rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A4: Panel B – Multinomial Regressions, 2006**

Log Odds ratio between Outright Owners and Mortgage holders (Marginal Effects)

||||||||||**Hausman F**|**-**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**parttime**|**unemp**|**retired**|**urban**|**lage*secedu**|**lage*teredu**|**inc*secedu**|**inc*teredu**|**Statistic**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0450|-0.0355|-0.0454|-0.1474 ***||0.0708||-0.0294|8.11 ***|0.26|
|Estonia|0.0418|0.0408|0.0048|0.0204|0.0421|0.1354 *|0.0310|0.0212|1.33|0.18|
|Hungary|0.0287|-0.0109|0.0103|-0.0608 ***|0.1454 **|0.1464 **|0.1147 **|0.1360 ***|1.98 ***|0.13|
|Latvia|0.0264|0.0802|0.0767|-0.0035|0.0722|0.1812|0.0085|0.0552|0.91|0.07|
|Lithuania||-0.0622||0.0219 **||0.0361||-0.0036|0.95|0.24|
|Poland|0.0020|-0.0907 **|-0.0233|-0.1970 ***|-0.0192|-0.0662|-0.0280|-0.0558 *|1.73 **|0.32|
|Slovak Republic|0.0765|0.0308|0.0639|-0.0882 ***|-0.6625|-0.7351|-0.0228|0.0092|3.28 ***|0.29|
|Slovenia|-0.1908 *|-0.2158 **|-0.1704 ***||0.1200 ***|0.1704 ***|0.0582 ***|0.0401|1.60 *|0.10|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.1330 ***|-0.1598 **|-0.0691|-0.0711 ***|-0.4666|-0.3479|-0.1646|-0.1571|3.42 ***|0.24|
|Belgium|-0.0509|0.0610|0.1914 ***|-0.0670 ***|0.2081|0.2625|0.1696 *|0.2631 ***|2.25 ***|0.35|
|Cyprus|-0.2080 ***|-0.1814 *|-0.2058 ***|-0.1085 ***|0.2418 ***|0.1117|0.1170 ***|0.0815 *|1.45 *|0.16|
|Finland|0.0237|-0.0642|0.0019|-0.0391 **|0.0288|0.1886|-0.0232|0.0514|2.43 ***|0.31|
|France|0.0055|-0.0536|-0.0702 **|-0.0157|0.0071|0.0009|0.0102|0.0736 **|3.15 ***|0.32|
|Greece|-0.0147|-0.0436|0.0411|-0.0832 ***|0.0362|-0.0758|0.0819 ***|0.1113 ***|1.51 *|0.14|
|Ireland|-0.0952|-0.0782|0.0096|-0.1754 ***|-0.1571|0.0709|0.0752 *|0.0719|1.30|0.28|
|Italy|-0.1374 ***|-0.1278 ***|0.0147|-0.0812 ***|-0.1951 ***|-0.1205 **|0.0569 ***|0.0896 ***|4.70 ***|0.13|
|Luxembourg|-0.0778|-0.1344|0.0044|-0.0221|0.0739|0.0153|0.0461|-0.0261|1.13|0.35|
|Portugal|-0.1358|-0.1356|-0.1543 *|-0.1694 ***|-0.1151|0.0087|0.0506|0.1773 ***|2.64 ***|0.19|
|Spain|-0.0643|-0.0703|0.0322|-0.0028|-0.1731 ***|-0.1232 *|0.0142|0.0215|2.30 ***|0.14|
|Sweden|-0.0142 ***|-0.0136 ***|-0.0012|0.0035|-0.0572|-0.0415|-0.0018|0.0006|1.35|0.33|
|United Kingdom|0.0934 ***|0.0251|0.2498 ***|-0.0229 *|-0.2802 ***||-0.0562 ***|0.0340 **|12.09 ***|0.33|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

Source: EU Statistics on Income and Living Conditions (SILC) database, 2006 34
extended the analysis presented in Beyene et al. (2015) using similar data but covering a shorter period than the current study, concluded that Free Treatment is most effective at promoting sustained and increased long-run usage, because Pay Treatment households initially on average used the Mirt stove so much less than those who randomly received the other two treatments. In the very long run, however, we find that Pay Treatment households have continued to increase their average regular usage, with period 5 average usage virtually identical to free treatment. As initial average Pay Treatment regular usage was so low, we therefore find dramatic increases over the life of our study.

