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

## **District sample**

The eight MESIP disadvantaged districts were selected on the basis of being among the worst-off in the nation in terms of PqTR; female/male teacher ratio; shortages of classrooms; and repetition, promotion, and dropout rates in lower primary. The selection was also conducted in such a way as to ensure the inclusion of at least one school from each of Malawi’s six sub-regional education divisions. Ultimately, the chosen districts were Lilongwe Rural East, and Dedza from the large Central region; Kasungu from the smaller Northern region; and Chikwawa, Machinga, Mangochi, and Phalombe, all from the largest region, Southern.

For our comparison sample, we first identify the 26 districts not targeted by the main MESIP interventions. We remove the four districts into which these interventions were extended by MESIPExtended.<sup>16</sup> Although MESIP-Extended supported similar interventions to MESIP, there were differences in sequencing and implementation modality which make it difficult to robustly and simultaneously evaluate both programs.

We also exclude Malawi’s four urban districts, as the MESIP interventions were designed for implementation in rural areas. Finally, we also exclude Likoma district, a small island in Lake Malawi with a very particular set of challenges relating to schooling stemming from its particular geography.

With these exclusions, we were left with a group of 17 rural education districts as a comparison group. See **Table 1** for a summary of the selection.

## **School Sample**

For indicators drawn from EMIS, our sample is all public primary schools in the eight disadvantaged districts and the 17 comparison districts (a total of 4,428 schools).<sup>17</sup> For test scores which are drawn from MLSS, our sample is all public primary schools in the eight disadvantaged districts and the 17 comparison districts which are also part of the MLSS sample and for which baseline and endline data is available (a total of 444 schools). The MLSS sample is constructed using stratified probability proportional to size (PPS) sampling was used, with strata defined based on the six educational divisions. From each stratum, a random sample of schools was selected using PPS, using the number of schools in each stratum as measure of size. The PPS process generated a recommended sample size of 700 schools, 571 within the 25 study districts. From these

> 16 MESIP-Extended extended the MESIP interventions focused in the eight disadvantaged districts to four more districts which were similarly disadvantaged: Dowa, Mulanje, Nkhotakota, and Rumphi. MESIP-Extended did not extend the RBF incentives to new districts.

> 17 There were 1,856 schools in the eight disadvantaged districts and 2,572 schools in the 17 comparison districts. We include schools which are present in both EMIS 2016 and EMIS 2020 data.

15
**Table 5.** OLS regression analyses of determinants of contributions in the agreement <u>treatment if the group had formed an agreement.</u>

|||Nepal|Et|hiopia|
|---|---|---|---|---|
|Variable|Coeff.|Std.error|Coeff.|Std.error|
|Contribution in standard public goods experiment|0.146|0.257|0.312***|0.092|
|If believe that all others will keep their promises|1.540|1.367|0.605|0.410|
|(dummy=1)|||||
|Amount pledged to contribute if group did not|0.448|0.380|0.428***|0.077|
|agree|||||
|Amount pledged by other two group members if<br>group did not agree|0.339|0.211|0.018|0.050|
|Constant|-3.606|3.194|0.712|0.915|
|Number of observations||21||71|

# **5. Discussion**

This report summarizes the results from public goods experiments investigating what happens when it is possible for the players to form a nonbinding agreement requiring all players to fully contribute to the public good. There are many nonbinding agreements in the real world, for example in working groups, households, boards, and organizations. Arguably, this is a natural way to start a cooperative endeavor when external enforcement is frail or absent. Nevertheless, it is difficult to infer clear conclusions from them because we do not know the counterfactual situations of no agreement or an agreement designed differently. Also, the pure effect of having an agreement on behavior is often confounded with many other factors in the negotiation process. In an experimental setting, it is easier to separate out the different effects to investigate whether an agreement _per se_ fosters cooperation.

Previous studies on the effect of nonbinding agreements on contributions to public goods have used students exclusively from Western democratic countries as subjects. The results of those studies suggest that nonbinding announcements are not enough to foster cooperation, which may not be entirely surprising when social ties are weak as is typically the case among students.

18
<!-- Start of picture text --> wo<br>g Dry © °<br>&<br>@ oO<br>o@<br>Se ie)<br>2oO °<br>:oO - * eo<br>1 AES 2 2.5 3<br>Log Inspectors per Firm in the State<br><!-- End of picture text --> <!-- Start of picture text --> N<br>o . .<br>2 e<br>@ 8<br>: @<br>By @ ©<br>bi |<br>o<br>='|@ e<br>hr<br>2<br>:<br>|<br>}<br>e<br>©<br>1<br>1 ss 2 aes 3<br>Log Inspectors per Firm in the State<br><!-- End of picture text -->
initially less developed in trade facilitation are assumed to make larger progress in absolute terms but we still keep the rankings across countries unchanged. Some ECA countries have further to go to reach best practice in regulatory reform or in port efficiency, for example, than do others.

