rafmacalaba/data-use-mentions-extended huggingface dataset:
two other types of competitors in the same NAICS 6d sector: (i) other firms from the same country (compatriots), and (ii) US firms. Other compatriots’ prices are constructed from BLS microdata in a similar manner to foreign competitors’ prices and aggregate US producer price indices by NAICS 6d sector are taken from the BLS website.

# Appendix C. Intermediate inputs in the quantitative model.

In a model with imported intermediate inputs, Equation 11 is replaced by Equation 17, where Vc is the price of imported intermediates from country c, and φi,c is the input weight in the production function of inputs from country c used in production in country i and φi,i = 1 −<sup>�</sup> c=i<sup>φi,cistheweightondomestic</sup> costs. In the model in the main text φi,c = 0 ∀i=c.

I assume that imported intermediates are produced in competitive markets only with labor, such that Vc = Wc/Ac (κ is a normalizing constant). Log-linearizing and rearranging, with aijk = ΓAB(sijk)[ρ − 1]/ [1 + ΓAB(sijk)[1 − sijk][ρ − 1]] and bijk = [1 + ΓAB(sijk)[1 − sijk][ρ − 1]]<sup>−1</sup> as in the main text: One can then solve of the system of prices as in the model without imported intermediates (in the main text and online appendix), where now the “structural β-coefficients” will be a combination of effects through competitors’ prices, and effects through imported intermediates.

C.1. **Calibration.** Across major US trade partners, OECD data suggests the average imported intermediate share is 0.32 for manufacturing exports (Figure 4), though there is substantial variation across countries. In general, larger or more closed countries/regions have a lower intermediate
**Figure 14 Top 10 developing countries largest importers of environmental goods from China**

<!-- Start of picture text --> 7<br> 6<br> 5<br> 4<br> 3<br> 2<br> 1<br> -<br>India Brazil Vietnam Indonesia Thailand Malaysia Mexico Türkiye Pakistan Philippines<br>USD billion<br><!-- End of picture text --> _Source:_ Authors’ calculation based on 2022 data from the World Bank’s WITS. Note: Data is collected based on the APEC list of environmental goods.

# **4. Conclusion and policy recommendations**

**The select climate policies of the EU, US, and China bode well for the environment but present challenges and opportunities to developing countries** . These policies aim to cut carbon emissions, encourage renewable energy, and enhance environmental and social governance, which are commendable goals. However, these policies will also likely reshape trade, pricing, investment, and access to technology in developing countries, with mixed possible consequences.

**The policies are likely to reduce demand for fossil fuels, energy-intensive manufactured goods, and commodities that contribute to environmental degradation** . Specifically, the EU ETS, EU EED, EU RED, US IRA, and China's renewable targets and ETS will lower fossil fuel demand, negatively affecting countries whose exports heavily depend on these markets. The EU CBAM and CS3D policies will add costs to carbonintensive exports to the EU, reducing in the case of the EU CBAM competitiveness for nations like Zimbabwe, Ukraine, Georgia, and Mozambique, while improving it for places like Albania, Colombia, Jordan, and Morocco. Additionally, the exporters of EUDR-impacted products will likely see increased compliance and due diligence costs or loss of market access to the EU.

**Conversely, these climate policies are expected to create new opportunities in sectors critical to the green transition, such as critical minerals, EV
|**Gini after PEE**|0.514|0.538|0.540|0.476|0.544|0.512|
|**Difference –RS**<br>**index **|-0.003|0.000|-0.002|-0.001|-0.003|-0.003|
|Source: ICEFI, 2010|||||||

> 27 Specifically . Where is the Reynolds Smolesky index, the Gini index based on current income reported ( _y_ ) and the Gini index of the current income plus the average expenditure in the public service ( _y+b_ ).

> 28 The above analysis refers only to the generation currently studying and considers only short term benefits (the actual public expenditure).  If those attending and/or completing the tertiary level of education generate further benefits to those in lower quintiles, such as employment generation, larger tax payments or improvements in the education system, long term income inequality could be reduced.

34
Moreover, upon replication of this procedure with levels of data, the null hypothesis cannot be rejected for all variables with the exception of the change in the nominal exchange rate. This step concludes the existence of strong evidence that all the data series robustly follow an I(1) process in levels with the exception of the change in the exchange rate that follows an I(0).

