rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Between municipality variation in school expansion

||(1|)||(2)|
|---|---|---|---|---|
|_Outcome_|_New schoo_|_l indicator_|_Timing o_|_f new school_|
|||Standard||Standard|
||Coefficient|<br>error|Coefficient|<br>error|
|Teenage birth rate|-0.0007|(0.0018)|0.0057|(0.0109)|
|Enrolment rate (primary year 8)|0.0003|(0.0003)|-0.011***|(0.0019)|
|Secondary schools per 100|-0.064***|(0.0047)|0.322***|(0.0405)|
|Pre-school rooms per 100|0.0000|(0.0000)|-0.0008|(0.0007)|
|Male/female sex ratio (teen)|0.066**|(0.0257)|-0.1122|(0.1844)|
|Public spending—Total|-0.0012|(0.0032)|-0.044**|(0.0180)|
|Public spending—Welfare|0.0031|(0.0024)|0.0026|(0.0150)|
|Public spending—Education|0.0061|(0.0049)|0.0179|(0.0287)|
|Public spending—Health|-0.0031|(0.0045)|0.0059|(0.0253)|
|Public spending—Transport|-0.004**|(0.0019)|-0.0032|(0.0106)|
|Public spending—Housing|-0.0021|(0.0024)|-0.0079|(0.0146)|
|Population per km2|0.0000|(0.0000)|0.0000|(0.0001)|
|Municipality size (<10,000 excluded)|||||
|10,000-49,999|0.247***|(0.0205)|0.329**|(0.1328)|
|50,000-99,999|0.271***|(0.0397)|-0.0884|(0.2282)|
|100,000-499,999|0.184***|(0.0526)|-0.3055|(0.3068)|
|>=500,000|0.0425|(0.1016)|0.1856|(0.7288)|
|Log(total population)|0.095***|(0.0135)|-0.437***|(0.0816)|
|R2/Pseudo R2|0.3774||0.055||
|Observations|4,884||3,160||
||F-stat|p-value|Chi2|p-value|
|Joint significance—Municipal<br>spending|1.34|0.2345|8.51|0.2029|
|Joint significance—State dummy<br>variables|9.36|0.0000|226.8|0.0000|

Notes: This table reports the results of a regression of two variables reflecting the school expansion. Column 1 reports a linear probability regression in which the outcome is a binary indicator equal to one if a new school was introduced to a municipality in the period 1998–2009, and 0 otherwise. Column 2 reports an ordinal logit regression in which the outcome reflects the timing of a municipality’s first school new school (number of years after 1997). Only municipalities that received a new school during the period1998–2009 are including in the second regression. Both regressions include state dummy variables. Population size is based on the average total population between 1997 and 2009. Robust standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5% and 10%.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

32
40 Table 2: Usage of Enhancements and Flunchng of Enhancements (Mions of dolbers

|||Usage of En<br>Usage ofEn|thanicements<br>hancements||Fin<br>Offici|ancing Sources f<br>fa<br>Debt|or Enhancemen<br>i|ts<br> <br>|
|---|---|---|---|---|---|---|---|---|
||Buyback|Principal|Interest|Total|<br>Additional'|<br> <br>St Aid<sup>2</sup>|CommercaL<br>|<br>Country<br><sup>4</sup>|
|MEXICO|S0|S3465||<br>||e ses|New Mone9|Liquidity|
|PHILIPPINES<br>||<br>670|,<br>So|$3,717<br>|$7,182<br>|S3,732<br>|S1,847<br>|$1.027|$2,423|
|<br>COSTA RICA|<br>$188|<br>S0|S0<br>|S670<br>|$107<br>|$320|$690|($127)|
|VENEZUELA|<br>$647|<br>$809|$37<br>|$225<br>|$177|S0|S0|$42|
|URUGUAY|<br>$352|<br>$57|S924<br>|S2.380<br>|S687<br>|$1,010|S1.166|<br>$527|
|TOTAL|<br>$1857|<br>$4331|$54<br>|$463<br>|S140<br>|$86|$89|S234|
|Source:<br>World Debt|,<br>Tables(1990/91a|,<br>nd 1991/92)<br>and|$4,732<br>Quarterly<br>Revie|$10,920<br>w<br>of Financia<br>|$4,843<br>l<br>Flows (various<br>i|$3,263<br> <br>ssues)|$2,972|<br>$3,099|

