rafmacalaba/datause-encoder-data huggingface dataset:
. : . A . ; Ka ¥ abo S fe) ‘3 as] N S| xR we ERseponensggnaencegeegeeenengnanananengennngnergsonanON REreesnsenanenangneninnsenettgneamgyRE On BE co DAE EE OP ODEsaptnnenemangngnanontanarannnanagsoesgonrpaneesOO. OI OSE: EET Ba: vecreaeenngeaennREen eee . 5B bee eee SPORE TRS SPER ASE ERE ERE en SUN rR PLE SEE RE ON ER BE EA Wi toe io) CREEAhese WIG mie FS ORR: FRco BA ERE BAKE ce EE 00 FEE: oy He TER 00 EEE wn FRG ern He! RSE 00 FRE 28 oe EGE em UA | A=} Git] COPS:e+eCEM EREBARERA: EMEBeEO ESSERESF EM TS **R** SNS:HETTSOE OS FEMRSO MAMANEY: OP CONE WeEE OO ERE EGY Wee ME 7) ak i 8 re re rr ee ee ee ee ee | we InPEEpS NESEBe co SRCHEEE **B** eirE OEES OBEEE EsRSE URES EEEee en EDRl en 2522 on 22RE SE#22 wo ERBP CeEPP neeESS **EE** SRBE O **E** Re A=F ob ‘g [itl v **e** n NaSLAG:s sanERE O **e** SeeRR OEESF RATBRSE ON Ee: St URE: Nt ERR 0 EE **O** n EE CEETece msQR Be 8ae Se an} ~ eo] feb EE FP Ee EES 22 Be B22 EPP 52E EP) 282 Hep Bee Ee) EE 22 Ee 58 BBP Hep geo; ° o ; Seal Bee BEE 552 ES BES BRT BE ERE: HEE GE: Ee Bie BS: EP GES BS: Eee Be: 2ES2 ERP BEE eee WES g OQ a ee ee ee re rr ee Bee Se: Hp DUR USE ES: bE EEP BE: 22 285 522 EES eel 25 os) ole Sg a ae Sg. ce e559. on an BEE EEE DE Eee aig 4 om TE EE SS EE ERED VUES BEL OSB: OER QUES qe MSR Re SS EES BER 9 HES OPE SRE OESE ORE SEG oO Hepesfoe: SSO Sse 38 Se agE2ESR Sst ag:Bes PoaDUR BOSE B88USS: OSG DeeSe .On:<sup>ice!</sup> SeOIdEe ee: OFSES greMem SeeOey 4 oeSe ate EES8 idei Ofi CS See: FS. ESE OS ONS: Bee SEO: OFS O28 sla REE: Sees Deo: Rae oie Vue! OUR: Go OPS See ye FiM 8 esUSLROR RECS SESSA az ee eo ese a ARR OO RSetke RECA Ske “ig 2 Sans EE nant PRaenet he ans eto an naonabncnstbaonestnintoamseibatntan A Hasnan tit dinstitosinteoat tt Soames tt tivominaman een Ei tim timminnd Ga g YQ & is)vo . nmaeBDa 29 q 2 aea¢eOo .5 ae) wm 2 o ao a w ay Qe . a8 & — Bm77)@ [r} jaa} , <p) os So5 &kh ~A wy
_Congestion_ . Marginal congestion costs depend on the added delay to all other road users caused by the additional congestion from an extra vehicle mile, as well as how people value travel time. We employ a widely used function relating travel time to traffic volume in order to obtain marginal delay (see the appendix). The value of travel time is assumed to equal one-half the average gross hourly wage in Cairo, based on US studies of the wage/value of time relationship. This might be viewed as a conservative estimate to the extent that car ownership and use among Cairo residents is concentrated among people with higher than average wage rates.

Overall, the marginal congestion cost for autos is 9.7 cents per _vehicle_ mile. We assume the ―passenger car equivalent‖ for a microbus and a public bus vehicle mile are 3 and 5 respectively. Thus, marginal congestion costs for microbuses and buses are 29.2 and 48.7 cents per _vehicle_ mile respectively. However, the ranking of marginal congestion costs reverses when expressed on a per passenger mile basis, due to the high occupancy rates of buses. Marginal costs are 2.1 and 1.1 cents per extra passenger mile by microbus and public bus respectively, compared with 3.9 cents per passenger mile for autos.

