rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Characteristics of marine protected areas in Tanzania**

|_Marine Protected Area_|_Sub-Tidal_<br>_Area (km_<sup>_2_</sup>_)_|_Date_<br>_established*_|_Comments_|
|---|---|---|---|
|**Mainland Tanzania**||||
|Dar es Salaam Marine<br>Reserves System|26.0|1975|Comprises of 4 islands, designated as Marine<br>Reserves in 1975, and placed under the mandate of<br>MPRU in 1998.|
|Mafia Island Marine Park|615|1995|Multiple use marine park with zoning<br>(822 km<sup>2</sup>total area).|
|Tanga Collaborative<br>Fisheries Management Area|26.4|1996-2000|Covers coastal areas of three districts. Set up as<br>joint initiatives between local communities and<br>districts and includes closed reefs and the Maziwe<br>Island Marine Reserve (est. 1981, 2.6 km<sup>2</sup>). Total<br>area under management is about 1,603 km<sup>2</sup>, of<br>which 29 km<sup>2</sup>is no-take.|
|Mnazi Bay-Ruvuma<br>Estuary Marine Park|200|2000|Total area under management is 650 km<sup>2</sup>.|
|Bagamoyo Collaborative<br>Fisheries Management Area|tbd|2005|Initiated in 2004 in Bagamoyo district, seven<br>villages have established a collaborative fisheries<br>management area and are in the process of making<br>final decisions on sites which will be under<br>communitymanagement.|
|**Zanzibar**||||
|Chumbe Island Coral<br>Sanctuary|0.3|1994|Management delegated to a private company. All<br>no-take.|
|Menai Bay Conservation<br>Area|470|1997|A community-managed MPA.|
|Misali Island Marine<br>Conservation Area|21.6|1998|An NGO and community-managed MPA, total area<br>of 23 km<sup>2</sup>includes terrestrial; 1.4 km<sup>2</sup>is no-take<br>zone.|
|Mnemba Island Marine<br>Conservation Area<sup>**</sup>|0.15|2002|Currently all a no-take zone, privately managed<br>MPA by the Conservation Corporation Africa.|
|Ngezi Forest Reserve|14.4|1959|Proposed for re-designation as a Nature Reserve;<br>includes mangroves and beach area.|
|Jozani Chwaka Bay<br>Conservation Area||2004|A pilot ICM site and protected forest area,<br>Zanzibar’s first national park.|

# Notes:

* The Date Established normally shows the date that the sub-tidal area was included; some sites that include land were designated as terrestrial protected areas earlier.

** Mnemba Island Marine Conservation Area is to expand beyond the current no-take zone to include wider area under management. It will extend from Nungwi (north of Unguja) to Chwaka Bay, through Mnemba, Kiwengwa, Marumbi, <u>Charawe, Michamvi and Ukongoroni</u>

Source: Adapted from Ruitenbeek and others, (2005) 11
3,000 public primary schools across 19 major Indian states found that over 25 percent of teachers were absent from work on a typical working day in 2003 (Kremer et al., 2005). Although administrative data from the government’s official records<sup>2</sup> suggest that SSA has led to an improvement in various observed measures of school quality, there is little evidence on whether these investments have translated into improvements in education system performance, both with respect to intermediate metrics such as teacher absence and final outcomes such as test scores.

Our study of this nationwide campaign to improve school quality in India uses a new nationally-representative panel dataset of education inputs and outcomes that we collected in 2010. We constructed this data by revisiting a randomly-sampled subset of the villages originally surveyed in 2003 (see Kremer et al. (2005)) and collecting detailed data on school facilities, teachers, community participation, monitoring visits by officials, and teacher absence rates. Thus, in addition to reporting updated estimates of teacher absence, and independently-measured summary statistics on input-based measures of school quality, we are able to correlate changes in input-based measures of school quality with changes in teacher absence. The panel data help mitigate concerns arising from fixed unobserved heterogeneity at the village-level, and our results provide the best available estimates using nationwide data of how sharp increases in public education spending over the last decade have affected school quality.

