rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 5 – Net Quarterly Upward Transitions between Employment, Unemployment and Inactivity (% of total transitions)**

<!-- Start of picture text --> Urban Rural<br>40.00 40.00<br>30.00 30.00<br>20.00 20.00<br>10.00 10.00<br>0.00 0.00<br>-10.00 -10.00<br>q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4<br>07/08 08/09 09/10 10/11 07/08 08/09 09/10 10/11<br>Male Females Male Females<br><!-- End of picture text --> Source: Authors’ estimations from LFSs 2007-2011.

If we bring unemployment into the picture, we can appreciate how changes in unemployment are determined. Figure 6 shows the net bilateral transitions between employed, unemployed and inactive. For example, a 10% net flow between unemployment and employment indicates that the flow from unemployment to employment has been larger than the reverse flow from employment to unemployment. Thus, positive values indicate positive developments and negative values indicate negative developments.

We can see that the net flows are all positive for men while for women there are negative quarterly spells for all flows. In particular, for men the best performance is shown by people moving from unemployment into employment while for women the best performance is from inactivity to employment over the period. Therefore, when the economy performs relatively well, women are attracted into employment directly from inactivity while they are pushed back into inactivity when the economy performs less well. In essence, women, especially rural women, do not pass through unemployment when joining or leaving employment and downward mobility dominates for women as opposed to men where upward mobility dominates.

**Figure 6 – Net Quarterly Flows between Employment, Unemployment and Inactivity (% of total transitions)**

<!-- Start of picture text --> Men Women<br>14.0 12.0<br>12.0 10.0<br>10.0 8.0<br>8.0 6.0<br>Unemp-Empl Unemp-Empl<br>6.0 4.0<br>Inac-Empl Inac-Empl<br>4.0 2.0<br>Inac-Unemp Inac-Unemp<br>2.0 0.0<br>0.0 -2.0 T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4<br>T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4 T1 T2 T3 T4<br>-4.0<br>2007-2008 2008-2009 2009-2010 2010-2011 2007-2008 2008-2009 2009-2010 2010-2011<br><!-- End of picture text --> Source: Authors’ estimations from LFSs 2007-2011.

11
the labor market are arguably the relevant sample for analysis, so selection at this level is of less concern.

Conditioning on self employment, the sampling criteria may still result in different selectivity of females and samples. The resulting sample clearly does not reflect the entire population of self employed in Sri Lanka. But it does reflect the population of self employed with less than 100,000 LKR of capital stock. With regard to the analysis in this paper, our main concern is whether the truncation on capital stock results in different selectivity in the female and male sample. Indeed, the data suggest that the sampling criteria eliminated more male then female enterprise owners. This reflects both the fact that males are more likely than females to be self employed in fishing and transportation, and partly this reflects the fact that males have higher levels of invested capital generally. The initial screens eliminated about 42 percent of the male self employed and 20 percent of the female self employed in eligible industries. A further 20 males and 3 females were eliminated when they reported capital stocks in excess of 100,000 LKR at the beginning of the baseline enterprise survey. If ability and capital stock are complements, then the sample will exclude the most able entrepreneurs for both males and females. The more severe truncation for males will result in a more selected sample of males with respect to ability. In particular, if males and females have identical ability distributions in the population of self employed, males in our sample will have lower ability on average than females in our sample. This together with the robustness of the results to inclusion of controls for ability leads us to believe that the empirical patterns discussed in the paper do not result from differential selectivity.

33

-  -
- 38

recently increased by KD 670 million to KD 1.2 billion, AZ/ presumably to cover the rnet deficit in the Fu-nd's capital position.

0 Loans against shares: The two investment companies in which the government has a controlling interest lent KD 897 million against KSC shares and real estate. The banks were also asked to lend to debtors of the settlement organization against both KSC and KSCC shares so that dealers owing funds to distressed dealers transferred to the settlements organization could pay cash. The KD 200 million lent was part of the settlement program, so that the provisioning costs were shared by the banks and the government. The KSC share index moved down from the support level of about 460 to 232 in 1987, or 50.6 percent. The real estate index also moved from 206 in 1982 to 94.9 in 1985. Since most of the dealers joined the DCFSP, the cost to the government would be similar to that of having purchased the shares and real estate. Assuming a 25 percent real estate anl 75 percent share portfolio, the approximate cost would be KD 435 million _4.V_ .

