rafmacalaba/datause-encoder-data huggingface dataset:
30 The general impression one gets from Table 12 is that average pre-Uruguay tariffs facing middle-Eastern countries were generally low in Europe and Japan (with one or two exceptions) and higher in the United States. In the EU duties on all non-oil goods average about 1.2 percent although they reach 4 percent for non-oil exports from Cyprus. Factors accounting for the relatively low overall rates facing the middle-East include the extension of GSP treatment for many middle-Eastern products and the EU-Israel and EU-Turkey Free Trade Agreement.'" Conversely, the relatively high tariffs that occur in the United States are a result of two factors: the exclusion of OPEC members from the US GSP scheme, and the fact that GSP preferences are not extended to textiles, clothing and footwear. Box 5 gives more detailed information on the tariffs facing Egypt's exports.

For the most part the regional NTM trade coverage ratios show in Table 12 are also low with one or two exceptions. More than 30 percent of Egypt's non-oil exports to the EU and USA encounter NTMs, but the product sectors affected differ in the two markets. In the EU, Egypt faces major import restrictions on foodstuffs while most of the US barriers are in the textile and clothing sector.

How did the Uruguay affect tariff barriers facing ME exports? The Round's accomplishments can be summarized as follows; (i). Manufactured goods. A 40 percent cut in industrial countries' tariffs on manufactures with an increase in bindings (legal maximum rates) from 94 to 98 percent of all imports. GATT data indicate tariffs will be lowered by approximately 2.4 percentage points to 4.0 percent. Lower than average cuts occur in sectors of major importance to developing countries such as textiles, clothing, footwear and transport equipment. Reductions will take place in five equal annual stages beginning with the entry into force of the World Trade Organization.

(ii). Industrial tropical products. These are goods like Jute, Hemp, Sisal, tropical wood and Rubber. A 57 percent reduction in tariffs will result. Tariffs should decline from 4.2 to 1.9 percent.

(iii). Natural Resource Based Products. Goods falling in this category include items like Preliminary information suggests a 38 percent cut for these items. Larger than average reductions will occur for some mineral and tietal Eiroducts with lower than average reductions for fish. Tariffs on natural resource based manufactures should decline from 3.2 to 2.0 percent - - a 40 percent reduction.

'<sup>5</sup> 0ne potential problem relating to the statistics in Table 12 is that ceilings are applied to some products receiving GSP treatment. Once these ceilings are exceeded additional imports are taxed at the MFN rate. Due to the lack of required information Table 12 assumes that all trade occurred within the pre-established GSP limits. If this is not the case Table 12 could understate the importance of applied tariffs.
are likely small, but they may include additional investments in new reservoirs and irrigation systems, as hydroelectric facilities and water supplies are affected by changes in the water flow from melting glaciers.

Finally, it should be warned that the impacts found for Chile cannot be generalized to apply to other countries. The impacts of climate change differ from country to country depending on the spatial distribution of the population, the types of activities they are engaged in, and the particular patterns of climate change.

# **References**

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- Baker, J.T., L. H. Allen Jr., K. J. Boote, P. Jones, & J. W. Jones (1989) “Response of soybean to air temperature and carbon dioxide concentration.” _Crop Science_ **29** : 98105.

- Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J. Räisänen, A. Rinke, A. Sarr and P. Whetton (2007) “Regional Climate Projections.” In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- Falvey, M., and R. D. Garreaud (2009) “Regional cooling in a warming world: Recent temperature trends in the southeast Pacific and along the west coast of subtropical South America (1979–2006)” Journal of Geophysical Research, 114, doi:10.1029/2008JD010519.

- Horowitz, J. K. (2006) “The Income-Temperature Relationship in a Cross-Section of Countries and its Implications for Global Warming.” Department of Agricultural and Resource Economics, University of Maryland, Submitted manuscript, July. - -

- <u>http://faculty.arec.umd.edu/jhorowitz/Income Temp i.pdf</u>

- Magrin, G., C. Gay García, D. Cruz Choque, J.C. Giménez, A.R. Moreno, G.J. Nagy, C. Nobre and A. Villamizar (2007) “Latin America. Climate Change 2007: Impacts, Adaptation and Vulnerability.” Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 581-615.

