners’ (as identified by our algorithm) Bitcoin trade volume.

> 31 See The Guardian, “Venezuela: huge power outage leaves much of country in the dark”, from March 8th, 2019 or BBC News

30
by the better-off-seems likely to have a sizeable effect on child mortality among the poor. Table 12 shows the effects of raising the levels among the poorest 25 percent of children to the levels enjoyed by the richest 75 percent of children. In each case apart from drinking water, child mortality would fall. This reflects the fact that among the poorest 25 percent all determinants except water are estimated to have beneficial effects on survival, as are most health services. The largest impact would derive from extending health service coverage among the poorest quartile to the level of coverage enjoyed by the richest three quartiles.

**Table 12. Targeted improvements in health services, water and sanitation among poorest 25 percent**

||**IMR**|U5MR|
|---|---|---|
|Valuesamongpoorest25%in1998|34.6|50.2|
|Improvingsanitationqualityto leveloftop75%|32.5|47.1|
|Improvingdrinkingwaterqualityto leveloftop75%|35.5|_51.5_|
|Improvinghealthservicecoverage toleveloftop75%|29.3|42.6|

The changes in Table 12-while useful-are fairly small in absolute terms and relative to the reductions envisaged by the IDGs. For example, effecting all three sets of improvements in Table 12 simultaneously would reduce the U5MR among the poorest 25 percent of children from 50.2%o to 41.1%o-a reduction of 6 percent on the 1998 value. It is worth considering what other measures mnight be taken to accelerate further the decline in mortality among Vietnam's poor. Looking back at Table 3, it is striking that when the coefficients differ significantly across the two consumption groups, in all cases except one (medically trained delivery coverage) the poor fare worse. When variables contribute positively to child survival-such as living in the central Highlands and the southeast, good drinking water and antenatal visit coverage-the impact is larger for the better-off. When variables contribute negatively to survival-as is the case with being male-the impact is larger in absolute size for the poor. The impact of medically trained deliveries runs counter to this pattern-this contributes positively to survival for the poor, but not for the better-off.

This pattern suggests that some effort could usefully be directed at exploring the reasons for the apparent gaps in impacts between the poor and nonpoor, and finding ways to reduce them. For example, the gap in the impact of drinking water may well reflect the fact that the nonpoor know better how to use water to promote the health of their children-e.g., taking advantage of its plentiful supply to wash hands after defecation, before preparing meals, and before feeding their children. The gap in the gender impact may reflect a tendency for poor boys to be more likely to be involved than their better-off 30
response. This will especially be true if the expansion of government employment leads to a ganeral deterioration in government performance. Besides "employer-of-last-resOrt" policies, which generally seem to be on the wane, the often rapid growth in casual or daily paid labor may be motivated by employment objectives independent of government production requiroments. In Zambia, for example, from 1978 to 1983 the number of daily paid workers, most of whom were unskilled, grew at 4.2% per annum compared with 1.3% for all permanent civil servants.<sup>12</sup> During this period, copper prices fell precipitously and unemployment was a growing concern. While offering a temporary solution to pressing employment needs, public employment expansion has also strained the government budget and generally contributed to a less than optimal allocation of scarce public resources.

In other settings an increase in government resources, often because of favorab'L exutrnal tems-of-traude **hias** promuted public empLOyment expansion. This appears to have contributed to employment growth in Bolivia and Nigeria. Such a tendency often leads to employment redundancies and becomes even more problematic when terms-of-trade move unfavorably and the government finds it difficult to shed public workers. This then results in high government employment/low government wage policies which may maintain jobs but at the 1 3 cost of reduced government performance.

In general, redundancies in government employment in many developing nations have reached extreme levels. Ozgediz (1983) reports, "In Egypt, according to an ILO estimate, overstaffing was close to 42 percent of the total civil service employment in 1976. A recent consultant's study of the 19
# **Table C.12: Effects on other expenditures**

