rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

In many tropical developing countries poverty is believed to be the main driving force behind the degradation of marine and coastal environment. There is also concern that if policies adopted to address degradation of these resources do not take into consideration the poverty issues, they will be less likely to be effective and lead to greater poverty and marginalized livelihoods for those that depend on these resources. Using household survey data from coastal areas of Tanzania, this paper examines the linkages between poverty, MPA management activities and household use of destructive fishing gear. Our goal is to understand whether poverty contributes to the use of destructive fishing gear and how MPA activities impact household choice of fishing gear and household welfare.

While we cannot establish a direct link between income poverty and the use of destructive fishing gear, we do find that some aspects of poverty contribute to the use of destructive fishing gear. Poverty has many different dimensions, and hence we examine a number of measures of poverty in the analysis to understand how different aspects of poverty affect household fishing gear use. We find that food insecure households and female headed households are more likely to use destructive fishing gear. Higher levels of male education and higher asset wealth, on the other hand, decrease the probability that households use destructive fishing gear.

We also find that ownership of fishing gear matters. Households that own destructive fishing gear are virtually certain to use such gear. However, more than two-thirds of fishing households do not own the fishing gear they use. These households are only slightly more likely to use destructive fishing gear than not. Most of the fishing gear we classified as destructive also happens to be illegal. Enforcement of existing regulations banning the use of the illegal fishing gear is clearly important, but so is perhaps a policy that confiscates illegal gear. Fines and other types of punishment may do little to deter the use of illegal gear, particularly when the illegal fishing gear can be rented out. For other destructive fishing gear that is not illegal, a gear exchange program might be considered. Some MPAs in Tanzania do in fact already have such gear exchange programs, which are facilitating the exchange of destructive fishing gear for more environmentally sustainable fishing gear. Households that use destructive fishing gear do appear to benefit from it, at least to some degree, in terms of 37
**Table 5: Percentage change in real GDP relative to baseline (2020) due to carbon tax of US$25/tCO2 without biofuels subsidy and with 25% biofuels subsidy**

||**Agric**|**ulture**|**Biof**|**uels**|**Energ**<br>**uti**|**y and**<br>**lity**|**Manufa**|**cturing**|**Ser**|**vices**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy**|**0%**|**25%**|**0%**|**25%**|**0%**|**25%**|**0%**|**25%**|**0%**|**25%**|
|**World total**|-0.8|-0.3|3.6|67.3|-8.0|-8.2|-0.8|-0.9|-0.4|-0.4|
|**High-income**|-1.3|-0.7|2.8|72.8|-5.9|-6.0|0.3|0.3|-0.1|-0.1|
|**Aus-NZ**|-0.4|0.0|1.1|79.7|-11.3|-11.4|1.0|0.9|-0.2|-0.2|
|**Japan**|-1.6|-1.2|-1.9|75.7|-1.7|-1.7|0.0|0.0|0.0|0.0|
|**Canada**|-1.0|-0.7|6.4|82.4|-5.1|-5.2|1.4|1.4|-0.3|-0.3|
|**United States**|-0.7|-0.2|4.2|71.1|-9.2|-9.4|-0.1|-0.1|-0.1|-0.1|
|**France**|-2.1|-0.5|-0.7|73.8|0.3|0.2|0.4|0.3|0.0|0.0|
|**Germany**|-1.9|-0.9|0.7|74.5|-3.2|-3.4|0.4|0.4|-0.1|-0.1|
|**Italy**|-1.7|-1.0|-0.8|75.6|-1.2|-1.2|0.2|0.2|-0.1|-0.1|
|**Spain**|-1.8|-0.9|-0.5|76.4|-2.7|-2.7|0.3|0.2|-0.1|-0.1|
|**UK**|-1.7|-1.1|0.5|77.3|-7.0|-7.0|0.6|0.7|0.0|0.0|
|**Rest of EU & EFTA**|-1.7|-1.2|1.4|80.1|-3.5|-3.5|0.7|0.7|-0.2|-0.2|
|**Middle & Low-income**|-0.3|-0.1|4.4|62.3|-9.4|-9.7|-2.1|-2.2|-1.0|-1.1|
|**China**|-0.5|-0.3|23.7|102.4|-20.0|-20.3|-4.0|-4.1|-1.7|-1.7|
|**Indonesia**|0.2|0.3|8.0|76.2|-7.3|-7.6|-1.2|-1.3|-0.4|-0.4|
|**Malaysia**|0.7|0.8|2.3|77.8|-9.2|-9.5|0.4|0.3|-1.6|-1.6|
|**Thailand**|-0.2|0.1|0.3|57.9|-3.6|-3.8|-0.1|-0.2|-0.2|-0.2|
|**Rest of EAP**|0.0|0.1|3.2|79.2|-3.5|-3.4|-0.4|-0.4|-0.2|-0.2|
|**India**|-0.4|-0.3|0.4|59.9|-9.3|-9.6|-2.1|-2.1|-0.8|-0.8|
|**Rest of SA**|-0.1|0.0|11.3|98.1|-10.0|-10.1|-1.8|-1.9|0.2|0.3|
|**Argentina**|-0.4|0.0|4.1|81.6|-7.5|-7.8|-0.5|-0.7|-0.8|-0.8|
|**Brazil**|-0.9|-0.4|-0.5|48.1|-1.4|-3.0|0.3|-0.7|-0.3|-0.6|
|**Rest of LAC**|-0.3|-0.1|6.1|89.8|-5.3|-5.5|0.1|0.1|-0.9|-0.9|
|**Russia**|1.2|1.9|17.7|100.6|-10.1|-10.3|-2.6|-2.5|-1.8|-2.0|
|**Rest of ECA**|-0.4|0.1|10.1|96.6|-7.1|-7.2|-2.2|-2.3|-0.4|-0.4|
|**MENA**|-0.2|0.0|9.7|101.3|-5.5|-5.6|1.1|1.5|-1.4|-1.5|
|**South Africa**|-1.1|0.0|10.9|73.4|-17.0|-17.4|0.0|-0.1|-0.4|-0.4|
|**Rest of SSA**|-0.1|0.1|5.3|88.9|-3.0|-3.3|1.0|1.0|-1.3|-1.4|

