rafmacalaba/datause-encoder-data huggingface dataset:
|2%<br>1%<br>0%<br>2%)<br>-3%<br>“We|1988-92<br>1993-97<br>1998-02<br>2003-07<br>2008-12<br>2013-17<br>enWe<br>——<br>Relative GDP growth<br>mam Credit to households<br>mmsInflation volatility<br>mmmPublic sector expend<br>et NEA<br>sansaTrade openness<br>MsCredit toNFCs<br>f |<br>seOilprices<br>Relative income<br>LaggedCAB<br>meeFDI<br>mmREER<br>Terms of trade<br>——change in CAB|
|---|---|
**Table 4: Networks and export participation**

|Dependent variable:|||export pa|rticipation|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ln firm labor productivity|0.0031***<br>(0.0003)||0.0031***<br>(0.0003)||||
|ln firm employment||0.0083***<br>(0.0012)||0.0083***<br>(0.0012)|||
|ln GDP|0.0038***|0.0038***|0.0036***|0.0036***|0.0038***|0.0036***|
||(0.0009)|(0.0009)|(0.0008)|(0.0008)|(0.0009)|(0.0008)|
|ln GDP per capita|0.0016|0.0016|0.0021*|0.0021*|0.0016|0.0021*|
||(0.0013)|(0.0013)|(0.0012)|(0.0012)|(0.0013)|(0.0012)|
|ln distance|-0.0117**|-0.0117**|-0.0106**|-0.0106**|-0.0117**|-0.0106**|
||(0.0047)|(0.0047)|(0.0046)|(0.0046)|(0.0047)|(0.0046)|
|EU member|0.0091|0.0091|0.0099|0.0099|0.0091|0.0099|
||(0.0096)|(0.0096)|(0.0096)|(0.0096)|(0.0096)|(0.0096)|
|common currency|0.0571**|0.0571**|0.0567**|0.0567**|0.0571**|0.0567**|
||(0.0227)|(0.0227)|(0.0228)|(0.0228)|(0.0228)|(0.0229)|
|landlocked|-0.0095**|-0.0095**|-0.0090*|-0.0090*|-0.0095*|-0.0090*|
||(0.0048)|(0.0048)|(0.0048)|(0.0048)|(0.0048)|(0.0049)|
|common language|0.0835**|0.0835**|||0.0835**||
||(0.0348)|(0.0348)|||(0.0349)||
|ln emigrants|0.0014**|0.0014**|0.0016***|0.0016***|0.0014**|0.0016***|
||(0.0006)|(0.0006)|(0.0006)|(0.0006)|(0.0006)|(0.0006)|
|Angola|||0.2850***|0.2850***||0.2850***|
||||(0.0026)|(0.0026)||(0.0026)|
|Brazil|||0.0189***|0.0189***||0.0189***|
||||(0.0063)|(0.0063)||(0.0063)|
|Cape Verde|||0.1328***|0.1328***||0.1328***|
||||(0.0043)|(0.0043)||(0.0043)|
|Guinea Bissau|||0.0334***|0.0334***||0.0334***|
||||(0.0042)|(0.0042)||(0.0042)|
|Macau|||0.0100***|0.0100***||0.0100***|
||||(0.0034)|(0.0034)||(0.0034)|
|Mozambique|||0.0511***|0.0511***||0.0511***|
||||(0.0039)|(0.0039)||(0.0039)|
|Sao Tome and Principe|||0.0512***|0.0512***||0.0512***|
||||(0.0034)|(0.0034)||(0.0034)|
|Firm fixed-effects|No|No|No|No|Yes|Yes|
|Observations|2852926|2852926|2852926|2852926|2852926|2852926|
|Firms|14782|14782|14782|14782|14782|14782|
|Destinations|193|193|193|193|193|193|
|Notes: The estimation met<br>R2|hod is OLS.<br>0.06|Robust stand<br>0.07|ard errors<br>0.08|clustered by<br>0.08|importing c<br>0.11|ountry are in<br>0.12|

<u>Notes: The estimation method is OLS. Robust standard errors clustered by importing country are in</u> parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

3
Nepal introduced the decentralized Community Forestry Programme (CFP) in the late 1980s in the context of serious deforestation and forest degradation, as a centralized approach was not working (Guthman, 1997; Ojha et al., 2007; Hobley, 1996, Springate-Bejinski and Blaikie, 2007; Carter and Gronow, 2005) and a number of scholars have claimed that the Community Forestry Programme forests have in many areas stemmed and reversed deforestation and forest degradation (e.g., Pokharel and Mahat 2007; Yadav et al., 2003; Gautam et al., 2003).