# **Cointegration Analysis**

At this stage the set of relevant variables proposed by LCH for savings behavior proposed by Modigliani & Brumbreg (1954) are considered. It is now plausible to investigate the presence of a long run equilibrium relation between real gross private savings per capita, real interest rate and m2togdp. A VAR-model in levels is considered between these three series.

# **Selecting the Lag Length**

The reliability of the findings of the VAR system of equations depends on the lag length considered in running the system. This research adopts a robust procedure to test the optimal number of lags that maximize the efficiency of the model and ensure that the feedback information is adequately and efficiently maintained within the model. The optimal lag length is tested using several criteria including (AIC), Shwartz Criteria (SC) and the Log Likelihood test. The theory proposes that quarterly data as well as the propagation of the business cycle should be considered within a 6- year period (Cotis and Coppe, 2005).

The analysis begins with a VAR system using 6 lags and then a sequential reduction process is implemented thereafter to move from a general to a specific model specifically to avoid overparameterization and ensure parsimony. Individual equations and vector misspecification tests for the reduced rank 3 lags model residuals and model stability robustly support our proposition to proceed with modeling long run and short run dynamics by adopting 3 lags and conditioning the analysis on white type noise residuals.

# **Cointegration Tests**

The cointegration analysis is employed to a 3-lags system. The presence of a co-integrating relation is identified by Johansen’s maximum likelihood estimator approach. The
We present exponentiated coefficients (odds ratios) from these models, along with standard errors clustered on the enumerator level.

# **_3.3 Simulating the targeting performance of the QAS_**

To understand what we can learn from QAS, we simulate the performance of the QAS in terms of targeting different potential quality enhancement actions that could follow from the monitoring tool data of the QAS. We refer to these analyses simulating the targeting performance of the QAS for quality enhancement as “simulations” for short. Specifically, we compare who would be the targets of different policy actions using the QAS tool and diagnostic research tool data. We examine:

- Differences in the top 10% and top 25% of districts, schools, and classes/teachers identified with the QAS overall, which might be used for recognition, pay, or promotion

- Differences in the bottom 10% and top 25% of districts, schools, and classes/teachers per the QAS overall, which might be used for shutdown, termination, or other sanctioning decisions

- Differences in targeting repairs (whether the bottom 10% and bottom 25% of districts, schools, or classes with the greatest number of safety hazards is consistent)

- Differences in targeting materials (whether the same districts, schools, or classes would be targeted for supplemental grants and training for materials, based on not having or using any materials)

- Differences in targeting pedagogy training or coaching (based on the poorest performing level in terms of pedagogy) for districts, schools, and classes

- Differences in supplemental reading tutors, based on the poorest-performing 10% and poorest-performing 25% on children’s letter recognition for districts, schools, and classes

Differences are parametrized as the percentage of the simulation targets in the QAS tool data that are identified the same way in the diagnostic research tool data. These different simulations using the QAS levels, the level of a particular QAS dimension, and specific items, illustrate how these different approaches to using QAS data to inform action might perform.
reported was 0.171 or about half of the high ICC estimated in this research. Brunton-Smith _et al._ (2016) found interviewer design effects between 2.5 and 3.3 using a similar number of interviews per interviewer as the survey we use here.

# _Interviewer characteristics_

Annex 2 shows the results using Specification IV. This specification adds to Specification II interviewer characteristics for the three dependent variables with the highest ICCs in Table 3 above. These results are presented only in the annexes because they are based on a limited sample since data from all interviewers was not available. Even if data for all 20 interviewers were available, this would not be a large enough sample from which to draw statistically robust conclusions. These results therefore should be interpreted with caution and be considered only as suggestive rather than empirical. The inclusion of different interviewer characteristics reduces ICCs by between 6 and 14 percent with a mean of 10 percent when compared to ICCs calculated using the same sample and model but without interviewer characteristic covariates. The most significant interviewer characteristics are having lived in Kassala State (14.1 percent lower ICCs), having a university degree or higher (13.4 percent lower) and experience working for CBS before (13.1 percent lower).