26
given that those children who were not measured are different, and they may be systematically different in other characteristics besides age as well.<sup>35</sup> Furthermore, since only a few children (less than 2 percent of the sample) experienced a positive pre- _canícula_ rainfall shock, the coefficient estimates for positive pre- _canícula_ rainfall should be interpreted with caution (Table 11).

Bearing these caveats in mind, Table 12 summarizes the average relationship between weather shocks and height-for-age. The full results for the specification are included in Appendix 4. A positive rainfall shock is associated with lower height-for-age scores. This is true for both a positive annual and a positive wet season rainfall shocks. The coefficient estimate of around 0.5 points is non-trivial given that a z-score of -2 is indicative of stunting and the average height-for-age z-score for the children in the sample is -1.09.<sup>36</sup> The earlier results based on the MxFLS consumption data suggest that there is no correlation between a positive rainfall shock and non-health expenditures, and that households spend more on food. Yet, the height of children under three years of age is negatively affected after such a shock. Together, these results suggest that direct environmental effects are important and that an analysis of the impacts of weather shocks at the household level has serious limitations in terms of capturing the impacts of these shocks on certain individuals in the household.

The negative impacts of a positive rainfall shock during the prior agricultural year or wet season are consistent in both of the regional subsamples (Table 12). The biggest impact is from a positive rainfall shock during the wet season in the North. Children who experienced such a shock are 0.7 points shorter than children who experienced an average amount of rain. Negative rainfall shocks appear to have different impacts in the Centre/South regions than in the North. Children living in the Center/South region are taller if the prior agricultural year or wet season was at least one standard deviation drier than average. In the North, however, children are shorter after such a shock.

Not all children experience the same kind of health outcomes from weather shocks. Tables 13, 14, and 15 present the results between weather shocks and sex of the child, the

> 35 If those who are not measured are more likely to be sick (and some of these illness are due to the weather), then the coefficient estimates of the weather shock variables is likely to provide a lower bound of the impact of the weather shock.

> 36 The average does include the 144 children who lived in a locality where the rainfall was at least one standard deviation more than on average.  However, excluding these children the average z-score does not change significantly and is -1.08.

24
on wages is negative but for the least routine (skilled) occupations, the net e¤ects are positive. Recalling the Grossman and Rossi-Hansberg (2008) model of Section 2.3.1, one might interpret these ...ndings roughly as implying that the labor-supply e¤ect of o¤shoring exceeds the productivity e¤ect for the lower-skill workers who specialize in the more-easily-o¤shored routine tasks.

Hummels, Jorgensen, Munch, and Xiang (2010) analyze the relationship between o¤shoring and workers’wages and employment opportunities. They use a matched worker-...rm data set that encompasses the Danish labor force from 1995-2006 as well as a data set on o¤shoring at the ...rm level. They estimate the impact of exogenous shocks to o¤shoring and exporting on ...rm characteristics and on the wages of individual workers. Further, they assess the dependency of these estimates on the education and occupational characteristics of the workers so as to understand the relative sensitivity of types of workers and occupations to o¤shoring. They ...nd that exogenous import shocks will have signi...cant and opposite e¤ects on skilled and unskilled labor wages: skilled labor wages will increase by 8.5 percent while unskilled labor wages will decrease by 7.3 percent. In contrast, shocks to exporting will increase both skilled and unskilled labor wages but low and medium skill workers will see a greater increase.