3The NPV of the new 'The face value of the additional 2The face value of non-additional moneys to be disbursed official existing by coamercial debt (including official debt (includirg banks.IMF) disbursed IMF). We consider in the context here the IFIs' of the deal.set-asides as the only non-additional funds. "The usage of the country's own reserves, including set-asides. * Does not add up due to a grant and other external financing.
**<u>Table 3: The Land Registry in Rwanda, Kigali, and the Study Area, Parcel Characteristics and Transactions since 2013</u>**

||<br>**Rwanda**|<br>**Kigali city**|<br>**Study area**|
|---|---|---|---|
|**Characteristics of registered parcels**||||
|Number of registered parcels|11,437,040|395,978|136,555|
|Area of registered parcels (ha)|2,064,176|69,831|14,210|
|Parcel size (ha)|0.18|0.18|0.10|
|Residential|0.13|0.09|0.07|
|Agricultural|0.19|0.20|0.19|
|Share of parcels (area) by land use type||||
|Residential|11 (8)|35 (17)|46 (72)|
|Agricultural|64 (66)|49 (56)|30 (16)|
|Commercial|1 (1)|1 (2)|4 (1)|
|Forest|8 (9)|3 (7)|3 (1)|
|Admin./science/social|0.3 (1)|0.5 (3)|4 (0)|
|Not categorized|13 (13)|9 (12)|10 (6)|
|Parcels under lease (%)|97.76|98.90|100|
|**Transactions since 2013**||||
|No. of properties sold|85,797|40,253|19,036|
|..._of which_residential|47,287|28,879|16,730|
|..._of which_agricultural|38,510|11,374|2,306|
|Sale value, residential property ($/m<sup>2</sup>)|27.52|39.98|51.16|
|Sale value, agricultural land ($/m<sup>2</sup>)|3.84|7.47|10.56|

_Source:_ RNRA LAIS database as of Dec 31, 2016. _Note:_ The study area covers that part of urban Kigali for which imagery to generate building heights was available (see figure 1).

17
**58**

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- Cameron, Rondo; Crisp, Olga; Patrick, Hugh T. and Tilly, Richard, eds. Banking in the Early Stages of Industrialization: A Study in Comparative Economic History. New York: Oxford University Press, 1967.
would be reduced but consumption of staple foods would be maintained. Instead, households are cutting the consumption of the most fundamental food items in their consumption basket.

## **Table 5. Difference in expenditure on cereals and quantities of cereals consumed between the lean and non-lean season waves** _Panel A: Expenditure_

||Burkin|a Faso|Ni|ger|Sen|egal|All except C|had and Mali|
|---|---|---|---|---|---|---|---|---|
||No|Full|No|Full|No|Full|No|Full|
||controls|controls|controls|controls|controls|controls|controls|controls|
|Lean season<br>wave|-0.1898***|-0.1720***|-0.1383***|-0.1430***|-0.1450***|-0.1425***|-0.1517***|-0.1575***|
||(0.0419)|(0.0333)|(0.0316)|(0.0267)|(0.0266)|(0.0216)|(0.0230)|(0.0165)|
|N|6898|6824|5918|5832|6944|6910|19760|19566|
|R-squared|0.0237|0.1938|0.0166|0.1587|0.0163|0.1286|0.0165|0.2064|