- Masters, W. A. and M. S. McMillan (2001) “Climate and Scale in Economic Growth,” _Journal of Economic Growth_ , **6** (3): 167-186.

- Mendelsohn, R., W. Nordhaus and D. Shaw (1994) “The Impact of Global Warming on Agriculture: A Ricardian Analysis,” _American Economic Review_ , **84** (4): 753-71.

- Poorter, H. (1993) “Interspecific variation in the response of plants to an elevated ambient CO2 concentration.” _Vegetation_ 104/105: 77-97.

26
- Senn, Stephen (2004) “Added values: Controversies concerning randomization and additivity in clinical trials”, _Statistics in Medicine_ 23(24): 3729-53.

- Skoufias, Emmanuel (2005) “PROGRESA and its Impacts on the Welfare of Rural Households in Mexico _”,_ IFPRI Research Report 139, IFPRI, Washington D.C.

- Soares, J.F. and C.F. Wu (1983) “Some restricted randomization rules in sequential designs”, _Communications in Statistics – Theoretical Methods A_ 12: 2017-34.

- Taves, DR (1974) “Minimization: A new method of Assigning Patients to Treatment and Control Groups”, _Clinical Pharmacology and Therapeutics_ 15: 443-53.

- Therneau, Terry M. (1993) “How many stratification factors is “too many” to use in a randomization plan?”, _Controlled Clinical Trials_ 14(2): 98-108.

- Treasure, Tom and Kenneth MacRae (1998) “Minimisation: the platinum standard for trials?”, _British Medical Journal_ 317(7155): 362-63.

- 37 -
<!-- Start of picture text --> reer tby rrate_us<br>.001<br>.003 .0015<br>.001 0<br>.002<br>.0005 −.001<br>.001<br>0 −.002<br>0 −.0005 −.003<br>0 5 10<br>gdp govexp<br>.015 .008<br>.006<br>.01<br>.004<br>.005 .002<br>0 0<br>rrate gdp_us<br>.002<br>.006<br>0<br>.004<br>−.002 .002<br>−.004 0<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> <u>Figure 4. Impulse Responses of GDP to Various Shocks: Developed economies</u> <!-- Start of picture text --> reer tby rrate_us<br>.001 .001 .0015<br>.001<br>.0005<br>0 .0005<br>0<br>0<br>−.0005 −.001 −.0005<br>0 5 10<br>gdp govexp<br>.008 .003<br>.006<br>.002<br>.004<br>.001<br>.002<br>0 0<br>rrate gdp_us<br>.001 .002<br>.0015<br>0<br>.001<br>−.001 .0005<br>0<br>−.002<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> 17
regions, and Bangladesh’s export portfolio is less diversified than those of its South Asian neighbors (Figure 1). The concentration in apparel help identify the effects of a sector-specific export shock clearer in Bangladesh than in other, more diversified, economies. Furthermore, the concentration of exports in RMG supports our identification strategy for two reasons. First, most production for these exports (and employment) is concentrated in few geographic districts. Second, females dominate employment in the sector; in the language of international trade models, RMG is a “female-intensive” industry.<sup>4</sup> To the extent that males and female are imperfect substitutes in production (Acemoglu et al., 2004), general equilibrium neoclassical models predict that the positive export shock in the RMG sector should increase the wages of women throughout the economy, not just in the RMG sector, and shrink the gender wage gap.

**<mark>Figure 1. A big range in export portfolio concentration</mark>**

_Sectorial breakdown of country-wise exports from South Asia and other developing countries, 2016 (%)_ <!-- Start of picture text --> 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Bangladesh<br>Pakistan<br>Sri Lanka<br>India<br>Brazil<br>China<br>Poland<br>Russia<br>South Africa<br>Turkey<br>Textiles and apparel Fabricated metals Fishing, forestry and logging<br>Chemicals Food, beverages and tobacco Agriculture and mining<br>Basic metal industries Other manuf. Industries Wood<br>Non-metallic minerals Paper<br>South Asia<br>Countries<br>Other Developing<br><!-- End of picture text --> _Source:_ Artuc et al., (2019).

