rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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- Altındağ, O., O'Connell, S. D., Şaşmaz, A., Balcıoğlu, Z., Cadoni, P., Jerneck, M., & Foong, A. K. (2021). Targeting humanitarian aid using administrative data: Model design and validation. _Journal of Development Economics_ , _148_ , 102564.

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- Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. _Annual Review of Economics_ , _11_ , 685-725.

- Beegle, K., De Weerdt, J., Friedman, J., & Gibson, J. (2012). Methods of household consumption measurement through surveys: Experimental results from Tanzania. _Journal of development Economics_ , _98_ (1), 3-18.

- Beegle, K., Christiaensen, L., Dabalen, A., & Gaddis, I. (2016). _Poverty in a rising Africa_ . World Bank Publications.

- Carpenter, J. R., Bartlett, J. W., Morris, T. P., Wood, A. M., Quartagno, M., & Kenward, M. G. (2023). _Multiple imputation and its application_ . John Wiley & Sons.

- Christiaensen, L., Ligon, E., & Sohnesen, T. P. (2022). Consumption subaggregates should not be used to measure poverty. _The World Bank Economic Review_ , _36_ (2), 413-432.

- De Weerdt, J., Gibson, J., & Beegle, K. (2020). What can we learn from experimenting with survey methods?. _Annual Review of Resource Economics_ , _12_ , 431-447.

- Dang, H. A. H., & Lanjouw, P. F. (2023). Regression-based imputation for poverty measurement in data-scarce settings. In Silber, J. (Ed.), _Research handbook on Measuring Poverty and Deprivation_ (pp. 141-150). Edward Elgar Publishing.

- Dang, H. A. H., & Verme, P. (2023). Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation. _Journal of Population Economics_ , _36_ (2), 653-679.

- Dang, H. A. H., Lanjouw, P. F., & Serajuddin, U. (2017). Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country. _Oxford Economic Papers_ , _69_ (4), 939-962.

- Dang, H. A., Kilic, T., Abanokova, K., & Carletto, C. (forthcoming). Poverty imputation in contexts without consumption data: a revisit with further refinements. _Review of Income and Wealth_ .

28
Page 76

# Lele, U. 1986. "Women and Structural Transformation." Economic DeveloRment and Cultural Chance. 34 (2).

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- Lycette, M.A. 1984. "Improving Women's Access to Credit in the Third World: Policy and Project Recommendations." International Center for Research on Women, Washington, D.C.

- Lycette, M.A. 1986. "Improving Basic Educational Opportunities for Women in Developing Countries." International Center for Research on Women, Washington, D.C.

- Mahran, M. 1981. "Medical Dangers of Female Circumcision." IPPF Medical Bulletin (15-2), London.

- Maine, D. and others. 1986. "Prevention of Maternal Deaths in Developing Countries." New York. Columbia University, School of Public Health, processed.

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- Molnar, A. and G. Schreiber. 1989. "Women and Forestry." World Bank PPR Working Paper No. 184. Washington, D.C.

- Pitt, M. and M. Rosenzweig. 1988. "Estimating the Intra-Family Incidence of Health: Child Illness and Gender Inequality in Indonesian Households." Minneapolis, M.N.: University of Minnesota, processed.

- Ravindran, S. 1986. "Health Implications of Sex Discrimination in Childhood." World Health Organization and UNICEF, Document WHO/UNICEF/FHE 86.2.

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- Rosenzweig, M. and R. Evenson. 1977. "Fertility, Schooling and the Economic Contribution of Children in Rural India." Econometrica, 1977.

- Schultz, T.P. 1987. "Labor Market Discrimination: Measurement and Interpretation." Paper prepared for Nancy Birdsall and Richard Sabot, eds., Labor Market Discrimination in Developing Economies. Washington, D.C., World Bank, forthcoming.