# 6. **Conclusions**

This paper analyzes the extent to which the Mirt stove continues to be regularly used 3.5 years after first being randomly distributed to 360 households in three regional states in rural Ethiopia. We also examine the long-run effects of different distribution methods, which were subsidized, for free and user incentivized, applied in a randomized field experiment at the time the Mirt stoves were distributed in summer 2013.

The main conclusion of this paper is that households in rural Ethiopia on average regularly used the Mirt stove – unless it broke – 3.5 years after they were randomly chosen to receive it. This is an important finding that points to the enduring positive net benefits that users receive from the stove compared with exclusive use of the three-stone tripod. This contrasts to other studies where widespread abandonment occurred long before stove life was complete (e.g. Hanna et al., 2016). That the stove is quite cheap and can provide fuelwood savings benefits (Gebreegziabher, 2018) over such a long timeframe highlights the benefit of promoting it.

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on total deaths by external causes. However, when we break down to individual causes, we find that homicide is strongly _negatively_ correlated with unemployment. What this means is in counties with higher job losses, the number of deaths from homicide is actually lower. Moreover, the relationship is robust and not driven on outliers (scatterplot not shown). The unemployment and crime relationship has been widely studied in the literature, with mixed results. Our finding is related to that of Raphael and Winter-Ebmer (2001), who found murders and rape procyclical. The authors postulated that higher incidents of rape during economic upswing is due to greater frequency of interactions between potential victims and offenders when a larger proportion of the population is working. However, the authors do not propose a rationale for murders. Note that Ruhm (2015a) found mortality from homicide having an unstable relationship with unemployment.

Table 5.4a: Unemployment and mortality by external causes (IV with controls)

|VARIABLES|External<br>Causes|Transport<br>Accidents|Non-<br>Transport<br>Accidents|Suicide|Homicide|
|---|---|---|---|---|---|
|Instr. Job Losses|-0.121|-0.455|0.413|-0.490|-2.645***|
||(0.201)|(0.281)|(0.263)|(0.322)|(0.642)|
|Constant|-0.0928|-0.102|0.0238|0.153|0.489|
||(0.141)|(0.204)|(0.192)|(0.216)|(0.454)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Observations|2,213|1,971|2,126|1,526|576|
|R-squared|0.185|0.071|0.178|0.033|-0.021|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 23
**Figure 5** : Availability of emergency transport* by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Hospital Non-hospital Hospital Non-hospital Hospital Non-hospital<br>Kenya Sierra Leone Tanzania<br>Has ambulance Other emergency transport No emergency transport<br><!-- End of picture text -->

* “Other emergency transport” arrangements include other official vehicles, private vehicles or another mode of emergency transport.

# Availability of WaSH and acceptable waste disposal

Availability of appropriate water, sanitation, and hygiene (WaSH) facilities is critical for infection prevention and control, especially in health care settings. Hand hygiene (hand washing) and appropriate waste segregation and disposal, for instance, are critical for both patient and health care worker safety. By the end of the 2018 Ebola epidemic, the rate of infection among health care workers was several folds higher than that of general population, partly owing to lack of robust infection prevention and control measures in place in the health facilities.<sup>15</sup> How do health facilities in Kenya, Sierra Leone and Tanzania fare in terms of WaSH arrangements?

The evidence from the SDI surveys is worrisome. Disquietingly, less than half of the facilities across these three countries have appropriate handwashing facilities for patients, with fewer rural facilities compared to their urban counterparts equipped with this basic amenity (Figure 6). Disaggregating by facility ownership, these data indicate that 65% of public facilities and 42% of private facilities in Kenya lack appropriate handwashing arrangements for patients (Appendix Table 5). Over 70% of facilities in all three countries have access to an improved water source; however, between 20% and 30% of facilities have experienced water interruptions in the last three months. This evidence highlights the need to address existing gaps in the health system and to offer context-specific guidance for infection prevention and control during pandemics.

