rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1.  Review of Student Achievement Tests**

|_No_|_Year_|_Organization_|_Abbr._|_Subject_|_Countries/ Areas_|_Grade/Age_|_Inclu_<br>_ded_|_Survey Series_|
|---|---|---|---|---|---|---|---|---|
|1|1959-1960|IEA|Pilot Study|M,S,R|12|7,8||-|
|2|1964|IEA|FIMS|M|12|7, FS|■|A.1|
|3|1970-71|IEA|SRC|R|15|4,8, FS.||A.1|
|4|1970-72|IEA|FISS|S|19|4,8, FS.|■|A.1|
|5|1980-82|IEA|SIMS|M|19|8, FS|■|A.2|
|6|1983-1984|IEA|SISS|S|23|4,8, FS|■|A.2|
|7|1988, 1990-91|NCES|IAEP|M,S|6, 19|4,7-8|■|A.1|
|8|1990-1991|IEA|RLS|R|32|3-4,7-8|■|A.1|
|9|Every four years since 1995<br>(latest round is 2015)|IEA|TIMSS|M,S|45, 38, 26, 48, 66, 65, 65|3-4,7-8, FS|■|A.1 (1995), A.2. (Other<br>years - except 2011)|
|10|1992-97|UNESCO|MLA|M,S,R|72|6,8|■|B|
|11|1997, 2006, 2013|UNESCO|LLECE|M,S,R|13, 16(only6 for science)|3,6|■|B|
|12|1999, 2002, 2007|UNESCO|SACMEQ|M,R|7, 15, 16|6|■|B|
|13|1993-2001,2002-2012, 2014|CONFEMEN|PASEC|M,R|22 (before 2014), 10|Until 2014: 2,5<br>After 2014: 3, 6|■|B|
|14|Every five years since 2001<br>(latest round is 2011)|IEA|PIRLS|R|35, 41, 55|4|■|A.1 (2001), A.2. (Other<br>years - except 2011)|
|15|Every three years since 2000<br>(latest round is 2015)|OECD|PISA|M,S,R|43, 41, 57, 74, 65, 71|Age 15|■|A.1 (2000 for reading,<br>2003 for math, 2006 for<br>science),<br>A.2. (remaining rounds)|

Note: For the meaning of abbreviations, please consult page 21. Only assessments for which there is an information in "Survey Series" column are included in our dataset. Subjects: M=math; S=science; R=reading.

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**Chart 25**

<!-- Start of picture text --> Fiscal Indicators OECD Aggregate<br>15 100<br>Public debt (% of GDP) ‐right axis Budget balance (% of GDP)<br>Primary balance (% of GDP) 80<br>10 75.10% 60<br>60.20 % 63.90% 40<br>5<br>20<br>0<br>0<br>‐20<br>‐5 ‐40<br>‐60<br>‐10 ‐80<br>Source: Economist Intelligence Unit<br>(% of GDP) (% of GDP)<br>2007 2008 2009<br><!-- End of picture text -->

# **The Response to the Crisis from Debt Managers in EMs**

**The global financial crisis heightened the role played by debt managers beyond meeting the unexpected borrowing requirements; their actions affected the effectiveness of the policy response to the crisis** . In several countries, debt managers were requested to raise additional funding for the implementation of vital fiscal stimulus packages in an environment of investors’ retrenchment. But more importantly, this had to be done in a manner that contributed to the stability of the domestic financial market shaken by the flight of foreign investors and strong risk aversion.  In this delicate environment the borrowing decisions needed to carefully weigh the potential impact on interest rates, the fiscal space taken by debt servicing costs, the investors’ response and the overall effect on the financial markets.

**The debt managers’ response to the crisis varied from country to country depending on the array of instruments available and their experience to work in a new adverse environment** . The global financial crisis tested the debt managers’ flexibility to adapt their borrowing strategies after the macro and market fundamentals shifted dramatically<sup>23</sup> . The responses of the debt managers in the sample of selected EMs can be grouped in three categories:

- A. Reducing the pressure on the market by filling part of the funding gap by other mechanisms;

- B. Adapting the funding program to shifts in the demand for government paper; and

- C. Implementing liability management operations to support the market.

## A. **Measures that reduced the pressure on the market by filling part of the funding gap by other mechanisms**

> 23 Romania is the most striking example: at the beginning of 2009 the forecasts were GDP growth of 2.9% and a deficit of 2% of GDP. At the end these numbers were -7% and 7.2% respectively.

