rafmacalaba/datause-encoder-data huggingface dataset:
34 conducive to higher participation rates and better social inclusion. But their effectiveness and sustainability are contingent to a broader industrialization strategy.<sup>21</sup> Lin and Monga (2011) suggest a practical six-step growth identification and facilitation (GIF) framework to help policy makers in Sub-Saharan Africa and in other developing regions identify reliable growth paths and facilitate the emergence of employment-generating industries: First, identify those tradable goods and services that have existed for a period of about 20 years in dynamically growing countries that have similar endowment structures but with a per capita income that is about double their own. The focus should be not on natural resources which do not directly create many employment opportunities but on labor-intensive industries that can be relocated across borders. Simply looking at Sub-Saharan Africa’s GDP per capita of $1,200 in 2011,<sup>22</sup> it appears that a relatively large group of upper middle-income countries such as Peru, Brazil, Chile, China, Malaysia, or Thailand could be good targets/anchors for its job creation strategy.

In a globalized world where the private firms that are competitive in international markets must cluster in sectors consistent with their country’s comparative advantage (keep both production costs and transaction costs in check), certain specific sectors must be given priority. Government capacity must be devoted to the facilitation of these carefully selected industries—and to a small number of localized reforms that are realistic in any given sociopolitical context. By facilitating the emergence and clustering of competitive private firms in some geographic locations with excellent logistics (without using the distortive instruments of old industrial policies such as tariffs or other forms of heavy protection), governments can foster the backward and forward linkages that brings capital and knowledge to national economies. Ultra-selectivity in the choice of potentially competitive industries and reforms therefore requires both realism and boldness in public policies: focusing on a limited number of initial sectors with high likelihood of success (because they are consistent with a country’s comparative advantage) is the most sustainable strategy for economic development (Lin 2012a, 2012b).

> 21 For a critical review, see Betcherman et al. (2001).  The debate over ALMP is often framed in terms of their supposed superior benefits when compared to _passive_ labor market policies such as social transfers or unemployment insurance, which are typically designed to mitigate the pain of unemployment and provide financial support to the unemployed—without directly improving their skills and marketability. 22 Source: World Development Indicators.
with both exports and EPA budgets, which will also result in spurious correlations between the two variables of interest. Third, the direction of causality might go from exports to the EPA budgets, as countries with higher exports might tend to provide more generous funding to their EPAs than other countries.

The estimation of a Heckman selection equation, which explains why some countries were not surveyed, and why some agencies did not answer, addresses the potential selection bias (Heckman, 1979). Our experience collecting contact information for EPAs helped us identify variables that should be part of this selection equation. It was clear that obtaining contact information for the relevant agency in poor and small economies was difficult, and even when we did, it was difficult to get them to answer the survey. So GDP per capita and GDP are part of the selection equation. Aid per capita also seemed to be an important selection variable because many of the poorest agencies were substantially funded by bilateral and multilateral donors. More formally, the selection equation that explains the latent variable _zc_<sup>_∗_,which</sup> captures the likelihood of obtaining a response to the export promotion survey in country _c_ , is given by: where _ξ_ is a vector of parameters and **x** _c_ is a matrix of independent variables determining the probability that the EPA in country _c_ answered the survey. The latter includes explanatory variables of the export equation (see below), except the budget of the EPA and the activities of the agency that help us identify the export equation, plus the log of GDP, the log of aid per capita discussed above, and regional dummy variables. The two exclusion restrictions are aid per capita and GDP. Admittedly, economic size and aid per capita (our exclusion restrictions) may be correlated with exports. However, the coefficient on aid per capita is not statistically significant after controlling for the presence of EPAs in the export equation. This suggests that it is a valid exclusion restriction.