Availability of toilets, especially gender-segregated ones, is important in any environment, and especially important for inpatient care. Not only are improved toilet facilities imperative for infection prevention, but also, gender-segregated facilities promote women’s safety and may increase comfort with (or even 15 World Health Organization. Health worker Ebola infections in Guinea, Liberia and Sierra Leone: Preliminary Report. World Health Organization, 2015. https://www.who.int/hrh/documents/21may2015_web_final.pdf 13
adopted by the Malian government, SD Bioline, shows zero false positives in WHO tests, a detection rate of over 90% for low parasite loads, and an almost 100 percent detection rate for high parasite loads, which accompany severe malaria cases (World Health Organization, 2014b). Despite the wide availability of RDTs, many clinics have blood test laboratories and use microscopes to perform blood smear tests on site. In our study area, RDTs were free of charge 70 percent of the time, while microscopy tests were free less than 3 percent of the time.

The relative performance of RDTs and microscopy differ depending on the setting. While microscopy tests are typically considered a “gold standard” malaria test (Lee et al., 2002; Trampuz et al., 2003), they require high-quality equipment and skilled technicians who visually identify malaria parasites on blood slides. The advantages of microscopy include the ability to assess parasite load and differentiate between types of malaria parasites. A primary disadvantage is variable performance in resource-constrained settings, which can result in misdiagnosis of malaria, including false positives (Wongsrichanalai et al., 2007; Berzosa et al., 2018). By comparison, RDTs are much simpler to administer (apart from a finger prick, procedures are similar to those required for a COVID-19 rapid test) and can be performed by trained laypeople..<sup>10</sup> RDTs test for antigens that remain in the bloodstream for some time after a malaria infection has cleared (Abba et al., 2011). We take advantage of this property in this study because it allows us to test for malaria even after treatment was potentially started.

**Health Provider Beliefs in the Study Sample.** To describe status quo provider attitudes, Appendix Table A1 reports average beliefs about malaria prevalence, confidence in RDTs, and confidence in microscopy tests among health providers in the group of clinics that did not receive training on RDT accuracy.<sup>11</sup> Panel A shows that providers think malaria prevalence is very high, afflicting 28 percent of the general population and 44 percent of patients at their clinic. Alongside, 51 percent of the workers report feeling pressure from patients to prescribe unnecessary medications, with 68 percent of those workers saying that most of the pressure they receive is to prescribe antimalarials.

Panel B summarizes confidence in RDTs among providers. Ninety-eight percent of providers use RDTs to diagnose malaria, but they believe that only 56 percent of patients with simple malaria will test positive. In line with this concern, 47 percent of providers report they would give an antimalarial to a sick relative despite a negative RDT. Providers

> 10A limitation of some RDT brands is that they can detect only p. falciparum malaria. P. falciparum is the most dangerous malaria parasite and accounts for more than 85 percent of infections in Mali (Cissoko et al., 2022).

> 11These data come from a post-intervention survey that we conducted with health providers at the end of the study (see a detailed description in subsection 3.3).

6
are aware of being in a study and, specifically in a trial, are aware of their treatment allocation (Leonard and Masatu 2006; McCambridge, J. et al. 2014; Noseworthy et al. 1994; Zwane et al. 2011). This can skew treatment effect estimates either upwards or downwards. In medicine, blinding of participants is often used to minimize this type of bias. In many economics studies and some medical studies, however, blinding is either ethically or logistically infeasible. For example, in the study of village-level education interventions, blinding participants with a placebo intervention would be unethical (although analysis could be conducted blind to allocation). In some economics studies, blinding may even be contrary to the goals of the research.

The relevant lesson from medicine is that extra scrutiny must be applied in two cases. The first case is when outcomes are subjective (e.g., self-reports of pain or personal opinions). A meta-analysis of studies of acupuncture treatment on back pain showed that while acupuncture was superior to control interventions in unblinded studies, it could not be proven to be superior to sham-interventions in blinded studies (Ernst and White 1998). Though all outcome assessments can be influenced by lack of blinding, there is greater risk of bias with more subjective outcomes. Lack of blinding was associated with a 30 percent exaggeration in treatment effect estimates in a meta-analysis of studies with subjective outcomes (L. Wood et al. 2008).