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involved are by no means trivial—exogenously lifting constraints to credit access could increase yields by at least 17 percent according to our estimates—and further exploration of the mechanisms for nonfarm participation in the context of overall structural transformation would be of interest.

A key policy that has traditionally been advocated to address credit constraints relates to formal and semiformal lenders’ ability to identify and transfer land at low cost so as to reduce the cost of using it as collateral for loans. Rwanda is one of the few countries in Africa to have undertaken a large program of systematic land tenure regularization that aims to deliver titles for all the 10 to 11 million land parcels in the country by 2013.<sup>20</sup> In theory, formalizing land rights can affect investment demand due to farmers’ higher tenure security and credit supply due to a collateral effect (Feder and Nishio, 1999). While such effects have been rather elusive in Africa (Migot-Adhola et al., 1991; Place and Migot-Adholla, 1998), high levels of population density as well as land market activity in Rwanda, as well as higher global demand for agricultural commodities, imply that it will be of great interest to see whether this program will affect credit markets. At the same time, many studies from developing countries show that risk may be a more relevant constraint to investment (Karlan et al., 2012), consistent with the high incidence of risk rationing in our sample. Further study to explore not only the mechanisms through which credit constraints affect participation in nonagricultural self-employment but also the extent to which riskrelated concerns may attenuate the impacts of supply-side factors will be of great interest not only for Rwanda but also for other African countries.

> 20The Land Tenure regularization Project and Rwanda’s context is explained in details in Ali et al. (2012). DFID annual review provides the most updated data on the advancement of the project. http://projects.dfid.gov.uk/project.aspx?Project=200284

15
problems in service. More generally, the findings are in line with those from Niger (Beasley and Huillery, 2015) that communities are unlikely to successfully monitor school resource use without significant investments in capacity.

Neither MEET nor SMS had any significant impacts on learning outcomes. This finding is consistent with the lack of observed impact on teacher effort with no improvement in teachers’ presence at school. It is also possible that, while individual students did experience some benefit in learning, these effects were offset by the impacts of increased retention of lower-performing students, reducing average performance; this is aligned with Crawfurd (2018) where the authors find null effects on learning from contracting-out of public schools in Punjab, Pakistan to a private company, despite significant impacts on access.

# **5.5 Cost effectiveness**

**<u>Figure 6</u>** presents the implementation cost for all three interventions: MEET, SMS and ELECTIONS-CAPACITY. Costs for both MEET and SMS were similar, totaling US$129,059 for MEET and US$138,369 for SMS. Taking the total endline enrollment of treated schools as the population of impact, this suggests a per-student cost of US$16.26 per student for MEET and US$19.41 for SMS. With 25 percent of expenditures invested at the design stage, per-student expenditures for ongoing and scaled-up activities are estimated to be significantly lower: US$12.85 for MEET and US$15.66 for SMS.

Taking this variable cost as a starting point, **<u>Table 13</u>** presents cost effectiveness estimates of the intervention. Improvement in enrollment is achieved from two intervention impacts: increase in enrollment in schools which remained open at baseline and endline, and additional enrollment 50
Elbers et al (2004).<sup>23</sup> These studies document that, in Ecuador, there is a considerable amount of heterogeneity across _parroquias,_ in terms of both poverty and inequality. At the aggregate level, rural poverty rates are generally highest in the Eastern (Amazon) region. However, at the local level, pockets of very high poverty are also discernable in the central, mountainous, Sierra region and along the Coast. Elbers et al (2004) note that inequality levels vary markedly across _parroquias_ , and emphasize that there should be no presumption that inequality levels are somehow lower in poorer communities.

# _Additional Control Variables_

In addition to the community-level poverty and inequality estimates that are of primary interest in our investigation of the determinants of project choice, we include a number of control variables intended to capture the influence of other factors affecting project choice. From the 1990 census data, we calculate population figures at both the province and the _parroquia_ level. The Census also allows us to calculate the percentage of the population in each _parroquia_ that is of indigenous ethnic origin (based on language spoken). These demographic characteristics could be thought to influence project choice in a variety of ways, and in the case of population are also important to the assessment of whether the FISE program is well targeted at poor communities. Project documents note explicitly that the targeting of FISE funding was to be based on a combination of measured poverty and population of provinces (although the targeting was based on an ad-hoc map of poverty entirely unrelated to the poverty map outlined above). A simple correlation between presence of a FISE project and incidence of poverty at the parroquia level finds no significant association – suggesting very poor targeting. However, once the parroquia population is controlled for, the association becomes positive and strongly significant.<sup>24</sup> As was found by Paxson and Schady (2002) for the case of the FONCODES Social Fund in Peru, geographic targeting of Ecuador’s FISE project