- 0 KSC share suDDort Rr2gram: The government purchased sharesworth KD 1.1 billion in 1983 (at support prices) directly from the market and another KD 144 million from the Settlement Organization. At 1987 prices the value was KD 634 million, with net costs of KD 624 million.

- Restructuring: To enable it to liquidate, restructure or merge firms, the government bought KD 243 million in KSCC shares and KD 34.7 million in Gulf shares. It is quite possible that the government will make a profit on the sale of these assets after the restructuring.

- SuRRort of bAnkini system: A total of KD 500 million was placed with the banks; the average 5 percent interest loss amounts to about KD 27.5 million annually.

# ISSUES FOR EVALUATION

Two fundamental issues are under government review: the government's future role in the economy, and the structure of the nonoil sector. Fiscal policy management and extensive ownership of domestic institutions and services is at the heart of the first issue. Assuming crude oil prices of $25 to $27 a barrel, economists from the World Bank and other sources.^have forecast that the present expenditure structure _41/_ Kuwait Ministry of Commerce. Law number 42. 1988.

- _4&/_ Real estate index available only up to 1985. An index developed by a private consulting firm (Al-Shall) shows further deterioration in real estate prices during 1986 with some improvements in 1987.
<!-- Start of picture text --> roe By =" TMo—=——5 10km<br>= | ie<br>A ateiBEGREEEREEESeo<br>- SEEEREREEECC ae Beean o6 oeEE SEEEES oe<br>— | aes a ast TT ia<br>| ii_ SEDEEeSEGGeeR SRSet tert6<br>(SPSS EEREEEEEOD 5 6<br>Ean_ BEDS[aEmToeeats@ En teSnaEEESSESEEEeCHEERTTT8<br>BEEEREEEEEREEEES 6h he Bee<br>BERBERS SERREEEE BERS eeees 6<br>eeiseaeee|| c= ies aeseatengSeeeBLa<br>os oomCy Tl x<br>Bre - 4 Share of accessible opportunities<br>& N We . by car within an hour<br>z "” HB %0-%10 EEE %30 - %40 ES) %70-%80<br>y \ HBTB %20-610 - %20%30 EEI) %50-%40 - %60%50 MHHE % 98 0-% 19 0 0<br>4 TF) %60- %70<br><!-- End of picture text -->

<!-- Start of picture text --> {4 ye ray—=—5 10km<br>. o «<br>q<br>= = Baaf one | |<br>> OG BREEES 6<br>BEa BERRGRGEeBeesBe<br>EEEERE 88<br>SER|||GE BG| BEEaaaeet)SaneBG|<br>GE BEEBE<br>Laielel<br>= fa<br>— — = ! Share of accessible opportunities<br>oN a . by matatu within an hour<br>\ - MEHG B %0-%10 % 12 0 --%3 %2 0 CGBGD 2) %30-%40%60 % 45 0- - %5%6%70 0 M GBEM %70 % 8090--- %80 %100%490<br><!-- End of picture text -->