8
Consistent with a limited trade openness, Brazil’s participation in global value chains (GVCs) is low and is relatively stronger on the seller (forward) side than on the buyer (backward) side. GVCs represent a new path for trade, whereby a country does not need the capability to produce an entire export good but instead contributes a segment of its production process. The latest OECDWTO TiVA data for 2011 suggest that Brazil’s participation in GVC is reduced when compared to international peers and is relatively stronger on the seller (forward) side than on the buyer (backward) side.<sup>8</sup>

**Figure 3. Participation in GVC Brazil vs comparator countries, 2011 (value added as share of domestic and foreign gross exports)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Forward Backward<br><!-- End of picture text --> Source: World Bank staff elaboration using OECD TiVA data The limited openness to trade reflects the use of protectionist trade policies. This includes high tariff barriers to import. Brazil’s average (trade-weighted) tariff rate was 8.3 percent in 2015, the highest rate in comparison to other emerging and advanced economies (Figure 4).<sup>9</sup> Overall, apart from the trade liberalization episode of the early 1990s, when tariffs fell from extremely high levels of 90 percent to 20 percent in wearing apparel, and except for small occasional amendments – such as the inclusion of tariff hikes in 1995 to account for the Mercosur list of exceptions, the generalized tariff increase in 1997 and the temporary rise (for 100 products) in 2012 - the import tariff structure in Brazil has not seen major changes since 2004 (Figure 5).

> 8 The first measure captures the foreign value-added content embodied in Brazil’s gross exports, while the second one is measured as domestic value added embodied in the third-party country’s gross exports.

> 9 This number considers bilateral preferences. The simple average MFM tariff rate was 13.5 for Brazil in 2016.

4
- Gatti, Roberta, Kathryn Andrews, Ciro Avitabile, Ruben Conner, Jigyasa Sharma, Andres Yi Chang. 2021. _The quality of health and education systems across Africa: Evidence from a decade of service delivery indicators surveys_ . Washington, DC: World Bank.

- Grogan, Louise. 2009. “Universal primary education and school entry in Uganda.” _Journal of African Economies_ 18(2): 183-211.

- Hoogeveen, Johannes, and Mariacristina Rossi. 2013. “Enrolment and grade attainment following the introduction of free primary education in Tanzania.” _Journal of African Economies_ 22(3): 375-393.

- Hoogeveen, Johannes and Mariacristina Rossi, eds. 2019. _Transforming education outcomes in Africa: Learning from Togo_ . Springer Nature.

- Keats, Anthony. 2018. “Women's schooling, fertility, and child health outcomes: Evidence from Uganda's free primary education program.” _Journal of Development Economics_ 135: 142159.

- Johnson, David, Andrew Cunningham, and Rachel Dowling. 2012. “Teaching Standards and Curriculum Review: Final Report.” Report No. 174257. World Bank Group, Washington, DC.

- Jones, Sam, Youdi Schipper, Sara Ruto, and Rakesh Rajani. 2014. “Can your child read and count? Measuring learning outcomes in East Africa.” _Journal of African Economies_ 23(5): 643-672.

- Lincove, Jane Arnold. 2012. “The influence of price on school enrollment under Uganda's policy of free primary education.” _Economics of education Review_ 31(5): 799-811.

- Le Nestou <mark>r, Alexis, Laura Moscoviz, Justin Sandefur. 2022. “The long-run decline of education quality in the developing world” CGD Working Paper 608. Washington, DC: Center for Global Development.</mark>

- Lucas, Adrienne M., and Isaac M. Mbiti. 2012a. “Access, sorting, and achievement: The shortrun effects of free primary education in Kenya.” _American Economic Journal: Applied Economics_ 4(4): 226-53.

- Lucas, Adrienne M., and Isaac M. Mbiti. 2012b. “Does free primary education narrow gender differences in schooling? Evidence from Kenya.” _Journal of African Economies_ 21(5): 691-722.

- MacJessie-Mbewe, Samson. 2002. “An analysis of free primary education reform policy in Malawi.” _International Journal of Educational Reform_ 11(2): 94-105.

- Makate, Marshall, and Clifton Makate. 2016. “The causal effect of increased primary schooling on child mortality in Malawi: Universal primary education as a natural experiment.” _Social Science & Medicine_ 168: 72-83.

