rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix**

## **Summary of Appendix**

## **Sections**

- A Timing and Equations for FPE Intensity Measure

- B Additional Results: Tables and Figures

- C Mother Tongue Instruction

- C.1 Background

- C.2 MTI Intensity Measure, by Region

- C.2.1 Oromia C.2.2 SNNPR

- C.2.3 Dire Dawa C.2.4 Tigray

- C.3 National Estimates: Accounting for FPE and MTI

- C.4 Combined Instrument and Reduced Form Estimates, by Age

- D Alternative Samples and Specifications

- D.1 Pre-Treatment Trends and Placebo Estimates

- D.2 Alternative Samples and Specifications

A.1
# S. DATA

Trends and instability in agricultural export earnings (for individual commodities and as a group) are examined over the period 1961-1987, which is divided into two sub-periods: 1961-1973 ("first period") and 1974-1987 ("second period").'<sup>6</sup> For each country, in the analysis, its major foreign market oriented exports are coffee, tea and tobacco, and major import substitute exports are cotton and sugar (the "Big-5"). Two additional agricultural import substitutes are considered for Malawi (maize and groundnuts), Tanzania (sisal and cashew), and Zimbabwe (maize and beef), which together with the "Big-S" commodities give each country's 'Big-7" exports.

Data on export values in $U.S., volumes in metric tons, and domestic agricultural production in metric tons come from a computerized FAO database.'<sup>7</sup> Export unit values are calculated by dividing export values by volume, which gives an f.o.b unit value SUS per metric ton.'<sup>8</sup> Aggregate world trade data, volumes in metric tons, are taken from various FAO Trade Yearbooks. Data on world commodity prices are from publications by the World Bank's International Economics Department, International Commodity Markets Division.

Nominal prices in $US are converted to constant prices in 1985 $US by using the manufacturing unit value (MUV) Index.'<sup>9</sup> Data on MUV are from publications by the World Bank's International Economics Department, International Commodity Markets Division.

# **6.** RESULTS

The first results presented deal with international commodity markets. Trends and instability of international commodity prices, and volumes traded set the stage for the country specific analysis, since world market conditions directly affect each country's potential for export earnings growth and stability. Following the evaluation international conditions, each country's performance will be evaluated and compared to the world market conditions.

16
Table Al Order of Integration: Unit Root Test St.atistics

|Country|Variable||ADlFtest|PIPtest|
|---|---|---|---|---|
|||k|teststatistic||
|Chile|?r|1|-2.63|-334*|
||y|3|-3.22**|-8.21***|
||z|1|-.3.43**|-2.95**|
|Korea|7r|0|-3.81*"||
||y|3|-3.83***'|-1.64*<br>|
||z|G|-4.37'>*|-4.12***|
|Philippines|7r|1|4512***.99**||
||y|0|-4.35"6|-4.2***|
||_z_|0|_44*':*_<br>*|_.56*_|
|Turkey|_7r_|1|-3.14**|-2.S4*|
||y|0|-8.24";*:c*|-8.24***|
||z|C|-3.5***|-3.83***|

Notes: Variables and estimated period are as defined in the<sup>text.</sup><sup>_1c_denotesthe number</sup> of lags in the ADF test. Asterisks *, ** and<sup>''**denote rejection of thenull hypothesisof</sup> a unit root at the 10%, 5%, and 1% significance levels.<sup>Criticalvaluesare from McKinnon</sup> (1991).
363). Schattschneider (1935, 8) reports that in 1928, the cost- equalization formula was the principle of protection of both major parties.

We thus see that advocates of antidumping regulation presented no unique reasons for this unique form of import protection. As in Canada in 1904, the voice that called for antidumping action was the voice of ordinary protection.

_Antldumplng law of 1921_ Where there is a way, however, there is a will. The 1916 antidumping act did little to dimin.sh pressure for a Canadian-style antidumping law, and proposals to the effect were soon introduced before the Congress. And although most of the complaints about foreign competition brought forward by the 1919 Tariff Commission study were not complaints about foreigners selling at a lower price in the United States than in their home markets, the commission nonetheless went on to recommend Canadian-style antidumping legislation.