# **5. Conclusions and recommendations**

Researchers have long been aware of the need to consider geographic clustering of responses in multi-stage cluster designs. Estimated design effects are used to calculate the sample size increase needed to account for increased variance due to within-cluster homogeneity of responses. No equivalent practice, however, is used to account for potential non-random distribution in measurement errors that results in homogeneity in data collected by the same interviewer. Using data from a pilot survey in Sudan, we find that clustering of responses by interviewers can in fact lead to larger design effects than area clustering. This finding is consistent with the findings of Schnell and Kreuter in Germany (2005).

This paper provides evidence that simpler questions lead to smaller interviewer design effects. This finding reinforces
a|6080|22.0%|10.75|3.10|4.9%|
|Poland|n/a|5480|17.1%|8.41|2.95|5.6%|
|Slovakia|n/a|6450|25.8%|n/a|3.05|_5.5%_|
|Slovenia|n/a|6230|21.6%|n/a|2.65|5.3%|
|CEE Average|n/a|6628|22.4%|9.58|3.15|5.3%|

Notes: Fastest Growing Economies includes Hong Kong, Korea, Singapore, and Taiwan. The Fast Growing Economies refers to Indonesia, Japan, Malaysia, and Thailand. The notation n/a stands for data not available. The data on human capital is from Barro and Lee (1993). No equivalent human capital data is available for Czech Republic, Slovakia, and Slovenia. We use for these countries the average from Hungary and Poland. The data on investment corresponds for all non-CEE countries to the average for 1993-94 and for all CEE countries to the average for 1994-95. The GNP per capita data for 1994 is from the 1996 World Development Report. The Index of Policy Z for CEE countries is based on the original IEF Index.

Using the econometric results discussed in Section IV, we examine how CEE countries will perform under different scenarios. More specifically, we first calculate the number of years it takes for CEE countries to reach, under different scenarios, 75 percent of the GDP per capita of the EU average country. This threshold is picked since most EU structural funds allocate resources to regions which are below it. We then perform some growth exercises and discuss how much faster the growth rate of GDP per capita for CEE countries can be relative to the EU _15_ average country, once again assuming different policy scenarios 15 The
The randomization among eligible schools is critical for our study: it ensures that the three successive cohorts are statistically comparable, which in turn ensures unbiased estimates of the eect of the program. In this process it is crucial that the control group contains only schools that were judged as eligible but were not selected to receive funding by the randomization process until a later date.

# 3 Data and Balance

In order to gather data for this study, three waves of surveys were administered to students and their families, teachers, and principals in these schools. A baseline survey was conducted at the start of the 2009-2010 academic year (in November), right as the rst round of grants were able to be executed. Subsequent surveys took place in November 2010 at the beginning of the 20102011 academic year (rst follow-up), and in May 2011 at the end of the 2010-2011 academic year (second follow-up).

At baseline, we administered written assessments in mathematics and French to a random sample of 6 children in each of grades 2 and 4, and an oral reading assessment (similar to Early Grade Reading Assessment, or EGRA) to a random sample of 3 of those 6 children in grades 2 and 4. Importantly, the same tests were administered across all waves. In addition, we randomly selected 2 of the 3 children in each grade who took all three assessments, and conducted a household survey that included demographic and nancial information on all household members. Finally, we collected classroom and school level information by surveying the school principals and the teachers of the students in our sample.

In the rst follow-up, we surveyed and tested the same children again (at the start of 3rd and 5th grade, respectively) and their households, teachers and principals. Schools who received grants in the rst cohort answered a set of questions on the use of the extra funds. To examine the possibility that funds were disproportionately channelled to students preparing to enter secondary school, we also administered written assessments in mathematics and French to a
10<br>20<br>30<br>40|50<br>60<br>70<br>80<br>90<br>0|13|Tauco|5.3|
|||10<br>FEI|4|Tranaquepe*|5.4|
||||11|Perales|5.9|
|||Maule||||
||IPF Ma|pping||MCE Ranki|ng|
||Mau|le|ID|Name|Score|
||||19|La Bruja|2.38|
|100|P18||17|Peralito*|3.12|
|90|||18|Peralito 2|3.17|
|80|P2|P27<br>P19|25|El Molino|3.22|
|70|5|P23<br>|26|Derivado Porvenir 2|<br>3.31|
|60<br>EI|P16|P26<br>|21|Sauzal|4.50|
|50<br>S|P24<br>P21|P17|24|Manantiales|5.01|
|40|P15<br>||27|Limávida|5.11|
|30|||23|Vaquería|5.45|
|20|P20<br>P22||20|El Guindo|5.55|
|10|||22|Botacura|6.20|
|0<br>0|10<br>20<br>30<br>40|50<br>60<br>70<br>80<br>90<br>100|15|Huencuecho I|6.65|
|||FEI|16|Huencuecho II|6.65|