_Accidents_ . External accident costs are driven primarily by fatality risks to pedestrians and cyclists (fatality risk to vehicle occupants is assumed to be internal, though there is some dispute about to what extent risks from multi-vehicle collisions are internal or external). Fatality risks are valued in the same way as pollution/health risks. Based on studies for other countries, other external costs from traffic accidents (e.g., non-fatal injury risk, third party property and medical burdens) are assumed to be 12 percent of those from fatality risk. Overall, external costs are 8.7 cents per vehicle mile for autos—for reasons noted in the appendix costs per vehicle mile are taken to be the same for buses. On a per passenger mile basis, external accident costs are 3.5 cents for autos, 0.6 cents for microbus, and 0.2 cents for public bus.

_Prices and operating costs_ . Gasoline is heavily subsidized in the GCMA. As of 2009, the fuel subsidy amounted to $1.20 per gallon, leaving a retail price of $1.60 per gallon (compared with a supply cost of $2.80 per gallon).

16
-4-

of fewer.

Theoretical justification for the current practice, when any is offered at all, consists of an appeal to the Arrow-Lind theorem. This says that risk aversion can be ignored when evaluating a public investment project if (i) the project is small in relation to the national product, and (ii) the risk in the project return is uncorrelated with that of the national product.

The assumptions are not innocuous. The first is violated for some major power, transport and irrigation projects. These are not the focus of our study; the implications of risk for such large projects are being examined by other units in the Bank. 1 The second assumption is violated in many circumstances, especially for countries with significant specialization in a crop or raw material whose price fluctuates on world markets. When a new investment project in the same sector is being considered, its return will be perfectly correlated with the return to all previous investments in that sector, and therefore significantly positively correlated with national income or wealth as whole. In other words, such a project carries a significant systematic or undiversifiable risk, and considerations of risk aversion become important.

The capital asset pricing model (CAPM) handles exactly- this a/ See a draft report entitled "Preliminary Stage of a Research Program on Improved Methodology for Incorporating Risk and Uncertainty in Power Systeu Planning: Literature Review and Survey of Current Practices," prepared for the World Bank by Information for Investment Decisions, Inc., nd.
the Economic Administrative and Social Research Center. For the evaluation of CETROCRECE, the Ministry of the Economy commissioned the Institute of Engineering at the National Autonomous University of Mexico ( _Instituto de Ingenieria de la Universidad Nacional Autónoma de México_ —IIUNAM).

Past program evaluations have varied considerably in design and methodology, in part due to vague legal requirements concerning the type of evaluation required. The evaluation methodologies used can be categorized into three main groups in order of the statistical rigorousness of their designs (Table 13): a) non-experimental evaluations (CIMO-PAC, CONOCER, and a recent terms of reference for Fondo PYME); b) operational evaluations (CRECE, FIDECAP, FAMPYME, and PAT); and c) case studies (PMT, PAIDEC and COMPITE).

**Table 13 Evaluation Studies in Mexico**

|**Program**<br>**Evaluated**|**Responsible**<br>**agency**|**Evaluating institution**|**Date**<br>**completed**|**Type of Analysis**|
|---|---|---|---|---|
|CIMO|STPS|STPS|1995, 1997|<br>Comparison with match<br>firms|
|CIMO|STPS|Alduncín y Asociados|2002|Comparison with match<br>firms|
|CIMO|STPS|World Bank|2005|Comparison with match<br>firms|
|CONOCER|STPS|Grupo de Economistas y<br>Asociados|2001|Comparison with match<br>firms|
|COMPITE|SE|Lateral Investment Fund from<br>IDB*|2003|Recipient views of program<br>impact|
|CRECE|SE|IILUNAM|2003|Recipient views of program<br>impact|
|FIDECAP|SE|IPN|2003|Recipient views of program<br>impact|
|FAMPYME|SE|IPN|2003|Recipient views of program<br>impact|
|PMT|CONACyT|CONACyT|2000|Recipient views of program<br>impact|
|PAT|BANCOMEXT|BANCOMEXT|2000|Recipient views of program<br>impact|

13
Table 11. Estimates **of** the Elasticity of Revenue with Respect to Volume for a 20% Real Devaluation by SSA.

|E _ ~~~~~~~~~~~~~~|~~~~~~~~~~~~~~~~|~~~~~~~~~~~~~~~~~~~~~.<br>... .... .... ..||
|---|---|---|---|
|Wi'**'**<br>~<br>~<br>~<br>00<br>0UMgS<br>i '<br>X.9<br>''X' '9<br>g|993|09|09<br>093S1{:|