<!-- Start of picture text --> 7A =o 5 10 km<br>Nee =<br>*<br>: e .<br>3<br>- Bi<br>a a5a onC<br>. SeSRSSS00888stfaaees =S<br>, SERRE RRee<br>5 Bo Seene5<br>O SS500 S58055”<br>OS SRBSRRE Oo<br>Pe22ootSen<br>5 SSee0c8Sn8o0m8<br>SEGS0 000000885<br>BH EE EH<br>an Cate<br>.<br>=<br>; -<br>— J Share of accessible opportunities<br>E N _— . by matatu within an hour<br>MB 0-410 GB %30- %40 HE %70- 80<br>~ GB %10-%420 KD %e0-%50 ME %80 - %90<br>f GB % 20 - %430 BBI %60-%50- %70%660 ME %90 - %100<br><!-- End of picture text --> <!-- Start of picture text --> te. i 0 5 10km<br>i I a |<br>‘ * Oe<br>* Co<br>~ - - Coo is<br>c ar<br>ae aaee 6B<br>- aoCo<br>va Hi<br>a8 bySe5e886 oc |<br>3 i a aE 5a<br>‘ aon Pry<br>La on oan<br>faET SHEE SSSE0R8<br>acl tet PCSSSSS00800880i ee ©<br>oe Hp 5<br>ao 2 sence<br>Co Poo<br>a a © 1<br>LitCCC a oanoo<br>in|Coo)| oie a<br>i oe o<br>Z LOO Ul -<br>F =! - ; Share of accessible opportunities<br>aN _— . by car within an hour<br>> 7 ME %0-%10 GD %30-%40HE %70-%80<br>“ GB %10-%20 7) %e40- 50 ME %480-%690<br>= a Gy %20- %30 BW(7) %50-%60%60-%70 ME %90-%100<br><!-- End of picture text -->
**Table 5: Where do the grants go?**

||Capital<br>Stock<br>FE|Truncated<br>Capital<br>Stock<br>FE|Made a<br>Transfer<br>Out<br>OLS|Amount<br>Transferred<br>Out<br>OLS|Weekly<br>Food<br>Spending<br>FE|Quarterly<br>Clothing<br>Spending<br>FE|Quarterly<br>Health &<br>Education<br>Spending<br>FE|Quarterly<br>Ceremonies<br>Spending<br>FE|Total<br>Quarterly<br>Spending<br>FE|Log<br>Quarterly<br>Spending<br>FE|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A: Males and Females**|||||||||||
|Cash Treatment*Female|82.61|49.17|0.05*|8.05**|3.81|3.38|‐1.05|1.39|120.54***|0.08*|
||(72.01)|(37.27)|(0.03)|(3.46)|(2.44)|(3.90)|(13.42)|(3.17)|(45.61)|(0.04)|
|In‐kind Treatment*Female|135.34**|120.24***|0.02|1.76|‐0.07|‐0.50|‐6.08|2.33|45.36|‐0.02|
||(65.55)|(34.51)|(0.03)|(2.92)|(2.60)|(4.39)|(13.03)|(3.46)|(44.36)|(0.04)|
|Cash Treatment*Male|31.36|2.21|0.03|‐4.06|3.93|9.52*|0.98|3.27|63.94|0.03|
||(70.33)|(61.10)|(0.04)|(3.93)|(3.12)|(5.08)|(11.26)|(3.92)|(50.82)|(0.04)|
|In‐kind Treatment*Male|157.71|83.74|0.01|‐6.01|‐2.82|3.63|‐0.85|4.36|20.95|‐0.01|
||(102.12)|(69.85)|(0.04)|(3.95)|(3.42)|(5.83)|(23.28)|(5.20)|(65.12)|(0.05)|
|Number of Observations|4256|4256|2033|2203|4268|3911|3713|4286|4495|4299|
|Number of Firms|765|765|722|722|765|761|753|765|765|765|
|P‐values testing:|||||||||||
|Cash = In‐kind Females|0.573|0.107|0.294|0.137|0.198|0.478|0.776|0.817|0.172|0.054|
|Cash = In‐kind Males|0.212|0.291|0.693|0.630|0.111|0.428|0.942|0.856|0.573|0.611|
|**Panel B: Female Sub‐sample**|||||||||||
|Cash Treatment*Low Profits|‐6.77|‐6.78|0.07**|6.13**|7.26**|4.66|15.39|2.94|197.84***|0.16**|
||(29.67)|(29.69)|(0.03)|(2.80)|(3.32)|(4.24)|(18.93)|(4.11)|(58.16)|(0.06)|
|Cash Treatment*High Profits|238.00|145.84*|0.02|11.54|‐2.13|8.29|‐25.71|‐8.05|‐53.38|‐0.07|
||(185.23)|(85.70)|(0.04)|(8.35)|(4.27)|(8.08)|(18.79)|(5.48)|(81.92)|(0.06)|
|In‐kind Treatment*Low Profits|59.17**|59.17**|0.01|‐0.40|1.11|4.10|3.83|‐2.38|32.92|‐0.02|
||(28.45)|(28.46)|(0.03)|(2.02)|(3.93)|(5.20)|(18.81)|(3.09)|(63.98)|(0.06)|
|In‐kind Treatment*High Profits|262.60|223.24***|0.03|5.12|‐1.99|‐2.43|‐18.48|3.11|18.07|‐0.04|
||(166.25)|(77.66)|(0.05)|(6.76)|(3.94)|(8.01)|(17.24)|(7.79)|(68.53)|(0.06)|
|Number of Observations|2654|2654|1260|1260|2657|2440|2323|2666|2790|2670|
|Number of Firms|475|475|446|446|475|475|468|475|475|475|
|P‐values testing:|||||||||||
|Cash Treatments Equal|0.193|0.093|0.351|0.540|0.083|0.691|0.124|0.109|0.013|0.007|
|In‐kind Treatments Equal|0.228|0.048|0.769|0.435|0.578|0.494|0.382|0.513|0.874|0.827|