* Not recommended due to environmental restriction

34
02|0.72|0.01|0.02|0.28|
|Total duration of school day (hours)|4.36|4.35|0.48|4.46|4.48|0.39|
|Number of observations|2,098|1,670||1,976|1,467||

_Sources:_ MEC/FNDE, INEP, _Prova Brasil,_ and School Census _Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2010 and remained until 2011 (columns (1) and (4)) and for the control group selected by propensity score matching (columns (2) and (5)). Columns (3) and (6) report the _p_ value of the _t_ test performed to check mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. In control group, the means are weighted using propensity score matching weights. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample. Our sample include only schools that have information for all variables over all the analyzed period.This table compares treated to control schools in 2009, after propensity score matching.
<!-- Start of picture text --> a) Correlation of RISE 2017 and Perceived Readiness from Survey Data<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>80<br>@ South Africa<br>70 R? = 0.4741<br>2 -~7<br>° @ Ghana -<br>Pr 60 _- - Kenya<br>5 cameron @ Cote d’lvoire -o -<br>be Ethiopia<br>é~ 50 Zimbabwe @ Burkinaur S F asog . co oeAbia” ~ “eoRwanda “Uganda<br>o 40 Congo a eon ® senegal<br>N Niger @ Tanzania yo» fe law 8<br>Ww -<br>no Sudan g ew” | . a<br>2 30 B urun diio € DRC- Bw:eit AngolaGuinea® Sierra Leone @ Nigeria<br>20 ~~ Mali®@ e<br>——~Chad @ Mauritaniaeo @ Liberia @ SouthMadagascarSudan @ Mozambique<br>Central African Republic<br>@ Somalia<br>0<br>-8 6 4 -2 0 2 4 6 8 10 12 14 16 18 20<br>Perceived Readiness (Net Positive Score)<br>b) Correlation of Power Sector Reform Index and Perceived Readiness from Survey Data<br>90<br>cy 80 @ Nigeria @ Uganda<br>8 R? = 0.3684<br>”<br>”<br>£ 70 @ Ghana - -7<br>iS -~<br>wr 60 e Cameroon ” -<br>$ Angola e @ @Coted’lvoire we na K<br>3 Senegal ” enya<br>: 50 uo?<br>5 Mauritania piers fear Mozavepiaue<br>‘wo 40 T a oe Zambia<br>© Zimbabwe @
markets than national owned exporting firms. Exporting firms with ISO certification have a 41% higher proportion of sales originating in foreign markets; whereas exporting firms that use email sell 31% more in foreign markets.

Even though not all signaling and technological tools are significant this study provides evidence that ISO certification and having access to the Internet not only increases the probability that a firm exports but also its export intensity. Firms that use these tools are more likely to export than firms that do not use them. Among firms that export, those using these tools sell a higher proportion of their total sales in foreign markets.

It is interesting that amongst the Internet marketing tools only the use of email is significant in both the probit and tobit models, whereas the use of a website to communicate with clients and suppliers is only significant in the probit model. Again, it is very likely that _email_ is picking up the effect of whether the firm has access to the Internet. Having access to the Internet will not only affect the probability to export but also how much the firm can sell abroad. Furthermore, the results suggest that having a website works only as a signaling mechanism in the sense that a firm’s website attracts foreign consumers; however, it has no effect on the export intensity.

# **7 Robustness Checks**

# _7.1 ATFP vs. ATFP2_

As indicated in previous sections, ATFP is estimated using a cost share of capital of 1/3 which is consistent with income accounts data for developed countries. However, because the firms in this sample are located in developing countries the share cost of capital might be lower than in developed countries. The estimation of a constant return to 22
and 54 _._ 7%, respectively) but NE students participate less (47%). Average monthly wages for BP students are about 23% higher than for SC students and almost twice as high as for NE students.

Table 2 presents average characteristics of students’ high school municipalities. Relative to SC and BP students, on average NE students live in municipalities that are smaller and less urban, with lower GDP and higher homicide rates. SC students, in turn, come from larger, denser, slightly more urban, and higher-GDP municipalities than BP students. As explained below, these patterns are related to the geographic location of SC HEIs.

