rafmacalaba/datause-encoder-data huggingface dataset:
- Fortems-Cheiney, A., Saunois, M., Pison, I., Chevallier, F., Bousquet, P., Cressot, C., Montzka, S. A., Fraser, P., Vollmer, M. K., Simmonds, P. G. _et al._ (2015) Increase in hfc-134a emissions in response to the success of the montreal protocol, _Journal of Geophysical Research: Atmospheres_ , **120** , 11–728.

- Frankel, J. A. and Rose, A. K. (2005) Is trade good or bad for the environment? Sorting out the causality, _The Review of Economics and Statistics_ , **87** , 85–91.

- Gonzalez, M., Taddonio, K. N. and Sherman, N. J. (2015) The montreal protocol: how today’s successes offer a pathway to the future, _Journal of Environmental Studies and Sciences_ , **5** , 122– 129.

- Hannesson, R. (2000) Renewable resources and the gains from trade, _Canadian Journal of Economics/Revue canadienne d’ ́economique_ , **33** , 122–132.

- Hofmann, C., Osnago, A. and Ruta, M. (2017) Horizontal depth: A new database on the con- tent of preferential trade agreements, Policy Research Working Paper WPS 7981, World Bank, Washington, DC.

- Holmes, G. J., Mansouripour, S. M. and Hewavitharana, S. S. (2020) Strawberries at the crossroads: Management of soilborne diseases in california without methyl bromide, _Phytopathology_ , **110** , 956–968.

Hotte, L., Van Long, N. and Tian, H. (2000) International trade with endogenous enforcement of property rights, _Journal of Development Economics_ , **62** , 25–54.

Ivanova, K. (2007) Corruption, illegal trade and compliance with the montreal protocol, _Environmental and Resource Economics_ , **38** , 475–496.

- Kreickemeier, U. and Richter, P. M. (2014) Trade and the environment: The role of firm heterogeneity, _Review of International Economics_ , **22** , 209–225.

- Leblois, A., Damette, O. and Wolfersberger, J. (2017) What has driven deforestation in developing countries since the 2000s? Evidence from new remote-sensing data, _World Development_ , **92** , 82–102.

- L ́opez, R. and Galinato, G. I. (2005) Trade policies, economic growth, and the direct causes of deforestation, _Land Economics_ , **81** , 145–169.

- Maggi, G. (2016) Issue linkage, in _The Handbook of Commercial Policy_ (Eds.) K. Bagwell and R. W. Staiger, Elsevier, Amsterdam, Netherlands, vol. 1B, chap. 9, pp. 513–564.

- Managi, S., Hibiki, A. and Tsurumi, T. (2009) Does trade openness improve environmental quality?, _Journal of Environmental Economics and Management_ , **58** , 346–363.

- Mattoo, A., Rocha, N. and Ruta, M. (2020) _Handbook of Deep Trade Agreements_ , World Bank, Washington, DC.

- McKenzie, R., Bernhard, G., Liley, B., Disterhoft, P., Rhodes, S., Bais, A., Morgenstern, O., Newman, P., Oman, L., Brogniez, C. _et al._ (2019) Success of montreal protocol demonstrated by comparing high-quality uv measurements with “world avoided” calculations from two chemistryclimate models, _Scientific Reports_ , **9** , 1–13.

22
learning-by-doing arguments wherein CGSs could foster informational spillovers in markets where SME lending is not well developed because lenders do not have the skill/experience and the stock of credit information to lend to SMEs.

While the use of CGSs has been increasing all around the world, there have been very few rigorous evaluations undertaken largely due to the challenges in identifying an appropriate control group, so that firms which have accessed guaranteed loans can be analyzed against other firms which have not benefited from guarantees. The key question related to the impact of CGSs is whether they lead to _additionality,_ that is, whether they generate additional loans for targeted firms and allow them to borrow at better terms. Most empirical studies find evidence of financial additionality as seen in the case of Chile’s FOGAPE (Cowan, Drexler, and Yañez (2015)), Canada (Riding, Madill, and Haines, 2007), the Special Credit Guarantee Program in Japan (Wilcox and Yasuda, 2008), the Small Firms Loan Guarantee in the U.K. (Cowling, 2010), and the U.S. Small Business Administration (Hancock, Peek, and Wilcox, 2007).