Since 1993 when the first CFP groups were registered, the CFP has expanded to include 18,133 forest user groups, with the involvement of over 2.2 million households (almost 35% of the total) managing 1.7 million hectares of forest. Almost 65% of all forests are in the hills are CFP forests, but only 18% of _Terai_ forests are Programme forests (MOFSC, 2013). This difference reflects a variety of circumstances, including that the hills are historically the more populated areas of Nepal and government officials have been less willing to devolve _Terai_ forests. In its short history of 30 years, the Nepal Community Forestry Programme is believed to have improved forest quality (Branney and Yadav 1998; DoF, 2005; Shrestha and McManus, 2008), expanded community infrastructure, increased incomes (Kanel and Niraula, 2004; Hobley and Jha 2012), and supported conflict resilient and democratic community institutions (Hobley and Jha, 2012) The CFP is a form of local collective action that is formalized by government, but is driven primarily by communities. Joining the CFP requires paperwork, time and negotiation outside the community that is generally costly; communities therefore have skin in the game when they apply for CFP status. For example, using a survey of 309 households belonging to eight different forest user groups in the middle hills of Nepal, Adhikari and Lovett (2006) find 8
(14) and These two equations cannot be solved for individual values of _R1, R2,_ only for their sum. Doing so implies that the total of regulation _R1 + R2_ is set optimally at a point that trades off financial stability for competitiveness. But this can be done through any arbitrary sharing of the regulatory burden. Given this indeterminacy, countries would no doubt prefer that the other country did the regulating. In these circumstances, harmonization would be necessary to rule out a downward spiral of deregulation.

# **IX. Concluding Remarks**

The indicators that we have developed in this paper should prove useful to track changes in confidence as we go forward. Already, there has been a small upturn, as credit spreads have narrowed and stock markets have experienced a substantial rebound. Time will tell whether these improvements prove to be durable. It may be the case that a lasting revival of confidence requires more far-reaching changes to the regulatory environment than have already occurred. So far, reforms have been modest, and the hopedfor agreement on regulatory issues has not yet materialized.

We have argued that the existence of a common shock to confidence can be a powerful motivating force for governments to agree on harmonized regulatory reforms. However, getting there faces a number of difficult hurdles. Most important is the reluctance of countries with major financial centers to weaken their competitive advantage. In the absence of a supra-national regulatory authority, any agreement would have to be on a voluntary basis, and countries face somewhat different regulatory challenges and institutional environments. National regulatory authorities retain responsibility for their financial sectors and will guard their freedom of maneuver. Finally, as confidence improves, even if only partially, the pressure on politicians to undertake a significant remake of the global financial regulatory system and/or make progress on national regulatory reform may wane.

## **References**

Balduzzi, P., E. Elton and T. Green. 2001. “Economic News and Bond Prices: Evidence from the U.S. Treasury Market.” _Journal of Financial and Quantitative Analysis_ 36 (4): 523-43.

Borio, Claudio E.V. and William R. White 2004, “Whither Monetary and Financial Stability? The Implications of Evolving Policy Regimes,” BIS Working Paper No. 147, Basel: Bank for International Settlements, February.

Brandt, Michael W., and Kenneth A. Kavajecz. 2004. “Price Discovery in the U.S. Treasury Market: The Impact of Orderflow and Liquidity on the Yield Curve.” _The Journal of Finance_ 49 (6): 2623-54.

25
production of X is relatively large; and (iii) the cost share of P in production of X is relatively large'<sup>2</sup> . The term l/(a-,i) S 0 is decreasing in a and increasing in rq. Hence, the more price elastic are the supply of X and the demand for X, the larger is the reduction in P for a given tax, **g.** If either a or **ij** is zero, the output effect vanishes and only a substitution effect remains.

Second-Best Policy: A Tax on X. If P cannot be monitored so the tax instrument X is unavailable, a natural alternative is to tax X. The salient difference between the two consumption goods is that production of X uses the environmental input while production of Y does not. Taxing X has no effect on relative -input prices, so production of X takes place along the same isoquant expansion path as in the untaxed regime. Taxing X discourages its consumption, however, and thereby shifts the demand schedule for P Inward. The reduced demand for P then leaves more of the environmental resource in Its natural state, to provide the consumption benefit V.