The second case is when patients are likely to change their behavior given their knowledge of which group they are assigned to. Knowledge of allocation status has been known to induce some control group participants to seek extra care, which, if effective, would introduce a systematic downward bias on treatment effect estimates. In economics this is often the stated purpose of the research, as in Akresh et al. (2013). In studies 14
# **5.2. Digging into the Impacts of Training**

It is already clear from the household results that the training program did not have transformative impacts. We now delve into this further by examining group behavior in more detail, as well as looking at ways that the training may have changed the relative contributions of group leaders. The simplest way to analyze the training experiment at the group level is to use only the Early-treated TASAF groups, within which the training experiment was performed, and compared the trained groups with the untrained. This treatment differential remains cleanly present in this group throughout the study, and so we can use the group-level surveys at RR, midline, and endline to examine the additional effect of training using an interaction between the training dummy and round dummies. Table 3 examines two sets of group-level outcomes; in columns (1) – (4) we look for changes in the types of business practices the training was supposed to engender. At RR and R2 we do see that trained groups are more likely to be keeping written records of group accounts, but these effects fade by R3, and do not carry over to other practices. Trained groups are no more likely to make these accounts available to all group members, to have a written business plan, or to issue written receipts to customers. Untrained groups are likely to have written records that are available to members, and are very unlikely to have written business plans or issue receipts.

Columns (5) – (7) of Table 3 then examine the key entrepreneurial outcomes at the group level (sales over the past month, stock value of group assets, and 6-month profits). All but two of the groups in the early treatment arm are operating an income generating activity at RR– with average total business stocks in the untrained groups valued at approximately $5,564, mean sales during the past month just over $40, and net losses in their first six months of $67. Relative to this base, we find no impacts of training either at midline or endline, although the point estimates are large and imprecise due to the small sample size of groups and large variation in these outcomes.

23
TABLE 2

# CHARACTERISTICS OF WORLD BANK CODIITY NOELS

|Commodity Model|Modeling Purposes|Modeling Structure|Erpirical<br>Scope|Model Contribution|
|---|---|---|---|---|
|Cocoa (1982)|Sinulate market iqsats<br>of<br>large production changes<sup>**and**</sup><br>of buffer stock stabilization<br>schemes. Forecast long<sup>term</sup><br>quantity and price mDvements.|Coupetitive<br>arket including<br>private<br>inventories**and**public<br>buffer stock adjustments.<br>30<br>equations (est.).|World. Anusl<br>1961-78 and<br>1979-90.|Provide realistic<br>view of<br>current and future cocoa<br>market adjustmnts.|
|Coffee (1982)|Simulate market iqrts<br>of<br>large production changes.<br>Forecast long term quantity<br>and price mvements.|Competitive market<sup>including</sup><br>inventory adjustments.<br>23<br>equations (est.).|World. Arnnal<br>1965-79**nd**<br>1980-90.|Provide realistic<br>view of<br>current and future<sup>coffee</sup><br>market adjustments.|
|Jute (1981)|Siriulate buffer stock<br>stabiLization<br>schemes.<br>Simulate alternative<br>stabilization<br>policies<br>including<br>compensatory finncing,<br>tartffs<br>and taxation,<br>and crop area<br>limitations.|Competitive market excluding<br>inventory adjustent,<br>but<br>including buffer stocks.<br>Supply equations iriclude<sup>farmer</sup><br>response to risk.<br>39<br>equations (est.)|3 supply regions,<br>7 demand regions<br>approximatirig<br>World.<br>Anmual<br>1955-1975,<br>1976-1991.|Developed**farmer**sopply<br>equationswhichrespond<sup>to</sup><br>price variance orOrisk.u|
|Tea (1981)|Siinulate iepsct of long<sup>term</sup><br>structural<br>changes in supply<br>andsensitivity<br>to coffee<br>andfertilizer<br>price increases.<br>Forecastlongterm quantity<sup>and</sup><br>pricemovemnts.|Competitive mrket<br>including<br>Including inventory adjustments.<br>56 equations(est.).|Vorld.<br>Ariwal<br>1957-79.|Supply equations cobine<br>area planted<br>nd yield<br>sub- eat<br>ions.|
|Tea(1986)|Simulate impact of<sup>changesin</sup><br>income,<br>inflation,<br>production<br>subsidiesandother exogenous<br>shocks.|Competitive mrket<br>including<br>inventory adjustments<sup>**and**</sup><br>featuringalinear<sup>ratiomal</sup><br>expectations approach<sup>to</sup><br>to market price determination,<br>43 equations.|World. Anmual<br>1959-1983.<br>Simulations<br>1984-2000.|Supply equations distinguish<br>between long**ad**short<sup>run</sup><br>adjustments.<br>Rational<br>expectations included<sup>in</sup><br>price formations.|
|Sugar (1980)|Forecast medium and long<sup>term</sup><br>movements in prodection,<sup>demand,</sup><br>trade and price based<sup>on baseline</sup><br>as well as conditional<br>variable<br>assurptions.|Competitive market including<br>inventory adjustments.<br>67<br>equations|World.<br>**Anuul**<br>1951-1977.|**Trade aquations**included<br>as well as production<br>**nd**<br>demnrd equations.|
# **4. Possible Future Consequences of Out of School and Out of Work Youth: The Presence of Scarring Effects**

This final section explores whether being Osow at youth can have consequences for future labor market prospects in the income and the employability dimensions.