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Table 4: Treatment Effects of Additional Information in Audit Study

||(1)<br>Any re|(2)<br>sponse|(3)<br>Interview|(4)<br> request|
|---|---|---|---|---|
|Certifcate (_β_1)|0.015|0.016|0.009|0.010|
||(0.009)|(0.009)|(0.004)|(0.006)|
|Certifcate _×_ HighIntensity (_β_2)|-0.027|-0.028|-0.014|-0.017|
||(0.013)|(0.014)|(0.009)|(0.010)|
|Mean outcome|0.130|0.130|0.088|0.088|
|# applications|3992|3992|3992|3992|
|# vacancies|998|998|998|998|
|# resumes|717|717|717|717|
|Vacancy fxed efects||_×_||_×_|
|Email address fxed efects||_×_||_×_|
|Resume covariates||_×_||_×_|

Note: Coefficients are from regressing each outcome on a vector of treatment assignments and, in columns 2 and 4, vacancy fixed effects, email address fixed effects, a vector of prespecified workseeker covariates (measured skills, education, age, gender, past employment, and the scan quality of documents they include in their application). The vacancy-level treatment HighIntensity _v_ is included in columns 1 and 3 but omitted in columns 2 and 4 because it is collinear with the vacancy fixed effects. Heteroskedasticity-robust standard errors shown in parentheses clustered by resume and vacancy. The mean outcomes are for applications sent without public certificates to vacancies that receive only one application with a public certificate.

effects, both equal to 11% of the control group means, although they are only statistically significant at the 10% level.

These results show that more informative applications lead to higher callback and interview invitation rates in a low-information environment. This suggests firm-side information frictions play a role in the earnings and employment effects of public certification. Combining this result with the observed effects of the public and private certification on workseekers’ beliefs, search behavior, and outcomes in the labor market suggests that both firms and workseekers’ face information frictions.

_β_ 2, the difference between the effect of being the only application with a public certificate sent to a vacancy and the effect of being one of multiple applications with public certificates sent to a vacancy, is negative. Applications that include a public certificate are 2.8 percentage points less likely to get a response and 1.7 percentage points less likely to get an interview invitation when they compete against other applications with reports (Table 4, columns 2 and 4). However, more informative applications may still be valuable in a higher-information environment for job offers, which we do not observe in the audit study, than callbacks and interviews. If firms use callbacks and interviews to get more information, then certificates may allow them to interview fewer candidates and still make better-matched hires. Jarosch and Pilossoph (2019) make a similar argument, both theoretically and empirically, about audit studies that manipulate firms’ information about applicants’ employment history.

There are, however, some caveats to the interpretation of the audit study results. This examines only one hiring method (online applications) and one stage of that process (interview invitations).

24
<!-- Start of picture text --> 5<br>5<br>4 4<br>3 3<br>2 Potential 2 Potential<br>1 1<br>MtCO2/yr in 2020 MtCO2/yr in 2030<br>D2020 D2030 D2020 D2030<br>(a) Achievable by 2020 (b) Achievable by 2030<br>Marginal cost $/tCO2 Marginal cost $/tCO2<br><!-- End of picture text -->

**Figure 6:** In this example, the 2020 MAC curve (a) suggests that the 2020 target ( _D_ 2020) should be met with options 1–4, disregarding option 5 before 2020. But then only a fraction of option 5 could be implemented between 2020 and 2030. The 2030 MAC curve (b) however shows that options 1–5 should be implemented by 2030 to meet the _D_ 2030 target. For option 5 to deliver all the abatement listed by 2030, it should be implemented before 2020. A MAC curve built for longer-term horizons (e.g. 2050) would show that even more options should be deployed early.

The short-term target masks the longer-term target, such that long-toimplement options required in the long term are not promoted soon enough. In this example, reaching the 2030 target requires to implement 95 additional MtCO2 between 2020 and 2030. However, a 2020-2030 strategy would be able to save 84 MtCO2 additionally at best, since not enough time would be left to deploy time intensive solutions. This new low-carbon scenario would therefore be short 11 MtCO2 or 12% in 2030 compared to the first one. In other words, the 2030 target becomes impossible to achieve after 2020, as the limited diffusion speed prevents high-abatement-potential options to achieve their optimal 2030 level in 10 years.

# **3. Conclusion**

This paper confirms with a case study on Brazil the theoretical findings from Vogt-Schilb and Hallegatte (2014). Using a MAC curve to design a mitigation strategy for the 2010-2020 period without accounting for a longer-term goal for 2030 would lead to insufficient investments in options with low implementation speed and large abatement potential. In this example, we find that omitting the 2030 target would lead to less investment in clean transportation infrastructure, and more investment in marginal improvements of existing refineries by 2020. This would even lead to a lock-in in carbon-intensive patterns, making it impossible for a subsequent 2020-2030 strategy to reach the 2030 target, as better transportation infrastructure is critical to reduce emissions on the long term.