> 23 A question of some importance to this study is whether the poverty map estimates should be seen to correspond the year 1990 (the year of the census) or 1994 (the year of the household survey).  Hentschel et al (1999) argue that because the period between 1990 and 1994 was essentially one of economic stagnation it is not unreasonable to assume that the relationship observed between consumption in 1994 and household characteristics in that year was essentially unchanged from the relationship that held in 1990.  As a result, one can view the poverty map as a reasonable snapshot of the spatial distribution of poverty in both years. For further discussion of these issues see also Elbers et al (2005).

> 24 This evidence on targeting of FISE is also discussed in the next section and illustrated in Figures 3 & 4.

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how many days) as well as water sharing practices (whether households were giving/receiving piped water to/from other households). All but 23 households covered by the baseline survey provided quarterly information throughout the experiment.

**Experimental sample** Our final experimental sample consists of 1,462 water customers.<sup>8</sup> Of these, 114 or 8 percent had been unconnected as of July 2015, but subsequently received a (free) piped water connection through the project in time to be included in the baseline survey. As noted above, prior to our interventions, a larger fraction of commune households (16 percent) did not have connections, presumably because they were not willing to pay the fee. When we turn to optimal pricing in section 4.4, and specifically to the computation of social welfare for the commune, we augment the estimation sample by randomly drawing from the 114 households so that previously unconnected households (inclusive of the original 114) make up precisely 16 percent of the sample.

**Price discounts** The actual price experiment started in January 2016, 6 months into the price linearization; see Figure 1 for a timeline. All the households surveyed at baseline agreed to participate in the price discount scheme and were randomly divided into three groups. Each group would, according to staggered schedules, receive price discounts of 50 or 75 percent and thus be charged 50 or 25 percent of the July 2015 flat price, respectively (Figure 1).<sup>9</sup> Each price subsidy regime ran for 6 months with the water utility reimbursed for the difference between official and subsidized price. However, households were not informed of the overall duration of the price experiment or of any particular discount more than a month in advance.

The experimental design incorporated a six-month pause in all discounts beginning January 2017, one year into the price experiment. The purpose of the pause was to allow a reduced-form test of demand persistence, exploiting the fact the each of the three groups had been exposed to different price discounts over the previous year yet were currently facing the same price. We discuss this test in section 2.4.

> 8While the baseline survey collected data on 1,488 households, 3 were dropped due to consumption exceeding 50 m<sup>3</sup> for 5 or more months during the experiment; they were most likely operating a business out of their homes. We dropped a further 23 households due to non-participation in the quarterly monitoring surveys (so that no time-varying demographic information could be collected).

> 9As mentioned earlier, a universal 50 percent increase in the base price was implemented in the whole province of Bac Ninh in June 2017. This price increase was also passed on to the experimental sample; total subsidies are reflected accordingly in the lower panel of Figure 1, which indicates that 50 (resp. 75) percent rebates translate into a price equal to 75 (resp. 37.5) percent of the July 2015 price.

7
# **Section 4 Revisiting the Conventional Wisdom on Transportation Costs**

We complemented previous macro-analyses on transportation cost cif/fob margins with data from exporters/importers and freight forwarders operating in LLDCs.<sup>18</sup> Road and rail are the main inland surface freight modes for LLDCs<sup>19</sup> . Historically, rail played a crucial role in the early development of most LLDCs (the railways in Central Asia still do). However, railways have ceded market shares to trucking during the last two decades due to their lower performance and reliability in most regions. Containerized traffic for high value goods is usually through trucks. For most shippers, railway discounts (typically 30% or more in Africa and Central Asia) cannot compensate for the induced costs in terms of delays and reliability. As road transport is dominant, we focus on road transport costs. We use the traditional measure of transport costs by ton and by kilometer expressed in US cents.

The long distance ton*km value is in the range of 4-6 US cents in industrial economies, but varies more than tenfold among corridors serving LLDCs:

- As low as 1.5-2 US cents in Western Asia (Iran, Pakistan).

