rafmacalaba/datause-encoder-data huggingface dataset:
- 71 -

then the analysis of the impact of a change in the tax rate on one fuel requires, for a first approximation, the own price elasticity for the tax fuel and the cross price elasticities for the competing fuels.

Let the tax rate imposed be such that the price of fuel i at the burner tip rises by t1 percent. For an own price elasticity of en the demand for fuel i will fall by (t1eu) percent, while the demand for substitute fuel j will rise by (t1ep) percent, where ej is the price elasti-ity of fuel j with respect to price i. These valuee van then be applied to the emissions characteristics to obtain the net change in pollution. To allow for energy substitution caused by the rise in the aggregate energy price it is first necessary to compute the - percentage rise in the aggregate energy price for small changes in the shares of different fuels this is a (constant) welghted average of the indlvidual fuel price changes (where the weights are the original shares in total energy) so that if just price i changes then the aggregate energy price will change by (t1s1), where ml is the share of fuel i. With this change in the aggregate energy price and an own price elasticity for energy (versus other inputs) of 3i, the fall ln the demand for energy will be (Eitjs1<sup>). The demand for all fuels will be reduced</sup> by this fraction thus contributing further to the reduction in the output of pollutants.

The use cf this elasticity approach, as well as depending on the lack of feedback through supply responses and macro-economic effects, is limited by the limitations of the assumptions used in econometric modelling. Not only may the incorrect form of cost functions be used, but the lack of dynamic adjustment gives no indication of the timetable of the response. Elasticities drawn from engineering studies are likely to be more accurate for the plants from which the data were derived, but they too omit feedback effects. For example, vLrtually all engineering studies take demand levels and fuel prices as given. Allowing for the repercussions of fuel swltching on the parameters of such models can lead to enormously complicated optimization where whole eystems are involved. At a - plant level indeed the feedbacks will usually be negligible plants are rarely
i

# Table of Contents

1. Introduction ..................................................... 2. Financial Markets. Efficiency and Growth;_ Some Theories....... 2 2.1 Argpume.ntsim for LIberAlixA1 d Finanecial jj&rkAti........ 2 2.2 Argumentsfor Reressed or Directed Financial Markets.4 2.3 Portfolio Qualitv . itortions and Economic Efficiency ....... 5 2.4 e ndogeneity of Real nteret ts ...... ................. 7

3. Some Existing Studies ............................................. 10 4. Some Cross-Section Relationshigp for 34 Countrieg .... ............. 12 4.1 Datao ................ i......................................... 12 4.2 Cross-Country Reressios ................................... 19 4.2.1 Interest Rates, Growth and Efficiency................... 19 4.2.2 Interest Rates and Other Distortions .................... 22 4.2.3 Financ-aliation and Efficiency ........ 22 4.2.4 Financial Depth and Growth .. 25 4.2.5 Tnterest Rates and the Current Acogun. 26 I.2.6 Growth and Stock Market Performance ..................... 26 5. Conclu1ion ........................................................ 28 Financial Policies. Efficiencye and Growth - References . ............. 30
remove them immediately and after they are cool replace them on the stove with the heat resistant tape.

In the first treatment aspect sites were randomly chosen to receive a 50 Birr payment if the SUM devices indicated that _Mirt_ stoves were used at least twice per week during the first monitoring period. The 50 Birr payment was made after checking the recorded SUM data at the end of the first round (about 6 weeks after installation of the SUM device). This treatment aspect tests the hypothesis that use incentives increase fuelwood savings in a manner similar to Charness and Gneezy (2009).

The second treatment aspect is cost. One-third of the sample paid 25 Birr for their _Mirt_ stoves and the remainder received their stoves for free. This is about 13% of the real stove cost. This treatment aspect tests the same type of hypothesis examined by Cohen and Dupas (2010) that those who pay for their stoves get better outcomes.