A case in point is the Mullins and Toro (2017) analysis of Chile’s credit guarantee scheme for bank loans to small and medium enterprises (SMEs). They carefully use a regression discontinuity around the eligibility cutoff and find that credit guarantees have large positive effects on firms’ total borrowing without large increases in default rates. They also find that the scheme has a strong amplification effect: firms increase borrowing from other banks in the 18 months following a loan guarantee; firms receiving guarantees build new bank relationships; and firms use the credit increase to expand their operations. Since Chile’s scheme has many 34
14). The globalizing developing countries here are the top third of developing countries in terms of increased trade integration over the past 20 years (Dollar and Kraay 2004). The nonglobalizers are the rest of the developing world. The point is that the fastest wage growth is occurring in developing countries that are actively increasing their integration with the global economy.

Figure 14. Poor countries that globalized have seen the fastest growth in wages Wage growth between 1980s and 1990s (percent) <!-- Start of picture text --> 30<br>20<br>10<br>0<br>Less globalized Rich countries More globalized<br>countries countries<br><!-- End of picture text --> While the general rise in wages is good news, however, the detailed findings from the Freeman, Oostendorp, and Rama study are more complex and indicate that certain types of workers benefit more than others. First, increased trade is related to a decline in the gender wage gap. More trade appears to lead to a more competitive labor market in which groups that have been traditionally discriminated against, eg., women, fare especially well (Oostendorp, 2002).

Second, the gains from increased trade appear to be larger for skilled workers. This finding is consistent with other work showing that there has been a worldwide trend toward greater wage inequality – that is, a larger gap between pay for educated workers and pay of less 25
Table 7: IV estimates of the effect of ICT use on the Number of workers: Manufacturing

|(1)|(2)|
|---|---|
|**Dependent variable:** ∆**ln(Number of white-collar **|**workers)**|
|∆Share of labor that uses computers<br>0.0116***<br>(0.0029)|0.0128***<br>(0.0031)|
|F- frst stage<br>40.43|38.71|
|Sargan p-value<br>0.4598|0.4251|
|∆Share of labor that uses Internet<br>0.0097***|0.0107***|
|(0.0023)|(0.0023)|
|F- frst stage<br>73.39|70.25|
|Sargan p-value<br>0.6130|0.5853|
|∆ln(stock of computer equipment/worker)<br>0.3110***|0.5972***|
|(0.0928)|(0.2041)|
|F- frst stage<br>26.36|11.19|
|Sargan p-value<br>0.6596|0.7048|
|**Dependent variable:** ∆**ln(Number of blue-collar **|**workers)**|
|∆Share of labor that uses computers<br>0.0039*|0.0080***|
|(0.0022)|(0.0024)|
|F- frst stage<br>40.43|38.71|
|Sargan p-value<br>0.1677|0.3352|
|∆Share of labor that uses Internet<br>0.0029*|0.0063***|
|(0.0017)|(0.0018)|
|F- frst stage<br>73.39|70.25|
|Sargan p-value<br>0.1337|0.2164|
|∆ln(stock of computer equipment/worker)<br>-0.0607<br>(0.0588)|0.1139<br>(0.1209)|
|F- frst stage<br>26.36|11.19|
|Sargan p-value<br>0.1856|0.1079|
|**Controls**||
|ln(capital/worker)<br>No|Yes|
|Observations<br>11,438|11,438|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

31
Khan, S.R. and Kiefer, D. (2007) “Educational Production Functions for Rural Pakistan: A Comparative Institutional Analysis”, _Education Economics_ , 15 (3): 327-342.

