rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Omani Patients Treated Abroad, 2006<br>Egypt<br>1 Thailand<br>Jordan 1 Other<br>USA 1 6<br>3<br>Kuwait<br>4<br>Germany<br>8<br>UK<br>31<br>India<br>182<br><!-- End of picture text -->

**Source:** MOH of Oman, 2006 **Source:** Suleiman (2005) and Ministry of Health of Oman (www.moh.gov.om)

# <u>Mode 3</u>

Foreign investment plays a crucial role in many parts of the economy, both in developed and developing countries. However, due to the structure of many health systems, the role of the public sector in health, and the difficulty to articulate public and private interests in the provision of health services, foreign direct investment (FDI) in the health sector has remained underdeveloped in many countries. Potential gains are nonetheless important and should not be neglected.

Foreign investment could bring new resources to the health system, foster competition among health services providers (raising standards, quality, and lowering prices), and encourage technology and knowledge upgrades and transfers (e.g. training of medical and management personnel, or economies of scale on medical supplies). It could reduce the burden on public spending, including by reducing the need for sponsoring expensive treatments abroad, and create more jobs. The case of India (case-study n°2) perfectly illustrates these effects (and their limits).

It should be noted that the provision of health-insurance services (primarily through modes 3 and 1) by foreign companies also could have an important impact on the sustainability of the domestic health systems and facilitate access to health services.

In India, for example, Hindustan Latex Ltd and Acumen Fund (USA) created a joint venture to develop a small chain of high-quality and affordable (30-50 percent of regular price) maternity hospitals designed to serve the low-income population of underserviced Indian regions. Initiators of this initiative hope it could serve as a global model for increasing access to qualitative and affordable health care for the world’s poor.<sup>2</sup>

> 2 See http://www.lifespringshospitals.com.

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During the slowdown, in contrast, the marked differences in poverty reduction observed across MCC and SA countries seem to be disappearing. A new story of convergence emerges if we look at the evolution of changes in poverty reduction across the region. Each dot in Figure 6 represents two-year average annual changes in poverty rates—as percentage points—for each country over the 2004-14 period. Dots above the horizontal line reflect an increase in poverty, whereas dots below the line refer to a decrease in poverty. The further away the dot is from the horizontal line, the greater is the poverty change in absolute value. Figure 5, then, presents some interesting findings. First, though most countries in the region continued to reduce poverty over time, the pace of poverty reduction has slowed. Second, this slowing of poverty reduction across the countries of LAC suggests that the region as a whole may be converging to the same place, in contrast to the recent decade of “two regions” within LAC. Third, if this convergence towards low poverty reduction continues, we are likely to find poverty reduction in the region continue to flatten in the near future, or even begin to see an increase in poverty rates.

_Figure 6: The convergence in poverty reduction rates is due to ‘commodity boom countries’ now looking more like the rest of LAC_

- (a) Boom (2003-11 annual average, excl. 09) (b) Slowdown (2012-14 annual average)

<!-- Start of picture text --> 2 2<br>0 0<br>-2 -2<br>-4 -4<br>Commodity boom countries Commodity boom countries<br>-6 -6<br>-1 1 3 5 7 9 -1 1 3 5 7 9<br>GDP growth (%) GDP growth (%)<br>Poverty Change (ppts) Poverty Change (ppts)<br><!-- End of picture text --> Source: LAC Equity Lab tabulations using SEDLAC data (World Bank and CEDLAS). Note: Commodity-boom countries are identified as those whose terms of trade grew by more than two percent in annualized terms between 2003 and 2013 as defined in World Bank (2015). Commodity boom countries are Chile, Bolivia, Colombia, Peru, Ecuador, Brazil, and Argentina; the other countries included are Dominican Republic, Guatemala, Honduras, Mexico, Paraguay, Uruguay, and El Salvador. Since growth rates in 2009 are significant outliers, they are excluded from the first period.

This convergence in poverty reduction is largely explained by the change in growth performance among countries that benefited from the commodity boom. Figure 6 shows the relationship between poverty change and economic growth during the commodity boom period (2003-11) and the slowdown (2012-14). The best performers in each period are those countries in the bottom right of the figure. Commodity-boom countries have jointly moved to the upper left during the slowdown period, reducing the poverty reduction gap with the non-commodity countries.