Source IECT, Wodd BankR 1/ Price Elasicty of Demand = - 0.2; Income Ellastiity of Demand 0.5; Growth Rate in Real GDP 2% p.a. 2/ Price Elastict of Demand = - 0.1; Other Parameter as in Base Simulation. 3/I ncome Elasticity of Demand = 0.25; Other Paameers as in Base Simulation. 4/ Inme Grwth Rate = 1%; Other Paramters as in Base Simulation.

21
# Chart 13. Fiscal<sup>Position of Some Major Industrial Countries:</sup> An Illustrative<sup>Scenario</sup>

# (a) Budget Deficit (Percent of GDP)

<!-- Start of picture text --> 35<br>Italy<br>30 Y<br>25<br>15 Canada<br>1990 95 2000 05 10 15 20 25 2030<br>200.U.S.<br><!-- End of picture text --> 800 <!-- Start of picture text --> 00' Italy<br>600 -<br>400 _ - ,_' o ~~~~~~~~~~Gerrnany<br>1990 95 2000 05 10 15 20 25 2030<br><!-- End of picture text --> _Source: McKinsey (1994)_ _21_
example, Cox (1997), Strom (1990), and Cox and McCubbins (1993) all argue that electoral rules affect the cohesion and discipline of parties. Bowler, Farrell, and Katz (1999), Cox (1997) and Cox and McCubbins (1993) also emphasize that parliamentary systems increase incentives for party cohesion. Nevertheless, although the political system variable is, in particular, significantly related to our measure of programmatic parties, results are robust to controls for all these variables.

**Table 1: Summary Statistics**

||Count|Mean|Standard<br>Deviation|Minimum|Maximum|
|---|---|---|---|---|---|
|IEG, Institutional<br>Development Impact Rating|295|.41|.49|0|1|
|IEG,Overall Outcome Rating|301|.42|.49|0|1|
|Programmatic Parties|415|.52|.38|0|1|
|LogGDPper capita|418|1.27|1.66|.08|9.02|
|LogPopulation|418|16.59|1.47|12.95|20.94|
|LogLand Area|426|12.63|1.64|5.83|16.61|
|FundingAmount|439|99.26|198.33|.49|2525.25|
|Project Duration|439|3.78|2.53|1|12|
|IEGQualityat Entry|305|4.21|1.42|1|6|
|ICRG BureaucraticQuality|354|2.51|1.13|0|6|
|Loan % Allocated to PSR|439|44.64|21.01|11|100|
|Newspaper circulation|321|34.55|42.18|.09|200.15|
|Gross secondary school<br>enrollment|411|48.72|30.06|5.19|110.36|
|Natural Resource Exports (%<br>GDP)|365|2.69|5.38|0|49.19|
|PR|401|.54|.48|0|1|
|Political System|423|.39|.74|0|2|
|Mean District Magnitude|393|15.19|34.19|.7|281.5|