Fourth, relative to other countries studied, such as India, dispersion of wages—a heuristic measure of local labor-market integration—is lower in Bangladesh. While admittedly an imperfect measure of labor market integration (Robertson, 2000), the lower geographic wage dispersion suggests that Bangladesh may be more likely to experience the kind of long-run general equilibrium effects predicted by neoclassical theory.

To compare the local labor market effects with general equilibrium predictions, we first apply the Bartik (1991) approach. The results show significant short-run local labor market effects that fade over time. Sub-districts more exposed to these export shocks experience a 3,062 _taka_ increase in average annual wages in the short-run—between 2005 and 2010 period relative to less exposed sub-districts. A US$100 gain in exports per worker between 2005 and 2010 lead to a 0.7 percent decrease in informality in sub-districts with a higher degree of exposure to trade. More fullyemployed groups—males, high-skilled, and more experienced workers—seem to benefit the most

> 4 Several studies have documented the RMG sector to be a key in driving female-intensive job creation across the world. In Bangladesh, the expansion of RMG sector led to a sustained increase in female labor force participation rates from 27.5 percent in 2003 to approximately 37 percent in 2010. Several studies have documented this “female-intensive” nature of the RMG sector (World Bank, 2012; World Bank, 2016).

3
Research that studies the climate change externality challenge posed by income growth posits that no single durables owner or buyer has an incentive to internalize the social costs caused by fossil fuel energy consumption. As documented by Davis and Gertler (2015) and Gertler et al. (2018), there is a rising demand for durable goods across the developing world. For the case of air conditioning, Davis and Gertler (2015) estimate ownership increases by 2.7 percentage points per $1,000 of annual household income. They use their estimates combined with income growth projections, data on utilization, and an estimate of the grid’s future carbon intensity (i.e., tons of GHG emissions per MWh of power generation) to predict the impact of the world’s growing middle class on future GHG emissions. Biardeau et al. (2020) document how cooling degree days in developing nations with extremely hot summers translates into increased electricity consumption as air conditioning ownership rates increase. Despite dramatic improvements in energy efficiency over time, economic growth in LDCs is not less energyintensive than past growth in developed countries (van Benthem 2015).

We build on this past research by presenting new Engel curves estimates and we focus on what these Engel curves mean in terms of stakeholders in the status quo carbon intensive economy. We augment these data with national data on the emissions intensity of the grid to discuss how the social costs of operating these various durables differs across nations.

In developed nations, we present new evidence on the political economy challenge posed by durable internal combustion engine vehicles and by high-carbon cities within nations. Using data on California vehicle registrations, we document the slow progress in increasing the share of electric vehicle ownership. For each European nation, we document the variation in the crosscity carbon emissions per-person. The results from these data sets document that even in rich nations there are many people whose carbon footprint is large and thus they bear more of the economic incidence of carbon pricing (Glaeser and Kahn 2010).

The growth of the middle class in developing nations is fueled by urbanization and rising educational attainment. These secular changes raise the possibility of increased support for environmental protection over time. Richer people are willing to pay more than poorer people to avoid risk and climate change raises our risk exposure along many margins (Costa and Kahn 2004). U.S based research documents that the more educated are more patient and more likely to support environmental protection (Becker and Mulligan 1998, Kahn 2002). We explore the role of both income and individual education in determining one’s stated support for environmental 4
equation (5) with _μ_ = _α_ 0 + _α_ 1 _E_ [ _prize_ ] and where the expected prize is expressed per lottery round. The number of risky sexual acts fell by 0.4 for every $1 increase in the expected prize, while the number of unprotected sexual acts was reduced by approximately 2.4 for every $1 increase in the expected prize per round over the trial period.

As a comparison, table A6 reports treatment effects on self-reported sexual behavior (panel A) and reproductive health outcomes (panel B). The overall effect of treatment (practice safe sex, column 5, panel A) was significantly positive. Quantitatively, the effects were relatively small for two out of the four individual indicators (1.8% reduction in number of partners, 4.3% increase in condom use, 15.9% reduction in sex with partner with high likelihood of HIV, and 29.1% reduction in extramarital sex).

