rafmacalaba/datause-encoder-data huggingface dataset:
3. Teachers who are in Group 3 should be attempted not to be transferred to another school during the implementation of this study.

4. Check the accuracy of teacher data sent by the principals. If there are discrepancies between the actual data and the sent data, please report this to us as soon as possible and send the revised teacher data at latest by July 15, 2009.

5. If we found teacher data that may be invalid from a certain school, for example the student teacher ratio is too small, we will clarify to the respective school, and the evaluation of the portfolios of all teachers from this schools will be put on hold until the clarification process has been completed.

Should you need further information you can contact the Teacher Profession Directorate at the Director General of Quality Improvements of Education and Education Personnel at the following phone number 021-5774122 (through Santi and Ubaidah) or online consultation through website <u>www.sertifikasiguru.org during office hours.</u> For your attention and cooperation, we thank you.

Signed, Director General of Quality Improvements of Education and Education Personnel.

Copies sent to:

- (1) University Rectors responsible for teacher certification

- (2) Secretary, Director General of Quality Improvements of Education and Education Personnel

(3) Directors, Director General of Quality Improvements of Education and Education Personnel (4) Executive Secretary, Teacher Certification Consortium (5) Head of Education Quality Assurance Agencies, as applicable

- (6) Principals of applicable schools
**Table 6: Children’s Occupational Choices in Ecuador 2003-2005** Actual and Counterfactual Enrollment Rate for Target Population

|||TT|**_Ex post_**<br>Probability of<br>receiving benefits if|ITT||**_Ex ante_**|||
|---|---|---|---|---|---|---|---|---|
||d1|(a)|eligible<br>(b)|(c)=(a)(b)|Simulated ITT<sup>1</sup>|Simulated ITT<sup>2</sup>|Simulated ITT<sup>3</sup>|Simulated ITT<sup>4</sup>|
|Table 4 - Results|||||||||
|2SLS|10.00%|10.00%|77%|7.7%|14.58%|7.63%|5.86%|9.00%|
||4.72%|4.72%|||0.53%|0.82%|1.10%|0.60%|
||Average Tran|sfer per House|hold with Ceiling of $15|per household|15.00|11.91|9.80|12.31|
||||Number|of households|645|631|596|603|
|||||Annual Cost|116,100.00|90,192.00|70,080.00|89,100.00|
||Avera|ge Transfer per|Household without Hou|sehold Ceiling|32.33|11.92|10.35|15.95|
||||Number|of households|645|631|599|605|
|||||Annual Cost|250,200.00|90,264.00|74,400.00|115,830.00|

Source: Schady and Araújo (2008) table 4 column (3).

Note:

- 1 - R$15 per child assuming unitary child household.

- 2 - $15 divided by number of children age 6-17 in the household. Minimum transfer $3 per child.

- 3 - $5 per child.

- 4 - $7.5 per child.

- ** Significant at 5% level; * Significant at 10% level.

Standard Deviation computed by bootstrap method.

32
rose significantly at the onset of the COVID-19 pandemic. The VIX spiked in late March 2020 amid a broad-based erosion of confidence in financial markets and subsequently fell on the back of massive policy support but remained volatile throughout the pandemic (Figure 7). In robustness checks, we use the U.S. BBB corporate bond spread as an alternative measure of risk appetite (higher values indicate higher risk aversion).

- _United States longer-term inflation expectations._ We focus on the longer-term inflation outlook in the United States as a core relevant factor for financial markets and investors. If crypto-assets are deemed to be a global macro hedge, crypto volume would increase if inflation expectations increase. When inflation expectations are well-anchored, changes in breakeven inflation may also reflect improvements in growth expectations and increased risk appetite. We use the 5-year, 5-year Forward U.S. Inflation Expectation Rate, a widely monitored market-implied proxy for longer-run inflation expectations. It is a measure of the average expected inflation over the five-year period that begins five years from today and is derived from yields on nominal and inflation-adjusted Treasury securities. On the onset of the COVID-19 pandemic, the economic outlook deteriorated which caused inflation expectations to fall dramatically (Figure 7). Inflation expectations recovered to pre-pandemic levels due to massive economic and monetary stimulus. In robustness checks, we instead use the U.S. 10-year Breakeven Inflation Rate, another widely used proxy for inflation expectations which is similarly derived.

- _Unites States real 10-year Treasury yields._ 10-year U.S. Treasury yields are a widely observed indicator in global markets as they reflect economic prospects and drive global financial conditions. We use the yield on 10-year U.S. Treasury securities which are indexed to inflation (TIPS). Higher rates are often associated with tighter global financial conditions, producing cross-border spillover effects, including to EMDEs. During the onset of the pandemic in March 2020, Treasury yields fell strongly as economic prospects deteriorated and investors fled to safety. However, yields briefly spiked in late March as investors turned to cash amid a wider erosion of market confidence and broad-based selling of financial assets. Real 10-year yields turned negative in our sample period due to the impact of the COVID-19 pandemic and the subsequent policy response which included purchases of Treasury securities by the U.S. Federal Reserve which put downward pressure on yields to support market functioning and lower longer-term interest rates to stimulate the economy (i.e., quantitative easing) (Figure 7). Low or even negative (real) yields

26
Loayza, N., A. Mier y Teran, and J. Rigolini. 2013. “Poverty, inequality, and the local natural resource curse.” Discussion Paper Series, Forschungsinstitut zur Zujunft der Arbeit, 7226, Bonn.