8
Policy Research Working Paper 7887

# **Abstract**

Natural disasters have an impact on poverty through many different channels—economic growth, health, schooling, behaviors—that are difficult to quantify. It is nonetheless possible to assess the short-term impacts of income losses. A counterfactual scenario is built of what people’s income would be in developing countries in the absence of natural disasters. This scenario uses surveys of 1.4 million households in 89 countries. Depending on where they live and work, what they consume, and the nature of their vulnerability, the additional income that each household in the survey could earn every year on average in the absence of natural disasters is calculated. The analysis concludes that if all disasters could be prevented next year, 26 million fewer people would be in extreme poverty—that is, living on less than $1.90 a day. A systematic analysis of the uncertainty suggests that this impact could lie between 7 million if all the most optimistic assumptions are combined, and 77 million if we retain only the most pessimistic assumptions.

This paper is a product of the Global Facility for Disaster Reduction and Recovery (GFDRR) and the Office of the Chief Economist of the Sustainable Development Global Practice Group of the World Ban, and a background paper to the GFDRR and World Bank report “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disaster.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at shallegatte@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(prob > <sup>2</sup> 0.33 to 0.62). This suggests that the effect on HAP is not due to cooking location. Instead, the difference appears to be due to Mirt _conditioned on_ cooking/baking area location. We also note that the sample size of those households who cook in their main houses (n=53), is not atypical for ICS studies and is sufficiently powered to detect a moderate HAP effect (e.g. see Edwards et al., 2007; Quansah et al., 2017; Balakrishnan et al., 2002; Grabow et al, 2013). To detect the observed 64% reduction in HAP with 80% power and estimated COV=0.7, we would need approximately 20 households in each arm compared to the 21 treatment and 32 control observations we have in this study.<sup>12</sup> We next estimate a difference-in-difference type specification to test whether cooking location is indeed important for effectiveness of the Mirt stove. The dependent variable is mean PM2.5 concentrations, and independent variables include dummies for whether a household received a Mirt stove, whether households cook/bake in the main house and these two variables interacted. The interaction term is our variable of interest and measures the additional change in mean PM2.5 for those households who use the Mirt in the main house relative to those who use Mirt elsewhere. In a second model, we add an indicator for roof type, the one variable out of 39 that is unbalanced in the full sample. Robust standard errors are clustered at the Kebele (peasant association) level.

Table 3 reports the difference-in-difference regression results. Based on the coefficient of the interaction term, we find that cooking with Mirt in the main house is estimated to reduce average HAP concentrations by approximately 1,000  g/m<sup>3</sup> , which is similar to the simple mean comparison estimate in Table 2. In both models, the coefficient estimate is significant at the 5% level. We therefore conclude that it is neither the Mirt stove nor baking in main houses alone that reduces HAP, but the two variables together that yield large effects.

> 12 Of course, because by definition people live in their main houses, their choice of cooking location affects HAP exposures.  This issue is addressed in a related paper by LaFave et al. (2018).  We note that injera baking and cooking of other foods, such as stews, coffees and breads, can in principle take place in different locations.  As discussed above, at baseline no household had two cooking areas.  The 2013 reported locations of baking injera and cooking other foods are virtually perfectly correlated.

13
Nearly 60% of owners have at least some secondary education,<sup>31</sup> while almost 30% has either a vocational or university education beyond secondary. Four of ten of informal operators reached are female.<sup>32</sup>