- As high as 20 cents of Euros in Chad, barely below current air cargo rates on some long distance routes between developed markets.

Due to the trade imbalance, (imports usually exceed exports in developing countries) exporters usually get a discount on outbound road transport. Although freight rates are

> 18 Collecting transport costs as part of field work can be confusing. Depending on the source or the commodity, there may be variations of transport tariffs for four reasons.  (1) Perimeter of services: depending on their responsibility in the logistic chain, operators include certain costs Typically, freight forwarders who organize the transit trade for the shipper/consignee, charge the latter with not only the transport service cost billed by the truck or rail companies, but also the fixed costs of organizing and administering the transit shipment. (2) Commercial discounts: given to high volumes/preferred customers. (3) Commodity and resulting service quality, and (4) Illegal payments for facilitating the process and these are usually not acknowledged in surveys or interviews.

> 19 Multimodal barges or ferry systems do play a role _inter alia_ in the case of the Parana River (Paraguay), Congo River, and also on the Caspian Sea.

18
Benchmark <!-- Start of picture text --> 0.1<br>0<br>04<br>I><br>—~ 0.3<br>Es.<br>Ss<br>&= 105<br>06<br>os<br>08<br>96 605° 04 03 02 01 0<br>(Core) b./¥<br><!-- End of picture text -->

# Eurobonds

<!-- Start of picture text --> 01<br>i)<br>4 DD<br>I><br>a)<br>—-_03 DR<br>6 RR<br>1g RR& DD<br>06<br>R=Repay<br>07 D=Default<br>03 06 4«05' 04 03 02 DA 0<br>(Core) b./¥<br><!-- End of picture text -->
Table 7. Determinants of productivity – Non Linear Effect of Bribery

|VARIABLES|(1)<br>LP|(2)<br>VA|(3)<br>TFP|
|---|---|---|---|
|**Capital**||||
|Log. Stock of capital|0.173***|||
||(0.0329)|||
|**Labor**||||
|Proportion of|0.412**|0.427|0.717***|
|temporary worker|(0.178)|(0.260)|(0.275)|
|Avg. Education of|0.240***|0.211**|0.149***|
|Workers|(0.0339)|(0.102)|(0.0463)|
|**Size and Age**||||
|Market share|76.20***|98.53***|48.20***|
||(16.24)|(26.18)|(8.965)|
|Young|0.135|-0.150|0.180|
||(0.146)|(0.295)|(0.146)|
|Mature|<br>0.132***|<br>0.0466|<br>0.0509|
||(0.0440)|(0.0480)|(0.0410)|
|**Access to finance**||||
|Credit line or overdraft|0.181|0.251***|0.142***|
|Facility|(0.133)|(0.0626)|(0.0456)|
|**Investment Climate (%)**|0.00675|0.0131|0.0160*|
|Cost of insecurity|(0.0193)|(0.0181)|(0.00894)|
|Cost power outage|-0.00585*|-0.0124**|-0.00397|
||(0.00322)|(0.00487)|(0.00255)|
|Bribe|0.103***|0.0451|0.0614***|
||(0.0312)|(0.0362)|(0.0180)|
|Bribe*Market share|<br>-7.583**|<br>-9.624**|<br>-5.126***|
||(2.952)|(4.770)|(1.688)|
|Time Tax|-0.00263|0.000759|-0.00158|
||(0.00930)|(0.00788)|(0.00748)|
|**Outward-orientation**||||
|Exporter|0.172|0.637|0.290|
||(0.663)|(0.532)|(0.573)|
|**Sector**||||
|Manufacturing|1.973|-0.151|0.881**|
||(1.302)|(0.995)|(0.398)|
|Observations|1,277|1,302|1,275|
|R-squared|0.508|0.508|0.352|
|Weak identification test|8.559|9.184|8.896|
|Region FE|YES|YES|YES|
|Year FE|YES|YES|YES|

Source: Authors based on the data from the Enterprise Surveys 2007/2009 and 2014.

Note: LP=labor productivity per worker; VA=Value added per worker; TFP are obtained using Levinshon and Petrin (2003)’s approach. Robust standard errors clustered at the States level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All the estimates use the weights from the survey.

