rafmacalaba/datause-encoder-data huggingface dataset:
Table Al.7 Goverr ent Budget (TL billion)

|||1987|1988|Source|
|---|---|---|---|---|
|H Fib<br>H P&Lc<br>H iFXbc<br>H OFIb<br>H TI<br>HTDo<br>HTDp<br>H Tfb<br>|Factor Income<br>Profits<br>andLosses of CB<br>Interest<br>Received from FX Deposits<br>at CB<br>Other Factor Income<br>Indirect<br>Taxes<br>Direct<br>Taxes from SEEs<br>Direct<br>Taxes from PS<br>Transfers<br>from Abroad<br>|1,392.1<br>1,433.3<br>50.0<br>-91.3<br>6,618.9<br>741.1<br>6,010.4<br>69.3|3,058.3<br>1,285.6<br>106.2<br>1,666.4<br>11,255.6<br>852.9<br>9,725.0<br>126.1|(Fiscal<br>data)<br>(Catc fromCB)<br>(CS data)<br>(Calculated)<br>(fiscal<br>data)<br>(Fiscal<br>data)<br>(Fiscal<br>data)<br>(SOP data)|
|H REVb<br>|Total Revenues<br>|14,831.8<br>|25,017.9|(CalcuLated)<br>|
|H Cb<br>HTbo<br>H Tbp<br>H Sub<br>|Consu ption<br>Transfers<br>to SEEs<br>Transfers<br>to PS<br>Subsidies<br>Interest<br>Payents<br>on:<br> <br>|5,320.0<br>216.2<br>1 941.4<br>1,247.9|8,814.7<br>426.3<br>2,888.9<br>2,229.6|(MA data)<br>(Fiscal<br>data)<br>(Residual)<br>(iAdata)|
|H iCRb<br>H ib<br>H iBbd<br>H iRbo<br>Hi iFGb<br>|<br>Central<br>Bank Credit<br>Domestic Bonds<br>ToBankingSystem<br>To Private<br>Sector<br>Foreign<br>ornds<br>|798.5<br>1,274.6<br>23.4<br>12512<br>1,039.7<br>|1,048.8<br>3,346.7<br>531.0<br>2,815.7<br>1,923.6<br>|(iR*CRb(t-1))<br>(Fiscal<br>data)<br>(iR^Bbd(t-1))<br>(Calculated)<br>(Table F19)|
|HECURb<br>|Current Expenditures<br>|11,838.3|20,678.6|(Calculated)|
|H Sb<br>|Savings<br>|2,993.5<br>|4,339.3|(Fiscal<br>data)|
|H lb<br>H dFXbc<br>H<br>iTbo<br>H KTbd<br>HKTbp<br>|Investmnt<br>Foreign Exchange deposits<br>atCS<br> Capital<br>Transfers<br>to SEEs<br>Capital<br>Transfers<br>to (public)<br>Banks<br>Capitat<br>Transfers<br>to Private<br> <br>|4,205.8<br>191.1<br>289.7<br>101.7<br>459.9<br>|6,321.2<br>183.9<br>666.2<br>213.3<br>472.1|(Fiscal<br>data)<br>(CS data)<br>(Fiscal<br>data)<br>(Fiscal<br>data)<br>(Fiscat<br>data)|
|H ECAPb<br>|Capital<br>Expenditures<br>|5,248.2|7,856.7|(Calculated)|
|H DEFb|Deficit<br>|2,254.7|3,517.4|(Calculated)|
|H dCRb<br>H dab<br>H dBbd<br>H dRdp<br>_H_dFGb|Financino:<br>Central Bank Credit<br>Domestic Borrowing<br>From Banking System<br>From Private<br>Sector<br>Foreign Borrowing|-1.4<br>2,910.4<br>802.5<br>2,107.9<br>-654.3|-494.4<br>3 348.5<br>1,401.5<br>1,947.0<br>663.3|(From CB stocks)<br>(Calculated)<br>(From BS stocks)<br>(Residual)<br>(Fiscal<br>data)|

Source: See explanation in text and right most coluan.

The cther public sector (non-financial SEEs) The current and capital accounts of the SEEs can respectively be written as: (Al.12) FIo + Tbo + iF*FXoc-i = TDo + iR CRo-l+ ic Bop-1+ icBod-1 + iF'EFMo=i+ So (A1.13) So + KTko + dCPo + dBod + dBop+ E*dFGo = Io - E'dFXocKXTop Given the fact that the SEEs do not raise financing directly in domestic capital markets there is no relationship with the private 26
Figure 8

# **PRODUCTION AND CONSUMPTION OF GAS IN CHILE Year 1996**

<!-- Start of picture text --> Type Production Energy Consumption<br>Millions m'3* Millions mr3*<br>Natural Gas 3.632 774<br>Manufactured Gas 507 486<br>Blast Furnace Gas 1.692 1.313<br>Biogas 77 77<br>LPG 466 897<br>e LPG in ton millions<br><!-- End of picture text -->