|_Table 2: Descriptive Statistics_|||[95% Con|. Interval]||
|---|---|---|---|---|---|
||Mean|Std. Dev.|[Lower|Upper]|n<sup>(u)</sup>|
|BRIBE (Y=1)|23.5%|42.5%|13.5%|33.6%|506|
|Sales in last month (log)|5.8|1.4|5.4|6.2|500|
|_Exp of log_(USD)|_317.7_|_4.1_|_213.5_|_472.5_||
|NECESSITY (Y=1)|77.9%|41.5%|69.4%|86.4%|513|
|NOWAGE (Y=1)|64.4%|47.9%|53.9%|74.8%|515|
|Workers in a normal month (log)|0.45|0.55|0.30|0.60|515|
|_Exp of log_|_1.6_|_1.7_|_1.4_|_1.8_||
|Age of the owner (log)|3.59|0.03|3.53|3.65|515|
|_Exp of log_|_36.3_|_1.0_|_34.2_|_38.5_||
|Permanent, fixed premises (Y=1)|15.6%|36.3%|3.3%|27.8%|515|
|Temporary, fixed premises (Y=1)|28.7%|45.3%|18.2%|39.2%|515|
|Household (Y=1)|18.9%|39.2%|10.6%|27.2%|515|
|Hawker (Y=1)|36.8%|48.3%|25.5%|48.2%|515|
|No or only primary education (Y=1)|12.5%|33.1%|4.0%|21.1%|515|
|Secondary education (Y=1)|58.7%|49.3%|46.0%|71.5%|515|
|Beyond secondary education (Y=1)|28.7%|45.3%|16.2%|41.2%|515|
|Female (Y=1)|37.9%|48.6%|26.7%|49.2%|515|
|Harassed by police or officials (Y=1)<br>|24.1%|42.8%|13.9%|34.3%|514|
|Harass 'donut' rate<sup>(d)</sup>|10.3%|19.2%|7.0%|13.6%|515|

(d) Rate of harassment within 500m but excluding nearest 200m.

(u) Observation counts are un-weighted.

The specific geo-location data available from the survey also allow for additional analysis using GIS<sup>33</sup> software. Appendix A.IV shows heat maps that help visualize the spatial occurrence of key variables. The first panel shows the occurrence of all enumerated informal businesses (whether given the long-form survey or not): the map shows clear clustering, a construction of the data collection process. The remaining three panels in red, purple, and orange show heat maps of the occurrence of BRIBE and HARASS, respectively. One notes immediately that these are not an exact

> 31 ZimStat’s 2014 Labour Force Survey (LFCLS) reports that 68% of workers in the informal sector had their highest level of education in as some form of secondary school, completed or not (Form 1-6, with 46% terminal at Form 4) (ZimStat, p. 92)

> 32 This compares to a figure of 52% from the ZimStat LFCLS (p. 87). Discrepancies can likely be explained by composition effects (i.e. the LFCLS covers areas outside of Harare), but these values are within confidence ranges. 33 That is, Geographical Information Systems.

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Poorer students who are not academically prepared are unable to gain significantly in learning (measured by test scores) from the additional schooling they receive as a result of the simple poverty-targeted incentive. Clearly more work is needed—in Cambodia and elsewhere—to establish the best approach to ensure that additional schooling translates into learning for these students. Remedial lessons for students in the early grades, or increasing school readiness among poorer students, for example through early child development programs, might be approaches to try. Indeed, data from Cambodia suggest that children suffer from substantial delays in cognitive development, which hamper their school readiness (Naudeau et al., 2011).

The experience evaluated in this paper suggests another approach, based on the finding that incentivizing school attendance in a way that recognizes academic potential can pay off in measurable learning outcomes. By changing the framing of the program, and the associated labeling of the recipient, the merit-based scholarships distributed by the program appear to have motivated recipients and their families to exert greater effort in education, with measureable impacts on learning outcomes.

While the findings cannot answer all potential questions—for example, does teacher behavior also respond to the framing of the scholarships?—the results suggest that it should be possible to frame demand-side incentive programs in a way that maximizes their impact on learning outcomes. Such an approach could be undertaken in a way that does not necessarily imply a tradeoff between equity and efficiency. Indeed, the results show that among poorer children who received merit-based scholarships, the impacts on school participation as well as test scores were large. Scaling up an approach that targets students with an incentive that recognizes their high academic potential—while ensuring that the poorest students are among that set—is likely to maximize both the equity and effectiveness objectives of the program.