The final treatment aspect is the network component. Network refers to formally informing households about who else in the site received the stoves and providing group training. One-third of respondents therefore not only received in-home training, but also were brought together with others in their village for a meeting with supervisors. The 10 villagers receiving the network treatment aspect in each of the 12 villages were assembled in a common area in the village. Participants also had the opportunity to ask questions. This treatment aspect tests whether making those who received _Mirt_ stoves aware of each other in a formal setting and allowing users to learn from and potentially network with each other increases fuelwood savings.

To test this randomization of our treatments we use wealth variance in forest user groups, existence of forest rules and regulations and percent of biomass change over 5 years. These variables/indicators are at the community level, which is the level of our randomization. Using Kruskal-Wallis tests we find no statistically significant differences in these important indicator variables across our sites. Test results are provided in Appendix E.

# _3.3 Empirical Methodology_

To analyze the fuelwood use per-kilogram food cooked and the per-meal cooking time data we use two-sample t tests, often referred to as a "paired" or "paired difference" 12 | P a g e
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|**Table 15: Compo**<br>|**nents of ID**<br>|**EB Middle s**<br>|**chools**<br>|||
|---|---|---|---|---|---|
|(1)<br>Prova<br>Brasil-Port<br>Mid (SD)|(2)<br>Prova<br>Brasil-Port<br>Mid (SD)|(3)<br>Prova<br>Brasil-Mat<br>Mid (SD)|(4)<br>Prova<br>Brasil-Mat<br>Mid (SD)|(5)<br>Approval<br>rate-Mid|(6)<br>Approval<br>rate-Mid|
|Carioca Middle School * Post<br>1.023<br>[0.124]***|0.757<br>[0.138]***|1.193<br>[0.163]***|0.768<br> <br>[0.161]***|0.087<br> <br>[0.011]***|0.031<br> <br>[0.010]***|
|Single-Shift * Post<br>0.172|0.099|-0.011|-0.087|0.014|0.000|
|[0.158]|[0.157]|[0.171]|[0.160]|[0.017]|[0.014]|
|School of Tomorrow<br>0.095|-0.007|0.103|0.004|0.017|0.01|
|[0.113]|[0.114]|[0.106]|[0.111]|[0.011]|[0.011]|
|More Education<br>-0.025|-0.014|0.018|0.026|0.017|0.014|
|[0.057]|[0.059]|[0.055]|[0.055]|[0.006]***|<br>[0.006]***|
|Mean items in the house|0.137||0.122||0.015|
||[0.033]***||[0.032]***||[0.004]***|
|% Mothers w/ HE|-0.112||0.202||-0.002|
||[0.267]||[0.260]||[0.028]|
|Participation rate-Prova Brasil||||||
|Mid|-0.015||-0.019||0.003|
||[0.030]||[0.032]||[0.003]|
|Age-grade distortion|-0.008<br>[0.004]**||-0.012<br>[0.004]***||-0.002<br>[0.000]***|
|Constant<br>-0.679|-1.305|-0.098|-0.995|0.792|0.879|
|[0.029]***|[0.281]***|[0.031]***|[0.296]***|[0.004]***|[0.032]***|
|Observations<br>1956|1635|1956|1635|1956|1635|
|Number of schools<br>326|327|326|327|326|327|
|R-squared<br>0.41|0.34|0.34|0.4|0.52|0.59|
|School FE<br>Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE<br>Yes|Yes|Yes|Yes|Yes|Yes|
|Restricted Sample<br>No<br>Robust standard errors in brackets|No|No|No|No|No|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: Prova Brasil Portuguese & Math 9th Grade. Approval rate index from 6st through 9th grades. Years analyzed: 2005, 2007, 2009, 2011, 2013 and 2015. Columns (2), (4) and (6) refer to years 2007 through 2015. Standard errors clustered in school level.