Panel B, table A6, reports the impact of the lottery intervention on pregnancy and births. Women in the pooled intervention group were 22.3% less likely to have given birth in the last four months or be As and births are a currently pregnant. pregnancies result of unprotected sex, the findings are consistent with the results reported in table 6. Columns (7)-(8) disaggregate the sample by whether or not the women live in a longterm relationship (here labeled as married vs. unmarried). Pregnancies among single women are more to be unwanted and due to sex with a likely unprotected non-regular partner. Despite the smaller samples, the results suggest that the effect in the full sample was mainly driven by the sample of single women (a 31% reduction in recent births and pregnancies among single women).

# **Heterogeneous effects: Preferences for risk**

The expected utility of a lottery with cash prizes depends on individuals’ attitude towards monetary risk. In this section, we explore whether individuals with preference for risk, based on the perceived value of a risky gamble, are more likely than risk-averse individuals to respond to a prevention scheme with a high but uncertain return conditional on behavioral change.<sup>15</sup> Better understanding of how to strategically target social behavioral programs to groups at higher risk of infection is often raised as a priority by policy makers.<sup>16</sup> Participants’ preferences for risk were measured using a hypothetical risk aversion question in the baseline questionnaire. Summary statistics are provided in table 2. Sixtytwo percent of the participants report they would prefer a fixed amount of money below the expected value of a lottery instead of taking part in the lottery (risk-averse), while

> 15Lammers and van Wijnbergen (2007) show, using experimental data from students in South Africa, and the same risk aversion measure as employed in the Lesotho trial, that less risk-averse individuals have higher (self-reported) risk of contracting HIV.

> 16Apart from specific groups (commercial sex workers for example), however, little is known about how to identify and target those at high risk, in particularly in a population at large (UNAIDS, 2013).

15
Table 10. Heterogeneity in the impact of the program on the probability of student dropout: LATE estimates.

|||Interaction:<br>||
|---|---|---|---|
||(i) Pre-treatment<br>dropout probability|(ii) School size<br>(=1 if large school)|(iii) Gender<br>(=1 if boy)|
|ENTRE_j_+ (ENTRE_j_) x (_Interaction j_)|-0.061*|-0.046***|-0.044***|
||(0.032)|(0.018)|(0.017)|
|_ENTRE j_|-0.007|-0.013|-0.018|
||(0.017)|(0.020)|(0.018)|
|Observations|124,626|124,626|124,626|
|R-squared|0.327|0.325|0.325|
|Controls at student level|YES|YES|YES|
|School fixed effects|YES|YES|YES|

Notes: (1) Local Average Treatment effects were estimated by instrumenting treatment (proxied as the effective reception of the ENTRE guide according to self-reported data in follow-up 3) with treatment assignment. (2) Standard errors clustered at schoolyear level in parenthesis. (3) *** p<0.01, ** p<0.05, * p<0.1. (4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. (5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that was treated) and _Postt_ : (1 = year of the intervention; 0 = year before the intervention). (6) _Interaction j_ in column (i) is given by the variable _Risk sjt_ (Probability of dropout for student _s_ that attends school _j_ in year _t_ estimated in the Early Warning System); in column (ii) by _Large school sjt_ : (1=Large school: size in 2017 > median), and in column (iii) by _Man sjt_ : (1=Man). (7) All specifications include the corresponding _Postt_ , _Entrejt_ and _Interaction j_ dummies. (8) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

# - <u>Scalability and cost benefit analysis</u>

As the size of the RCT suggests, the program was designed to be implemented at scale by relying on existing MINEDUC staff, including trainers, teachers, and school principals, and at a very low cost per student benefited by the program (roughly USD 2 dollars). The training and overall implementation of the program was chiefly in the hands of MINEDUC, with minor support from the World Bank. Expanding ENTRE to additional grades would reduce the costs per student by approximately 76%, given that the training and materials for principals (which represent roughly half of the budget for an average school) are a “fixed cost” for the school, there are many multiplegrade schools and the number of teachers per student is substantially larger in the sixth grade than in the first five years of primary education.