To determine the welfare effect of a marginal ad valorem tax on X, differentiate (21) with respect to (, and evaluate where t = X = x = 0: awwac = (Ux - CX)aRX/ - c - VQ{(aP/aX)aX/aQ + C, = - V0(aPfPaX)aX/a(}. (29) The expression in brackets is a pure output effect, and the welfare effect of the tax is positive if this output effect is negative. Negativity of the own price elasticity of demand for X implies aX/at < o. and aP/aX > 0 since P is normal.1<sup>3</sup> Hence, a marginal tax on the consumption good that uses the free access environmental resource as an input is welfare enhancing. As shown latpr, 12Note that elasticities ofdemand for P refer to a constant ou,ipRt demand function. 13 The consumer's choice of X satisfies Ux - (1 + CJCx(a,P,X) = 0. Dffferentiating this equation with respect to 4 and evaluating at (= 0 yields, after rearranging, UX/a = CX/(UXX - Cxx) _<_ 0.

12
example, in Ethiopia, Zerfu and Larson (2010) found that high transport costs, among other factors, were instrumental in suppressing inorganic fertilizer use. Also in Ethiopia, Minten, Koru, and Stifel (2013) study the effects of the “last mile” in input markets access, finding that traveling 10 kilometers to procure inputs doubled the price of the input.

Figure 6: Local linear non-parametric regression of total fertilizer use per hectare by distance to nearest major market (in km, geo-referenced) <!-- Start of picture text --> Ethiopia Malawi<br>Local polynomial smooth Local polynomial smooth<br>100<br>200<br>50 100<br>0<br>0<br>-100<br>-50 -200<br>0 100 200 300 0 10 20 30 40<br>distance from household to nearest market (km) distance from household to nearest market (km)<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = 9.73, pwidth = 14.6 kernel = epanechnikov, degree = 1, bandwidth = 1.7, pwidth = 2.55<br>Niger Nigeria<br>Local polynomial smooth Local polynomial smooth<br>100<br>300<br>50<br>200<br>0 100<br>-50 0<br>-100 -100<br>0 50 100 150 200 250 0 50 100 150 200<br>distance from household to nearest market (km) distance from household to nearest market (km)<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = 7.02, pwidth = 10.53 kernel = epanechnikov, degree = 1, bandwidth = 9.25, pwidth = 13.88<br>Tanzania Uganda<br>Local polynomial smooth Local polynomial smooth<br>10<br>60<br>5<br>40<br>0<br>20<br>-5<br>0<br>-10<br>-20<br>0 50 100 150 200 250 0 50 100 150<br>distance from household to nearest market (km) distance from household to nearest market (km)<br>95% CI lpoly smooth 95% CI lpoly smooth<br>kernel = epanechnikov, degree = 1, bandwidth = 12.58, pwidth = 18.88 kernel = epanechnikov, degree = 1, bandwidth = 8.06, pwidth = 12.09<br>household average kg/ha inorganic fertilizer household average kg/ha inorganic fertilizer<br>household average kg/ha inorganic fertilizer household average kg/ha inorganic fertilizer<br>household average kg/ha inorganic fertilizer household average kg/ha inorganic fertilizer<br><!-- End of picture text --> Note: In Malawi, the distance is to the nearest ADMARC (government parastatal) market, not market in general. In Uganda, the 2009/10 geovariables are used as a placeholder in the absence of an updated data set. Inorganic fertilizer application rates are unconditional and represent total, not nutrient, values.

42
<!-- Start of picture text --> z=—— a CHN<br>== — au Rts Wy EST<br>Pini vNM BIH TTo R via<br>FSs uca NHACBTNgi IRQSRB mg akHRV arsvN<br>Bood TH SFA,ML pOrsenia PEA SHESaeES 6TUNERdea "ERDcy BARTAGES HKNEEDS woR TUX<br>8 Gp one MPO E bseA Pav UR =<br>a NGOS 192 Teh Gas<br>a RWA UKR<br>a B01 TK<br>[o)aw coo<br>e<br>Ze o<br>6 re 8 9 10 11 12<br>Log GDP per capita (constant 2015 US$)<br>——— gdp_pc_gr =-1.2284 + .394 log_gdp_pc<br>Note: countries in red = BB; countriesin blue = EM; countries in green = OECD<br><!-- End of picture text --> <!-- Start of picture text -->
#2 MMR AZECHN<br>= ETH WWE<br>= KHM LAO ABAD BLR<br>=3° a _ oeIND nonUKA 4 peragenigttKara dir,wrv —<br>FS COniaer SSS — ae RME IRLRRRTREST, | KOR<br>s NER gy oa” HOG * Sy aN sal haar: ste a<br>S 5 too mona eo JAM GAB oar gsi BR SHE Lux<br>8 car SNR TM cre Kwr<br>a<br>aa sR<br>Owo'<br>e ssD LBY<br>Ze<br>6 7 8 9 10 1 12<br>Log GDP per capita (constant 2015 US$)<br>——— gdp_pe_gr= 5.8167- .374 log_gdp_pc<br>Note: countries in red = BB; countriesin blue = EM; countries in green = OECD<br><!-- End of picture text -->

