rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In early 2009, Manmohan Singh was reelected as Prime Minister of India following a successful election campaign in which he emphasized his success in protecting his country from the outcomes of the 2007/08 world food crisis. While world rice prices increased by 160% between June 2007 and June 2008, in India this increase was only 7.9% (World Bank 2010). In 2007, when the world rice price increase was accelerating, the Indian government was already aware of and concerned about the high world price of wheat, which would have made large wheat imports very costly. To secure domestic grain availability, in October 2007, India banned non-Basmati rice exports. The ban was soon relaxed and a minimum export price above the Indian export parity price was imposed, which had to be increased regularly as world prices were rising.<sup>1</sup> The Haitian government was less successful in its attempts to weather the crisis. Haiti imports 82% of its rice consumption, and in April 2008, after an annual increase of 81% in the price for imported rice, the Haitian president, acknowledging his helplessness, was reported to have said to protesters: "come get me at the palace and I will demonstrate with you."<sup>2</sup> The Haitian prime minister was soon voted out and decisions were taken to subsidize the price of rice. Many countries experienced food riots that threatened the stability of their governments but the situations in Haiti and India illustrate that public intervention in a period of high food prices is a matter of political survival in countries with large poor populations. Governments have to be "seen to be doing something" (Poulton et al. 2006). Inaction is not an option. But without appropriate preparation for such situations and pressed by emergencies, many countries rely on costly policies, such as universal food subsidies, or beggar-thy-neighbor policies, such as trade policy adjustments. The food crisis has increased the consciousness of many governments of the unreliability of world markets,<sup>3</sup> and that the stable food prices experienced in the previous decades must not be taken for granted. Anecdotal evidence and experience of what happened following the 1973/74 crisis would suggest that the recent crisis could trigger a wave of new stabilization policies relying on storage and self-sufficiency.

However, these developments would go against the recommendations made since the 1980s by academics and policy analysts that direct market intervention should be avoided, people should be assisted to cope with risks by their governments through the use of safety nets or the development of market-based risk management instruments, agriculture should be supported through investment in long-run productivity growth, and trade and private storage

> 1 For more on Indian rice policies see Slayton (2009), World Bank (2010) and Timmer (2010).

> 2 _New York Times_ , April 18, 2008, "Across Globe, Empty Bellies Bring Rising Anger".

> 3 The recent global “land rush”, which is strongly driven by net food importing countries (Arezki, Deininger, and Selod 2011), is a good illustration of this new distrust in world markets.
- 15 Wane W, Martin GH. Service Delivery Indicators Report - Uganda. World Bank 2013. https://www.sdindicators.org/countries/uganda (accessed 2 Mar 2020).

- 16  World Bank. Service Delivery Indicators Report - Senegal. World Bank 2012. https://www.sdindicators.org/countries/senegal (accessed 2 Mar 2020).

- 17 World Bank. Service Delivery Indicators Report - Tanzania. World Bank 2016. http://documents.worldbank.org/curated/en/162561467996712439/Tanzania-Health (accessed 2 Mar 2020).

- 18  Martin G, Shireen M. Kenya Public Expenditure Tracking and Service Delivery Indicator Survey (PETS-plus), Health Questionnaire 2012. World Bank 2017. https://microdata.worldbank.org/index.php/catalog/2751/related-materials

- 19  Pimhidzai O. Nigeria Service Delivery Indicator Health 2013. Worldbank 2017. https://microdata.worldbank.org/index.php/catalog/2559/related-materials

- 20  Wane W. Tanzania Service Delivery Indicators (SDI) Survey Health Questionnaires 2014. World Bank 2017.