<mark>Maldonado, S. (2017). The non-monotonic political effects of resource booms.</mark>

- <mark>Mamo, N., Bhattacharyya, S., & Moradi, A. (2019). Intensive and extensive margins of mining and development: evidence from Sub-Saharan Africa.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>139</mark>_ <mark>, 28-49.</mark>

- Michaels, G. 2011. “The Long Term Consequences of Resource-Based Specialisation.” _The Economic Journal_ 121: 31–57.

MineAtlas. 2013. https://mining-atlas.com/

- <mark>Rhee, S., Nyanza, E. C., Condon, M., Fisher, J., Maduka, T., & Benshaul-Tolonen, A. (2018). Understanding environmental, health and economic activity interactions following transition of ownership in gold mining areas in Tanzania: A case of private to public.</mark> _<mark>Land use policy</mark>_ <mark>,</mark> _<mark>79</mark>_ <mark>, 650-658.</mark>

- Standing, A., and G. Hilson. 2013. “Distributing mining wealth to communities in Ghana: Addressing problems of elite capture and political corruption.” _U4_ issue 2013 (5).

- van der Ploeg, F. 2011. “Natural Resources: Curse or Blessing?” _Journal of Economic Literature_ 49 (2): 366–420.

- <mark>von der Goltz, J., & Barnwal, P. (2019). Mines: The local wealth and health effects of mineral mining in developing countries.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>139</mark>_ <mark>, 1-16.</mark>

- Wilson, N. 2012. “Economic Booms and Risky Sexual Behavior: Evidence from Zambian Copper Mining Cities.” _Journal of Health Economics_ 31 (6): 797–812.

- World Bank. 2011. “Global Economic Prospects June 2011. Regional Annex.” World Bank, Washington, DC.

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# **_Direct and In-Kind Transfers_**

## **Direct transfers are all progressive in Albania and, to different extents, have positive redistributive**

**and poverty-reducing effects** (Figure 15 and 16). Looking at the set of transfers in Albania, with Ndihma Ekonomique (NE) and Disability Pensions as the relatively larger programs,<sup>39</sup> the positive Kakwani index shows progressivity across all, with NE as the most progressive program. They are also pro-poor,<sup>40</sup> as reflected in a negative concentration coefficient (Table 3), meaning that in absolute terms they are concentrated among the less well-off (Figure 17). For example, around 53 percent of the NE benefits in 2015 go to the bottom quintile, and 75 percent go to the bottom 40 percent of the distribution. Leakage seems to be low, given that the households in the top quintile receive only 4 percent of the benefits. This reflects a pro-poor design, currently being improved through piloting of a proxy means testing in certain regions. Nevertheless, while most programs have positive but small impact given their size with respect to households’ market income, NE and Disability contribute to reducing poverty the most by 0.8 and 1.2 percentage points ($2.5/day poverty line), respectively. Disability has larger poverty-impact despite not being a poverty-targeted benefit, which is driven by the larger amount spent: around three times the amount of NE.

Energy transfers in the other hand are not well targeted and represent a relatively small share of household budgets (low size), resulting in a marginal poverty and distributional impact. Unemployment benefits have a very low redistributive and poverty-reducing effect, mostly due to its small size (Figure 10), despite the fact that nearly 40 percent of the benefits go to the bottom quintile.

**Figure 15. Progressivity of Direct Transfers in**

**Figure 16. Marginal Contributions of Direct Transfers**

<!-- Start of picture text --> Albania (Kakwani Index) in Albania<br>0.035<br>0.9<br>0.8 0.025<br>0.7<br>0.015<br>0.6<br>0.5 0.005<br>0.4<br>0.3 -0.005<br>0.2<br>-0.015<br>0.1<br>0 -0.025<br>-0.035<br>Redistributive Effect<br>Poverty reduction effect ( $ 2.5 PPP )<br>Poverty reduction effect ( $ 4 PPP )<br>Source: Own estimates using the Albania HBS 2015. Source: Own estimates using the Albania HBS 2015.<br>Disability Pension Survivor Pension Unemployment Benefit Social care services elderly/disabled Ndihma Ekonomike Energy transfers All direct transfers excl contributory pensions<br>Disability Pension Survivor Pension Unemployment Benefit Social care services elderly/disabled Ndihma Ekonomike Energy transfers All direct transfers excl contributory pensions<br><!-- End of picture text -->

**Figure 17. Concentration of social spending and transfers (by market income plus pensions, by quintiles)**

> 39 Disability pension includes here both disability assistance and contributory disability pension.

> 40 A transfer is “pro-poor” if the proportion received in absolute terms decreases as income rises (globally progressive in absolute terms).