The examination by Hummels et al of the role of occupational characteristics in wage inequality reveals that workers who are exposed to unsafe working conditions and workers in the natural sciences and engineering will see their wages fall in the case of o¤shoring shocks while those workers in the social sciences and language industries will be less a¤ected by those shocks.

Hummels et al also consider the relationship between displaced workers and o¤shoring, ...nding that those workers displaced by o¤shoring generally experience greater and more persistent wage and earnings loss than workers displaced for other reasons. While initially both low and high skill displaced workers experience wage loss, this loss is smaller and less persistent for high skill workers. A year after losing their jobs to o¤shoring, skilled workers will 39
World Bank (2022a). Brazil Human Capital Review: Investing in People (English). Washington, D.C. : World Bank Group. Available at: <u>http://documents.worldbank.org/curated/en/099359007012217076/IDU0c9bcb58a08ac704dbe081eb0 77b28ef22453</u> World Bank (2022b). Brazil Poverty and Equity Assessment – Looking ahead of two crises. World Bank, Washington, DC. Available at: <u>https://openknowledge.worldbank.org/bitstream/handle/10986/37657/P1746910e33a8407d0b0850b8 f0f5bcf18c.pdf?sequence=1&isAllowed=y</u> World Bank (2022c). Coverage profile of Brazil’s Auxílio Emergencial and special design features for protecting women and other vulnerable groups: lessons from Brazil’s Auxílio Emergencial. World Bank and UK Government Technical Note No. 4. Available at:

- <u>https://documents.worldbank.org/en/publication/documents reports/documentdetail/099930005242219483/p1748360bd260a015090610fb0525fc0254</u>

<mark>World Bank (2022d).</mark> _<mark>Data World Bank</mark>_ <mark>. Available at: https://data.worldbank.org</mark> World Bank (2022e). _Global economic prospects, January 2022_ . World Bank, Washington, DC. Available at: <u>https://openknowledge.worldbank.org/bitstream/handle/10986/36519/9781464817601.pdf</u> 45
- 59 -

The total price elasticities are very different from the partial values and indicate that it is important to allow for substitution away from energy towards capital and labor. The resulting total picture shown very small substitution between coal and oil, coal and electricity and oil and gas except for Canada and the U.S.A., which over the estimation period were distinct in having domestic production of all three primary fuels. Coal is strongly substituted for gas but both oil and gas appear complementary to electricity.

There have been many subsequent studies of price elasticities for industry but few have attempted to give the range of results of Pindyck. Most have also concentrated on the translog cost function approach. For example, Uri (1979) pools sector and time series data (1960-71) for India using a translog cost function - the subsector coefficients were constrained to be equal. At the mean shares for each sector the own price elasticities for coal, oil and electricity were all negative while the cross price elasticities were all positive. The elasticities did show considerable variations between sectors due to the large differences in fuel shares between the sectors. Since the sectors were highly aggregated and very different (mining, transport, domestic, agriculture and commercial) this result is an immediate consequence of the tranalog form.

A series of studies by Sterner (89, 901 has looked at the economics of fuel switching within a single industry (cement) and a number of lndustries in Mexico. The cement study uses both aggregate time series on the industry and a cross section of sixty kilns in 1976 derived from a survey of fuel use. The cross section data allowed Sterner to relax the assumption of smooth and reversible substitution and use a 'putty-clay, approach as originally suggested by Johansen (1972). Once capital is in place there is a fixed input requirement, while in choosing how much capital to have firms can choose over a range of inputs. Due to differences in technology between plants firms can still substitute ex post between inputs by varying the degree of capacity utLiLzation (when there is spare capacity). Using this approach Sterner derives expressions for the short-run substitution of labor for energy. A similar exercise could be
experiments. Beginning in 1977 a very intensive program of provision of Maternal and Child Health and Family Planning (MCH-FP) services was introduced in a set of treatment villages in the Matlab region of Bangladesh (with a nearby set of villages served as a comparators). Mothers and children were visited every 15 days in the treatment area by a female worker with messages about family planning. Detailed vital records were kept in both areas. Although mortality among children fell (Muhuri and Preston, 1991), this has largely been attributed to measles immunization (Koenig, Faveau, and Wojtyniak, 1991, Menken and Phillips, 1990). An experimental design of the delivery of health care services for children which was intensively carried out in Narangwal, India, showed a six point drop in infant mortality over the three years of the project (from 96 per 1000) versus a one point increase in the control area (from 107 per 1000) with the difference being insignificantly different from zero (Taylor and Singh, ND, page IV.D.7).'<sup>6</sup> Taken together, these empirical results suggest that enhancing health outcomes is not simply a matter of providing additional funds, or increasing access to PHC-like services and facilities. This lack of demonstrated impact of PHC across a variety of countries and settings raises the importance of the framework discussed in Section 1, as the "chain" provides two likely explanations for why the impact on health status of public spending, even on PIM4ike interventions, might be low. First, the impact of provision will depend on the ability of the