_Panel B: Quantities consumed_

||Burkin|a Faso|Ni|ger|Sen|egal|All except C|had and Mali|
|---|---|---|---|---|---|---|---|---|
||No<br>controls|Full<br>controls|No controls|Full<br>controls|No controls|Full controls|No<br>controls|Full controls|
|Lean season<br>wave|-0.1347***|-0.1298***|-0.1272***|-0.1280***|-0.2164***|-0.2102***|-0.1524***|-0.1555***|
||(0.0408)|(0.0285)|(0.0313)|(0.0240)|(0.0259)|(0.0212)|(0.0229)|(0.0145)|
|N|6903|6829|5920|5834|6946|6912|19769|19575|
|R-squared|0.0140|0.2114|0.0137|0.2030|0.0371|0.1324|0.0173|0.2364|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the ‘grappe’ level in parentheses. Dependent variable is the log of total monetary expenditure on cereals per capita in Panel A and the log of total quantity of cereals consumed, in kilograms, in Panel B. Individual-level sampling weights applied. ‘Full controls’ specifications include controls for the gender, age, and education level of the household head, household size, the dependency ratio, household walls material, household roof material, household floor material, access to electricity, access to improved drinking water, and access to improved sanitation as well as an urban-rural dummy and region fixed effects. Source: EHCVM and World Bank estimates.

# 4.3. Subjective poverty

Sahelian households’ self-perceptions seem to echo the finding that welfare is lower in the lean season. In the EHCVM questionnaire, households were asked to classify themselves on a scale from ‘very poor’ to ‘very rich’; we can therefore re-estimate Equation 1 with a dummy variable for whether households classified themselves as ‘poor’ or ‘very poor’ as the dependent variable. Pooling the data from Burkina Faso, Mali, Niger, and Senegal, the share of people that were subjectively poor by this metric was 4.4 percentage points higher in the lean season than in the non-lean season (Table 6). This difference was statistically significant at the 1 percent level and was largely unchanged by the introduction of controls.

17
<!-- Start of picture text --> Figure 4<br>Opportunities and Economic Development - Completing Six Years on Time<br>r = 0.695*** r = -0.697***<br>0 20 40 60 80 100 0 20 40 60 80<br>HOI - Completing Six Years of Education On Time D-Index - Completing Six Years of Education On Time<br>Fitted values Americas<br>East Asia, South Asia & Pacific Europe & Central Asia<br>Middle East and Africa<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>Logaritm of GDP pc at Current USD (2005-2010) Logaritm of GDP pc at Current USD (2005-2010)<br><!-- End of picture text --> Before introducing our IV results, some results using OLS are helpful in setting the stage. Table 3 with the OLS results shows that once we take into account the effect of educational opportunity, measured by the HOI or D-Index using PISA data, inequality of outcomes (as measured by the Gini index of income) does not have a relationship with the level of economic development.<sup>15</sup> The next step is to assess the effect of unequal opportunities on development outcomes using the wheat–sugar ratio as an instrument for opportunities. The first stage regression shows a highly significant relationship between the wheat–sugar endowment ratio and the two measures of equality of opportunity. The F-statistics for the first stage regressions are above the critical values identified by Stock and Yogo (2005), which indicates a problem with weak instruments.<sup>16</sup> Table 4 shows the IV estimates of the effect of equality of opportunity (as measured by the HOI and D-Index) on economic development, measured by GDP per capita. Higher influence of circumstances on available opportunities is associated with lower degree of economic development. Once we account for the effect of opportunities, inequality of outcomes – measured by the Gini coefficient of income – has no causal effect on economic development. The relationship between opportunities and economic

> 15 The results are similar with equality of opportunity measures constructed with other variables (e.g. timely completion of primary school). But for the sample of countries for which we have those variables, the Gini coefficient does not have a significant negative relationship with GDP per capita in OLS regressions. Then we cannot make the case that accounting for equality of opportunity makes the Gini insignificant.

> 16 Staiger and Stock (1997) suggest that the F-statistic in the first stage regression should exceed 10. In most of our specification this value holds true for our case. For some specifications the F from the first stage are below 10, we also conduct sensitivity analysis for weak instruments and compute alternative confidence intervals following Chernozhukov and Hansen (2008). This confidence intervals are larger but produce the same insight the once we found using the small sample robust statistics we report in this paper.