- Gauri, V. and Galef, J. (2005) “NGOs in Bangladesh: Activities, resources, and governance” _World Development_ , 2005, vol. 33, issue 12, pages 2045-2065.

- Fruttero, A. and Gauri, V. (2005) “The Strategic Choices of NGOs: Location Decisions in Rural Bangladesh”, _The Journal of Development Studies_ , 2005, vol. 41, issue 5, pages 759-787.

- Khwaja, Asim, T. Andrabi and J.Das (2007) “Students Today, Teachers Tomorrow? Identifying Constraints on the provision of Education”.  Harvard University (mimeo).

- Sukontamarn, Pataporn (2005) “The Entry of NGO Schools and Girls’ Educational Outcomes in Bangladesh”, STICERD, LSE.

- World Bank (2006) “Economics and Governance of Nongovernmental Organizations in Bangladesh”, Poverty Reduction and Economic Management Sector Unit South Asia Region.”

- World Bank, 2003. _Making Services Work for Poor People,_ World Development Report 2004, Washington, D.C., World Bank.

15
- 68 -

by air, thus partially dispensing with the need for a road. There<sup>are</sup> precedents for this elsewhere in South America, but Conoco questions their application to the Yasuni case. The company argues that the clearcut<sup>trail</sup> required for the pipe-laying equipment would still provide access for colonists.

Under the contractual arrangement covering exploration and exploitation, CEPE rather than Conoco would ultimately bear the extra costs of an air-built pipeline.

# 6.10. MBARACAYU WILDLANDS AREA - PARAGUAY

(The main source for this section is Hill, 1988) Mbaracayu is a cooperative project that originated in the continuing attempt by a group of Ache Indians to retrieve lands from which they were removed during the 1970's. The area, a territory traditionally occupied and used by the Ache, is a 58,000 ha tract of forest that was purchased by an Argentinian lumber company and developed with the help of a loan from the International Finance Corporation (IFC), the private sector equity arm of the World Bank group.

In 1979, the lumber company defaulted and the equity reverted to the IFC which has since then engaged a local security company to prevent logging. A team of anthropolists studying the Ache estimated that there were Indian habitation sites in the area and 350 Indians who used it frequently. This larger group was based at a 1,500 ha Catholic mission, 15 km from Mbaracayu.

After consultation with the Ache, in 1986, anthropologist Hill began to explore the prospect of converting Mbaracayu into a conservation unit. The Nature Conservancy and the Paraguayan Centro de Datos de Conservation (CDC) were supportive and on this basis, Hill undertook to outline the various possibilities to the Ache, with the focus upon a possible national park.

The Ache were unfamiliar with the concept of a national park and were most concerned about a loss of hunting rights if its main purpose was to protect animals. But they were interested in the possibilities of employment and were willing to refrain from hunting certain species in exchange for such economic benefits. Past studies revealed that none of the 4-5 main species taken by Ache hunters were listed as rare or endangered; in fact, such species comprised less than 1X of the Ache diet.

But the Ache insisted on gaining title to a tract of land near to the park which they could use for their own purposes. They asked for 5,000 ha. but eventually agreed to accept 3,000 ha. The CDC could not guarantee this but agreed to convey the request to the Nature Conservancy, which was funding the planning work.

After a survey by CDC staff, in which Ache were contracted for guiding and logistics, the community presented their position and ideas on the project in the form of a five year plan (Hill 1987a). Its three major provisions are:
# **Table 5a**