Congress passed such an antidumping law in 1921, and the present U.S. antidumping law traces back to that law. The law empowered the secretary of the treasury (whose department included the customs service) to impose a special dumping duty when he determines that a U.S. industry is being or is likely to be injured or prevented from being established by imports of a product at a price below its fair value in the exporting country or in other export markets. Congress has since reassigned the determrination of injury to the U.S. International Trade Commission (originally the U.S. Tariff Commission) and has pressed the president to assign the determination of dumping to the U.S. Department of Commerce. Though numerous amendments have expanded its technicalities, the form of the law has remained basically unchanged.

- 15 -
condition of the country is a challenge for the growth of their business. Shortage of credit is also an important constraint, with ICT firms mentioning it twice as often as other firms, but it was one of the most chosen options for both industry groupings. The competition from local firms is also perceived as an important constraint, point again to the key role of the domestic market. Uncertainty places a key role, with uncertain government policies and political instability being the fourth most chosen option for both industry groupings. Results are in line with the recent takeaways from the OECD workshop on Strengthening the Palestinian business and climate investment (October 1, 2019) that mentions among the major constraints access to finance and better market access. However, the key market of reference is not the international market but rather the domestic market.

Table 7: What are the major constraints to the growth of your business? Pick up to three options. Fraction of owners that chose the following options:

|**ICT**<sup>_∗_</sup>||**non-ICT**||
|---|---|---|---|
|Current economic conditions of|34.2%|Current economic conditions of|30.7%|
|the country||the country||
|Shortage of credit|22.5%|Too much competition /unfair<br>competition<br>from<br>Palestinian<br>frms|12.1%|
|Too much competition /unfair<br>competition<br>from<br>Palestinian<br>frms|14.8%|Shortage of credit|10.4%|
|Uncertain<br>government<br>policies<br>and political instability|7.2%|Uncertain<br>government<br>policies<br>and political instability|10.0%|
|Size of the internal market|6.7%|Size of the internal market|9.4%|
|Difculty to import|2.6%|Difculty to access local markets<br>(within Palestine)|6.9%|
|Difculty to access local markets<br>(within Palestine)|2.2%|Lack of technical know-how|4.4%|
|Lack of skilled workers in the local<br>job market|1.7%|Shortage<br>of<br>modern<br>machin-<br>ery/equipment/physical capital|3.1%|
|Lack of raw material or lack of ac-<br>cess to raw material|_<_1.0%|Lack of skilled workers in the local<br>job market|2.1%|
|Difculty to export|_<_1.0%|Difculty to import|1.8%|
|Lack of marketing services or<br>transport facilities|_<_1.0%|Difculty to engage with foreign<br>frms|1.4%|
|Difculty to engage with foreign<br>frms|_<_1.0%|Too much interference by foreign<br>ofcials|1.2%|
|Too much interference by local of-<br>fcials|_<_1.0%|Too much competition /unfair<br>competition from foreign frms<br>(including Israeli frms)|1.1%|

_Continued on next page_ 22
these likely ranges often overlap for countries that are being compared with each other.<sup>12</sup> This is not to say however that the aggregate indicators cannot be used to make cross-country comparisons. To the contrary, there are a great many pair-wise country comparisons that do point to statistically significant, and likely also practically meaningful, differences across countries. Our 2008 Control of Corruption indicator for example covers 208 countries, so that it is possible to make a total of 21,528 pair-wise comparisons of corruption across countries using this measure. For 64 percent of these comparisons, 90% confidence intervals do not overlap, signaling quite highly statistically significant differences across countries. And if we lower our confidence level to 75 percent, which may be quite adequate for many applications, we find that 74 percent of all pair-wise comparisons are statistically significant. In sum, the likelihood that a comparison between any given pair of countries does exhibit a reasonably significant difference in governance performance is close to three-quarters.