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||**Table 6**<br>**SM**|**: Split sample**<br>**Es (<100 work**|**results for gro**<br>**ers)**|**wth rate of la**|**bor productiv**|**ity**<br>**Large fi**|**rms (>=100**|**workers)**||
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:<br>Labor Productivity (logs)<br>**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
|Ethnic<br>-18.968***|-14.914***|-14.743***|-14.596***|-14.056***|-12.257***|-7.187|-7.016|-3.243|-2.190|
|Fractionalization<br>(4.749)|(4.925)|(4.946)|(4.945)|(4.679)|(4.420)|(5.028)|(4.919)|(4.606)|(4.295)|
|Initial Labor<br>-5.500***|-5.801***|-5.918***|-5.958***|-5.893***|-6.971***|-7.310***|-7.502***|-7.470***|-7.511***|
|Productivity (logs)<br>(0.602)|(0.582)|(0.561)|(0.581)|(0.607)|(0.659)|(0.730)|(0.718)|(0.698)|(0.718)|
|GDP per capita|3.034**|2.952**|2.897**|4.031***||3.771**|3.750**|3.856**|2.929|
|(logs)|(1.254)|(1.296)|(1.276)|(1.434)||(1.658)|(1.731)|(1.623)|(2.125)|
|Age of firm (logs)||0.092|0.196|0.367|||2.523|2.775*|2.980**|
|||(1.022)|(1.026)|(1.014)|||(1.534)|(1.438)|(1.477)|
|Manager experience (years, logs)||0.986|0.852|0.937|||0.889|0.374|0.409|
|||(1.126)|(1.088)|(1.057)|||(1.302)|(1.278)|(1.310)|
|Exports (proportion of sales)||6.229***|5.961***|6.109***|||1.437|0.733|-0.330|
|||(2.130)|(2.126)|(2.136)|||(2.282)|(2.103)|(1.943)|
|Foreign Ownership||2.996|2.737|2.993|||2.882*|2.950*|2.390|
|Y:1 N:0||(1.833)|(1.874)|(1.896)|||(1.637)|(1.556)|(1.584)|
|Firm is Financially Constrained||0.119|0.103|0.183|||-3.849|-4.191|-4.128|
|Y:1 N:0||(1.908)|(1.828)|(1.788)|||(2.603)|(2.727)|(2.732)|
|Female Top|||0.460|0.344||||-2.347|-2.441|
|Manager Y:1 N:0|||(1.334)|(1.398)||||(1.801)|(1.767)|
|Firm Provides|||0.011|0.010||||-0.021|-0.023|
|Training Y:1 N:0|||(0.013)|(0.013)||||(0.016)|(0.016)|
|Courts Obstacle|||-0.675|-0.795||||-4.791***|-4.441**|
|Y:1 N:0|||(1.274)|(1.278)||||(1.675)|(1.714)|
|Total hours of|||-0.002|-0.001||||-0.029**|-0.021*|
|power outages|||(0.010)|(0.009)||||(0.012)|(0.012)|
|Firm Purchased Fixed Assets Y:1|||1.362|1.264||||3.301|2.758|
|N:0|||(1.097)|(1.104)||||(2.075)|(2.019)|
|Time Tax|||-0.061|-0.061||||-0.026|-0.046|

54
case in different applications are patchy. Therefore, more analytical work is needed to underpin any policy intervention to promote off-grid applications. Second, to identify policy options for shifting the diesel subsidy savings towards financial incentives for off-grid users, such as tax incentives or customs exemptions for imported PV technologies. If financial support is to be provided to users directly, this support should take the form of performance-based payments in order to avoid that technologies sit idly or being resold on black markets. Third, to identify options for aggregating users, for example in the 1.5 Million Feddan Project or in poorer areas in Upper Egypt. Because off-grid solar applications are affected by policies of different government ministries, including the Ministry of Finance (for diesel subsidies), the Ministry of Petroleum, the Ministry of Agriculture and the Ministry of Tourism, any intervention should be coordinated between the relevant ministries.

Third, the government should strengthen private-sector activity in the sector, including by addressing nonfinancial barriers such as transaction costs and gaps in the availability of data, and by building capacity among users, suppliers, and financial institutions. The complexity and administrative procedures of any new policy incentive should be minimized to reduce transaction costs and broaden access to the scheme. The government should make easily accessible any data it has collected that could be useful to estimate the potential for distributed solar in Egypt and should continue doing so for any new analyses conducted in the future. Further, any future financial support by the government for distributed solar should be accompanied an awareness campaign targeted at end-users, distribution companies and other market participants, including banks. Furthermore, capacity building in the form of specialized trainings and certification schemes should be availed.

26