12
<!-- Start of picture text --> Comparing 2005 and 2011 PPPs (benchmark year 2008)<br><4 “ “"<br>a aie<br>ie s e SYCa<br>=a oss<br>of SN,<br>ran]pee ° ggPn. AM<br>s eona °MRT<br>=]rol onceCl<br>= son SZ<br>28 #$SSD<br>c @eSIP -<br><!-- End of picture text --> <!-- Start of picture text --> is)= ° Mbt.7GO@ UGA2<br>s ® NGA 7 CAF<br><<br>=o ~ a e NER<br>—_ a GNB e ees<br>8 PA<br>©= fAI<br>w sige<br>o -*CO<br>0 10 20 30 40<br>Rank of mean consumption (2005 PPP)<br>Note: The dash line is a 45-degree line.<br><!-- End of picture text --> <!-- Start of picture text --> Between-group contribution: 2005 and 2011 PPPs<br>GE(0), benchmark year 2008<br>MWI =z<br>LSO \<br>BWA |<br>BDI eg<br>SSD ij<br>LBR Bs<br>GIN i<br>CIV a<br>NER EF<br>UGA =<br>GHA P|<br>BFA aoe]<br>SDN —<br>ZMB rf<br>MOZ \cosios]<br>MDG | __<br>TZA _$$<br>ANC<br>AGO -—<br>CoD | _—____________________==""*<br>NGA QB 2008 eR _<br>-.1 -.05 0 .05 Bl 15<br>Note: Out of a total of 34 countries observed in 2008, we show the 20 countries with the largest<br>absolute difference in the between-group contribution according to 2005 and 2011 PPPs.<br><!-- End of picture text -->
**Table 1. Variable Descriptions and Summary Statistics**

|Variable|Description|Mean|SD|
|---|---|---|---|
|Employment growth|Employment growth [(ln_S it_- ln_S i,t-3_)/3]|0.052|0.127|
|Laborsize|Number of permanent employees [ln_S i,t-3_]|3.112|1.350|
|Age|Years of firm's operation|2.602|0.741|
|Multi|Equal to 1 if firm is independent, single establishment; 0 otherwise|0.138|0.345|
|Manuf|Equal to 1 if firm is in manufacturing or construction sector; 0 otherwise|0.555|0.497|
|Exporter|Equal to 1 if direct exports account for more than 10 percent of firm's sales; 0 otherwise?|0.130|0.336|
|Foreign|Equal to 1 if firm has 10 percent or more of foreign ownership; 0 otherwise|0.117|0.321|
|Govt|Equal to 1 if firm has 10 percent or more of government ownership; 0 otherwise|0.017|0.129|
|Loan|Equal to 1 if firm has loan, line of credit, or overdraft facility; 0 otherwise|0.573|0.495|
|Credit constraint|Equal to 1 if firm did not apply for loan for some reason; 0 otherwise|0.334|0.472|
|Sales credit|Equal to 1 if firm has positive sales paid for after delivery; 0 otherwise|0.702|0.458|
|External finance|Equal to 1 if firm has a positive amount of external funds; 0 otherwise|0.237|0.425|
|Access to finance|How much of an obstacle to firm's operation is access to finance?|1.725|1.564|
|Informal competition|How much of an obstacle to firm's operation are informal sector competitors?|1.627|1.453|
|Labor regulations|How much of an obstacle to firm's operation are labor regulations?|0.958|1.181|
|Inadequate education|How much of an obstacle to firm's operation is an inadequately educated workforce?|1.408|1.353|
|Electricity|How much of an obstacle to firm's operation is electricity?|1.843|1.526|
|Transport|How much of an obstacle to firm's operation is transport of goods, supplies, and inputs?|1.224|1.310|
|Customs and trade|How much of an obstacle to firm's operation are customs and trade regulations?|0.954|1.242|
|Access to land|How much of an obstacle to firm's operation is access to land?|1.031|1.334|
|Courts|How much of an obstacle to firm's operation are courts?|1.025|1.280|
|Crime|How much of an obstacle to firm's operation are crime, theft, and disorder?|1.423|1.382|
|Tax rates|How much of an obstacle to firm's operation are tax rates?|1.828|1.374|
|Tax administration|How much of an obstacle to firm's operation is tax administration?|1.439|1.319|
|Licensing and permits|How much of an obstacle to firm's operation are business licensing and permits?|1.095|1.238|
|Political instability|How much of an obstacle to firm's operation is political instability?|1.615|1.504|
|Corruption|How much of an obstacle to firm's operation is corruption?|1.780|1.530|