We find significant improvements in almost all input-based measures of school quality between 2003 and 2010. The fraction of schools with toilets and electricity more than doubled, and the fraction serving mid-day meals nearly quadrupled. There were significant increases in the fraction of schools with drinking water, libraries, and a paved road nearby. The fraction of teachers with college degrees increased by 41 percent, and student-teacher ratios (STR) fell by 16 percent. The fraction of teachers not paid on time fell from 51 to 22 percent, and the fraction of teachers aware of recognition programs increased from 50 to 81 percent. Finally, the frequency of school inspections and parent-teacher association (PTA) meetings increased significantly.

However, reductions in teacher absence rates were more modest. The all-India weighted average teacher absence in rural areas fell from 26.3 to 23.6 percent.<sup>3</sup> While increased teacher hiring brought the STR down from 47 to below 40, the effective STR (after accounting for teacher absence) was still over 50 (having reduced from 64 in 2003 to 52 in 2010). The variation in teacher absence across states remains high. At one end, several top performing

> 2These come from the “District Information System for Education” and are commonly referred to as the DISE data.

> 3The all-India weighted average teacher absence estimated in 2003 was 25.2 percent; the corresponding figure for the rural sample was 26.3 percent. The panel survey only covered the rural sample.

2
annex B for more on the methodology and the various iterations of the estimation of tobacco price elasticities by decile.).<sup>9</sup> The estimated average cigarette price elasticity in Russia is −0.52, which is within the estimated elasticities found in the literature (−0.4 and −0.8), as well as those previously estimated for Russia.<sup>10</sup>

**Table 1 – Cigarette Price Elasticities, by Decile**

|_Price Elasticity_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|-0.48|-0.41|-0.38|-0.34|-0.32|-0.31|-0.29|-0.26|-0.25|-0.21|
|Medium|-0.68|-0.61|-0.58|-0.54|-0.52|-0.51|-0.49|-0.46|-0.45|-0.41|
|Upper bound|-0.88|-0.81|-0.78|-0.74|-0.72|-0.71|-0.69|-0.66|-0.65|-0.61|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles were created using per capita household expenditure. Lower- and upper-bound elasticities show average differences of −0.2 and +0.2, respectively, with the medium-bound elasticity. Demographic controls include area, macroregion, and year fixed effects.

# **Mortality and morbidity**

Data on mortality, years of life lost, and morbidity have been obtained from the Global Burden of Disease Study. Ischemic heart disease, stomach cancer, and stroke are among the most prevalent diseases among men and women in Russia. In 2016, approximately 243,252 deaths were attributed to tobacco-related diseases, of which nearly 86 percent were men (208,451) (table 2). Similarly, more than 2.5 million cases of tobacco-related disease were reported in Russia the same year (table 3). A total of 5,979,509 years of life lost were estimated for all tobacco-related deaths in 2016 (table 4).

> 9 To make this paper comparable to the other cases analyzed by the authors in the past (Chile, Ukraine, Moldova, South Africa, Indonesia, Bangladesh and Bosnia Herzegovina) it was decided to use household level data to estimate price elasticities. However, a full replication of the analysis using individual level data is also performed and included in the annex. Although there is a difference in levels, both analyses yield similar and consistent results in terms of trends and incidence.

> 10 Only partial elasticity of consumption is estimated and used here unless specified otherwise. Using the full elasticity, including the effect on participation, would make the results more precise, but, because of computational difficulties, this step was omitted on the assumption that participation in smoking is less sensitive to relatively small changes in prices.

6
# **3.3. Implications of the Macro and Micro Evidence**

Taken together, this evidence suggests that much of the aggregate growth in remittances during the 1990 to 2010 period was the result of changes in measurement, rather than changes in actual remittances. Many countries had remittance growth that far exceeds what would be predicted from the growth in their migrant stocks and the growth in the income that these migrants were receiving. Large surges in the macro remittance data also do not appear to be accompanied by corresponding surges in the micro remittance data in many countries.