<!-- Start of picture text --> =O<br>- r<br>= IRL<br>3 ETH ee VNM CHN<br>Bo IND soqpye'on, oom POU<br>gE eeeey<br>G car eeSOMBRE TZA ae Ree cite T ES@R .<br>Bo pero ASER LRA Pt — rep hYO1A7 Trea<br>‘ag MG SOE2i wen”Nga oN agCU BHR crARG SRBE ot Om PSNI<br>.a BDI ie"Teo SON oR no KW<br>20a‘ cos =<br>Lo)<br>o<br>S<br>So<br>qa ssigEM<br>6 7Log GDP per8 capita. (constant9 2015 US$)10 11 12<br>——— gdp_pe_gr= .21569+ .18 log_gdp_pc<br>Note: countries in red = BB; countriesin blue = EM; countries in green = OECD<br><!-- End of picture text -->
<!-- Start of picture text --> fifth in terms of export volume. On the other hand, El Salvador captures a much lower share of<br>the tariff rent at 46%. When we calculate the average preference margins faced by the countries<br>over time, we see that Dominican Republic, Honduras, El Salvador and Costa Rica have similar<br>trade weighted averages of between 10-11.5% (after 1991) while it is only 6% for Guatemala . This<br>implies, the average export price increases due to preferential market access are between 8.5-9.5%<br>for Honduras, Dominican Republic and Costa Rica. On the other hand, the export price increases<br>are around 4.5% for El Salvador (due to lower portion of the rents being captured) and Guatemala<br>(due to lower preference margins)!”.<br>The other three countries (which export significantly lower volumes than the top five) exhibit<br>different patterns. Jamaica actually has a negative and significant coefficient which is probably<br>related to why it is the only country with declining exports to the US. On the other hand, Haiti<br>has a coefficient that is larger but not statistically different than 1 which means it is capturing all<br>of the preference margin of 15%. Thus, it is no surprise that Haiti had one of the fastest growth<br>of export volume to the US in this time frame. Finally, Nicaragua has a coefficient that is not<br>statistically different from zero implying that it receives none of its average 5.7% tariff preference<br>rent.<br>What can explain the different patterns in terms of the tariff preference rents captured? It<br>is natural that larger exporters capture a larger portion of the rent. This result also appears in<br>Olarreaga and Ozden’s [2003] analysis of AGOA preferences in apparel. The next point is about<br>the categories in which these countries specialize. The average export unit prices for Dominican<br>Republic, Honduras, Guatemala and Costa Rica are around $58. Furthermore, the average prices<br>for all US imports in categories where these countries export is $63. On the other hand, the average<br>export price for El Salvador is $37 and the average US import price in these categories is $44. These<br>numbers imply that these four countries, when compared to El Salvador, are specializing in higher<br>value categories and are producing higher quality products relative to average US imports in these<br>categories. This is consistent with the results of Evans and Harrigan [2003] who emphasize the<br>increasing importance of just-in-time manufacturing and retailing in apparel. By specializing in<br>high-value categories where other product features (such as quality, timely delivery and production<br>~ country-level 18Detailed data are not presented due to space limitations, but are available on request.<br>14<br><!-- End of picture text -->
<!-- Start of picture text --> 35 mmm Zambia2019 :<br>: —@— Sub-Saharan Africa i<br>A uv H<br>E20 18<br>10 8<br>0<br>i Access to Electricity Practices of | Corruption Taxrates Customs and Political Tax Access to land Labor H<br>: finance the informal trade instability administration regulations i<br>i sector regulations H<br><!-- End of picture text -->
Fighting corruption was mentioned as the most important reason for the Arab Spring by 64.3 percent of respondents, followed by betterment of the economic situation (63.4 percent) and social and economic justice (57.2 percent). These findings are in line with a poll by Zogby in 2005, in which respondents in developing MENA countries indicated that the lack of employment opportunities, corruption, health care, and schooling were seen as the most pertinent problems in developing MENA countries (Zogby 2005). Strikingly, civil and political freedom (42.4 percent) only comes in fourth place and is, hence, neither found associated with dissatisfaction in developing MENA nor regarded as one of the most important factors related to the uprisings. Likewise, relations with the West (7.5 percent) and Israel (14.6 percent) as well as rule of law (15.7 percent) and dignity (28.8 percent) were less often mentioned as important reasons for the Arab Spring, and were not found to be an important determinant of dissatisfaction with life in developing MENA. Hence, standards of living, labor market conditions, and ‘wasta’ are not only strongly related to dissatisfaction with life prior to the Arab Spring, but also mentioned as the main reasons for the Arab Spring uprisings.