22
|Legal Status|Legal Status is a binary variable that measures whether an enterprise is registered as<br>a sole proprietorship or as a company. 0 indicates sole proprietorship, while 1<br>indicates the enterprise is registered as some form of company.<br>Source: WorldBank Enterprise Surveys (www.enterprisesurveys.org).|
|---|---|
|Binary Bribe Index|Binary Bribe index is a variable that measures whether and enterprise has been<br>asked for an informal gift or payment in the past year for one or more of the<br>following: Connection/access to public utilities, obtaining business permits,<br>obtaining government contracts, to deal with safety, fire, or environmental<br>inspections, for tax purposes, for customs, courts, or to influence legislation. )<br>indicates no bribes were solicited, 1 indicates that at least one bribe was solicited.<br>On average, 68% of enterprises report at least one bribe.<br>Source: World Bank EnterpriseSurveys(www.enterprisesurveys.org).|

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-12-

# **IV. DATA AND** MEASUREMENTS

# **1. The Sample**

The sample of companies analyzed here represents all non-financial corporations that had been listed on the Korea Stock Exchange from 1983 through 1988. **The** primary data source is the Pacific-Basin Capital Market (PACAP) Data Base from the University of Rhode Island. The PACAP data is supplemented by the data provided by National Infonnation and Credit Evaluation (NICE) Inc. of Korea. NICE compiles the raw data from which the PACAP data base is constructed and hence provides a more detailed breakdown of balance sheet items than does PACAP.

Due to new listings, delistings, mergers, and bankruptcies, the number of non-fiancial frms listed on the Korea Stock Exchange varies from a low of **275** in 1983 to a high of 441 in 1988. To estimate the parameters of equations **(1)** and **(I1)** we require sufficiently detailed balance sheet, income statement, and stock market data for all sample **finns** throughout the sample period. Thus, to be included **in** the sample, firns must be listed on **the** Korea Stock Exchange throughout the 1983-88 period, thereby limiting our sample to 241 fims. The sample covers 27 industries which account, in aggregate, for 62.7 percent of the total market value of non-financial corporations listed on the Korea Stock Exchange **at** the end of 1988.

# **2. Subsidized Loans**

As discussed in the previous section, we use the sum of short and long termn domestic loans plus foreign loans to proxy for the firm's supply of subsidized loans.12 The net flow of these loans received by each finm in a given year is estimated by taking first differences of their

**To** the extent that some of these loans are not subsidized, our measure overstates the true amount of subsidized loans. Note, however, that in equilibrium risk adjusted rates of return on speculative assets are the same as the risk adjusted cost of non-subsidized loans; consequently, **furms** have no incentive to take out non-subsidized **loans** to make speculative investments. Ihus the upward bias in our estimate of subsidized loans works against detecting a significant relation between our measure of subsidized loans and speculative asset holdings.
specifications. If fertility declines are concentrated among the B40, the economic benefits of lower dependency rates and more income earners as a share of the population will accrue to the poorest. The effect of changes in the share of children, instead of the share of working-age population, is also tested and the results are similar (Table 11). The results are also consistent if an alternative poverty line is considered.<sup>18</sup>

**Table 10 Impact of changes in the share of WAP on poverty**

||Fist-Differ|ence||Panel Fixe|d-effects||Generalize|d Method of|Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|-0.873**|-0.656**|-0.727**|-1.086**|-0.619|-0.324|-0.714|-0.712|-0.752*|
||(0.377)|(0.324)|(0.308)|(0.531)|(0.399)|(0.364)|(0.607)|(0.466)|(0.393)|
|Initial GDP-pc||1.614|2.441*||1.451|1.173||1.010|-0.602|
|||(0.997)|(1.302)||(2.210)|(2.142)||(2.809)|(2.632)|
|Schooling (years)|||0.265|||1.698|||1.145|
||||(2.125)|||(4.351)|||(5.201)|
|British colony|||-0.416||||||-3.236|
||||(2.194)||||||(6.111)|
|French colony|||0.353||||||-7.909*|
||||(1.435)||||||(4.798)|
|Non-colony|||0.681||||||-1.440|
||||(1.212)||||||(2.260)|
|Landlocked|||-1.211||||||-6.113|
||||(1.647)||||||(7.396)|
|Latitude|||-0.0347||||||0.0136|
||||(0.0367)||||||(0.0980)|
|Observations|350|341|298|350|341|302|350|341|298|
|Countries|105|104|82|105|104|86|105|104|82|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

18 Estimates based on a poverty rate of $3.10, instead of $1.90, where the results are shown to be qualitatively similar.

22
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