_Source:_ World Bank Enterprise Surveys (2006-10).

21
|**App**|**endix Table**<br>|**7: First Stage**<br> <br>_1 if At_|**, Separately**<br> <br>_tended Daycare_|**by Gender**<br> <br>_for..._||
|---|---|---|---|---|---|
||0 Years|1 Year|2 Years|3 Years|4 Years|
|Boys * Treatment|-0.05|-0.01|0.05|0.01|-0.01|
||[0.03]|[0.03]|[0.04]|[0.03]|[0.04]|
|Treatment|-0.17|-0.01|0.02|0.09|0.07|
||[0.02]|[0.03]|[0.03]|[0.03]|[0.03]|
|Boys|0.05|-0.02|-0.04|0.02|-0.01|
||[0.03]|[0.03]|[0.03]|[0.03]|[0.02]|
|Mean of Y among||||||
|_Notes_: The table show<br>Control Girls|that treatment b<br>0.20|os and treatme<br>0.18|nt irls were mo<br>0.24|re likel to atte<br>0.25|d dacare than<br>0.13|

_Notes_ : The table shows that treatment boys and treatment girls were more likely to attend daycare than their control counterparts. The dependent variables are indicators for participants attending daycare for 0, 1, 2, 3, and 4 years. _N_ = 1,857 .

42
Table A3: Green firms <u>per</u> NAICS sector, 2012-2021

|NAICS Sector|Number of green frms|
|---|---|
|Accommodation and Food Services|39|
|Administrative and Support and Waste Management and Remediation Services|47|
|Construction|75|
|Finance and Insurance|171|
|Information|142|
|Manufacturing|1271|
|Mining, Quarrying, and Oil and Gas Extraction|202|
|Professional, Scientifc, and Technical Services|122|
|Real Estate and Rental and Leasing|138|
|Retail Trade|124|
|Transportation and Warehousing|135|
|Utilities|201|
|Wholesale Trade|53|

Notes: The table presents the number of green firms (that mentioned at least one green technology in ECTs) for each NAICS sector from 2012 to 2021 and the percentage of green firms (that mentioned at least one technology in ECTs) in each WIPO green sector.

42
## **Other**

There are other subsidy expenditures in the FY14/15 budget: for example, subsidy expenditure that delivers loans at concessional rates or transportation subsidies. However, these expenditures are minor compared to energy subsidy spending and these additional subsidies are not allocated in the CEQ.

# **Revenues**

Egypt’s revenue system is comprised of direct and indirect taxes as well as nontax revenue streams like property and asset income (for example, from the Suez Canal and other public sector economic authorities). Table 2 provides a snapshot of public revenue sources in FY15. Income taxes account for approximately two-fifths of tax revenue, and corporate income tax makes up approximately 70 percent of income tax revenue. Personal income taxes and real estate transaction taxes represent approximately 9 and 6 percent of tax revenues, respectively.