35
# 1. **Introduction**

Adaptation to climate change will require living with sea level rise and increased storm surges in many coastal areas (IWTC 2006; IPCC 2013; Rahmstorf 2007; ADB 2008; ScienceNow 2008; Dasgupta and Meisner 2009a; WMO 2010; World Bank 2010a, World Bank 2010b). Coastal protection from storm surge and flooding is partly provided by built infrastructure (Dasgupta et al. 2010; World Bank 2010c; Nicholls et al. 2010). Mangroves<sup>2</sup> are a form of natural infrastructure that also provides coastal protection in tropical regions. The protective role of mangroves and other coastal forests and trees against coastal hazards has received considerable attention in the aftermath of the 2004 Indian Ocean tsunami. This paper describes the extent of coastal mangrove forests in developing countries with previous exposure to tropical cyclones, how mangroves will be affected by climate change, the geographic area and human resources at risk due to loss of coastal protection from mangroves in a changing climate, and the potential for adaptation.

The idea that mangroves may protect coastal communities from coastal hazards (coastal erosion, tidal bores, wind and salt spray, cyclones, etc.) is well known in tropical coastal ecology and increasingly by coastal managers (Chapman, 1976; UNEP‐WCMC, 2006; Doney et al. 2012; Waite et al. 2014). Various modeling and mathematical studies have shown that mangrove forests can attenuate wave energy (Brinkman et al., 1997, Mazda et al. 1997, 2006; Massel et al., 1999; Quartel et al., 2007, Barbier et al. 2008, Gedan et al. 2011; Zhang et al. 2012; McIvor et al. 2013; Liu et al. 2013; Pinsky et al. 2013). However, these studies indicate that the magnitude of the energy absorbed strongly depends on forest density, diameter of stems and 2 Mangroves are salt‐tolerant evergreen forests found along sheltered coastlines, shallow‐water lagoons, estuaries, rivers or deltas in 124 tropical and subtropical countries and areas (Tomlinson 1986; Ellison and Stoddart 1991). A “mangrove” has been defined as a “tree, shrub, palm or ground fern, generally exceeding more than half a meter in height, and which normally grows above mean sea level in the intertidal zones or marine coastal environments, or estuarine margins” (Duke 1992). The term ‘mangrove’ describes both the ecosystem and the plant families that have developed specialized adaptations to live in this tidal environment. The mangrove ecosystem represents an inter phase between terrestrial and marine communities, which receive a daily input of water from the ocean (tides) and freshwater, sediments, nutrients and silt deposits from upland rivers. Mangroves may grow as trees or shrubs according to the climate, salinity of water, topography and edaphic features of the area in which they exist.

2
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Market (worsening economy, bad time to find a job, not getting ahead by working hard, unemployed, out of workforce). We use variables that are similar or that we believe represent good proxies of the ones used in Milasi (2020). A few of the variables cannot be proxied by any of the variables we have in our data. In such cases, we exclude these variables from the regression. The variables available in our data overlap quite well with the specification of Milasi (2020) in terms of push factors, to the exception of social networks. Our data only contains information on social networks abroad. For this reason, we replace the network variables by the number of acquaintances abroad. Moreover, we do not have any variables that can capture context, except for village-level FLFP and MLFP, which provide some information on local labor market opportunities.

Interest in migration can therefore be estimated by the logistic model presented in Equation 4.

Where LiteraturePredictors is a vector that includes age, having a child dummy variable, years of schooling, household asset score, whether the household benefits from a social program dummy, number of friends/acquaintances abroad, labor market status, village male and female LFP rates.

The predictive power of this model, shown in Table 7 is smaller than the one obtained from the ML determinants model.<sup>23</sup> This result suggest a better predictive power of the ML models but note that this result can be due to the fact that there are fewer variables included in this model than in the ML models.

**Table 7. Performance indicators of the literature-driven models**

|**Model**|**Accuracy**|**Sensitivity**|
|---|---|---|
|Female logistic regression with variable list derived|80.84%|2.56%|
|from the literature|||
|Male logistic regression with variable list derived from|68.65%|18.13%|
|the literature|||

Source: Authors’ calculations from DESMIGRATIF (2018).

> 23 The estimation results are presented in Appendix Table 4.