- https://microdata.worldbank.org/index.php/catalog/2582/related-materials

- 21  Rockmore C. Togo Enquete sur les Indicateurs de Prestations de Services (SDI), Santé 2013. World Bank 2017. https://microdata.worldbank.org/index.php/catalog/2560/related-materials

- 22  Wane W. Uganda Service Delivery Indicator Health Questionnaire, Module H1 - Service Delivery 2013. World Bank 2017. https://microdata.worldbank.org/index.php/catalog/2750/related-materials

- 23  Peabody JW, Luck J, Glassman P, _et al._ Comparison of Vignettes, Standardized Patients, and Chart Abstraction: A Prospective Validation Study of 3 Methods for Measuring Quality. _JAMA_ 2000;283:1715–22. doi:10.1001/jama.283.13.1715

- 24  Peabody JW, Luck J, Glassman P, _et al._ Measuring the Quality of Physician Practice by Using Clinical Vignettes: A Prospective Validation Study. _Ann Intern Med_ 2004;141:771. doi:10.7326/0003-4819-141-10-200411160-00008

- 25  Chaudhury N, Hammer J, Kremer M, _et al._ Missing in Action: Teacher and Health Worker Absence in Developing Countries. _Journal of Economic Perspectives_ 2006;20:91–116. doi:10.1257/089533006776526058

- 26  Ibnat F, Leonard KL, Bawo L, _et al._ The Three-Gap Model of Health Worker Performance. The World Bank 2019.

- 27  Daniels B, Kwan A, Pai M, _et al_ . Lessons on the quality of tuberculosis diagnosis from standardized patients in China, India, Kenya, and South Africa. _Journal of Clinical Tuberculosis and Other Mycobacterial Diseases_ 2019;16:100109. doi:10.1016/j.jctube.2019.100109

- 28 Mohanan M, Vera-Hernández M, Das V, _et al._ The know-do gap in quality of health care for childhood diarrhea and pneumonia in rural India. JAMA pediatrics Published Online First: 2015. doi:10.1001/jamapediatrics.2014.3445

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The possibility that ORT could completely<sup>eliminate deaths from diarrhea does not</sup> appear unrealistic. Countries where<sup>conditions are likely to be worse than in Mexico have shown</sup> dramatic reductions in the rates of<sup>diarrhea-related child mortality. "An estimated 4 million</sup> youngsters under age 5 worldwide succumb<sup>to the disease each year, accounting for more than a</sup> quarter of the 14 million annual deaths<sup>in that age group. ORT now saves a million small children</sup> annually. No one, adult or child, would<sup>die of diarrhea if only every family in the world knew how</sup> to prepare and deliver some form of ORT.<sup>The ingredients needed for an adequate formulation can</sup> be found in virtually every household" (Hirschhorn<sup>and Greenough II 1991).</sup> In the case of Mexico, ORT has not<sup>proved so effective, probably because of</sup> inadequate administration coupled with<sup>lack of knowledge on the beneficial effects of the therapy,</sup> or even its own existence (Latorre et al.<sup>1990). The costs of the therapy are approximately US$1 '</sup> plus administration. "A child with diarrhea<sup>may need to be spoon fed more than half a liter of</sup> solution each day, in sessions spaced<sup>as little as three minutes apart, for five to seven days"</sup> (Hirschhorn and Greenough III op.cit.).<sup>Administration is estimated at US$12 (average of three daily</sup> wages of US$4, which is more than the minimum<sup>wage in Mexico, assuming thiat ORT administration</sup> does not require any qualification of the person<sup>administering the serum). Additionally, since ORT</sup> does not stop diarrhea, but only prevents dehydration,<sup>we added an estimated treatment cost of US$3</sup> per case. Applying the figures (US$16 per<sup>case) to all 28,000 annual lethal cases would lead to an</sup> estimated cost of US$448,000.