# Argentina: Annual **Indicators**

' 1970-1974 1975-1979 227.58 38.30 3-47 4.60 15.10 1.84 8.36 8.88 45.53 6.84 0.44 2.07 112.44 40.19 4.93 40.35 0.96 141.52 135,50 187.39 -0.07 0.38 1980-1882 123.34 -3.39 5.37 7.07 SO.S1 5.54 -3.47 1.26 96.03 140.59 2.42 198 **3** 43.82 2.92 8.61 5.12 55.59 10.71 -3.77 5.75 96.20 218.85 -0.46 1984 626.72 2.46 7.12 4.01 51.86 7.65 -3.21 5.12 97.00 184.78 -4.58 1985 672.15 -4.38 6.51 3.89 52.09 2.16 -1.46 7.41 89.80 207.83 -6.36 t98B 90.10 5.40 3.46 5.20 50.58 2.22 -3.6 **3** .10 85.30 200.51 -6.04 1987 131.33 1988 342.96 t989 307.a1 Mi~~...~ -3.77 9.00 3.05 33.26 3.27 -2.16 9.47 89.60 -2.72.37 4.03 4.43 50.0 _ANR_ **_.All..'_ 5** .17 3.24 37.53 **_.vp_** ¢) 5.10 -1.75 4.73 86.20 **5** .54 -5.13 1.23 81.80 215.13 222.21 165.25 -4.99-2.54-5.04 1990 23t3.97 -t.96 4.81 2.38 31.08 0.97 2.29 11 **.** 05 n.a 176.09 ;3.57O SNW*-P _Based In Mif. .UNIC Defict .Ovral ff tt, op ot th Con.kfltPul,k ncto. tS701IM FfEL. ON tf..g.l ExWf Ssefot A NOftEX Exchag Rteh :tNomf *xdw rof. odkbf mrket rfof r.a ttS Term of Trade_ T em _dX0 trade_ Indx 100- OD _AHSREt R*ealExchRt Rel fmANf nf W hai rest aindswh rcstMtothp top ImytyradingprNtra 1flS0sStI Hrt_ n" _o Worldf_ Dow _tab/ea, stion tW lorng wm not transfrs truiefrtv ftsF. sterf i n IMS it esxclds rtg trwntf 19: 19371-890 4_
ability, rather than local conditions such as geographical terrain or the level of economic development.<sup>30</sup> Still, some variation of the location (and timing) of family planning center may stem from differences in local governments’ resources: communes with more resources might have been more likely to build a family planning center earlier. We argue, however, that once this channel is controlled for in the regressions (as proxied for by commune infrastructure in several model specifications we examine later), the location of the family planning center is exogenous to each household’s decision on number of children. While it is impossible to directly test for the instrument’s exogeneity, we use a three-pronged approach for extra precaution to ensure its validity. First, we use the distance to family planning centers in 2002 to instrument for the impacts of family size on household investments in education four years later, in 2006, which can help reduce any contemporaneous correlation between the former and the latter.

Second, as a check we will also restrict our analysis later to a sample where the family planning centers had already been established before the 1997 round of the household survey. If family planning centers were more likely to be first established in locations with stronger demand for family planning, older centers would be more effective in reducing family size and would consequently allow households to increase investment in their children’s education. Thus an analysis showing similar impacts of family size for the sample with older centers compared to those for the overall sample would provide evidence for the instrument’s exogeneity.<sup>31</sup>

> 30 San et al. (1999) also provide some evidence that their selected 15 provinces share many characteristics of the overall functioning of the national family planning program.

> 31 This check does not hold in the opposite direction since older centers may also be effective through other channels that are uncorrelated with endogeneity of location (e.g., longer existence simply increases the chances

18
# **5.2 Early Development Instrument (EDI)**

Figure 2 presents gender gaps in each domain of the EDI for the sample of 6-8 year old children observed in 2013. Gender gaps in the EDI are statistically significant for the social competence, emotional maturity, and language and cognitive development domains at all ages. However, the magnitude of this difference varies across the domains. For social competence, the gender gap ranges from 0.166 SD at age 6 to 0.247 SD at age 8. Similarly for emotional maturity, the gender gap ranges between 0.279 SD at age 6 to and 0.296 SD at age 8. The gender gap in the language and cognitive domain is smaller: at age 6 it is 0.157 SD and shrinks down to 0.089 SD at age 8. For the physical health and well-being domain, there is no gender difference at age 6 and 7 but a slight statistically significant gender gap (0.068 SD) is apparent at age 8. Finally, for the communication skills and general knowledge domain, we also only observe a significant gender gap of 0.094 SD at age 8.