27
# **Table 3: Panel A – Multinomial Regressions**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.1905 ***|0.0141|-0.0088|-0.1391 ***|-0.0475 **|-0.0008|-0.2820 ***||-0.5195 ***|-0.0365|
|Estonia|-0.2852 ***|0.0081|-0.0506 ***|-0.0358 **|-0.0976 ***|-0.0155|0.0374 **||0.2462|-0.0130|
|Hungary|-0.0695|0.0017|0.0007|-0.0398|-0.0224 *|-0.0027|-0.1290 ***|0.4006|0.2215|-0.0434 ***|
|Latvia|-0.4303 ***|-0.0250|0.0117|-0.0982|-0.0480 **|-0.0452 *|-0.1245 ***|-0.9904 ***|-0.9248 ***|-0.0311|
|Lithuania|-0.2006 ***|-0.0183 *|-0.0132|-0.0412 ***|-0.0339 **|0.0127|0.0103||0.0793|-0.0032|
|Poland|-0.2896 ***|0.0150|-0.0100|-0.0749 ***|0.0258|0.0394|-0.5823 ***|-0.5512|-0.5621 ***|-0.002|
|Slovak Republic|-0.1963|-0.0150|0.0004|0.0114|-0.0307 *|0.0298|-0.0964 ***|0.1450|0.7868|0.0347|
|Slovenia|-0.1687 *|-0.0285 ***|-0.0196 ***|-0.1244 ***|-0.0625 ***|0.0873 ***|-0.1136 ***|-0.0579|-0.0918|0.1402 ***|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.6764 **|0.0043|-0.0252 **|-0.2457|-0.0187|-0.0102|-0.4861 ***|-0.9232 ***|-0.9671 ***|-0.0343|
|Belgium|-0.2716 **|0.0405 *|0.0013|-0.2824 ***|-0.0722 **|-0.0061|-0.4598 ***|-0.0055|-0.0205|-0.1544 ***|
|Cyprus|0.0764|0.0133|-0.0288 *|-0.1987 ***|0.0135|-0.0424|-0.2652 ***|0.4060|0.7276|0.0209|
|Denmark|-0.3377|-0.1023 ***|0.0637 ***|-0.5600 *|0.0810 **|-0.0723|0.5214 ***|-0.9997 ***|-0.9998 ***|0.2031 ***|
|Finland|-0.3005 **|-0.0034|-0.0030|-0.3458 ***|0.0148|0.0612|-0.3450 ***|0.2375|0.6248|-0.0842 ***|
|France|-0.3813 ***|0.0364 *|-0.0039|-0.1541 ***|-0.0479|-0.0413|-0.4723 ***|0.9512 ***|0.9655 ***|0.0438|
|Greece|-0.5006 ***|-0.0296 *|-0.0798 ***|-0.0014|-0.0890 ***|0.0461|-0.2025 ***|0.2732|-0.4130 ***|-0.0288|
|Ireland|-0.5735 ***|0.1005 ***|0.0192 *|-0.1396 ***|0.0338|0.0289|-0.6699 ***|-0.2334|0.8394 **|-0.1362 ***|
|Italy|-0.3677 ***|-0.0094|-0.0226 ***|-0.0863 ***|-0.0584 ***|0.0322|-0.1085 ***|0.0700|-0.2201 **|0.0286|
|Luxembourg|-0.2668 **|-0.0301|0.0076|-0.3874 ***|0.0771|0.0547|-0.4626 ***|-0.5687|-0.9415 ***|-0.0365|
|Netherlands|0.3008 **|-0.0085|-0.0049|0.3158 ***|0.0362|0.0535|0.3215 ***|0.5840|-0.1458|0.2447 ***|
|Portugal|-0.2721 ***|0.0354|-0.0113|-0.1106 ***|-0.0459|-0.0437|-0.0626 ***|0.3513|-0.2865|-0.0797|
|Spain|-0.2110 ***|0.0263 **|0.0012|-0.0799 ***|-0.0625 ***|-0.0692 ***|-0.0618 ***|-0.2463|-0.0398|0.0213|
|Sweden|-0.3274 **|0.0204|0.0018|-0.2552 ***|-0.0195|0.0422|-0.5192 ***|-0.9495 ***|-0.8644 ***|-0.0871 ***|
|United Kingdom|-0.6659 ***|0.0336 **|0.0062|-0.1588 ***|-0.0449 **|0.0456|-0.4159 ***|-0.7518 ***||-0.2984 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

3
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**2** 9-

firm voluntarily. Disability insurance was offered by 92% of DB plans in 1988, but 39% of these paid only a deferred benefit. This is an annuity that begins after retirement not at the date of disability. (Trends in pensions 1992, Table 9.16). DB plans do not offer survivor's insurance. Only 34 * of DB plans allowed full-time participants to withdraw funds for hardship reasons in 1989 (Table 9.25, Trends in Pensions 1992). One important drawback of DB pensionz is that they are nominal. Once a worker retires, his pension is not adjusted by inflation except in the uncommon situations where the ex-employer gives pension increases for which there is no contractual obligation. The same happens to deferred pensions, whose amount is defined as of the date of exiting the firn but which begin to be paid only upon reaching an age like 65. Therefore, the benefits of these plans have lower quality than in Chile's AFPs because they are subject to inflation risk. On the other hand, in the phase where investment returns are guaranteed by the firm they resemble the fixed real annuities sold in Chile with the difference that the rate of return is the rate of growth of real wages plus the rate of growth of the number of years in the plan.