## **Panel A: Outcomes in % of household income**

||(1)<br>Food|(2)<br>Furnishing<br>durables|(3)<br>Entertainment<br>durables|(4)<br>Kitchen<br>durables|(5)<br>Housing|(6)<br>Savings|(7)<br>Recreation|
|---|---|---|---|---|---|---|---|
|selected|0_._80|_−_0_._01|_−_0_._96|_−_0_._82|0_._33|_−_0_._92|_−_0_._08|
||(5_._53)|(0_._30)|(0_._59)|(1_._67)|(2_._82)|(2_._03)|(0_._09)|
|N|363|363|363|363|363|362|363|
|Nominated mean|7932_._48|106_._07|319_._83|171_._69|4005_._68|1767_._16|126_._87|
|Nominated SD|5435_._51|456_._33|1152_._81|1222_._58|5121_._33|2687_._65|136_._61|
|||**Pa**|**nel B: Outcome**|**s in Taka**||||
|selected|978_._45|19_._71|_−_230_._44|37_._67|_−_27_._09|_−_86_._23|_−_20_._33|
||(733_._86)|(68_._20)|(140_._62)|(166_._26)|(700_._12)|(437_._66)|(17_._13)|
|N|363|363|363|363|363|362|363|
|Nominated mean|7932_._48|106_._07|319_._83|171_._69|4005_._68|1767_._16|126_._87|
|Nominated SD|5435_._51|456_._33|1152_._81|1222_._58|5121_._33|2687_._65|136_._61|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

**Table C.13: Effects on remittances (% in HH income)**

||All||Ma|rried couples|with child|ren||
|---|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Total|(3)<br>Wife’s<br>family|(4)<br>Husband’s<br>family|(5)<br>Total|(6)<br>Wife’s<br>family|(7)<br>Husband’s<br>family|
|selected|3_._48**|3_._53**|0_._87|3_._03**|1_._29|0_._11|1_._68|
||(1_._40)|(1_._69)|(0_._96)|(1_._46)|(1_._70)|(0_._92)|(1_._28)|
|selected*Child outside HH|||||3_._60|1_._11|2_._00|
||||||(3_._20)|(1_._87)|(3_._08)|
|Child outside HH|||||4_._07|1_._74|2_._42|
||||||(2_._78)|(1_._66)|(2_._89)|
|N|362|237|238|237|237|238|237|
|Nominated mean|6_._00|6_._46|2_._73|3_._73|6_._46|2_._73|3_._73|
|Nominated SD|8_._63|8_._31|4_._52|6_._87|8_._31|4_._52|6_._87|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

80
- Deaton, A., & Muellbauer, J. (1980). _Economics and consumer behavior_ . Cambridge University Press.

- Douidich, M., Ezzrari, A., Van der Weide, R., & Verme, P. (2016). Estimating quarterly poverty rates using labor force surveys: a primer. _The World Bank Economic Review_ , _30_ (3), 475-500.

- Eckman, S., Kreuter, F., Kirchner, A., Jäckle, A., Tourangeau, R., & Presser, S. (2014). Assessing the mechanisms of misreporting to filter questions in surveys. _Public Opinion Quarterly_ , _78_ (3), 721-733.

- Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. _Econometrica_ , _71_ (1), 355-364.

- Gourlay, S., Kilic, T., & Lobell, D. B. (2019). A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale-Productivity relationship in Uganda. _Journal of Development Economics_ , _141_ , 102376.

- Ha, Jongrim, M. Ayhan Kose, and Franziska Ohnsorge. "One-stop source: A global database of inflation." _Journal of International Money and Finance_ (2023): 102896.

- Jenkins, S. P., Burkhauser, R. V., Feng, S., & Larrimore, J. (2011). Measuring inequality using censored data: a multiple-imputation approach to estimation and inference. _Journal of the Royal Statistical Society Series A: Statistics in Society_ , _174_ (1), 63-81.

- Kilic, T., & Sohnesen, T. P. (2019). Same question but different answer: experimental evidence on questionnaire design's impact on poverty measured by proxies. _Review of Income and Wealth_ , _65_ (1), 144-165.

- Kilic, T., Moylan, H., Ilukor, J., Mtengula, C., & Pangapanga-Phiri, I. (2021). Root for the tubers: Extended-harvest crop production and productivity measurement in surveys. _Food Policy_ , _102_ , 102033.

- Kreuter, F., McCulloch, S., Presser, S., & Tourangeau, R. (2011). The effects of asking filter questions in interleafed versus grouped format. _Sociological Methods & Research_ , _40_ (1), 88104.

- <mark>Little, R. J., & Rubin, D. B. (2019).</mark> _<mark>Statistical analysis with missing data</mark>_ <mark>. 3</mark><sup>rd</sup> <mark>Edition. John Wiley & Sons.</mark>

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29
volatility of consumption in emerging economies becoming greater than one.