Morbidity. In 1990, the number of non-lethal<sup>cases of diarrhea in Mexico was 3.36</sup> million (Secretaria de Salud 1991). Sixty<sup>percent of these cases were children under age 14 and</sup> adults over age 65. For both these age groups<sup>we assumed: (a) the necessity to treat all cases and</sup> to (b) apply ORT to 50 percent of the total<sup>number of cases (both costs described above); and, (c)</sup> that 5 percent of all cases require laboratory<sup>analyses at an average cost of US$20 each. Total costs</sup> for those age groups are estimated at US$20<sup>million. For the other age groups we assumed the</sup> necessity to treat all cases and to apply ORT<sup>to 20 percent of the cases. We additionally assumed</sup> that productivity effects are not significant.<sup>Total treatment costs are estimated at US$8.5 million.</sup> Total morbidity costs associated to diarrheal<sup>diseases in Mexico are estimated at US$30 million.</sup> _22. Estimated costs -vary between US$0.17 to more_<sup>_than US$10. The USSI estimate used here_</sup> _may still be an overestimate (Martines et al._<sup>_1991)._</sup> 24
criterion.<sup>10</sup> In order to be eligible for PROGRESA services one needs to have a low discriminant score. The localities for which data were collected were grouped into seven broad geographical regions. For each region a separate discriminant analysis was performed to calculate the discriminant score which resulted in a situation were different regions have different threshold scores to determine if one is eligible or not. Figure 1 presents kernel estimates of the density of the discriminant score in each region along with the threshold score applied in each region. These kernel densities are estimated by pooling all the households in treatment and control localities in the region. As it can be seen in most regions the threshold scores fall frequently at the mean of the distribution of the discriminant scores which implies that there is a considerable number of households just to the right of the threshold score that have a discriminant score very close to that of the eligible households. This is precisely what the RDD approach relies upon to estimate program impact: by comparing individuals just below and above the threshold score.

Using more formal notation, a child’s participation in the labor market or his/her school attendance<sup>_Y_</sup> _i_<sup>may be modeled by an equation such as:</sup> where α _i_ and β _RDD_ are parameters to be estimated, and _B_ is the treatment indicator that equals one if child _i_ is eligible for PROGRESA benefits and zero otherwise. In our case, the treatment indicator is a function of _DSi_ , the discriminant score. Of course, if one were only 10 An alternative and increasing popular method is that of propensity score matching. A key prerequisite for the application of propensity score matching is that individuals with equal probability of being selected as beneficiaries of the program are left out of the program (Heckman, Ichimura and Todd, 1997). Given that this “overlapping support” condition is not satisfied by PROGRESA’s methods of selecting eligible households we are unable to apply this method and test its performance using the PROGRESA sample. See Diaz, Handa and Orozco (2003) for an analysis of the evaluation bias of matching estimators using comparison households outside the PROGRESA evaluation sample.

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_Table 3: An outline of the methods used to evaluate the thirteen green recovery measures. The list of criteria is a condensed form of the detailed list appearing in Table 2._ <!-- Start of picture text --> Criteria for assessment of short-term and long-term impacts<br>Net<br>Effect on skills,<br>Air Other employment<br>Energy Carbon Economic resilience,<br>Measure pollutant environmental effect per<br>use emissions multiplier productivity<br>emissions impact million euros<br>etc.<br>invested<br>1. Immediate launch of grant scheme for energy renovations of buildings from<br>unused budget of 2020-21<br>2. New grant scheme for energy renovations of buildings, 2021-27<br>3. Grants for energy renovations of buildings under construction for upgrade to<br>Near-Zero Energy Buildings<br>4. Installation of smart electricity meters<br>5. Virtual net billing for encouragement of photovoltaic installations by<br>enterprises<br><!-- End of picture text --> 2. New grant scheme for energy renovations of buildings, 2021-27 3. Grants for energy renovations of buildings under construction for upgrade to Near-Zero Energy Buildings 4. Installation of smart electricity meters 5. Virtual net billing for encouragement of photovoltaic installations by enterprises 6. Subsidy to loans of businesses which have been certified with an environmental management system 7. Business4Climate scheme - grants to enterprises with a verified low-carbon action plan up to 2030 8. Implementation of existing Sustainable Urban Mobility Plans (SUMP) 9. Construction of tram in the capital city of Nicosia 10. Scrappage scheme for old cars to be replaced with battery electric vehicles 11. Replacement of streetlights in municipalities and villages with energy efficient lighting 12. Tree planting along urban and intercity roads 13. Fiscally neutral carbon taxation for economic sectors out of the EU Emissions Trading System Calculations with computational energy/environment/economy models Simple bottom-up calculations based on data from National Energy and Climate Plan of January 2020 Qualitative assessment based on stakeholder input 10
_<u>Table 7: Trade shock – Tariff reductions and international prices changes</u>_