35
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 2. Infrastructure’s contribution to the productivity handicap of firms, 2002-2006**

- a. Overall contribution of infrastructure

<!-- Start of picture text --> Namibia<br>South Africa<br>Kenya<br>Tanzania<br>Ethiopia<br>Uganda<br>Malawi<br>0% 20% 40% 60% 80% 100%<br>infrastructure non-infrastructure<br><!-- End of picture text -->

- b. Contribution of infrastructure by sector

<!-- Start of picture text --> Tanzania<br>Malawi<br>South Africa<br>Kenya<br>Uganda<br>Namibia<br>Ethiopia<br>0% 20% 40% 60% 80% 100%<br>electricity customs transport ICT water<br><!-- End of picture text --> _Source:_ Escribano and others 2009.

5
where U(.) is a standard utility function capturing the trade-off between consumption (c) and leisure (l); _v_ t is the probability of survival to age t<sup>7</sup> ;  is the rate of time preference; _y_ is income; _w_ is the wage; _r_ the real interest rate; _h_ the available working time during period _t_ ; _e_ is equal to one if the individual is employed in a “formal sector job” and zero otherwise;  is the social security contribution rate (paid by the employee); _R_ is the retirement age; _X_ is the maximum number of years a human being can live; _a_ is the entry age to the labor market, and **Z** t ={ _wt,et,rt_ }. The function _Sp(.)_ gives the value of the pension at retirement that depends on past wages, interest rates, career histories, as well as the parameters **ψ** p of the pension system. The model allows for work after retirement from the mandatory system. Thus, with probability  p , individuals who retire work in the informal sector at a fraction δp of the formal sector wage. Similar to pension benefits, the function _Su(.)_ gives the value of unemployment benefits which also depend on past values of **Z** and policy parameters **ψ** u .

One innovation in this model is the formalization of transitions in and out of the social security (or between formal and informal jobs). These transitions are assumed to reflect, at least in part, decisions by individuals. Clearly, depending on preferences, many workers might not want to risk formal sector jobs under any circumstance. Others, on the other hand, are more likely to weight the pros and cons of formal vs. informal jobs and choose sometimes the later. This formulation can be controversial, but seems consistent with the analysis of labor market dynamics in Brazil presented in Section 2.

Thus, we assume that transitions between formal and informal sector jobs can be modeled by a Markov-type stochastic process that depends on factors which are exogenous to the worker (i.e., that the worker cannot control or change at least in the short term) and factors that are endogenous (i.e., that the worker controls). Exogenous factors refer, for instance, to the economic

> 7 Based on the IBGE’s mortality table.

10
35 As Fig. 2.17 below shows, when moving into a new building, practically all households (95.8%) managed to improve their housing conditions, while when moving into secondary market housing, only 69.8% did so.

Fig. 2.17. Change of housing conditions when moving into new and previously occupied housing (% of mover households in 1993-1995) <!-- Start of picture text --> Newly constructed Previously occupied<br>1.7% 4.G% 14.6%<br>2.5% H improved<br>E no change<br>I5.8% l691.8%I became worse<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996 As for the methods of obtaining housing (Fig. 2.18), its free allocation through the waiting list or purchase was generally accompanied by an improvement in the living conditions. In the case of apartment exchanges, however, 17% of the households reported moving into inferior apartments.

**Fig. 2.18.** Change in mover household living conditions in Moscow by methods of obtaining housing (% of mover households in 1993-1995; same legend as Fig. 2.17.)

<!-- Start of picture text --> Waiting list Purchase Exchange<br>0.0% 7.0% 55% 17.0%<br>_ ~~3.4% 1|- 8.2%<br>96.6% ~<br>86.5% 64.8%<br><!-- End of picture text -->

Source: The Moscow Longitudinal Household Survey, January 1996.

This can be explained, in part, by the social composition of the mover households. Those households who resort to housing exchanges (and, to a lesser degree, purchases) include quite a few external migrants among whom 28% think that their new apartments are worse than their old ones (among the Muscovites, this proportion is only 7%). That is, these are households that have moved from another city where they gave up an apartment that was better than the one they received in Moscow, presumably in the<sup>belief that</sup> there were other benefits to be had from moving to the capital.