These results suggest that using a 2030 MAC curve to plan a mitigation strategy that has a long-term objective (for 2050 and beyond) will lead to similar issues, namely the under-investment in some of the most important mitigation options and the creation of a carbon lock-in.

We propose a few solutions to this problem.

13
||(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|---|---|---|---|---|---|---|---|
|Completed four years of education beyond high<br>school and/or 4-year college degree.|0.901***<br>(0.057)|0.569***<br>(0.050)|0.679***<br>(0.054)|0.918***<br>(0.053)|0.899***<br>(0.053)|0.539***<br>(0.068)|0.486***<br>(0.136)|
|_(Reference group: Not a migrant)_||||||||
|Migrant|-0.141<br>(0.102)|-0.200**<br>(0.098)|-0.184*<br>(0.102)|-0.142<br>(0.102)|-0.155<br>(0.101)|-0.272***<br>(0.096)|-0.718***<br>(0.188)|
|Female|0.223***<br>(0.029)|0.157***<br>(0.027)|0.203***<br>(0.031)|0.215***<br>(0.029)|0.206***<br>(0.029)|0.140***<br>(0.038)|0.226***<br>(0.072)|
|Age<br>Age ^2|-0.039***<br>(0.006)<br>0.000***|-0.029***<br>(0.006)<br>0.000***|-0.038***<br>(0.006)<br>0.000***|-0.039***<br>(0.006)<br>0.000***|-0.038***<br>(0.006)<br>0.000***|-0.029***<br>(0.006)<br>0.000***|<br>-0.022**<br>(0.009)<br>0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_(Reference group: Married with children)_||||||||
|Married without children|0.091*|0.038|0.083*|0.100**|0.087*|0.047|0.036|
||(0.047)|(0.044)|(0.046)|(0.047)|(0.047)|(0.044)|(0.076)|
|Single with children|-0.125**|-0.096**|-0.090*|-0.134***|-0.147***|-0.081|0.066|
||(0.053)|(0.047)|(0.049)|(0.050)|(0.050)|(0.057)|(0.096)|
|Single without children|-0.081|-0.095**|-0.067|-0.076|-0.089*|-0.072|-0.015|
||(0.050)|(0.047)|(0.049)|(0.050)|(0.050)|(0.047)|(0.082)|
|Separated/Divorced/Widow with children|-0.106|-0.032|-0.100|-0.123|-0.105|-0.011|0.111|
||(0.086)|(0.076)|(0.082)|(0.083)|(0.082)|(0.089)|(0.124)|
|Separated/Divorced/Widow without children|-0.403***|-0.273***|-0.343***|-0.385***|-0.399***|-0.252***|<br>-0.290*|
||(0.099)|(0.091)|(0.095)|(0.099)|(0.098)|(0.093)|(0.153)|
|_(Reference group: 1 person older than 15 in house_|_hold)_|||||||
|2 people older than 15 in household|0.010|0.017|0.007|0.023|0.024|0.010|-0.037|
||(0.087)|(0.084)|(0.085)|(0.086)|(0.086)|(0.089)|(0.125)|
|More than 2 people older than 15 in household|0.015|-0.022|-0.032|0.030|0.030|-0.032|-0.018|
||(0.085)|(0.080)|(0.081)|(0.081)|(0.081)|(0.093)|(0.135)|
|Constant|5.159***|5.452***|5.471***|5.364***|5.116***|5.570***|5.446***|
||(0.213)|(0.159)|(0.182)|(0.196)|(0.168)|(0.198)|(0.254)|
|Observations|25,244|25,244|25,244|25,244|25,244|25,244|9,065|
|R-squared<br>**Statistics**|0.077|0.193|0.117|0.074|0.079|0.204|0.076|
|Underidentification test: P-value|83.04|1105.78|194.93|503.81|454.68|29.508|23.872|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.013)|(0.475)|
|Cragg-Donald Wald F statistic|42.14|563.35|31.23|729.53|425.07|1.826|1.533|
|Stock-Yogo VC 10%|10.27|10.27|10.89|19.53|10.83|NA|NA|
|Hansen statistic|4.25|4.63|5.13|0.133|0.924|8.327|7.516|
||(0.234)|(0.200)|(0.953)|(0.715)|(0.921)|(0.871)|(0.873)|

21
exceptions where cluster wealth and fever are significantly related. On the other hand, the average level of fever in the cluster is significantly related to wealth (with the exception of CMte d'Ivoire, Ghana, Mozambique, and Zimbabwe).