Notes: All expenditure data truncated at the 99.5th percentile of the data. All estimation includes wave effects which vary by gender, and by category in panel B. Standard errors in parentheses, clustered at the firm level. High and Low profits refers to groups defined on pre‐treatment profits. Trimmed sample used. OLS estimation includes dummies for the matched quadruplets. *, ** and *** denote significant at the 10%, 5% and 1% levels.
issue. Finally, in order to be sure that the choice of PCT applications to build the coinventorship variable does not bias the results, the baseline regression is replicated using alternative patent data sources, such as the USPTO, the EPO, and “Triadic Patent Families” (TPF) (OECD Triadic Patent Families database, January 2014). TPF consist of a set of patents filed at the EPO, the Japan Patent Office (JPO), and granted by the USPTO that share one or more priority applications. If anything (columns [4] through [6]), it seems that using alternative sources of patent applications may overestimate the relationship between high-skilled migration and international co-patenting.

Appendix S7 replicates the baseline regressions but splits the count of co-patents and foreign inventors into five broad technology fields (Schmoch 2008). Appendix S8 uses alternative count data methods intended to zero-inflated dependent variables, with no important differences with respect to the main results.

TABLE 7. Robustness Checks. Co-inventorship

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS||Citation||||
||<br>co-inv.+1|NegBin|<br>weighted|USPTO|EPO|TPF|
||||<br>Co-inve<br>|ntorship<br>|||
|ln(Diaspora)|0.232***|0.221***|0.165***|0.229***|0.174***|0.219***|
||(0.0136)|(0.0267)|(0.0268)|(0.0230)|(0.0234)|(0.0263)|
|ln(Distance)|-0.0136|-0.453***|-0.291***|-0.213***|-0.243***|-0.207***|
||(0.0156)|(0.0486)|(0.0621)|(0.0546)|(0.0626)|(0.0663)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|1.089|-6.659|-0.0461|-10.27|-2.879|-1.395|
||(0.835)|(5.554)|(7.122)|(6.523)|(5.891)|(6.584)|
|Obs.|26,160|26,160|26,160|26,160|25,760|24,700|
|R2|0.571|0.354|0.903|0.980|0.937|0.883|
|Sending FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Receiv. FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

34
data provided by Vattenfall for its 3090 MW Forsmark power plant in Sweden. These E data permit us to compute the fraction<sup>grossproductionexpendedfortheinitial</sup> 'PnpT<sup>of</sup> emplacement of the plant and the fraction h of O&M plowback. From (2), (11) and (12) the implied values for EROI; �, and �2 are 74, .90, and .8 years respectively.