If most of the measured growth in remittances at the global level is illusory, resulting from changes in measurement, rather than genuine growth, then we should not expect to see changes in GDP growth coming from it. Nevertheless, if remittance growth was overstated in a consistent way across countries, then countries with higher measured remittance growth would be the ones with higher actual remittance growth, and so this would still offer the hope of correctly establishing the correlation between remittance growth and GDP growth, even though the estimated magnitude of the effect would be understated. But a second implication of our macro and micro estimates above is that there appears to be lots of heterogeneity in the extent of changes in remittance measurement across countries. Countries which saw the highest growth in recorded remittances therefore need not be those with the highest growth in actual remittance flows. As such, even establishing the correlation between remittance growth and GDP growth will be problematic. Furthermore, it seems highly likely that changes in the extent of measurement error will be correlated with institutions and financial infrastructure in the remittance receiving countries, raising further complications for studies endeavoring to examine the heterogeneity in remittance effects.

Finally, it is frequently claimed that the presence of informal transfers and measurement concerns lead recorded remittances at the macro level to be an undercount of true remittances (e.g. World Bank, 2006; Mohapatra and Ratha, 2011). The implicit assumption is then that over time we are getting closer to the truth. However, there are also a number of reasons why macro data can overstate remittances, by recording other types of transfers as remittances. For example, IMF (2009) notes that many other types of small transactions such as transfers to family 20
generally used by the USDOC in its ADD calculations. Products were identified at the HTS 10 digit level. To assist in comparability across the various products, we normalized the prices for each HTS product so that the mean price for each HTS product was 1 for the sample period. With that normalization we then computed the pricing variation over the period.

We used the World Bank’s country classification guide to divide countries according to their development status.<sup>37</sup> We group countries designated by the World Bank as “Low income” and “Lower-middle income” as _low income_ and those designated “Upper-middle income” and “High income” as _high income_ .<sup>38</sup> We can use regression analysis to test for the statistical significance of the difference in price variation. The OLS results for a linear specification are given in Table 7. We also control for whether a supplying country was subject to the investigation in these regressions. For each product, suppliers fall into one of four categories: (i) subject-high income; (ii) subject-low income; (iii) nonsubject-high income; and (iv) nonsubject low income. All parameters are measured relative to the subjecthigh income countries; i.e., the economies filing the zeroing disputes against the U.S. at the WTO. In Specification A we include just the basic controls; in Specification B we attempt to control for the possible correlation between price variation and price levels by also controlling for the general level of prices. In this specification “low prices” (“high

> 37 World Bank “WTI Country Classification by Region and Income, (July 2009-July 2010) “ at <u>http://tinyurl.com/y494rao</u>

> 38 Most countries in our sample we call “low income” fall under the World Bank’s category “Lower middle income”.

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2.16 Methodologv. Current thinking on monitoring and evaluation favors the use of multiple data collection approaches rather than one. Emphasis should be on rapid, cost-effective methods that capture the complexities of African households and the implications for extension of gender differences. Monitoring must be continuous, not perfunctory and intermittent, to ensure that interventions have the desired impact on household labor allocation and income streams. Many of the simple, gender-sensitive techniques outlined above are also useful for monitoring the impact of projects on women. For example, field trip reports by extension and research supervisors can be an effective tool for gender-disaggregated analysis. Such reports should include the following information as a start: How many people were contacted? Women -------- Men ---------Whose farms/fields were visited? Women -------- Men ---------Who received project benefits--inputs, credit, training, etc.? Women -------- Men ---------2.17 Including such information obliges extension and research supervisors to address the issue of involving women farmers in extension. This approach is much less expensive and time-consuming than extensive surveys and, provided interviewees are selected randomly, the results can be generalized over a larger population. (Russo and others 1989).

2.18 To evaluate the effectiveness of extension projects in reaching women, at least four questions should be asked:

- Is the project attempting to reach women?

- * Is the project, in fact, reaching the targeted women (for example, the women farm managers)?

* Are the targeted women benefiting from project activities? * w'hat effect is the project having on targeted women?