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The initial estimation results, based on the Investment Climate Survey of the World Bank, show the importance of total factor productivity (TFP) in the production process of firms across sectors in India and Brazil during 2002‐05. It needs to be noted that since the number of observations is very limited and the time period is short, the initial results need to be taken cautiously. In both countries, the contributions of capital, labor, and total factor productivity are calculated at the industry level. The results are obtained running value added at the firm level (defined as total sales minus the sum of new investment, energy, and material cost) on

- Capital (Lcapital_n): natural log of firm’s property, plant and equipment.

- Labor (Lworker): natural log of number of permanent workers plus temp workers weighted by working months.

- Alternative measure of labor (Lworker_edu): natural log of summation of number of permanent workers plus temp workers weighted by working months and weighted schooling years of workers times 0.1.

Our expectation is that major changes in the level productivity can be explained by changing in policies and reforms. We expect that policies combining improvements in domestic markets (such as higher flexibility in labor or financial markets) with integration to global markets (such as trade liberalization, higher foreign direct investment) produce the highest productivity gains. We also expect that the convergence in the productivity level of developing countries towards the levels observed in developed countries is faster in relatively more competitive industries at the international standards, which can be accomplished by reforms.

One purpose of further study is to study the level of productivity at different layers of aggregation (i.e. at the firm level, at the industry level, and national level). Previous empirical studies show that stylized facts associated with productivity are different at different level of aggregation. A comparative study of these facts across countries in a long time period is essential to better understand productivity, which is considered as one of the major sources of economic growth. With the help of long time series, we can study impact lags of different external or internal factors, especially policies and reforms, in determining productivity. In addition, future work could focus on the following issues which will hopefully improve our understanding of productivity as one of the essential determinants of growth: (1) micro‐level analyses can inform us about how the performance of businesses is affected by changing policies such as export, import, FDI policies, privatization. (2) We can also observe the reallocation of resources from inefficient to efficient ones (turnover rate) and how businesses are affected by this allocation process. (3) Depending on data availability on firm characteristics, such as access to foreign technology, R&D expenditures, the level of corruption, we can also study within‐firm improvements in productivity and factors determining it.

18
18 Figure 12. Export Quality by Region, 2009 <!-- Start of picture text --> Export Quality Relative to Destination Markets<br>(World Frontier = 1)<br>Emerging Asia<br>Frontier Asia<br>Sub-Saharan Africa<br>Western Europe<br>0.6 0.7 0.8 0.9 1<br>Average Quality Demanded in Destination Countries<br>Quality Exported<br><!-- End of picture text --> _Sources_ : Penn World Table 7.0, UN COMTRADE, and author calculations.
# Annex A Methodological annex

## Measures of trade costs

Conceptually, trade costs may be defined as the excess cost of an internationally traded good compared with a similar good traded domestically. By construction, trade costs can therefore move without any change in external costs of trading, simply as a result of changes in domestic trading costs. To measure trade costs, two main approaches have been developed in the literature: direct and indirect approaches (Chen and Novy 2012).

Direct approaches rely on observable data that serve as a proxy for individual components. For instance, measures of costs faced at the border are often based on counting the average number of days that is needed for a good to cross the border, while transport costs are often inferred from the cost of ocean and air shipping (Hummels et al. 2007). Policy barriers such as tariffs and nontariff measures are directly available from a range of statistical sources. Direct approaches suffer from a series of limitations, including the fact the underlying variables are only partially observable and may not be easily converted to plausible ad-valorem tariff equivalents, which makes it difficult to compare them but also to aggregate them into a summary measure of trade costs (Anderson and van Wincoop 2004). Therefore, trade cost estimates taken from such measures tend to be only partial.

Indirect approaches aim to circumvent these difficulties. These infer trade impediments top-down, from measures of trade flows and aggregate value added. Under this approach, trade costs correspond to the difference between the trade flows that would be expected in a hypothetical “frictionless” world and what is observed in the data and are computed relative to domestic trade costs. Measures built through the indirect approach can be tracked over time and include all observed and unobserved factors that explain why trading with another country is more costly than trading domestically. Novy (2013) developed a micro-founded measure of aggregate bilateral trade costs using a theoretical gravity equation for the trade cost parameters that capture the barriers to international trade. The resulting solution expresses the trade cost parameters as a function of observable trade data, providing a micro-founded measure of bilateral trade costs. The measure is easy to implement empirically for a number of countries with readily available data. One drawback is that the contribution of the individual cost factors cannot be easily disentangled by simple inspection of the measure. A way proposed in the literature to overcome this is to combine indirect and direct measurements into a single regression (Arvis et al. 2013).

## Determinants of trade costs

To estimate the contribution of different determinants of trade costs, a gravity model is estimated for a panel of up to 23 advanced economies and 72 EMDEs with annual data for both trade costs and all determinants of trade costs over 2007-18. The sample includes 25 industrial commodity (energy and metals) exporters, all of which are
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the results obtained when using this alternative measure. We observe that both the direction and the magnitude of the estimated relation between employment in mining and per capita income growth is similar to those obtained using the (conditional) measure of employment in mining.

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