A comparison of Tables 1 and 2 reveals signi...cant improvements in the performance metrics of nuclear power, wind, and solar. The huge increase in nuclear power’s EROI comes about from converting to centrifuge technology and the improvements in capacity factors and expected lifetime of nuclear plants. Signi...cant increases in the power rating of wind turbines and concomitant reductions in the fraction of gross production expended for initial emplacement and for O&M led to an equally impressive increase in the EROI and dramatic reductions in the payback and doubling times of wind power. Similarly, improvements in the manufacturing technology, re...ning and puri...cation processes of silicon, led led to signi...cant increases in the EROI of PV systems.

||EROI|T|'|�1|E<br>'PnpT|h|q = <sup>E</sup><br>Pnp|~t|�|�|�2|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Nuclear|74|40|.85|.05|.001|.012|.043|6|.2|.90|.8|
|Wind|20|30|.20|.98|.032|.018|.193|1|.2|.15|4.8|
|PV|5.4-10|30|.15|4.67-2.67|.150-.088|.035-.012|.676-.395|1|.2|.01-.04|75.7-17.8|

Table 2: Technology-induced Improvements in the Energy Balance and Growth Potential of Renewables and Nuclear Power

# 5. Summary of Underlying Technological Characteristics

The historical evolution of energy infrastructures has led to very suitable energy carriers (electricity and liquid chemical fuels) for the distribution links of the supply chain. These carriers exhibit high energy density and exceptional versatility and convenience. They are unlikely to experience signi...cant substitution in the next several decades.

Table 3 provides a summary of the innate di¤erences among the alternative electricity generation technologies.

# 5.1 Longevity, Environmental Friendliness, and Carbon Intensity

Based on current consumption, proven economic global reserves of coal will last another 137 years, natural gas 63 years, and oil 43 years (EIA, 2009). Probable reserves for oil and natural gas may be substantially higher. Extracting these reserves will eventually become more di¢ cult and costly, putting upward pressure on prices and the required energy plowback— though how quickly remains a matter of debate. Regardless of how rapidly prices rise, however, they are likely to remain volatile. Thus there are fundamental issues 17
# **Annex A: List of countries covered by the OECD STRI data**

Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Costa Rica, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, the Republic of Korea, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, the Russian Federation, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States.

# **Annex B: List of sectors covered by the OECD STRI data**

Accounting and auditing services; Accounting services; Air transport; Architecture services; Audiovisual – Broadcasting; Audiovisual - Motion pictures; Audiovisual - Sound recording; Auditing services; Commercial banking; Computer services; Construction – Engineering; Construction services; Courier services; Distribution services; Engineering services; Insurance; Insurance – Actuaries; Insurance - Broking and agency services; Legal services; Legal services - Domestic law; Legal services - International law; Logistics cargo-handling; Logistics customs brokerage; Logistics freight forwarding; Logistics storage and warehouse; Maritime transport; Rail freight transport; Road freight transport; and Telecommunication.

# **Annex C: List of restrictive regulatory measures on MNP covered by the OECD STRI data**

A temporary licensing system is in place; Appointed actuaries must be nationals or residents; At least one engineer must be licensed for the issuance of construction permits; Domicile required for License to practice; Foreign construction engineers are required to practice locally for at least 1 year; Foreign construction engineers are required to take a local examination; Foreign professionals are required to practice locally for at least 1 year; Foreign professionals are required to take a local examination; Foreign providers have to completely re-do the university degree, practice and exam in the domestic country; Labour market tests: contractual services suppliers; Labour market tests: independent services suppliers; Labour market tests: intra-corporate transferees; Laws or regulations establish a process for recognizing qualifications gained abroad; Laws or regulations establish a process for recognizing qualifications in engineering gained abroad; Limitation on duration of stay for contractual services suppliers (months); Limitation on duration of stay for independent services suppliers (months); Limitation on duration of stay for intra-corporate transferees (months); Membership in the professional association is closed to foreigners; Memo: License or authorization is required to practice; Nationality or citizenship required for License to practice; Nationality or citizenship required for construction engineers; Other restrictions to movement of people; Prior or permanent residency is required for License to practice; Quotas: contractual services suppliers; Quotas: independent services suppliers; 45
<!-- Start of picture text --> 80<br>64.23<br>60<br>38.22<br>40<br>29.12 29.29<br>20 13.32<br>1.57<br>0<br>Africa and Asia Eastern Europe Latin America Industrialized Total<br>Middle East and Central Asia and the Countries<br>Caribbean<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

# **_5.2 Entry Rates_**

Entry rates are calculated as new registrations of companies as a percentage of total lagged (previous year) registered businesses. The data for 82 countries, summarized by region, are shown in Figure 3. On a regional level, industrialized countries had the highest entry rates in the three years period. Interestingly, we find that mean entry rates are consistently around 7-9% across emerging markets.