# **Description of the Database**

We utilize the database in WMO (2004).<sup>5</sup> Our measures indicate how far a country's performance is from the best-practice country in each of the four trade facilitation dimensions. The best-practice country is indexed to a value of 1.0. Among the 75 sample countries in 2001, Singapore is the best performer in port efficiency and Finland is the best performer in the other three areas. For each country, the indicator "Port Efficiency" is an average of the efficiency of the port facilities / inland waterways and air transport;<sup>6</sup> the indicator "Customs Regimes" captures the hidden import barriers other than published tariffs and quotas, and the irregular extra payments or bribes connected with import and export permits; the indicator "Regulatory Policy" is constructed as the average of the transparency of government policy and the control of corruption; and the indicator "Information Technology Infrastructure" is measure of the speed and cost of internet access and the contribution of internet to the reduction of the inventory cost.

# **Cross-Country Comparisons in ECA**

The 15 EU member countries (EU15)—the members that existed prior to May 2004— are relatively advanced in all four areas, with an average value of 0.82, 0.87, 0.79, and 0.78 in port efficiency, customs regimes, regulatory policy, and information technology infrastructure, respectively.<sup>7</sup> The development level of trade facilitation in the EU15 ranks well on average; with customs efficiency rated the highest.

The new EU8 member countries, however, are less developed in these four areas with an average value of 0.60, 0.73, 0.65, and 0.64, (development level of 72%, 83%, 81%, 81%

> 5 See annex 1 for a summary of the statistics and data sources of the four trade facilitation indicators.

> 6 Due to the data constraint, we could not measure the efficiency of the port facilities and inland waterways separately.

> 7 The data for Luxembourg is not available. We use the average of the other 14 member countries as that of EU 15.

6
# **1.** **_The puzzle_ : an open and growing economy, but investment is declining**

**The Filipino economy is open to trade and capital inflows.** After the political and debt crisis of the 1980s, the government implemented market-based economic reforms, resulting in a more outward-oriented economic system (Figure 1). The main policies included liberalizing trade, oil, telecommunications, and domestic shipping; opening up to foreign direct investment; privatizing government assets; and strengthening the central bank’s independence. Services are rapidly becoming the dominant sector in the economy and account today for more than 50 percent of GDP (Figure 2).

**Figure 1. Growth with declining investment ...**

<!-- Start of picture text --> 35 8<br>Aquino's assassination<br>Coup attempts<br>6<br>30<br>4<br>25<br>2<br>Asian crisis<br>20<br>0<br>Economic reforms<br>-2<br>15<br>Marcos leaves -4<br>10<br>-6<br>5<br>-8<br>Fixed Investment<br>GDP Growth (secondary axis)<br>0 -10<br>19801982198419861988199019921994199619982000200220042006<br>%<br>% GDP<br><!-- End of picture text -->

**Figure 2. ... in a service economy**

<!-- Start of picture text --> Share of Services in GDP (%)<br>China 2002<br>2006<br>Malaysia<br>Philippines<br>Thailand<br>0 10 20 30 40 50 60<br><!-- End of picture text --> _Source:_ National Statistical Coordination Board, 2007. _Source:_ Asian Development Outlook, 2007.

**Since 2002, growth has been higher than in the past.** Over the past 40 years, the country struggled to raise economic growth (from its historical trend of 3.8 percent) above population growth (Figure 1).  In 2002-06, the economy - driven by services - grew at 5.3 percent, well above its past performance<sup>4</sup> - but only average in its neighborhood (Table 1).

4 For the first time since the 1970s, growth performed at 5 or more percent for three consecutive years.
The upper poverty line determines the nonfood adjustment on the basis of the consumption pattern of households whose _food expenditures_ are equal to the food poverty line. Their spending on nonfood consumption is added to the food poverty line, as shown by segment B in Figure 1.

# Data

We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years. The sample of the 2017/18 round includes over 58,000 households in the whole country, comprising about 178,000 individuals. Data was collected between July 11, 2017, and July 9, 2018.