2.19 If the project is a pilot or has innovative features, a mid-term evaluation should be scheduled to adjust its course and to identify features that could be implemented on a broader scale.
|Received subsidized farm inputs (1/0)|-0.158<sup>**</sup>|(0.078)|
|---|---|---|
|Received food/cash transfer (1/0)|0.144|(0.120)|
|Subsidized inputs*transfer (1/0)|0.182|(0.200)|
|Panel D|||
|Received food/cash transfer (1/0)|0.257<sup>**</sup>|(0.104)|
|Community has irrigation scheme (1/0)|0.026|(0.098)|
|Irrigation scheme*food/cash transfer (1/0)|-0.412|(0.283)|
|Panel E|||
|Received food/cash transfer (1/0)|0.224<sup>**</sup>|(0.099)|
|HH uses an irrigation scheme (1/0)|-0.159|(0.187)|
|HH use an Irrigation scheme*food/cash transfer (1/0)|-0.548|(0.503)|

Notes: All models include control variables such as household characteristics and location fixed effects. Because the dependent variable is log transformed the obtained coefficients can be interpreted in terms of percentage change. Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

# **5 Discussion and conclusion**

A growing number of Malawian households are experiencing food and nutrition insecurity because of recent COVID-19 threats, supply chain disruptions caused by wars like the one between the Russian Federation and Ukraine, and climate shocks that result in the loss of produce, livestock, and livelihoods. Furthermore, the recent devaluation of the Malawi kwacha relative to the US dollar has made the macroeconomic environment even more dire, making it difficult to afford nutritious foods. Public programs to achieve food and nutrition security include provision of input subsidies, establishment of irrigation infrastructure and direct welfare transfers in form of food or cash. Prior evaluation of these programs found a positive association with food and nutrition security; however, there is a dearth of studies evaluating the impacts of participating in multiple programs (Tirivayi et al., 2016). We extend the literature by comparing and assessing the combined effects of social protection and irrigation investments using integrated household panel surveys for three years.

Overall, the findings suggest weak and insignificant joint effects from participating in several programs. In particular, the availability of irrigation infrastructure in a community and its use by households that receive food/cash transfers or input subsidies have no influence on food and nutrition security outcomes. This is contrary to evidence demonstrating stand-alone positive effects of irrigation infrastructure on production, livestock and livelihoods (Fan, Gulati, & Thorat, 2008; Nkhata et al., 2014), but these are localized studies and did not focus on food and nutrition 18
# **2. Stylized facts**

Indonesia has undergone sustained structural transformation since the early 1990s. The share of agriculture, hunting, forestry, and fishing in the labor force decreased from two-thirds in 1993 to about one-half in 2014. Services expanded from 18 to 24 percent, while manufacturing expanded from 14 to 20 percent.

**Figure 2.1: Indonesia: Employment, by Aggregate Sector (1993-2014)**

<!-- Start of picture text --> Mining and quarrying 14% 18% 17% 18% 18% 16% 18% 18% 19% 19% 17% 17% 18% 18% 18% 18% 18% 19% 20% 20% 19% 20%<br>1%<br>1%<br>1% 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2%<br>Manufacturing 3% 3% 4% 4%<br>18%<br>18% 21% 19% 21% 20% 19% 15% 17% 16% 15% 16% 16% 17% 18% 19% 20%<br>21%<br>Financing, insurance, 22% 22% 24% 24%<br>real estate, and<br>business services<br>Electricity, gas, and<br>water supply<br>Community, social, 65% 61% 59% 60% 58% 62% 60% 64% 61% 63% 65% 63% 63% 61% 60% 59% 58% 56%<br>and personal 53% 51% 51% 50%<br>services<br>Agriculture, hunting,<br>forestry, and fishing<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Authors’ calculations with data from Sakernas.

**Merchandise trade was, and continued to be, dominated by manufacturing.** Mining and quarrying account for about 20 percent of total exports value with the primary agriculture sector counting for less than 10 percent of the total value of exports. Some of the most important export products are coal briquettes (10.9% of total value), palm oil (8.21%), petroleum gas (4.48%), gold (2.16%), and rubber (2.07%).

**Indonesia’s six largest trade partners have persistently accounted for at least 60 percent of total exports value.** However, the relative relevance of each partner has changed substantially over time. Japan accounted for nearly one-third of exports by value in the beginning of the period and less than 10 percent by the end. Conversely, China grew in relative relevance from less than 5 percent to more than 15 percent of export values.