**Table 2. Government of Egypt’s Revenue Sources, Fiscal Year 2015**

||EGP<br>(billions)|Revenues<br>Share of<br>revenues (%)|GDP<br>(%)|Included in<br>analysis?|
|---|---|---|---|---|
|Total revenue and grants|465.2|100.0|19.0||
|Revenue|439.8|94.5|18.0||
|Tax revenue|305.9|65.8|12.5||
|Direct taxes|150.9|32.4|6.2||
|Personal income tax<sup>a</sup>|38.2|8.2|1.6|Yes*|
|Corporate income tax|91.6|19.7|3.7|No|
|Taxes on property|21.1|4.5|0.9|No|
|Indirect taxes|155.0|33.3|6.3||
|Sales tax (goods and services)<sup>b</sup>|65.5|14.1|2.7|Yes*|
|Excise taxes<sup>c</sup>|41.4|8.9|1.7|Yes*|
|Customs duties (stamp taxes)|7.7|1.7|0.3|No|
|Taxes on exports|21.9|4.7|0.9|No|
|Other taxes<sup>d</sup>|18.53|4.0|0.8|No|
|Nontax revenue|133.8|28.8|5.5||
|Sales of goods/services|26.5|5.7|1.1|No|
|Property income/ fines, penalties/ voluntary<br>transfers|83.2|17.9|3.4|No|
|Miscellaneous revenues|24.2|5.2|1.0|No|
|Grants<sup>e</sup>|25.4|5.5|1.0|No|

_Source:_ Ministry of Finance.

*Revenue collections included may not be fully allocated within the HIECS 2015 for various reasons. See the section on methodology for detail on the allocative methods and assumptions.

a Personal income tax includes taxes from employment, other activity, and capital gain taxes. b Sales of goods represent 53.4 LE billion.

c Includes 39.7 LE billion of excise taxes on domestic goods.

d Other taxes include an 8.3 EGP billion discrepancy in MoF accounts.

e Include grants from foreign governments (LE 24.9 billion), international organizations (LE 0.3 billion), and others.

14
_COTE D’IVOIRE: Volatility, Shocks and Growth_

**Trade indices share a cyclical pattern with cocoa prices** . Figure 7 shows cycles around trend of the trade indices and cocoa prices. Cycles in all three exhibit a very similar pattern, post-1980.  Cocoa price cycles move closely with the terms of trade cycles throughout the study period (except for 2002), which is not surprising as cocoa prices are an important part of the index of export prices from which the TOT measure is created. Cycles in the real exchange rate measure move against the cycles in TOT and cocoa prices before the 1980s.  From 1971 to 1976, the RER cycle was above trend, while the TOT cycle was below; and in 1976-79 TOT was above trend while RER was below. By the 1980s, both RER and TOT cycles were moving together, and moving with cocoa price cycles.

**Cocoa world prices, Cote d’Ivoire’s production and yields also share a common cyclical pattern.** Figure 8 show data on cycles in the cocoa markets.  Cocoa production cycles in physical units in the Cote d’Ivoire have often moved with international real cocoa price cycles.  Prices above trend in 1976 to 1980 coincided with production above trend in 1978 to 1980, and thereafter the world price and Cote d’Ivoire production cycles around trend move together. This may suggest a feedback or “cobweb” mechanism. Above-trend real world prices may stimulate above-trend output by local producers (keeping in mind that real prices tended down over the study period while physical production trended upwards.). Output was down sharply in 2001 and 2002 due to conflict.  The cycle in cocoa yields per hectare moves from above trend in 1963-1974, to below trend during Cote d’Ivoire’s boom and subsequent crash in 1975-1986,  to above trend from 1987 through 1999.  Yields have fallen precipitously from 2000 on, probably due to conflict.

**Coffee prices correlate with cocoa prices, and prices, output and yields also share a pattern much like that of cocoa.** See Annex II.

**While not perfectly correlated, the descriptive patterns brought out by HP filtering reveal considerable co-movement in the deviations from trend of output, trade indices, and cocoa and coffee prices in Cote d’Ivoire.** This suggests a strong relationship between macroeconomic productivity, trade, and commodities in Cote d’Ivoire.  Examination of this data strongly indicates that regular cyclical patterns have appeared in the economic times series, and the pattern is shared by output, trade and cocoa variables.  In the next section, we evaluate these inter-relationships more rigorously.