The survey collects very detailed data on the household budget composition and on the living conditions of the population, including the subjective perception of quality of life and information on the nutritional profile. It consists of seven questionnaires that collect information on demographics, work and income, quality-of-life perceptions, expenditures, and food consumption (at the household and individual level). For the estimation of the poverty line, we use data from the three expenditure questionnaires (POF 2-4), in addition to key demographic data collected in the general household questionnaire (POF 1). The expenditure questionnaires collect information on monetary consumption expenses as well as the value of nonmonetary consumption.<sup>6</sup> In addition, we need information on the caloric value of the food consumption expenditures incurred by households. This information can be obtained from the TBCA. It has nutritional values per 100 grams, including calorie intake, for an extensive list of meals and food items typically consumed in Brazil.<sup>7</sup> 3. Estimating the poverty line for Brazil We now describe the two main stages of our poverty line estimation: the food poverty line and the total poverty line. To keep the presentation simple, in this section we describe the procedure and assumptions made for our preferred estimation. Robustness checks and the associated results for varying our assumptions are presented in the following section.

# Estimating the food poverty line

As described in equation [1] above, the food poverty line is obtained from multiplying the minimum daily caloric requirement deemed necessary by the cost per calorie of a reference group chosen from the population of interest. We describe each of these steps next.

> 6 Monetary expenditures are expenditures made in cash or by credit card, while the value of nonmonetary consumption is obtained for goods and services acquired through donation (in-kind transfers), withdrawal from business, exchange (trade with no money involved), or own production.

> 7 The TBCA can be accessed at http://www.tbca.net.br/base-dados/composicao_alimentos.php.

5
11 for each year. Latin America and the Caribbean had the highest median vulnerability in 2008 and 2009, and South Asia in 1999.

The change in vulnerability between 1999 and 2008 is shown in figure 5 (importers) and figure 6 (exporters). Nearly one-third of net importers spent at least 5 percentage points more of GDP on oil imports in 2008 than in 2009. Brazil was the only country that managed to decrease vulnerability, and it did so by increasing domestic oil production. Among exporters, one-quarter derived less of GDP from oil revenue, mostly as a result of falling production. Equatorial Guinea, Nigeria, and Vietnam all increased oil production but, unlike other exporters in a similar situation, diversification of the economy led to a lower share of GDP derived from oil revenue, signifying lower vulnerability to oil price shocks.

Figure 5: Distribution of changes in vulnerability among net oil importers, 1999–2008 <!-- Start of picture text --> Below 0% 1<br>0 to 2.5% 30<br>2.5 to 5% 37<br>5% to 7.5% 15<br>7.5% to 10% 7<br>Above 10% 10<br>0 5 10 15 20 25 30 35 40<br>Percentage of countries<br>Sources: Table A1.1 and author’s calculations.<br>Note : The import status is that in 2008.<br>Figure 6: Distribution of changes in vulnerability among net oil exporters, 1999–2008<br>Below -20% 12<br>-20 to -10 19<br>-10 to -5 9<br>-5 to 0 35<br>0 to 5 12<br>Above 5% 14<br>0 5 10 15 20 25 30 35 40<br>Percentage of countries that were net oil exporters in 2008<br>percentage of GDP<br>Change in vulnerability as<br>percentage of GDP<br>Change in vulnerability as<br><!-- End of picture text --> _Sources:_ Table A1.1 and author’s calculations. _Note_ : The export status is that in 2008.