19
37 Table **4.** Reduced **Foim** Equations for Current Contraceptive<sup>Choice for Tunisia</sup>

|Variables|| Pills/Condom/<br>I<br>Vaginal|IUD|Traditionall<br>Methods|Sterilization|
|---|---|---|---|---|
|Age15-25|-0.24(1.22)|-0.20(1.16)|-0.98(4.64).'|-3.25 (5.59)0*|
|Age 26-30|0.12 (0.68)|0.27 (1.74)'|-0.48(2.58)"'|-1.43 (5.54)..|
|Age 31-35|0.36 (2.04)"-|0.31 (2.03)"|-0.23(1.44)|-0.15(0.85)|
|Educationspouse|0.01(0.51)|004 (1.96)"|0.13 (4.13)"'|-0.04 (0.94)|
|Education 4 to 6|-0.15(0.71)|-0.03(0.17)|0.04 (0.17)|-0.02(0.08)|
|Education 7+|0.23 (0.78)|-6.08(0.36)|-0.14 (0.35)|-0.13(0.30)|
|Rural age 12|-0.16(0.90)|-0.18(1.19)|-0.42 (2.06)*|0.04 (0.21)|
|Small city|-0.02(0.06)|-0.09(0.51)|-0.05(0.18)|0.55 (1.79)-|
|Urban|0.60 (0.62)|-0.42(0.56)|0.69 (0.61)|-1.89(1.94)'|
|Remote|-0.13 (0.41)|-0.44(1.85)-|-0.56(1.47)|-0.12 (0.41)|
|Message|0.29 (2.05)"|0.46 (3.85)**|0.13 (0.82)|0.35 (2.13)"*|
|Sum assets|_0.09_(1.95)'|0.03 (0.65)|0.05 (0.98)|0.06 (0.88)|
|Doctor<br><5 km|0.54(2.49)..|0.61 (1.55)|0.43 (1.08)|0.41 (0.69)|
|Clinic 2<5 km|0.04 (0.16)|-0.05(0.29)|_-0.30_(1.22)|0.17 (0.52)|
|Clinic3<5 km|0.26 (0.84)|0.08 (0.41)|0.09 (0.41)|0.42 (1.35)|
|Hospital<5km|-0.07(0.26)|0.17 '1.05)|0.42 (1.98)"|-0.46(1.48)|
|Pharmacy c 5 km|1.21 (1.72)'|-0.13 (0.22)|-0.00(0.00)|0.49 (0.50)|
|Clinic 3<10 km #|0.58 (1.05)|-0.34(0.66)|-0.22 (0.34)|-0.33 (0.76)|
|Supplies #|0.20 (0.61)|0.25 (0.52)|-0.52(0.39)|-0.02 (0.03)|
|Frequency supplied #|-0.02(0.04)|-1.12 (2.09)"-|1.07 (0.73)|-0.03(0.04)|
|Husband Absent|0.54 (2.49)".|-0.39 (2.42)".|-0.72(3.22)"'|-0.67(2.48)"'.|
|Community mortality|2.94 (0.73)|-9.90 (3.14)"*'|0.52 (0.10)|-14.77 (2.92)" '|
|Education opp. <_5_|-0.35 (2.01)"|-0.14(1.10)|-0.12(0.83)|0.09 (0.38)|
|Education opp. < 10|0.15 (0.76)|0.08 (0.48)|0.27 (1.25)|0.07 (0.31)|
|Good water|-0.12 (0.61)|0.21 (0.99)|-0.10(0.40)|0.08 (0.34)|
|Good sanitation|0.50 (1.76)'|0.45 (2.35)"'|0.52 (1.11)|-0.31(1.18)|
|Own land|-0.28 (0.72)|-0.46 (1.60)|4.22 (0.67)|0.18 (0.71)|
|Constant|-2.99 (4.33)"'|-0.22(0.35)|-2.54(2.51)'|0.66 (0.63)|
|Choice based coefficient|0.58 (2.92)***<br>|_-<br>_|||
|N|3064|3064|3064|3064|

# Rural areas Significant at 1l0o level

to Significant at 5% level *es Significant at 1% level
used trade shares as a proxy for openness to decompose the short and long run effects of openness on economic growth. Using annual data, they found that most of the observed positive relationship between openness and growth was due to short-run cyclical fluctuations. Consequently, we also compute period averages for 1960-66, 67-73, 74-81, and 82-88. These results are given in Tables 6 and 7.

The six-year averages do seem to indicate a robust relationship between openness and growth. Five of the seven variables show a positive relationship between openness and growth, after controlling for investment, education, and countryspecific effects captured through the use of dummy variables. Again, the null hypothesis that country dummy variables are jointly insignificant is generally rejected, although with less regularity than for the annual data.