The benefit of improved precision of aggregate indicators with increased data availability over time can also be clearly seen from this calculation. Consider our 1996 Control of Corruption indicator, which was based on a median of only four data sources per country, as opposed to a median of 11 sources in 2007, implying substantially higher margins of error in 1996. Of the 11,781 possible pair-wise comparisons in 1996, only 45 percent are significant at the 90% confidence level, and only 58 percent at the 75 percent confidence interval (versus 64 percent and 74 percent respectively, in 2008).

We also emphasize that the WGI are unusual in that we generate and report these margins of error, which allow an explicit assessment of the significance of observed cross-country and over time differences in estimates of governance. Although rarely explicitly disclosed -- or even acknowledged-- all other measures of governance are subject to margins of error as well, which in our past work we have shown to be at

> 12 Of course, asking whether 90% confidence intervals overlap or not corresponds to a hypothesis test at a significance level that is more stringent than 10%.  The assumptions underlying our statistical model imply that the standard error of the difference between two country scores is the square root of the sum of the squared standard errors of the two sources, which is always smaller than the sum of the two standard errors themselves.  It is more convenient -- and more conservative -- for users to simply inspect confidence intervals and see whether they overlap.

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# **1. Introduction and Motivation**

Many countries have embraced school-based management reforms to improve the quality of their education systems, based on the notion that decision-making will be improved by devolving responsibility to those who can more easily identify the needs of schools (Barrera-Osorio, Fasih and Patrinos 2009). Decentralization also has the potential of increasing the accountability of school-level decision-making agents to local communities, which could in turn increase effort and ultimately learning outcomes (World Bank 2003).

The evidence on the effectiveness of school-based management reforms in low and middle income countries is, however, mixed.<sup>2</sup> In Nepal (Chaudhury and Parajuli 2010) and Mexico (Gertler, Patrinos and Rubio-Codina 2012; Skoufias and Shapiro 2006) school-based management reforms led to increases in school participation and grade progression, but not to significant increases in test scores. In the Philippines, impacts were documented on participation, grade progression as well as learning outcomes (Khattri, Ling and Jha 2012; Yamauchi 2014). In Madagascar, there were no detectable impacts (Glewwe and Maiga 2011). One reason for these differences is that the details of implementation matter for success (Bruns, Filmer and Patrinos 2011). In Indonesia, alternative approaches to supporting the decentralization process led to different outcomes: complementary interventions that led to increased engagement of parents with the school were associated with positive impacts on learning; other approaches were not (Pradhan et al. 2014).

There are clearly a number of potential factors that modulate the extent to which school-based management reforms translate into better learning outcomes. Building on the earlier results from Indonesia, this study focuses on how to build parent engagement by providing information.

Disseminating information to parents in order to improve education outcomes is an attractive approach since it can be done relatively cheaply, especially if the information disseminated has already been collected as a part of routine administrative tasks (Hoxby 2002).<sup>3</sup> There are three

> 2 The evidence for high income countries is likewise mixed (Hanushek and Jorgenson 1996).

> 3 The World Development Report 2004 “Making Services Works for Poor People” (World Bank 2003), argues that lack of information weakens client power (to hold providers accountable) and it weakens citizens’ voice (relative to policy makers and politicians). Information-based interventions can affect relationship of school (provider) accountability and sooner or later education intermediate outcomes (i.e. teacher attendance, facility improvement, textbook provision) and ultimately student learning outcomes. When looking at schools as a service provider, greater

2
# **1.  Introduction**

Open defecation is thought to be a major cause of the persistent worldwide burden of diarrhea and intestinal parasite infection among children < 5 years old [1]. Reducing open defecation requires access and use of improved sanitation facilities, which are defined as facilities that prevent human feces from re-entering the environment; in 2010, an estimated 47% of the world population did not have access to improved sanitation facilities [2]. India alone accounts for a quarter of the world’s deaths from diarrheal diseases (387 million) [3], a third of those without improved sanitation (814 million) and nearly 60% of those who continue to practice open defecation (626 million)—the lowest rung on the sanitation ladder [2].