Thus, the results for test scores and EDI suggest the presence of gender gaps in both cognitive and non-cognitive skills in the first few years of primary school in rural Indonesia. At age 6, we already observe gender gaps in language and mathematics test scores as well as in children’s social competence and emotional maturity. Given the existence of early gender gaps in rural Indonesia, we now examine whether there are gender differences in enrollment patterns and parenting practices to see if early schooling and family environment can be analyzed further as explanatory factors of the gender gap in test scores and EDI.

# **5.3 Education enrollment**

For each child enrolled in primary school, we collected enrollment histories with regard to enrollment in education, including preschool education, starting from age two. These are used to construct data on enrollment by age. Two variables are constructed. The enrollment variable indicates whether or not the child was enrolled at any time, and the number of months of enrollment indicates the number of months the child was enrolled over a 12-month period.

Figure 3 reports the gender gaps and associated 95% confidence interval at each age for children in our sample both in terms of whether or not children are enrolled and in terms of duration of enrollment. At age 2, the earliest age for which we have enrollment histories, there is no observed gender gap. At age 3, girls are 4 percentage points more likely to be enrolled in preschool than boys, a point estimate that increases to 6.7 percentage points at age 4. At age 5 they are 5.7 11
Comparison between urban and rural areas within each of the regions is analogous to that of national level: the concentration profile uses weighted parameters from specifications (1’) and (2’) and the means for urban and rural areas of the respective region, while the geographic profile was obtained by setting the mean at the regional level and the parameters estimated using (1’) and (2’). To compare across the regions of the same area (urban/rural), the concentration measure uses the means of each region only for the area of interest (urban/rural) and weighted returns for the two regions in the comparison from the regressions that were estimated separately for each region. The geographic effect uses the weighted means for the two regions in the comparison and the estimated coefficients for each region and area of interest. The results are presented below.

In the Sierra and Costa, the disparities in the concentration profile between urban and rural areas are larger than the differences in the geographic profile, which means that _characteristics_ are the major explanation behind welfare differentials in urban and rural areas. In the Oriente, which is mainly rural (approximately 61 percent of households), both returns and characteristics are important in order to explain the differences in welfare ratio between urban and rural sector (Figure 1). The same conclusions emerged using the poverty rates shown in Table 10.

**Figure 1. Decomposition of Welfare Ratios (logarithms) Within Regions**

<!-- Start of picture text --> Sierra Costa<br>1.0 1.0<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0.0 0.0<br>Geographic Concentration Unconditional Geo graphic Co ncentratio n Unco nditio nal<br>Urban Rural Urban Rural<br>Oriente<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Geo graphic Co ncentratio n Unco nditio nal<br>-0.2<br>-0.4<br>-0.6<br>Urban Rural<br> Source: Own calculations based on ECV Survey.<br><!-- End of picture text --> 14
# **Table 3 Estimates of the capital stock by sector in 1952**