The related issue of whether higher unemployment or underemployment levels among the youth can have long lasting effects in labor market trajectories, has been widely analyzed using panel or pseudo panel data in the literature. A recent reference is Cruces, et.al. (2013), who use cohort data for Brazil with the same approach as here, and find that levels of informality at young ages are related to higher unemployment rates and lower wages at later stages of the life cycle, although the effects tend to fade away over time. Other examples of evidence of negative implications of youth unemployment on future employability and/or wage levels are Cockx and Picchio (2011), Fairlie and Kletzer (2003), Gregg and Tominey (2005), Gregory and Jukes (2001), Jacobson et al. (1993), Neumark (2002), and Nilsen and Reiso (2011).<sup>29</sup> As for education, a large number of studies on its determinants and consequences for future outcomes are also available. Some examples using panel data are Hanushek, et.al. (2011), who examine the relationship between early education choices and future labor market outcomes using retrospective data to emulate a panel approach, and find considerable implications in terms of employability. Torche (2012) follows a cohort approach to identify the effect of economic shocks on schooling outcomes using synthetic panels for Brazil, Chile and Mexico, and uses smoothing techniques similar to those in Section 2 above for estimating time effects in the data. Through a descriptive approach, the author argues that cohorts exposed to negative economic conditions during school age have in general lower future education levels than other generations. Dynarski, Hyman and Whitmore (2011) use panel data and also find effects of school conditions to which cohorts are exposed during childhood on future college enrollment and graduation. Jacob and Wilder (2010) examine the effects of educational expectations during childhood on future attainment and also find positive effects using panel data, while as mentioned before, Boznick (2009) finds that community environments can have significant implications for time use choices and future labor market outcomes.

Following Cruces et al. (2013), the empirical model we estimate is: (13) Yit = α0 + α1Xit-20+ **βρ** it + νit + τt + εit where Y is the log of income or employment status of cohort _i_ at time _t,_ X is the share in the Osow youth status for the same cohort observed 20 years earlier, **ρ** is a vector of cohort characteristics,

> 29 A related literature measures the effects of economic shocks on unemployment and other variables and finds persisten significant effects on various dimensions overt time. Some examples are Bell and Blanchflower (2011), Giuliano and Spilimbergo (2014), Greg and Tominey (2005), Kahn (2009), and Oreopolus (2004).

29
scatter plots derived from Specification (1) of Table 4 complement the analysis and may be found in Figure 1.

(Insert Appendix B about here) (Insert Figure I about here) It is interesting to note the impact of the investment environment (Investment, Law and Order, Corruption) on small firm access to finance. For DPR nations, the stronger the legal system and the greater the observance of the law, the less likely it is that a small listed firm will issue securities. This could suggest that large firms have more influence on the laws that are put into place with regard to securities. In LDPR nations, investment has a negative and significant coefficient, implying that as a government’s attitude toward inward investment improves, small firms will be less likely to issue securities. It is possible that this variable describes more the government’s attitude toward foreign direct investment (versus foreign portfolio investment). Governmental policies put into place to level the playing field for foreign (direct) investors can increase competition and squelch investment. Indeed, Agosin and Machado (2005) find that foreign direct investment can, in some cases, actually hinder domestic investment. The coefficient on corruption is also negative for LDPR nations. This is likely due to the fact that bribery may have an instrumental role in small firms accessing capital in these nations.

(Insert Table 4 about here) These impacts, taken collectively, imply that FPI reaches small firms in both DPR and LDPR nations directly through the capital market, effectively increasing the access to finance for small listed firms. This implies that the financial markets of the U.S. and other developed nations can confer benefits on developing nations by helping to fund the growth of small firms in those nations whose markets cannot perform this function alone.

17