||<br> <br>O<br>r|<br> <br>wn Tar<br>eductio|<br> <br>iff<br>ns|<br> <br>Chan|<br> <br>ge in im<br>prices|<br> <br>port|<br> <br>Chan|<br> <br>ge in e<br>prices|<br> <br>xport|
|---|---|---|---|---|---|---|---|---|---|
||Full<br>Liber.|Deep<br>Doha|Weak<br>Doha|Full<br>Liber.|Deep<br>Doha|Weak<br>Doha|Full<br>Liber.|Deep<br>Doha|Weak<br>Doha|
|CerealGrains|-100|||8|2.0|2.1|16|5.9|6.0|
|OilSeeds|-100|||6|2.5|2.5|14|4.8|4.9|
|RawSugar||||2|0.9|1.0|14|5.3|5.4|
|OtherCrops|-100|0|0|2|0.9|0.9|13|4.7|4.8|
|Livestock|-100|||2|1.0|1.1|25|9.7|9.8|
|RawAnimalProducts|-100|||2|0.4|0.4|18|6.6|6.7|
|OilMinerals|-100|0||0|0.1|0.1|2|1.1|1.3|
|LightManufacturing|-100|-6|0|1|1.1|1.2|9|3.8|4.0|
|AgriIndustriesExp|-100|-4|-1|0|0.6|0.6|7|3.0|3.2|
|WoodProductsPaper|-100|-6|-2|0|0.0|0.0|4|1.8|2.0|
|ChemicalsOilPr|-100|-11|-3|-1|-0.1|0.0|3|1.4|1.7|
|MetalMineralProducts|-100|-6|-1|0|0.0|0.0|3|1.6|1.7|
|MachineryEquipment|-100|-7|-2|0|0.0|0.0|2|1.5|1.7|
|OtherServices||||0|0.0|0.0|5|2.0|2.2|
|Construction||||0|0.0|0.0|4|1.7|1.9|
|TradeCommunication||||0|-0.1|-0.1|5|1.9|2.1|
|PublicServices||||0|-0.1|-0.1|5|2.1|2.3|
|Agri|-100|0|0|5|1.5|1.5|14|4.8|4.9|
|Non-Agri|-100|-7|-2|0|0.0|0.1|4|1.9|2.1|
|Economywide|-100|-7|-2|0|0.1|0.1|5|2.2|2.4|

Source: Authors’ calculations.

The full liberalization scenario has the largest impacts: tariffs are completely eliminated and Brazil enjoys strong terms of trade gains; the other two shocks, representing two possible versions of the Doha negotiation outcomes, generate almost no own liberalization and fairly muted global prices effects. In order to fully appreciate their final effects, these shocks need to be mapped to the economic structure of Brazil. Table 8 presents this structure and helps in this regard. For instance, in the full liberalization scenario, export oriented sectors – those displaying high shares of export to domestic output – such as Oilseeds, Other Crops and the industrial sectors transforming agricultural products ( _AgriIndustriesExp_ which buys most of its inputs from agriculture) record considerable increases of their export prices. Conversely, import competing sectors, such as Chemicals and Oil derived products and capital goods, do not face high increases in their international prices. These combined export and import price movements result in fairly strong terms of trade gains, inducing significant reallocation of resources towards export oriented sectors. Additional push for this reallocation comes from Brazil’s own liberalization which entails a reduction of the anti-export bias implicit in the higher protection rates for manufacturing of the initial tariff structure.

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models (not reported) were also run with specific types of behavior change as the dependent variable and demographic and other characteristics as the independent variables.

The study was approved by the Institutional Review Boards of the Ifakara Health Institute, the University of California at Berkeley, and by the Tanzania National Institute for Medical Research. All participants provided written informed consent.

# _Outcome Measures_

This analysis is based on self-reported baseline intentions to change sexual behavior and selfreported actual change over the first 4-months of the trial, in addition to baseline self-reported risky sexual behaviors. As with any study that involves self-reported sexual behavior, there is the possibility that unsafe sexual behaviors were under-reported and behavior change was over-reported. Plummer (2004) reports on the validity of the collection of sexual behavior data using five different methods from a study done in Northern Tanzania. While this study was conducted amongst adolescents, and results among adults may be slightly more congruous across the five methods, they find striking inconsistencies in reports of sexual behavior from self-administered surveys, face-to-face surveys, in-depth interviews, participant observation and biological markers. The authors do find that in-depth interviews may be better at eliciting true responses (as measured by biological markers) than face-to-face questionnaires or self-administered questionnaires, but still inconsistencies were common. Social desirability bias is common in these types of studies, and in this case may have been exacerbated by the extended counseling on safer sex practices that participants were receiving throughout the study.