Observational studies of interventions that contain human feces and prevent them from entering the environment have been shown to reduce diarrheal disease [4,5] and intestinal parasite infections [6–8]. However, much of the research has focused on the provision of sewerage systems in urban centers, and to date there have been no published, randomized trials to measure the effect of sanitation provision on diarrheal disease or intestinal parasite infections in young children. Furthermore, in rural areas of low-income countries, the provision and maintenance of networked sewerage is usually prohibitively expensive in the short term. As a result, most government and donor financing in the rural sanitation sector has focused on the provision of non-networked toilets or latrines.

One approach to trigger large-scale toilet construction is Community-Led Total Sanitation (CLTS) [9]. Under CLTS, an external facilitator leads community exercises with the aim of changing community norms around open defecation. The activities highlight the magnitude of open defecation, they incorporate elements to elicit shame and disgust, and they attempt to mobilize community action for building toilets and leading the community to be “Open Defecation Free” (ODF). Facilitators encourage communities to come up with their own latrine designs that incorporate low cost, locally available materials. Individuals typically contribute their own time and materials to build toilets; for this reason, CLTS programs use relatively little public funds and rely heavily on private investment.

Since the early inception of CLTS in Bangladesh in the late 1990s, the practice has rapidly spread throughout the rest of Asia and Sub-Saharan Africa [9]. The Indian government launched the Total Sanitation Campaign (TSC) in 1999 with a goal of reaching universal rural sanitation coverage by 2012. India’s TSC used principles of CLTS to motivate private toilet construction by attempting to change community norms around open defecation; TSC also provided financial incentives for local governments to achieve high levels of coverage, and subsidies for households to offset the capital costs of toilets.

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regional leader’s figures would be the resource rich countries (that is, countries that are abundant in metals and minerals). This group is probably the most laggard in terms of export product diversification. Specifically, if these countries were to diversify their export product basket more, their growth rate per worker would increase by 1.18 percentage points. In terms of physical capital accumulation, the average annual gains would be 1.02 percentage points while TFP growth accelerates by 0.51 percentage point annually. Finally, note that non-resource rich countries attain the lowest potential growth benefits (0.30 percentage point per year) from narrowing the gap in terms of export product diversification. The gains in physical capital accumulation and TFP growth are about 0.26 and 0.13 percent per year.

**-** **<u>Table 9. Comparative Statistics, Product concentration (2010 2014)</u>**

|||Region<br>Leader|World excl.<br>SSA (10th<br>percentile)|Industrial<br>countries<br>(median)|East Asia & the<br>Pacific<br>(median)|
|---|---|---|---|---|---|
|Panel A.<br>Growth|SSA|0.40%|0.42%|0.38%|0.07%|
|-0.023|Resource Rich|1.18%|1.19%|1.16%|0.85%|
||Non-Resource Rich|0.30%|0.31%|0.28%|-0.04%|
|Panel B.<br>Capital|SSA|0.35%|0.36%|0.33%|0.06%|
|-0.02|Resource Rich|1.02%|1.04%|1.01%|0.74%|
||Non-Resource Rich|0.26%|0.27%|0.24%|-0.03%|
|Panel C.||||||
|TFP|SSA|0.18%|0.18%|0.17%|0.03%|
|-0.01|Resource Rich|0.51%|0.52%|0.50%|0.37%|
|_NOTE: The_|Non-Resource Rich<br>_estimations are calculated bas_|0.13%<br>_ed on the results f_|0.14%<br>_rom regressions in_|0.12%<br>_Table 7 Columns_|-0.02%<br>_[4] [5] and [6]._|