# **4. Treatment seeking behavior and poverty**

## _Seeking carefor malaria_

The focus on fever here is driven by its availability in the DHS data. Typically, however, episodes of fever are what prompt caregivers to seek treatment, and most often patients are treated presumptively (both by parents and medical personnel) for malaria. Based on their review of the literature, Brinkmann and Brinkmann (1991) conclude that between 8 and 25 percent of persons with malaria visit health services, with selftreatment being more common in urban than in rural areas (more than 60 percent versus between 2 and 25 percent). McCombie (1996) reviews the literature on treatment seeking for malaria and finds a substantial variation across countries. On average, close to 50 percent of cases rely exclusively on self-treatment-usually with antimalarials. Most episodes involve some form of self-treatment, which in general involves the purchase of drugs. The use the official health sector-hospitals, clinics, dispensaries, private practitioners, and village health workers-for treatment varied from 10 to 99 percent, depending on the country and the type of study (with about half the studies finding more than 50 percent). Very few cases rely exclusively on traditional methods (or not even at all for uncomplicated malaria). The review also identified urban-rural differences as the most common source of variation across studies. McCombie (1996) also observes that the community prevalence of malaria reduced the probability of seeking care from a doctor. In general, "[...] experience with malaria affects treatment seeking behavior and leads to diffusion of information on how to treat it (op. cit. p. 941).

Other more recent household survey-based studies conform to these results. In western Kenya 60 percent of fever episodes were treated at home with only 18 percent resulting in a visit to a health clinic or a hospital, with the remainder seeking no treatment (Ruebush and others 1995). In coastal Kenya 23 percent of mothers reporting that a child had malaria in the prior two weeks had taken the child to a health facility (Mwenesi, Harpham, and Snow 1995). Fifty-four percent had given over the counter drugs to the sick child and 24 percent had given no treatment, or had given a home remedy. In southern Ghana, fever is mostly treated at home with commonly available drugs and herbal remedies and a visit to a health center was the last resort after failure of home treatment (Ahorlu and others 1997). On the other hand, in Malawi a higher share of episodes, 52 percent, resulted in a visit to a clinic (Slutsker and others 1994). In that study higher socioeconomic status was found to be positively correlated with clinic attendance.

_19_
assets (Smith and Stulz, 1985; Froot et al., 1993) or they might try to time the market, issuing floating rate debt when the yield curve is steep (Faulkender, 2005). Although we do not directly test and compare these views, we do find that international bond issues have a higher fraction of fixed rate contracts, which seems to suggest that investors in international markets have a lower preference for interest rate risk. Taken together, these results indicate that firms issue very different types of bonds across different markets.

Fourth, the paper contributes to a large literature that studies financial integration using price-based measures.<sup>9</sup> This literature builds from the premise that under full financial integration the law of one price should hold. That is, securities with identical cash flows should command the same price irrespective of the market where they are issued, and thus the literature interprets pricing differentials as evidence of market segmentation. A key empirical challenge to this type of analysis involves identifying comparable assets across markets (Levy-Yeyati et al., 2009; Japelli and Pagano, 2010). Our sample of corporate bond issues around the world helps address this challenge, as many firms issue debt securities both at home and abroad, allowing us to compare borrowing costs between issues conducted by the same firm in different markets.

The rest of the paper is organized as follows. Section 2 describes the data and presents descriptive statistics. Section 3 characterizes the main non-price features of corporate bond issues in domestic and international markets. Section 4 analyzes how firms that issue debt abroad use domestic and international bond markets following their internationalization. Section 5 discusses whether differences between issues at home and abroad depend on the firms‘ country

> 9 Several studies use stock market data to analyze market integration (see, among many others, Bekaert and Harvey, 1995; Soydemir, 2000; Masih and Masih, 2001; Scheicher, 2001; Carrieri et al., 2007). Other papers focus on the (covered and uncovered) interest rate parity and the real interest rate parity conditions and analyze onshore-offshore return differentials (see, for example, Meese and Rogoff, 1988; MacDonald and Nagayasu, 2000). Obstfeld and Taylor (2003) and Kose et al. (2009) provide comprehensive overviews of the main operational measures of financial integration.