Tables 1 and 2 summarize ∆ C and ∆ V in decomposition analysis for 1999–2008 for net importers and exporters, respectively. The results are shown for the production and consumption terms, which in turn sum three and six factors, respectively. For the two-thirds of net oil importers that did not produce oil, ∆ C and ∆ V are the same and ∆ P is equal to zero. Only six countries, all net oil exporters and of which four are in Europe and Central Asia, managed to
|Mexico Environmental SustainabilityDevelopment PolicyLoan|Adriana Goncalves Moreira|
|---|---|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR LifelongLearningProject|Marcela Ines Salvador|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|AR Provincial Maternal-Child Health Investment APL 2|Andrew Sunil Rajkumar|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|UY PromotingInnovation to Enhance Competitiveness|Diego Ambasz|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|Haiti Transport and Territorial Development Project|Pierre Xavier Bonneau|
|P085133|2005|2013|6/3/2013|13 Closed|Indonesia|
|---|---|---|---|---|---|
|P085133|2005|2014|12/14/2013|14 Closed|Indonesia|
|P085133|2005|2014|6/30/2014|15 Closed|Indonesia|
|P085133|2005|2015|1/13/2015|16 Closed|Indonesia|
|P085133|2005|2016|7/30/2015|17 Closed|Indonesia|
|P085133|2005|2016|12/30/2015|18 Closed|Indonesia|
|P085192|2005|2005|6/23/2005|2 Closed|Rwanda|
|P085230|2004|2004||0 Closed|Burkina Faso|
|P085230|2004|2005|4/6/2005|2 Closed|Burkina Faso|
|P085230|2004|2006|11/5/2005|3 Closed|Burkina Faso|
|P085230|2004|2006|12/20/2005|4 Closed|Burkina Faso|
|P085230|2004|2007|10/5/2006|5 Closed|Burkina Faso|
|P085230|2004|2008|7/9/2007|6 Closed|Burkina Faso|
|P085230|2004|2008|2/29/2008|7 Closed|Burkina Faso|
|P085230|2004|2009|12/30/2008|8 Closed|Burkina Faso|
|P085230|2004|2010|7/4/2009|9 Closed|Burkina Faso|
|P085230|2004|2011|4/14/2011|10 Closed|Burkina Faso|
|P085231|2006|2006|12/20/2005|1 Closed|Yemen,Republic of|
|P085231|2006|2006|5/25/2006|2 Closed|Yemen,Republic of|
|P085231|2006|2007|12/21/2006|3 Closed|Yemen,Republic of|
|P085231|2006|2007|6/15/2007|4 Closed|Yemen,Republic of|
|P085231|2006|2008|12/14/2007|5 Closed|Yemen,Republic of|
|P085231|2006|2008|6/24/2008|6 Closed|Yemen,Republic of|
|P085231|2006|2009|12/9/2008|7 Closed|Yemen,Republic of|
|P085231|2006|2009|6/18/2009|8 Closed|Yemen,Republic of|
|P085231|2006|2010|7/13/2009|9 Closed|Yemen,Republic of|
|P085231|2006|2010|12/23/2009|10 Closed|Yemen,Republic of|
|P085231|2006|2010|5/21/2010|11 Closed|Yemen,Republic of|
|P085231|2006|2011|1/2/2011|12 Closed|Yemen,Republic of|
|P085231|2006|2012|12/25/2011|13 Closed|Yemen,Republic of|
|P085231|2006|2012|6/26/2012|14 Closed|Yemen,Republic of|
|P085231|2006|2013|12/23/2012|15 Closed|Yemen,Republic of|
|P085231|2006|2013|6/20/2013|16 Closed|Yemen,Republic of|
|P085231|2006|2014|12/27/2013|17 Closed|Yemen,Republic of|
|P085231|2006|2014|6/21/2014|18 Closed|Yemen,Republic of|
|P085231|2006|2015|12/28/2014|19 Closed|Yemen,Republic of|
|P085231|2006|2015|6/16/2015|20 Closed|Yemen,Republic of|
|P085260|2005|2005||0 Closed|Vietnam|
|P085260|2005|2006|5/15/2006|1 Closed|Vietnam|
|P085260|2005|2007|12/1/2006|2 Closed|Vietnam|
|P085260|2005|2008|8/28/2007|3 Closed|Vietnam|
|P085260|2005|2008|1/2/2008|4 Closed|Vietnam|
|P085260|2005|2008|6/12/2008|5 Closed|Vietnam|
|P085260|2005|2009|2/7/2009|6 Closed|Vietnam|
|P085260|2005|2009|6/29/2009|7 Closed|Vietnam|
|P085278|2003|2019|5/9/2019|1 Closed|Indonesia|
|P085302|2004|2004||0 Closed|Ecuador|
find new suppliers; and (ii) whether firms maintain links with suppliers. The first measure is summarized, for each firm _j_ and time period _t_ by the which measures the percent of suppliers who are new in the current period. A market is resilient if the entry rate does not decrease after a shock. The second measure is summarized by the which measures the percent of suppliers from the last period that no longer supply in the current period. A market is resilient if the separation rate does not increase after a shock. A net separation rate may also be measured as To evaluate the resilience of U.S. supply chains during COVID, we construct these measures for U.S. imports. The data source is Panjiva, which compiles the manifests for all container shipments coming into U.S. ports. Though the data do not include trade values, firm identifiers allow us to measure the extensive margin of trade. For each shipment, the data identify the U.S. firm receiving the shipment (consignee) and the foreign firm sending the shipment (shipper).

Figure **5** shows the quarterly averages of the entry and separation rates across all importing firms in the U.S. during COVID and a year before. Both measures have an average of about 35 percent, as many firms do not receive shipments from the same firms each quarter, and receive shipments only once or twice a year.

By these measures, supply chains appear resilient in the quarter of March 2020 to May 2020, the first wave of the pandemic. In that quarter the total value of imports dropped 13 percent in real terms relative to same quarter in the previous year, reflecting reduced demand and prices as the country entered lockdown, and potentially supply reductions elsewhere.<sup>18</sup> Despite this, the entry rate spiked to 42 percent. The separation rate also fell, as firms held on to previous

> 18 Total import values reported by the Census Bureau: https://www.census.gov/foreign-trade/balance/c0015.html. In the Panjiva data the volume of container shipments also declines in March to May 2020 relative to March to May 2019.

22