Many of the comments regarding the differences between charges and costs made in the previous section apply to defined benefit plans also. The same applies to the apparent saving of marketing costs due to vertical integration of the pension function with the employer.

One new area refers to the area of investment guarantees which are implicit in the pension offer. This is because the employer has promised the same pension regardless of actual returns in the capital market. Some argue that the employer is a natural provider of this financial guarantee to the worker, so vertical integration of pension provision with the employer is cheaper than direct contracting in the capieal market.

As the employer may be charging for this service through lower wages, there is no evidence in support of this position. Moreover, the- quality of this financial guarantee is open to question, given the fact that the survival rate of most firms over a 30 or 40 year period is consistently inferior to that of life insurance companies. This is the result of explicit solvency regulation imposed on life insurance companies, which is very costly to replicate at the level of employers. In the United States, Congress created the Pension Benefit Guaranty Corporation to cover the pension promises to workers in failing firms, at a cost to the federal budget that is being estimated only now. The PBGC is trying to impose risk-adjusted insurance preemie, but this raises again the issues associated to evaluations. of firm solvency by a government agency. From the point of view of the worker, the linkup of the
# **3. Performance Measures**

As our first measure of performance we use the real rate of return on assets (ROA) in local currency. This is calculated at the firm level as the earnings before interest and taxes (EBIT) in local currency over total assets minus the annual inflation rate in the country. The advantage of this measure is that it is not influenced by the liability structure of the corporate, as it excludes interest payments, financial income, and other income or expenses. Table 1 shows that across countries, East Asian corporates have had quite different ROAs. Relatively low profitability rates have been recorded by corporates from Hong Kong, Japan, Korea, and Singapore with real ROAs on average of about 5%. High-profitability countries, at least for most of the period we study, have been Indonesia, the Philippines, and Thailand. Corporates in these countries averaged real ROAs of about 9%-10% for the whole period. ROAs for corporates in Malaysia and Taiwan fall in between these two groups, but their returns of about 7% are still closer to the high performers. These ROAs can be compared to ROAs in Germnany and the United States<sup>3</sup> of about 5 percent, providing support to the notion that the corporate sector contributed significantly to the East Asian Miracle during most of this period.

**Table 1: Return on Assets for Nine Asian Countries, Germany and the US**

|||(|%, med|ians, in|real loc|al curre|ncy)||||
|---|---|---|---|---|---|---|---|---|---|---|
|Country|1988|1989|1990|1991|1992|1993|1994|1995|1996|1988-96|
|Hong Kong|5.1|5.3|4.9|4.8|4.5|3.8|3.9|3.9|4.1|4.6|
|Indonesia|n.a.|n.a.|9.4|9.1|8.6|7.9|7.4|6.2|6.5|7.1|
|Japan|5.7|5.4|4.6|4.7|4.8|4.5|4.1|3.8|3.6|4.1|
|Korea|4.4|3.9|4.1|4.0|3.9|3.6|3.4|3.6|3.1|3.7|
|Malaysia|5.4|5.6|5.4|6.2|6.0|6.5|6.3|6.1|5.6|6.3|
|Philippines|n.a.|n.a.|n.a.|7.1|6.4|8.1|8.5|6.8|8.4|7.9|
|Singapore|4.9|_4.5_|4.2|3.9|5.2|4.6|4.5|3.9|4.0|4.4|
|Taiwan|n.a.|n.a.|n.a.|5.1|6.2|6.5|6.8|6.5|6.6|6.7|
|Thailand|10.8|11.0|11.7|11.2|10.2|9.8|9.3|7.8|7.4|9.8|
|uS|4.7|4.8|5.1|4.9|5.2|5.4|5.3|5.2|5.2|5.3|
|Gernany|5.3|_5.5_|_5.5_|5.7|5.6|5.2|5.1|4.9|_5.0_|_4.7_|

Note: Table Al reports means, standard deviations, and sample sizes.

As a further comparison of the performance of East Asian corporates, we plot the average 1988-96 ROA for corporates in all other countries that report to Worldscope (Figure 1). Thailand, the Philippines, and Indonesia have the highest ROAs in this sample of 46 countries, while Taiwan and Malaysia are close behind. At the other end, Korea and Japan have the lowest ROAs in the sample, together with Norway, Sweden, and Austria. Singapore and Hong Kong also have relatively low ROAs in real local currency.

> 3 For all companies listed on the DAX in Frankfurt, and for all NYSE companies in the US.