The cost-benefit analysis was conducted using the J-PAL tool and following its cost guidelines.<sup>17</sup> As previously mentioned, the intervention is low-cost, with a total cost per student of USD 2.91, 36% of which corresponds to the design of the program and 45% to the opportunity cost of

> 17 See https://www.povertyactionlab.org/resource/conducting-cost-effectiveness-analysis-cea

35
might require certain expertise and fixed start-up costs, which gives large and potentially foreign-owned banks an advantage over smaller domestic banks.

Foreign bank entry can help, but is not sufficient to improve the efficiency of intermediation and the availability of credit. The presence of dominant government-owned banks reduces competitive pressures; the absence of a sound contractual and informational framework reduces the feasibility of small business lending.<sup>11</sup> The absence of other important elements of the financial infrastructure thus impedes countries from reaping the full benefits of opening their markets to foreign providers of financial services.

International capital flows have increased substantially over the past years, especially portfolio flows and foreign direct investment. More and more emerging markets have decided to open their economies for inward and outward equity and debt investment. Allowing outward investment helps private agents diversify investment and earn a higher return at lower risk. This is especially important for mutual and pension funds with limited long-term investment possibilities in the local markets. These limitations on investment and risk diversification possibilities are especially striking for small economies with limited domestic possibilities of risk diversification and high risk economies (Driessen and Laeven, 2005). Allowing inward investment can help attract additional resources and improve resource allocation. Cross-country comparisons have shown that countries experience higher capital inflows, lower cost of capital for enterprises raising funds through stock exchanges, higher investment growth, improved capital allocation and ultimately higher GDP per capita growth rates after opening their economies to equity inflows (Bekaert and Harvey, 2003). While countries have experienced more volatile asset price movements in the years following liberalization (Kaminsky and Schmukler, 2003), concerns that higher capital inflows after financial liberalization lead to higher volatility in the form of investment boom-and-bust cycles have not been confirmed by the

> 11 See Beck and Fuchs for a discussion in the context of Kenya (2004).

25
# Table D.2: **Household Survey** – Tests for Balance and Attrition

Panel A reports tests for balanced survey coverage. Each column represents results from a separate regression based on the entire sample frame (N=5,554). “Surveyed” is a dummy variable equal to one if a beneficiary household was located and completed the entire survey. Treated is a dummy variable equal to one for all households that had pledged _<_ 2 hectares of land as cllateral and were eligible for 100% debt relief. Panel B presents tests for balanced attrition across treatment and control. “Surveyed” includes duplicates, where the same beneficiary had multiple loans in the sample frame; 2,897 surveys were administered in total. “Other” includes a small number of surveys that were not attempted and respondents outside the sample area. Standard errors in parentheses. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

_Panel A : Survey Coverage_

||Surveyed=1||
|---|---|---|
||(1)|(2)|
|Treated|0.010|0.024|
||[0.01]|[0.03]|
|Log eligible balance||0.00|
|||[0.01]|
|Coop bank loan||0.051|
|||[0.04]|
|Crop loan||0.028|
|||[0.05]|
|Total land||0.027|
|||[0.06]|
|Year disbursed||0.023**|
|||[0.01]|
|Observations|5,554|4,808|
|R-squared|0.001|0.011|

_Panel B: Test for Balanced Attrition_

||Treatment|Control|Difference||
|---|---|---|---|---|
||100% Relief|25% Relief|Coeffcient|_SE_|
|Surveyed|0.551|0.5548|-0.00375|[0.01360]|
|Deceased|0.1186|0.1026|0.0160+|[0.00859]|
|Migrated|0.0723|0.0799|-0.00755|[0.00720]|
|Refused|0.0316|0.0367|-0.0051|[0.00492]|
|Not located|0.0938|0.1043|-0.0105|[0.00811]|
|Failed to administer|0.05|0.045|0.005|[0.00582]|
|Other|0.0827|0.0768|0.00592|[0.00741]|

57