_NOTE: The estimations are calculated based on the results from regressions in Table 7 Columns [4] [5] and [6]. These coefficients are expressed in negative numbers are higher figures would mean more concentration and less would mean more diversity._ Additionally, **Table 10** presents the potential increase in economic growth, physical capital growth and TFP growth if the gap in intra-regional trade was to close relative to the four benchmarks mentioned earlier. If SSA were to match the regional leader’s intra-regional trade in their overall export basket, growth would accelerate 1.54 percentage points, capital accumulation would be enhanced by 0.81 percentage points and TFP growth would rise by 2.19 percentage points. If we look at the potential gains (including the sub-groups) relative to the regional leader, the resource rich countries would benefit the most from closing the gap with the benchmarks in terms of growth, capital accumulation, and TFP growth.

27
# **Appendix A: Trade Data and Tariff Rates for Armenia’s Trade Partners**

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data published by the National Statistical Service of the Republic of Armenia<sup>21</sup> . The data is for the year 2007 and shows exports and imports by country and commodity.

The regions of our model are Armenia, the European Union, the CIS, and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. For the CIS, we include Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan. Rest of the World is the residual.

The data is reported according to the Harmonized System (HS) classification at the two digit level. We mapped the HS-commodities into the sectors of our model. The exact mapping is defined in the first table below. Results for both exports and imports are reported in the subsequent two tables.

# **Tariff Data.**

We use detailed collected tariff data from the Armenian Customs Authority. That is, we received data on collected import duties (tariffs) and import values at the four digit tariff line level (again using the Harmonized System classification). The collected tariff rates for the sectors in our model are obtained by first aggregating the four digit tariff line level tariff collections and import values to the sectors of our model. The ratio of tariff collections to import values for each sector of our model is then calculated to give estimates of the collected tariff rates, which in turn are incorporated into our dataset. The tariff rates are shown in Table 4. Applying these tariff rates across all sectors implies that tariff revenue in the revised database is about 1% of GDP, which is consistent with collected revenues in Armenia.<sup>22</sup> Given that Armenia participates in preferential trade areas with the other CIS member states, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from CIS countries at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are also reported in Table 4.

> 21 http://www.armstat.am/file/article/ft_2nish_07_14.pdf 22 For the year 2008, aggregate data from Armenia show that tariff collections are 1% of GDP.

72
34% in field offices (the remaining 1% were in WB offices in developed countries, mainly Europe). The fact that familiarity is lower for those in country offices suggests that the advances in communication technology—the WB has a well-developed ―intranet‖ to facilitate this—have not eliminated the advantages of physical proximity.

It is of interest to look for other evidence for or against the view that stronger incentives for learning translate into knowledge. An objective clue to how much the Bank’s internal system for knowledge management responds to differences in the incentive for learning can be found in how the demand for paid cross-support from the Bank’s research department varies with answers to the value question. Across VPUs, the mean score for the value of WB research is highly correlated with the per capita demand for cross-support from DECRG (using the data in Tables 1 and 12); the correlation coefficients between the three-year mean of cross-support per staff member GG+ and the mean value score is 0.72, and it is also 0.72 with the proportion of VPU staff giving a value core over 5. This is consistent with the view that incentives for learning in the Bank do generate a response.

# **7. Conclusions**

One can think of two stylized models of how research is used by development practitioners. In the first, they have a demand for knowledge that does not stem from its direct bearing on their work. Much development research is a public good. Practitioners might read research findings to better understand the world in which they work, even when that understanding is essentially irrelevant to the specifics of that work. Alternatively, in the second model, research has a direct value in the work of practitioners—such as by informing project choices at the entry stage and assessing impacts later on—and research findings are sufficiently relevant and accessible to assure that practitioners become well informed. To enhance the quality of development aid, we hope that the second model is the best characterization. But is it?

If it was true in general that research findings are largely consumed independently of their direct bearing on the work of development practitioners then we would not expect greater familiarity with research findings to be associated with a higher perceived value of research for the practitioners’ work, or higher stated reliance on research for that work. Indeed, given that 21