4
Figure 3 shows some indication of the exquisite detail in the IFLS data. Not only is it possible to replicate the net flows from, for example, “poor, rural, agriculture” to “non-poor, rural, agriculture” as in Figure 2, it is also possible to track the movements of the actual individuals who make up those flows. The central panels in Figure 3 replicate the net flows in Figure 2, and tell reasonably consistent stories. More of the action in the IFLS data comes from productivity gains in agriculture, with the rural, non-farm economy actually losing workers. There is a modest gain in urban employment, although in net more of that gain is in urban agriculture than in non-agriculture, reflecting the impact of the economic crisis, which was still a major factor in 2000.

The power of panel data is revealed by taking apart these net flows, interesting as they are, and tracking the individuals who come and go from each cell. The IFLS data reveal astonishing mobility of individuals across cells. The example picked in Figure 3, the cell representing workers who started and ended as poor, rural and agricultural, is especially revealing. In net, 2.5 percent of the total sample of workers tracked left this cell between 1993 and 2000, exiting rural, agricultural poverty. _But only 276 of the original 713 workers in this category in 1993 remained there in 2000._ Another 301 individuals joined them from other categories: rural, agricultural but non-poor in 1993 (162), rural, non-agricultural and poor in 1993 (69) and rural, non-agricultural and nonpoor (61). According to Table 5 (showing actual numbers of individuals) and Figure 3 (which shows the share of these individuals in the total of 5308 who are tracked between 1993 and 2000), there are even 4 individuals who were urban, non-poor and nonagricultural in 1993 who ended up as poor, rural and agricultural in 2000! This particular number is small, but clearly the economic crisis fostered great mobility in the workforce.

The mobility out of rural, agricultural poverty between 1993 and 2000 is just as striking. Of the 713 workers who started in this category, 307 exited poverty by improving their agricultural productivity and 68 exited poverty by moving to the rural non-agricultural sector (41 remained poor after this move). Only 21 poor rural farmers moved to urban areas.

24
**Table 2: Cost-effectiveness of improving learning – Percentage of draws that each program is in the bottom and top quartile of cost-effectiveness, and is as cost-effective as Girls’ merit scholarships, across 100,000 draws**

|**Program**|**Additional SD per**<br>**$100**|**Standard**<br>**deviation**|**Percentage of**<br>**draws in bottom**<br>**quartile of cost-**<br>**effectiveness**|**Percentage of**<br>**draws in top**<br>**quartile of cost-**<br>**effectiveness**|**Percentage of draws as**<br>**cost-effective as**<br>**gilrs'merit scholarships**|
|---|---|---|---|---|---|
|Providing earnings information, Madagascar|118.34|102.15|3%|96%|97%|
|Streaming by achievement, Kenya|34.78|25.03|1%|91%|99%|
|Linking school committee to local government, Indonesia|34.62|23.13|1%|92%|99%|
|Electing school committee and linking to local<br>government, Indonesia|13.34|9.45|2%|19%|98%|
|Teacher incentives (year 2), Kenya|6.29|5.40|7%|2%|93%|
|Textbooks for top quintile, Kenya|3.56|2.58|9%|0%|92%|
|Remedial education, India|3.07|1.72|7%|0%|95%|
|Camera monitoring, India|2.28|1.98|27%|0%|77%|
|Village-based schools, Afghanistan|2.13|0.87|15%|0%|92%|
|Extra contract teacher + streaming, Kenya|1.97|1.20|29%|0%|79%|
|Individually-paced computer assisted learning, India|1.55|0.37|55%|0%|78%|
|Girls' merit scholarships, Kenya|1.38|1.35|61%|0%|50%|
|Read-a-Thon, Philippines|1.18|0.74|82%|0%|33%|
|Minimum conditional cash transfers, Malawi|0.06|0.06|100%|0%|0%|

Source: Data adapted from J-PAL (2014). Calculations by authors. Notes: SD means standard deviations. Using the mean and standard deviation of the impact estimate for each program and assuming a normal distribution, we randomly select a simulated point estimate for each program, re-doing this 100,000 times. We then calculate the proportion of times, out of the 100,00 draws, that each program falls in the top quartile, in the bottom quartile, and the proportion of times it is more cost-effective than Girls'merit scholarships (i.e., produces at least 1.38 SD in learning gains per $100), which are the figures presented in this table.

18