Table 10 summarizes the relationship between openness and growth presented in Tables 4 through 9. Using six-year or annual data, the different specifications show a positive, generally significant association between the various measures of openness used in this study and productivity growth. However, using crosssectional data derived from long run averages only reveals a significant relationship between openness and growth for two of the seven indicators.

Figure 1 plots the partial correlations between all seven measures of openness and productivity growth, using six-year averages. The scatter plots in Figure 1 reveal an important 17
which has not been the case historically. This can be done via rapid switching to clean fuels, shifting to public transportation (including rail and water transportation), and an increase in transportation energy efficiency through vehicles efficiency improvements, road maintenance, and the reduction of traffic congestion. Fiscal instruments such as emission taxes and subsidies for clean fuels and clean vehicles would be more effective in slowing CO2 emission growth in countries where the economic activity effect is the primary driver for transport sector CO2 emission growth. On the other hand, regulatory instruments such as fuel economy standards and vehicle occupancy standards would be more effective in countries where the transportation energy intensity effect is the main driver for CO2 emission growth. Both fiscal and regulatory policy instruments would be useful in countries where both economic activity effect and transportation energy intensity effect are responsible for driving transport sector CO2 emissions growth.

# **References**

Ang, B.W., 2005. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33 (7), 867-871.

Ang, B.W., Liu, F. L., 2001. A new energy decomposition method: perfect in decomposition and consistent in aggregation. Energy 26 (6), 537-548.

Ang, B.W., Liu, N., 2007. Handling zero values in the logarithmic mean Divisia index decomposition approach. Energy Policy 35 (1), 238-246 Ang, B.W., Zhang, F.Q., Choi, K.H., 1998. Factorizing changes in energy and environmental indicators through decomposition, Energy 23 (6), 489-495. Bhattacharyya S.C., Ussanarassamee, A., 2004. Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000. Energy Economics 26 (5), 765781.

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Diakoulaki, D., Mandaraka, M., 2007. Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emissions in the EU manufacturing sector.

30
# C. The geographic composition of the sample

The benchmark measure of welfare is crucial for evaluating different prediction methods. However, because the census extract is only available for ten districts, it is not immediately clear whether it would be best to use only the survey data from those ten districts, or the full set of survey data to train the census model. The latter takes advantage of a wider set of training data, but the former may better capture the specific relationships between welfare and household characteristics in those poor districts.

Table 8 shows the results when varying the household survey sample used to estimate benchmark welfare. Specifically, we experiment with using only the UBR districts in the household survey data to train the census model, rather than the full sample. While the partial registry method remains the most accurate method by far, it doesn’t do nearly as well when the benchmark welfare measure is derived from a model trained on data from only the UBR districts. This is because the sample size used to train the models declines significantly from 6,000 to 2,000 poorest households when limiting the training sample to households in UBR districts, leading to a less informative benchmark welfare model and measure. The partial registry method is particularly sensitive to the weakening of explanatory power in the census model, due to limiting the training data to UBR districts. This is because the predictions from the census model are also used as the dependent variable to train the second stage geospatial model that generates estimates for non-registry villages. Interestingly, however, the predictive performance of the RWI also declines substantially, due to the increase in noise in the benchmark measure of welfare. Nonetheless, the predictive performance of the UBR improves, suggesting that the PMT may have picked up some of the heterogeneity in welfare patterns within the 10 districts.

**<u>Table 8.</u>** <u>Results when training models on sample data from UBR districts instead of all  districts</u>

||<br>**All districts-poorest 5**|<br>**0% HH**<br>**UBR di**|<br>**stricts-poorest 50% HH**|
|---|---|---|---|
|||**Rank correlation**<br>|**s**<br>|
|Partial registry (10% of the census sample)|0.753||0.399|
|PMT scores|(0.02)||0.15|
|IHS training sample|0.13||0.04|
|RWI|0.20||0.11|
|||**AUC**||
|Partial registry (10% of the census sample)|0.89||0.64|
|PMT scores|0.50||0.53|
|IHS training sample|0.53||0.50|
|RWI|0.60||0.53|
|||**R-squared**||
|Partial registry (10% of the census sample)|0.57||0.17|
|PMT scores|0.00||0.01|
|IHS training sample|0.01||0.00|
|RWI|0.04||0.01|