# **3.2 Identifying variation**

To capture variation in local supply of higher education, we use SNIES data from 2004 (again, one year prior to high school graduation in our sample). We first identify a student’s high school location as given by its administrative division or municipality and find the coordinates for its geographic centroid. We then construct the binary variables _Zj_ and _Hj_ , where _j_ denotes the municipality of interest. _Zj_ is equal to one if there is at least one SC HEI within a 10-km radius around municipality _j_ ’s centroid, and zero otherwise. _Hj_ is equal to one if there is at least one HEI offering bachelor’s (and perhaps SC) degrees–namely, at least one HEI belonging to the "Other HEIs" category.<sup>9</sup> As expected, the local supply of HEIs is correlated with students’ enrollment choices (bottom panel of Table 2). We observe that 73 _._ 7% of SC students went to high school in a municipality where there was at least one SC HEI, but this share drops to 64 _._ 6% and 56 _._ 2% for BP and NE students, respectively. In contrast, approximately the same share (84%) of SC and BP students attended high school in municipalities where
which to<br>implement policies that were part of their long-run reform<br>agenda rather than being carefully targeted countercyclical<br>fiscal policy.|
|United States|Mian & Sufi (2012)|957 U.S. metropolitan or<br>micropolitan statistical<br>areas (CBSAs) for|January 2004 -June<br>2010|The Car Allowance Rebate System<br>program - temporary subsidies to<br>encourage the trading in old cars and<br>buying new ones|<br>Program induced the purchase of an additional 370,000 cars in<br>July & August 2009.<br>reversal impact 10 months after the program expired|
|United States|Chodorow-Reich_et_<br>_al._(2012)|State level income and<br>employment|December 2008-<br>July 2009 (main<br>focus)|$88 billion of aid to state governments<br>through the Medicaid reimbursement|Positive relationship between receiving fiscal relief and<br>increases in employment|
|Europe<br>(17 countries)|Nickel and Tudyka<br>(2014)|Real GDP, government<br>consumption, private<br>investment, debt-to-GDP<br>ratio, trade balance|1970-2010||A one percentage point of GDP increase (expansionary fiscal<br>shock) in government consumption is first followed by<br>positive cumulative responses of real GDP and negative<br>responses of private investment and the trade balance as a<br>share of GDP|
|G20|Spilimbergo,<br>Symansky, and<br>Schindler (2009)||2008 Global crisis||The low set of fiscal stimulus multipliers included 0.3 on<br>revenue, 0.5 on capital spending and 0.3 on other spending|
|World|<br>Agnello_et al._(2017)|Panel of 157 countries<br>
removal of tree cover canopy. The authors reported the tree cover loss data to have a false positive rate of 13 percent, a false negative rate of 12 percent, and a ratio of total forest gain to loss over 2001-2012 of 0.34. The derived data differ from statistics reported by the UN-FAO’s Forest Resource Assessment, but due to the consistent methodology and definition of forests across countries, we believe these data are better suited for a global analysis. We define forests as pixels with a minimum canopy closure density of 30 percent. Finally, we convert the data to area measures and sum the data by country to calculate tree cover loss as a percentage of tree cover extent in 2000. Our intention is to examine “natural dense forests”, but note that the data also capture forest plantations. Our loss measure is thus only a proxy for deforestation, as there may be many other natural and anthropogenic processes (storm damage, fires, mechanical harvesting) that are reflected by the data. We refrain from including the tree cover gain data as there are significant differences in methodology that limit additivity or comparison with loss.

# **3.2 Pollution data**

We use concentrations of fine particulate matter (PM2 _._ 5), coarse dust particles of 2.5 micrometers in diameter, as a proxy for broader air pollution. The (0 _._ 01<sup>_◦_</sup> _×_ 0 _._ 01<sup>_◦_</sup> resolution) data were developed by van Donkelaar et al. (2016) and include global annual ground-level PM2 _._ 5 (1999-2014) derived from a combination of satellite-, simulation- and monitor-based sources. The dataset has been developed from satellite-derived Aerosol Optical Depth reflectance values calibrated to ground-based PM2 _._ 5 observations using a Geographically Weighted Regression.

Remote sensing methods aim to observe particulate matter but are prone to capturing fine dust released from barren lands that have