**16** latter rate is, by the authors' admission, a poorly measured one as it is based on a post-neonatal mortality rate increase in the three years of 49 percent. The authors explanation of this huge fluctuation is "due to incorrect age classification, [...] since in the control villages [...] vital statistics investigators who had no access to exact birth information and needed to rely on the age at death given by the mother of the family".

18
climate relationships in order to derive biomass (Saenger and Snedaker 1993, Berger and Hildenbrandt 2000, Simard et al 2006, and Hutchison et al. 2013); biomass then determines the mangrove density (Berger and Hildenbrandt 2000; Mazda 1997) and density finally determines the flow velocities (Horstman et al. 2013) and the wave attenuation function (e.g. Horstman et al. 2012).

In a cross country study like the one presented in this paper, specifying location‐specific bathymetry, mangrove species (their allometric characteristics: trunk width, root system and leaf area which determines the extent of bed resistance to the flow of water from storm surges), forest density, and forest width is beyond the scope of the analysis. Instead, we estimated the coastal protection services of mangroves using the algorithm described below:

1. The storm surge inundation zone protected by mangroves is derived from the inundation zone modeled for an extreme 100 year return period storm surge<sup>10</sup> with mangroves and a storm surge zone without mangroves (the counterfactual). The inundation area protected by mangroves (mangrove protection zone) is only calculated upstream of an area of mangroves greater than 3 arc seconds (90 sq. m).

# ��_�����∗����� � ��∗�����

Where _SS_PA_ refers to the storm surge inundation area that is protected, _SS_ refers to the 1 in 100 surge height in meters, _wave_ refers to the wave attenuation function, _n_ refers to without mangrove and _m_ refers to with mangrove.

2. For storm surge areas without mangroves, a linear distance decay of waves of 6.3 cm/km, where _d_ is the distance in meters, was adapted from observational data summarized in McIvor et al. (2012) for salt marsh:

> 10 <mark>It is a statistical measure of the average recurrence interval over a long period of time and is the inverse of the probability that the event will be exceeded in any one year. A 100 year storm surge has a 1% chance of occurring in any given year.</mark>

9
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**_56_**

> average monthly (quarterly) exchange rates and then summed to obtain the annual data instead of summing the monthly (quarterly) data and then converting with the average annual exchange rates. The major drawback of the latter instance is that the annual average exchange rates may not be in accord or conform with the known contributions of

> the annual data within the monthly (quarterly) time periods. Given country X's traditional,

> say, fourth quarter's trade or current account surplus together with fluctuating fourth quarter's exchange rate, any difference in the conversion procedure will result in noticeable

> effects on the longer period data expressed in the common numeraire.

> **78.** The recommendation is obviously clear **...** that whenever shorter period data are available these should be converted at the average of monthly (or even daily) rates and then summed to derive longer period data, e.g.,annual data. One should, however, draw

> the distinction between what is desirable and what is statistically feasible. Consideration of statistical feasibility would pr3clude deriving an overall conversion factor based on

> **highly** disaggregated levels involving various commodities at respective rates. More often than not many macroeconomic data (e.g., **GDP, GNP)** from developing countries are not available for the shorter periods but are usually available as annual observations. In the extreme circumstance where volatility in the exchange rates is accompanied **by** fluctuating

> volumes of international transactions, the adequacy of the official exchange rate as a

> conversion factor becomes questionable. An alternative conversion factor in such a situation

> may be in the form of weighted exchange rates at a **highly** aggregated level e.g.,

> merchandise trade weighted exchange rate (discussed in Section VII above) which will

> capture traditional cycles not only in the data but also in exchange rates.
Table 7. Regression of City Growth Residuals<sup>a,b</sup> (robust standard errors in parentheses)

|.|(1)|
|---|---|
||OLS|
|Laws to collectpropertytax|0.035|
||(0.042)|
|% ofpopunder land zone law|0.050***|
||(0.014)|
|Ln(no. formal firms /|0.046*|
|no. workers in formal firms)|(0.024)|
|Ln(popdensity)|0.001|
||(0.007)|
|Small citydummy|-0.044***|
||(0.015)|
|time dummies|Yes|
|Observations|245|
|R<sup>2</sup>|0.093|

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. Small city dummy has a value 1 if a city has less than median city population in each year.

- b. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

37