22
109

**"bl** 8A Senegp **Distrion** of **Mae Woks byMNrhi SM u**

||**1963**|**1986**|
|---|---|---|
|Single|13.8|10.1|
|One wife|612|63.1|
|Two wives|213|20.9|
|Three or more wives|3.7|4.6|
|Widowed or<br>divorced|-|4|

Sourfv: 1963 - Pfeffermann (1968), p. 168 1986 - Worker sample, 1986 survey of 17 industrial firms TABLE **82** SenegaL Avwaig Number of Ch&. **by Markel Sat**

||1963|1986|
|---|---|---|
|Monogamous|3.08|5.08|
|Polygamous|5.93|9.88|
|Total|_3A1_|5.87|

Source: 1963 - Pfeffermann (1968), p.<sup>168</sup> 1986 - Worker sample, 1986 survey of 17 industrial firms.

but the number of children per worker has also jumped. The rise in the average number of children is consistent with the rapid population growth rates reported by the official statistics (averaging 2.9 percent per year between 1972 and 1982) and it can probably be explained by the constant fertility rate and falling mortality rate.
**Table 3: Calibration parameters for the MAC and INV models for the 1.5**<sup>_◦_</sup> **C temperature target using the low cost, linear calibration.**

|_T_ = 80 yr|_T _<sup>_∗_</sup>=<br>_r_ = 2 % yr<sup>_−_1</sup>|1_._5 <sup>_◦_</sup>C<br> ̄_B_ = 440 GtCO2|_σB_ = 180 G|tCO2|
|---|---|---|---|---|
|Sector| ̄<br>�<br>$ tCO<sup>_−_1</sup><br>2<br>�| ̄_a_[GtCOr<sup>_−_1</sup>]|_δ_[%r<sup>_−_1</sup>]| ̄_c_<br>�<br>$ tCO<sup>_−_1</sup><br>2<br>�|
||_γ_<br><br>GtCO2 yr<sup>_−_1</sup><br>|2 y|y|GtCO2 yr<sup>_−_3</sup><br>|
|Waste|40.82|0.82|3.3|11557|
|Industry|17.54|5.47|4|4966|
|Forestry|7.12|8.25|0.8|2016|
|Agriculture|23.53|4.07|5|6662|
|Transport|6.12|3.74|6.7|1732|
|Energy|2.82|11.99|2.5|798|
|Buildings|12.99|3.2|1.7|3678|

policy to skyrocket. For context, the worst case carbon budget we sample is just higher than 100 GtCO2, which would give the social planner just north of two years to decarbonize in the absence of any abatement. Furthermore, we cannot rule out zero or negative RCBs for the 1.5<sup>_◦_</sup> C temperature target (Matthews et al., 2021; Dvorak et al., 2022), implying that even our sampling of the RCB is optimistic from a geophysical perspective. This framing provides context to our results: they should not be interpreted as “ruling out” limiting warming to 1.5<sup>_◦_</sup> C, but rather should demonstrate the urgency of this target in terms of global investment needs.

# **3.3.1 Calibration**

We calibrate our 1.5<sup>_◦_</sup> C target model using the low cost, linear calibration for the marginal abatement costs, i.e., the gold lines in Figure 1. We follow the same prescription as laid out in the main text, see Table 3.

# **3.3.2 Results**

We find that for the 1.5<sup>_◦_</sup> C temperature target, the main qualitative findings of our paper hold, as was the case in each other alternative calibration; see Figures 9 and 10).

However, we do find some differences with other temperature targets. For example, the overall cost of policy as a function of learning time does not follow an “S”-curve as much as an inverted “L”-curve, owing to even a marginal delay in learning about the RCB causing a large increase in cost. This is because the worst-case carbon budget is as low as 100 GtCO2, causing an immediate surge in precautionary investment. However, overall costs level out as the budget is learned later in the century, consistent with other temperature targets.

Furthermore, we find that the marginal cost of delay for the 1.5<sup>_◦_</sup> C target is more than twice that of the 1.7<sup>_◦_</sup> C or 2<sup>_◦_</sup> C targets, again owing to the urgency of the worst-case scenario.

15
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i This material has been funded by UKaid from the Department for International Development, however the views expressed do not necessarily reflect the department’s official policies. This project benefitted enormously from the excellent research assistance of Jaqueline Oliveira.

40