**Figure 3: Average Entry Rates, by Region, 2003-2005**

<!-- Start of picture text --> 12%<br>10.24%<br>10%<br>8.44% 8.55% 8.35%<br>7.84%<br>8%<br>6.70%<br>6%<br>4%<br>2%<br>0%<br>Africa and Middle Asia Eastern Europe and Latin America and Industrialized Total<br>East Central Asia the Caribbean Countries<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database, 2007.

# **_5.3 Business Distribution by Sector_**

The 2007 survey collected data on the number of total and new businesses disaggregated by sector of activity. In order to compare data across regions, the classification was truncated to: 16
## **Secondary School Project**

<!-- Start of picture text --> FY01 FY02 FY03 FY04 FY05 FY06 FY07 FY08 FY09<br>Implementation perod<br>Operating pefiod<br>Gross Recurrent Costs<br>Operating Costs 117.0 122.8 128.9 135.4 142.2 149.3 156.7 164.6<br>teacher salanes 100.0 105.0 110.3 115.8 121.6 127.6 134.0 140.7<br>teacher training 1.0 1.0 1.1 1.1 1.2 1.2 1.3 1.3<br>matenals and supplies 10.0 10.5 11.0 11.6 12.2 12.8 13.4 14.1<br>utilities 1.0 1.1 1.1 1.2 1.2 1.3 1.3 1.4<br>Indirect costs<br>new central admin staff wages 5.0 5.3 5.5 5.8 6.1 6.4 6.7 7.0<br>Maintenance 2.0 2.1 2.2 2.3 2.4 2.6 2.7 2.8<br>Total Recurrent Costs 119.0 125.0 131.2 137.8 144.6 151.9 159.5 167.4<br>Recurrent Cost recovery<br>recurrent costs covered by project 1.0<br>fees from students 4.0 4.2 4.4 4.6 4.9 5.1 5.4 5.6<br>Net budgetary Requirement 114.0 120.8 126.8 133.1 139.8 146.8 154.1 161.8<br><!-- End of picture text -->

# **R-Coefficients**

As a guide to estimating the desired level of recurrent expenditure with any given public investment proposal ratios of recurrent expenditure to investment expenditure have been calculated for 10 categories (and some 75 subcategories) of investment.<sup>3</sup> The ratios are based in World Bank and ADB projects in many countries. Because the ratios reflect averages over a variety of country situations, they should be viewed as no more than indicative of actual requirements in any given country.

|Comparison o|fADBandW<br> <br>|BProjects: <br>ADBProjects<br>|Summaryof <br>|'r'coefficients<br>W<br>|,bySector:<br>orldBankProjects<br>||
|---|---|---|---|---|---|---|
|Sector|Numberof<br>projects|Averager-<br>coefficient|Medianr-<br>coefficient|Numberof<br>projects|Averager-<br>coefficient|Medianr-<br>coefficient|
|Agriculture|7|0.023|0.010|22|0047|0019|
|Education|3|0.029|0.011|17|0.074|0032|
|Energy|7|0.047|0.037|14|0.013|0.002|
|Environment|5|0.074|0.056|12|0.017|0.014|
|Health|5|0.073|0.020|15|0.030|0.029|
|Telecommunications|4|0.043|0.027|3|0.003|0.000|
|Transportation|6|0.019|0.010|15|0.025|0.009|
|Urban Development|2|0.016|0.016|11|0.017|0.013|
|Water supply/sanitation|_5_|0.054|0.063|12|0.044|0.021|
|Averageallsectors (unweighted)|44|0.042|0.028|123|0.030|0.014|
|Averageallsectors(weighted)|44|0.043|0.028|123|0.035|0.017|

3This follows a method similar to that described in Heller, Peter; "Underfinancing of Recurrent Development Costs", Finance and Development. 16:1:38-41 March 1979, World Bank and IMF.

6