The primary outcomes used in this analysis were as follows: <u>Anticipated behavior change at Baseline: The following question was asked of all study participants at the</u> first study visit at baseline, before participants had received STI or HIV test results and before they had been randomized into one of the three study groups: _Do you anticipate that your enrollment in this study will change your sexual behavior?_ Participants could answer yes, no or don’t know. All those who answered _yes_ to the above question were then asked, _In what way?_ Possible responses included: abstain, 9
# **<<C>> 3.3.1. Heterogeneity by Income**

Above, we presented the results for the average impact of conditional cash transfer programs. We now examine possible differences associated with some characteristics of the beneficiaries: income (in this subsection) and age and gender (in the next).<sup>27</sup> A priori, there are reasons to expect that the impact of conditional cash transfers will differ depending on the level of household income.<sup>28</sup> Most importantly, because poor households are more likely to be affected by credit constraints, transfers targeted to these households are more likely to affect investment in human capital and to reduce inefficiently high levels of child labor.

Two studies of the PRAF conditional cash transfer scheme in Honduras find that although the program did not significantly reduce child labor overall (Figure 3), the effects of the program differed depending on household income and were statistically significant for the poorer households (Figure 5). Galiani and McEwan (2013) divide their sample of municipalities into height-for-age quintiles (arguing that height for age provides an indication of poverty in the municipality) and find that the program reduced children’s participation in economic activities by eight percentage points (significant at the 5% level) in the poorest quintile. In the richest quintile, no statistically significant change in child labor could be observed. Glewwe and Olinto (2004) interact their treatment indicator with the log of per capita expenditure and find a qualitatively similar result (not displayed in Figure 5): as the per capita expenditure decreases by one percentage point, the impact of PRAF-II on children’s participation in economic activities increases significantly by 0.45 percentage points.

Sparrow (2007) divides his sample into four per capita consumption quantiles to examine the heterogeneous effects of Indonesia’s Jaringan Pengaman Social. He too finds that program impact on child labor increases with poverty. In the lowest consumption quantile, children’s participation in economic activities decreased by 4 percentage points, whereas in the combined upper two quantiles, it decreased by only 3.3 percentage points (both are significant at the 1% level). Only Dammert (2009) does not identify a differential impact of cash transfers on child labor 12
13 inflationary shocks (which include also stabilization attempts). By contrast, persistence remained at high levels in Argentina and Brazil in the post HS period, suggesting a different process -presumably not a fiscal hyperinflation.

There are also difficulties in using the alternative fiscal view, i.e. that the underlying fiscal imbalance required a high but stable level of inflation towards which the cycles tend to converge. A pattern of inflation consistent with this approach would start with an acceleration coinciding with the removal of controls, after which inflation would tend to stabilize at a level consistent with the fundamentals. An initial overshooting of inflation during the flexibilization stage is consistent with the basic arguments of the approach, but-this increase should be reversed at a later stage as inflation stabilizes so that the inflation tax revenue stays in line with the need to finance the deficit through money creation.

This view that inflation was converging to a stable level is not supported by the available evidence. Tcble 3 shows the results of the aut7iregressive equations for the individual cycles. In both countries the large size of the coefficient of the trend in equation (1) shows that inflation was not converging to a stable level. They indicate that within each cycle the dynamics of inflation were akin to inflation explosions characteristic of hyperinflations, as opposed to the "inertia' observed in these two countries prior to the HSs. In fact, the coefficients of cpinf(-l) and the trend in equation (2) for the second and third cycle in Argentina bear striking resemblance to those of the Bolivian hyperinfiation. The only non-explosive cycle corresponds to the period between the Bresser and Summer plans in Brazil, when as a result of a gradual stabilization attempt between