A common feature of reform in emerging economies is financial sector reform. The increase in the access of households to finance resulting from reform allows households to smooth consumption over their lifetimes. But at the same time, emerging economies witness large shocks to the permanent component of income, relative to transitory income shocks. The combination of the response of households to permanent income shocks and the easing of financial constraints can yield an increase in the relative volatility of consumption.

The goal of this paper is to understand the joint impact of easing of financial constraints and permanent income shock on consumption volatility. This is analysed in a dynamic general equilibrium model with heterogeneous type agents. The model assumes that some households in the economy do not have access to finance. They can neither save nor borrow. These financially constrained households cannot smooth consumption over their lifetimes. The rest of the households in the economy are unconstrained and respond to a perceived income shock by smoothing consumption. Shocks to income that are perceived to be permanent lead to an increase in current period consumption higher than the increase in current period income. Only unconstrained households can increase consumption by more than the increase in income, either by borrowing against future income or reducing current savings. Constrained households can only increase consumption by the amount income has increased. Financial sector reform allows more households to access financial services. Now more households become unconstrained and can respond to the income shock that they perceive to be permanent. The key prediction of this model is that financial development in an emerging economy leads to an increase in relative consumption volatility.

This prediction can be tested. The model is calibrated to Indian data for the pre- and post-reform years. All of the parameters, except for the share of 3
...rst two rounds, 730 answered the ...nal wave survey. Appendix 1 details wave by wave attrition rates and shows the robustness of our main treatment e¤ects to corrections for attrition.

Each follow-up round collected data on changes over the quarter in ...xed capital from purchases, sales or repair; the current value of inventories and raw materials, and the value of the last month’s expenses, sales, and pro...ts. The most important ...rm outcome variable measured is ...rm pro...ts. Pro...ts were elicited via a direct question, following the recommendations of de Mel et al. (2009b). Firm owners were asked: “After paying all expenses, what was the income of the business (the pro...ts) during January 2009? (Consider all expenses, including wages of employees but not including any income you paid yourself as an expense)”. This de...nition of pro...t thus includes the return to the entrepreneur’s labor and managerial talent. Nominal pro...ts were converted to October 2008 real pro...ts using the Greater Accra region Consumer Price Index collected by the Ghana Statistical Service.

An innovation in this experiment was the use of computerized cross-sectional and panel consistency checks. Data was collected using PDAs, and a consistency check was triggered whenever reported pro...ts exceeded reported sales in the cross-section, whenever a ...rm reported sales but not pro...ts, and whenever the change in pro...ts from one quarter to the next was less than -33.3 percent or greater than +50 percent (provided the absolute change in pro...ts was at least 20 cedis). We discuss these consistency checks in more detail in Fafchamps et al. (2010), where we show that they lead to some improvements in data quality. We therefore use the pro...ts which incorporate the consistency checks in this paper. Nonetheless, our results are similar when we use the raw pro...t data.

Table 1 summarizes the basic characteristics of ...rms and their owners in our experimental sample, and compares the pre-treatment characteristics of ...rms in the control group to those assigned to either treatment group. The top of the table shows balance for the characteristics used for strati...cation or matching, while the remaining rows compare the characteristics of other variables of interest. Mean (median) monthly pro...ts in January 2009 were 130 (68) cedis, and mean (median) capital stock at the same point in time was 452 (172) cedis. The grants of 150 cedis were therefore approximately equivalent to two months’pro...ts and almost equal to the size of existing capital stock for the median ...rm. However, since we did not explicitly cap pro...ts or capital stock when selecting ...rms into the experimental sample, there are a small number of ...rms with much higher levels –the maximum pro...t reported in our pre-treatment waves is over 5000 cedis per month. The inclusion of these few larger ...rms does not have much e¤ect our 18
(community) level. We rely on the household survey for labor supply data and the communitylevel price survey for rice price variation.