In sum, it can be concluded that the Arab Spring uprisings in developing MENA countries were preceded by a decline in life satisfaction from already low happiness levels, despite economic and human development progress in the prior two decades. In many developing MENA countries, the so-called “unhappy development” paradox was accompanied by social discontent driven by poor or worsening standards of living, labor market conditions, and crony capitalism. In this light, our study highlights that not only objective conditions count, but also the subjective awareness of shortcomings in these objective conditions. The rising awareness of social ills is partly due to the modernization process in which society is seen to be less of a moral order given by God, and in which an increasing number of educated people call for meritocracy rather than autocracy.

Dissatisfaction alone does not bring political action, which typically arises only in combination with perceived chances for change (Klandermans 1997). This paper does not explore the question why some developing MENA countries experienced political violence and fall of regimes, whereas in other developing MENA countries the protests remained rather limited. This question should be addressed in future research.

# **References**

<mark>Angel-Urdinola, D. F., and A. Kuddo. 2011. “Key Characteristics of Employment Regulations in the Middle East and North Africa.” MNA Knowledge and Learning Fast Brief No. 84, World Bank, Washington, DC.</mark> 24
<!-- Start of picture text --> Proportion of Foreign Educated<br>Physicians in total US Physician<br>Workforce, 1970-2004<br>so<br>25<br>4 20<br>-_<br>eoo45<br>+<br>Le)<br>5<br>o<br>1sTo 1950 1940 zo00 200d<br>Tear<br><!-- End of picture text -->
power sector shares of total national emissions increased due to the reduction of these countries’ reliance on hydropower. The reverse is the case for Ecuador and Paraguay, and currently, Paraguay is reliant exclusively on hydropower for its electricity generation.

**Table 1: Contribution of the Transport Sector in National CO2 Emissions**

||198<br>TlCO|0|200<br>TlCO|5|
|---|---|---|---|---|
|Country|ota2<br>emissions from<br>fossil fuel<br>consumption<br>(MtCO2)|Share of the<br>transport<br>sector in the<br>total (%)|ota2<br>emissions from<br>fossil fuel<br>consumption<br>(MtCO2)|Share of the<br>transport<br>sector in the<br>total (%)|
|Argentina|95.9|32.7|140.9|28.0|
|Bolivia|4.3|49.1|11.9|30.3|
|Brazil|178.0|41.1|329.3|42.1|
|Caribbean<sup>a</sup>|30.2|19.5|55.3|19.2|
|Chile|21.2|28.8|58.6|29.2|
|Colombia|33.8|34.5|59.9|33.2|
|Costa Rica|2.2|61.2|5.4|75.2|
|Cuba|28.5|21.8|23.8|8.1|
|Ecuador|10.6|37.6|23.4|45.5|
|El Salvador|1.7|50.9|5.9|49.3|
|Guatemala|4.2|31.9|10.5|46.1|
|Honduras|1.7|36.7|6.4|32.5|
|Mexico|212.8|32.1|389.4|33.7|
|Nicaragua|1.8|49.4|4.1|35.1|
|Panama|2.9|34.9|5.7|41.2|
|Paraguay|1.4|79.8|3.4|87.3|
|Peru|20.6|28.7|28.4|34.0|
|Uruguay|5.6|30.0|5.3|43.2|
|Venezuela|92.4|29.2|142.3|29.3|
|Other Latin America<sup>b</sup>|10.0|9.9|17.2|30.9|
|Total|759.7|33.1|1,327.1|34.1|
|Source: IEA (2007)|||||

a Caribbean includes Dominican Republic, Haiti, Jamaica, Netherland Antilles, and Trinidad and Tobago. b Other Latin America includes Antigua and Barbuda, Aruba, Bahamas, Barbados, Belize, Bermuda, Dominica, French Guyana, Grenada, Guadeloupe, Guyana, Martinique, St. Kitts and Nevis, Anguilla, Saint Lucia, St. Vincent and Grenadines, and Surinam.

Source: IEA (2007) At the regional level, total CO2 emissions from the transport sector more than doubled from 251 million tons to 453 million tons in 1980-2005 with an average annual growth rate of 2.4%. Given the size of Brazil’s economy and population, it comes as no surprise that its transport sector has been the largest CO2 emitter, and also the largest consumer of energy, 6