8
|Number of hh member b/w 7 and 12|||0.104<br>(8.35)***||||||
|---|---|---|---|---|---|---|---|---|
|Number of hh member b/w 13 to 17||||-0.061<br>|-0.030||-0.075||
|||||(3.62)***|(2.24)**||(3.92)***||
|Number of hh member above 17||-0.079|||||-0.060||
|||(7.77)***|||||(5.97)***||
|Household head with higher education|||0.111<br>(1.83)*|0.111<br>(2.04)**|||||
|Household Adult Equivalent|||-0.072<br>(8.44)***||||||
|Household head with secondary education||0.124<br>(3.87)***||||0.103<br>(4.13)***|0.077<br>(2.61)**||
|Female household head|-0.070||||-0.053|||-0.104|
||(1.64)*||||(2.16)**|||(2.67)**|
|Proportion of hh member under 15||||0.477<br>(7.79)***|0.660<br>(11.18)***||0.404<br>(5.98)***||
|Household head married||0.056||-0.062|||||
|||(1.81)*||(2.13)**|||||
|**Household characteristics and appliances**|||||||||
|Household is in a separate household|||||-0.134<br>(3.48)**||||
|Number of room|-0.050<br>(3.31)**||||||||
|Gas cylinder|0.105<br>(2.54)**||0.067<br>(2.32)**||||||
|**Village mean**|||||||||
|Proportion of hh with gas cylinder per village|1.69<br>(3.11)**||-0.771<br>(4.86)***|-2.328<br>(3.96)***|2.34<br>(3.60)***||||
|Proportion of hh with gas line per village||||||||0.970<br>(3.01)**|
|Mean household living area per village||||0.008<br>(4.73)***|||-0.006<br>(5.07)***||
|Population per village||||-0.0002<br>(3.68)***|||||
|Average hh sixe per village||||||||0.065<br>(1.90)*|
|Total number of householdper village||||0.001|||-0.0002||

55
# A.  Who are the unbanked?

Table 13 contrasts the characteristics of the unbanked individuals with the banked. Here a banked person is defined as someone who has a deposit account in a formal-sector deposit institution or who has credit from such an institution. The unbanked thus include persons with “afore” account s- pension accounts opened by their employers on their behalf. This accounts for the approximately 25% of unbanked persons who reported formal savings.

|**Table 13. Characteristics of Unbanked an**|**d Banked Ho**|**useholds**|
|---|---|---|
||**Percentage**<br>**among the**<br>**banked**|**Percentage**<br>**among the**<br>**unbanked**|
|**Age**|||
|**18 to 29 years old**|**40.6%**|**43.2%**|
|**30 to 64 years old**|**50.5%**|**49.0%**|
|**65 or older**|**8.8%**|**7.9%**|
|**Sex**|||
|**Male**|**19.1%**|**19.8%**|
|**Female**|**80.9%**|**80.2%**|
|**Highest Level of Education**|||
|**Less than high school degree**|**44.2%**|**49.8%**|
|**High School degree or equivalent**|**31.1%**|**30.5%**|
|**More than high school**|**21.9%**|**15.7%**|
|**illiterate**|**2.8%**|**3.9%**|
|**Household composition**|||
|**No children in household**|**24.4%**|**21.2%**|
|**One or two Children**|**25.8%**|**23.3%**|
|**Three or more children**|**30.7%**|**35.6%**|
|**Have Economic Dependents**|**76.0%**|**68.7%**|
|**Housing status**|||
|**Own Home**|**69.3%**|**63.1%**|
|**Rent Home**|**18.0%**|**20.5%**|
|**Lended Home**|**12.7%**|**16.4%**|
|**Employment status & non - labor income**|||
|**Working full or part time**|**95.1%**|**78.5%**|
|**Not Working**|**4.9%**|**21.5%**|
|**Receive Social Security, old-age pension or other**<br>**payments**|**9.5%**|**8.2%**|
|**Receive medical insurance, life insurance or robbery**<br>**insurance**|**0.4%**|**0.2%**|
|**Non - Labor income**|**24.4%**|**12.8%**|
|**Household Income in dollars**|||
|**No - Income**|**10.2%**|**20.8%**|
|**$ 15, 000 or below**|**82.0%**|**78.0%**|
|**$15, 001 to $30, 000**|**6.4%**|**1.1%**|
|**$30, 001 to $45, 000**|**1.4%**||
|**More than$45, 000**||**0.1%**|
|**Did not maintain any formal savings**|**1.8%**|**74.9%**|
|**Source: ENDESFI**|||

39