Billion yuan, current prices

||Chow|Liu and<br>Yeh|Chen|Feenstra<br>_et al._|Wu|
|---|---|---|---|---|---|
|**Published estimates**||||||
|Agriculture|45.0|40.9|45.0|..|48.0|
|Industry|15.9|8.7|15.8|..|12.8|
|Trade|1.2|1.0|0.2|..|..|
|Construction|0.3|0.7|..|..|..|
|Other service sectors and transport|..|..|..|..|99.2|
|Business sector|62.4|49.9|61.0|..|..|
|Transport|14.2|13.9|13.9|..|..|
|Business sector and infrastructure|76.6|63.8|74.9|..|160.0|
|**Supplementary estimates**||||..||
|Government<sup>1</sup>|13.6|13.6|13.6|..|16.5|
|Housing<sup>2</sup>|69.4|69.4|69.4|..|69.4|
|**Estimates by asset type**||||||
|Structures|..|..|..|148.6|..|
|Machinery|..|..|..|20.3|..|
|Transport equipment|..|..|..|6.8|..|
|Whole economy|159.6|146.8|157.9|175.6|281.9|
|**Calculated capital-output ratios**||||||
|Business sector|0.9|0.7|0.9|..|..|
|Infrastructure|0.2|0.2|0.2|..|..|
|Business sector plus transport|1.1|0.9|1.1|..|2.4|
|Government|0.2|0.2|0.2|..|0.2|
|Housing|1.0|1.0|1.0|..|1.0|
|Structures|..|..|..|2.2|..|
|Machinery|..|..|..|0.3|..|
|Transport equipment|..|..|..|0.1|..|
|Total|2.3|2.2|2.3|2.6|3.6|
|**Memorandum item**||||||
|GDP|67.9|67.9|67.9|67.9|67.9|

_Notes:_ 1 The capital-output ratio for government is assumed to be the same as that in India in 1975 as calculated in Arestoff and Hurlin (2006).

2 Estimate taken from Herd (2013) adjusted for revisions in data.

_Source_ : See bibliography 6
**_<u>Table 2: Parameter values, changes and predicted growth effects (real GDP per capita).</u>_**

|||**2000**|**-13**|**Early 2**|**000s**|**Late 2**|**000s**|**Early **|**2010s**|
|---|---|---|---|---|---|---|---|---|---|
||**parameter**|**change **|**predicted**<br>**effect**|**change **|**predicted**<br>**effect**|**change **|**predicted**<br>**effect**|**change **|**predicted**<br>**effect**|
|_Persistence_|_0.781_|_0.005_|_0.28%_|_-0.010_|_-0.82%_|_0.039_|_3.07%_|_0.041_|_3.20%_|
|_Structural:_|||_3.84%_||_2.52%_||_3.01%_||_1.20%_|
|Δln(schooling)|0.018|0.064|0.20%|0.094|0.17%|0.090|0.16%|0.008|0.01%|
|Δln(credit/GDP)|0.074|-0.013|-0.16%|0.012|0.09%|-0.009|-0.07%|-0.042|-0.31%|
|Δln(trade/GDP)|0.082|0.020|0.28%|0.052|0.42%|0.019|0.16%|-0.012|-0.10%|
|Δln(govt C)|-0.262|-0.040|1.79%|0.009|-0.24%|-0.041|1.08%|-0.086|2.26%|
|Δln(tele lines)|0.141|0.072|1.74%|0.147|2.07%|0.119|1.68%|-0.051|-0.72%|
|Δln(institutions)|-0.003|0.000|0.00%|0.000|0.00%|0.000|0.00%|-0.000|0.00%|
|_Stabilization:_|||_-0.28%_||_0.09%_||_-0.58%_||_0.01%_|
|Δln(inflation)|-0.011|0.052|-0.10%|0.055|-0.06%|0.285|-0.32%|-0.182|0.21%|
|Δln(exch rate)|-0.064|0.016|-0.18%|-0.023|0.15%|0.040|-0.26%|0.031|-0.20%|
|Δln(bank crisis)|-0.040|0.000|0.00%|0.000|0.00%|0.000|0.00%|0.000|0.00%|
|_External:_|||_0.34%_||_-0.55%_||_0.95%_||_0.18%_|
|Δln(TOT change)|0.118|0.016|0.32%|-0.047|-0.55%|0.081|0.95%|0.014|0.16%|
|Δln(commodity prices)|10.482|0.000|0.01%|0.000|0.00%|0.000|0.00%|0.000|0.02%|
|**Predicted average annual**|**GDP per**|||||||||
|**capitagrowth rate**|||**4.18%**||**1.24%**||**6.45%**||**4.53%**|

Source: Authors’ calculation, obtained by inserting Ethiopian values for the explanatory variables and using the regression coefficients of the baseline model presented in Table 1 (column 1).

12