The first round of the VLSS was conducted between September 1992 and October 1993. The second round of the VLSS revisited 147 communes from the first round between December 1997 and December 1998 (all but 2 percent of the first round sample was targeted for the second round). In our analysis, we focus on households in the 115 _rural_ communes that are visited in both rounds of the VLSS. We refer to these revisited communes as panel communes. Table 1 reports basic summary statistics for the rural sample. In the panel communes during the second round of the VLSS, the survey teams were instructed to re-interview the same households interviewed in the first round of the survey.<sup>8</sup> If a household was not found, the missing household was replaced by a randomly selected household from the original enumeration list. Thus, our analysis is not restricted to households that are interviewed in both rounds of the survey. Hence, selective attrition is not an issue for our analysis that is based on commune-level variation in rice prices, because the sample is random within each panel commune. Of course, the reader should be cautioned that our results are not nationally representative by statistical design, though in practice they may generalize. A second major caution in interpreting the empirical work of this study is that any movements in labor supply affected by rice prices that result in the physical movement of a household or an individual will be missed. Edmonds and Turk (2004) consider how large a problem children missing for work might be in VLSS panel households. They find that at most 36 out of 6003 children who are 0-10 in the first round of the VLSS may be missing from the second round of the VLSS for work reasons. Thus, at least for

> 8 Mobility restrictions in Vietnam are rather severe, so 90 percent of the households in the first round of the VLSS that were targeted in the second round of the VLSS were recaptured.  Glewwe and Nguyen (2004) compare the household panel component of the dataset to the nationally representative part and conclude that panel households appear similar to the non-panel households.

10
Table 9: Treatment effects using variation in immigrant concentration in grade 4 within the same school across years

||Nati<br>languag|ves’<br>e score|Nat<br>math|ives’<br>score|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Treatment 1:Share of immirants**|-1.102|-1.200|-0.985|-0.957|
|**g**|(1.336)|(1.273)|(1.400)|(1.376)|
|**Ttt 2Sh f t iit**|-3.78**|-3.72**|-1.75|-1.67|
|**reamen : are o recen mmgrans**|(1.301)|(1.210)|(1.415)|(1.302)|
|**Treatment 3:Share of other immirants**|0.145|0.168|0.142|0.128|
|**g**|(1.347)|(1.267)|(1.368)|(1.333)|
|Grade-level controls||✓||✓|
|Year effects|✓|✓|✓|✓|
|School-by-grade effects|✓|✓|✓|✓|
|Number ofgrade-level observations|3,015|3,015|3,015|3,015|

_Notes._ ***: significant at the 1% level, **: significant at the 5% level, *: significant at the 10% level. Robust standard errors clustered at the school level are reported in parentheses. Each row reports the coefficients from separate regressions of the effect of the corresponding treatment on natives’ average test scores. Controls for mean characteristics at the grade level include: the share of students by level of parental education, the share of female students in the grade, the share of disadvantaged students according to the Dutch weighting system, the share of students that repeated a grade, the average class size in the grade, teacher’s years of experience, the total number of students in the grade and its square. Controls for school linear trends are also included. Estimates that do not control for linear school trends are quantitatively very similar and available upon request.

56
4 decentralized health system. But there was also another reason—to ensure that lessons could be learnt from local experimentation, and that that they could be fed into the scaling-up process.

To capture better the details of the scheme at local level, we administered a detailed program questionnaire in 17 NCMS counties.<sup>11</sup> The program survey was complemented by a qualitative study (Wu et al. 2006), involving focus-group discussions and semi-structured interviews with NCMS stakeholders, including NCMS management, the local NCMS monitoring committee, health care providers at county and township level, village leaders, village doctors, and rural residents themselves.

The voluntary nature of NCMS raises concerns about adverse selection. Participation rates in pilot counties are, however, for the most part high, with an average in excess of 80 percent (see descriptive information in Table 1). In part, high levels of participation are likely to be the result of features of the NCMS that are meant to address the problem of adverse selection, notably the relatively generous government subsidies and the requirement that participation be at the household level. However, the qualitative study suggests that local governments have also exerted considerable efforts to achieve high levels of participation (Wu et al. 2006).

While central government has issued broad guidelines for how the NCMS should be designed and implemented, provincial and county governments retain considerable discretion over the details. One area of discretion concerns the placement of the program. NCMS pilot counties were not randomly selected. Rather, a complex set of criteria, including local interest and capacity, level of economic development, and the status of the delivery system were considered. The implications of selective program placement for our identification strategy are discussed in the methodology section below.

Local government also has some discretion over the level of financing of the program, and the associated benefit package. Currently, the minimum requirement is a 10 RMB (per person) beneficiary contribution from households, supplemented by a subsidy of 20 RMB from local government (40 RMB in the case of eastern provinces), and a 20 RMB matching subsidy from central government in the case of households living in the poorer central and western

> 11 A short form of the program questionnaire was also administered to all other counties ( _N_ =162) with official NCMS pilots in the 17 provinces covered by the survey. The data are not used in the present paper.