4
the increase in the ownership rates of these items appeared to be driven by the poorest and lower middle class (Panels A and B, Figure 2), which is consistent with a reported substantial reduction in (consumption-based) poverty during this period, especially among the rural population (Tesliuc, 2004). The acquisition of the PCs by the upper middle and the richest class also took place mostly during the 2001-2003 period (Panel C, Figure 2), as these groups may have benefited more from the enhanced economic growth observed since 2000. On the other hand, the stock of cars has slightly fallen for the poorest and lower middle class between 1995 and 2001 (but rebounded thereafter),<sup>17</sup> while it increased steadily throughout the years among the upper middle and the richest class (Panel D, Figure 2).

# **3. CONVERGENCE ACROSS WEALTH CLASSES OVER TIME**

Different wealth classes are likely to accumulate assets at varying rates over time, as the data presented above indicate (Figure 2). It is thus interesting to investigate if the degree of inequality in assets distribution among wealth classes has increased or decreased in Bulgaria between 1995 and 2003. We explore this issue by looking at whether the wealth class means (average stocks of various durable goods) converged to or diverged from the overall (national) mean over time.

The convergence/divergence trends can be easily seen by looking at Figure 3. The figure shows the ratio of the average quantity per household of a given durable good in a wealth class to the overall (across all wealth classes) average quantity of the same good in a given year. The divergence or convergence can be measured by the distance of these ratios from the mean. For instance, looking at AWMs (Panel A, Figure 3) we find that in 1995 this distance is 0.51 for the poorest class and 1.47 for the richest class. In other words, the poorest class was at 51 percent of the overall mean, and the richest class was at 147 percent of the overall mean.

> 17 This decline in car ownership has happened mostly during the 1996-1997 economic crisis. Many poor households were selling cars at that time because of the skyrocketing maintenance costs and the intention to alleviate the negative shock to everyday consumption.

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the ground. Nevertheless, this was successfully implemented under Parametric Re, a special purpose reinsurer used by Tokio Marine Insurance to obtain securitized reinsurance against earthquake risk. The 'parametric trigger' was based on earthquake intensity as well as on two grids rings specified around Tokyo and other Japanese cities (an inner and an outer area grid) to determine what reinsurance would be provided in the event of an earthquake.

# **Parametric Re: Earthquake Intensity & Grids as used as Payout Triggers**

|Earthquake level:||||||||
|---|---|---|---|---|---|---|---|
|<br>Richter Scale|7.1|7.2|7.3|7.4|7.5|7.6|7.7|
|% Bond loss: Inner<br>City Grid Epicenter|25%|40%|_55%_|70%|85%|100%||
|% Bond loss: Outer||||||||
|Grid Epicenter|||25%|44%|63%|81%|100%|
|||**Grid Ar**|**eas for Epic**<br>Out<br>Inner|**enter Deter**<br>er Grid<br>Grid|**mination**|||
||||**Tok**|**yo**||||
|||**Yo**|**kohama**|**Chiba**||||

# **Insurance/Reinsurance** Flow **Structure**

<!-- Start of picture text --> 4-.-- = Reinsurance Coverage - = Premium/bond payment<br>Tokio Maritne Swiss Re Parametric Re<br>Insurance Co. *Company *(Cayman 4 nvestors\<br>Islands)<br>(Reinsures with ReinsurersiRe- Reinsures Purchase<br>Swiss RE.) >trocedes w, > Swiss Re /> Cat Bonds<br>Pararnetric Sells cat bonds\<br><!-- End of picture text --> Source: Goldman Sachs In line with this type of 'parametric trigger' design, the following illustrates a hypothetical catastrophe bond structure and its issuance terms in which a 'multi-hazard bundle' is incorporated into the risk profile of the bond. While such bonds have not yet been issued to date, investors wishing to diversify against not only financial market movements, but also against concentrations of single hazards, may find such an issue attractive since some of the hazards (rainfall versus drought) are mutually offsetting.