## PARTICIPATION IN ENERGY CONSUMPTION **Year 1996**

<!-- Start of picture text --> BLAST FURNACE GAS, WBGAS<br>NATURAL GAS .651%<br>LPG<br>MANUFACTURED GAS m9%<br><!-- End of picture text --> _Total 21.516 Teracal_ SOURCE: NATIONAL ENERGY COMMISION 21
**Table A1:  Countries from the Demographic and Health Surveys (DHS)**

|**Region**|**Country**|**Used**|**Region**|**Country**|**Used**|
|---|---|---|---|---|---|
||Cambodia|Yes||Angola|No|
||Indonesia|Yes||Benin|Yes|
||Lao PDR|No||Botswana|No|
||Myanmar|No||Burkina Faso|Yes|
|East Asia &|Papua New Guinea|No||Burundi|Yes|
|Pacific|Philippines<br>|Yes<br>||Cameroon<br>|Yes<br>|
||Samoa|No||Cape Verde|No|
||Thailand|No||Central African Republic|No|
||Timor-Leste|No||Chad|Yes|
||Vietnam|Yes||Comoros|No|
||Albania|Yes||Congo|Yes|
||Armenia|Yes||Congo Democratic<br>Republic|No|
||Azerbaijan|No||Cote d'Ivoire|Yes|
||Georgia|No||Equatorial Guinea|No|
||Kazakhstan|No||Eritrea|No|
|Europe &|Kyrgyz Republic|Yes||Eswatini|No|
|Central Asia|Moldova|No||Ethiopia|Yes|
||Romania|No||Gabon|Yes|
||Tajikistan|Yes||Gambia|No|
||Turkey|Yes||Ghana|Yes|
||Turkmenistan|No|SbSh|Guinea|Yes|
||Ukraine|No|u-aaran<br>|Kenya|Yes|
||Uzbekistan|No|Africa|Lesotho|Yes|
||Bolivia|Yes||Liberia|Yes|
||Brazil|Yes||Madagascar|Yes|
||Colombia|Yes||Malawi|Yes|
||Dominican Republic|Yes||Mali|Yes|
||Ecuador|No||Mauritania|No|
||El Salvador|No||Mozambique|Yes|
||Guatemala|Yes||Namibia|Yes|
|Latin America|Guyana|No||Niger|Yes|
|& Caribbean|Haiti|Yes||Nigeria|Yes|
||Honduras|No||Nigeria (Ondo State)|No|
||Jamaica|No||Rwanda|Yes|
||Mexico|No||Sao Tome and Principe|No|
||Nicaragua|No||Senegal|Yes|
||Paraguay|No||Sierra Leone|Yes|
||Peru|Yes||South Africa|Yes|
||Trinidad and Tobago|No||Sudan|No|
||Egypt|Yes||Tanzania|Yes|
|Middl E &|Jordan|Yes||Togo|Yes|
|e ast<br>h fi|Morocco|No||Uganda|Yes|
|Nort Arca|Tunisia|No||Zambia|Yes|
||Yemen|No||Zimbabwe|Yes|
||Afghanistan|Yes||||
||Bangladesh|Yes||||
||India|Yes||||
|South Asia|Maldives<br>|No<br>||||
||Nepal|Yes||||
||Pakistan|Yes||||
||Sri Lanka|No||||

_Notes:_ Data come from the Demographic and Health Surveys (DHS). 53 countries are used and accessed between April 2021 and July 2021. 42 countries are not used in the analytic sample where at least one DHS survey is available but the sample size is small. The total number of observations used in the analytic sample is 544624.

39
**Table 10. Effects of Agatha on Consumption and Poverty (Subsample Household Exposed to Precipitation Anomalies of z-scores** <u>≥</u> **6 )**

|_Coef_<br>(1)|_CI 90%_<br>(2)|_CI 90%_<br>(3)|
|---|---|---|
|**Panel A. Total**|||
|_Total Consumption_|||
|t * ( 2 < rainfall z-score <= 6)<br>-54.03*<br>[27.9]|-100.09|-7.9|
|t * (rainfall z-score> 6)<br>-111.4**<br>[53.1]|-199.05|-23.8|
|_Moderate Poverty_|||
|t * ( 2 < rainfall z-score <= 6)<br>0.026<br>[0.020]|-0.007|0.06|
|t * (rainfall z-score> 6)<br>0.034<br>[0.025]|-0.002|0.08|
|**Panel B. Urban**|||
|_Total Consumption_<br>t * ( 2 < rainfall z-score <= 6)<br>-<br>164.4***<br>[48.8]<br>-|-245.03|-83.9|
|t * (rainfall z-score> 6)<br>206.6***<br>[61.8]|-308.7|-104.5|
|_Moderate Poverty_|||
|t * ( 2 < rainfall z-score <= 6)<br>0.044<br>[0.027]|-0.0006|0.09|
|t * (rainfall z-score> 6)<br>0.071**<br>[0.029]|0.0223|0.1196|

Observations: 26,587. _Notes_ : Results from diff-diff regression controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. The Z-score indicates the number of standard deviations above the rainfall mean (since 1980). t is the before-after dummy. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

**Table 11. Agricultural Cycle of Main Crops in Areas Affected by the Shock**

||Agricultural land<br>in affected areas|Jan Feb|Mar<br>Apr|May<br>|Jun|Jul|Aug|Sep|Oct|Nov|Dec|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Maize|||||P||H|||||
||38%||||||p||||h|
|B|||||P||H|||||
|eans|||||||||p||h|
|Coffee|22%|H||||||||H||
|Sugar Cane|13%|H|and P||||||||H|

Note: H = first harvesting season; P = first planting season; h = second harvesting season; p = second planting season. Vertical gray bar corresponds to the timing of the Tropical Storm _Source:_ Guatemalam Department of Food Security.

33
# Can Free Provision Reduce Demand for Public Services? Evidence from Kenyan Education<sup>_∗_</sup>

Tessa Bold, Mwangi Kimenyi, Germano Mwabu and Justin Sandefur October 26, 2013

**JEL Codes:** H52, I22, O15 **Key words:** user fees, school quality, private schools **Sector Board:** Education (EDU)

> _∗_ Bold: Goethe University. Kimenyi: Brookings Institution. Germano Mwabu: University of Nairobi. Sandefur: Center for Global Development. Funding: This work was supported by research funding from the UK Department for International Development (DFID) as part of the iiG, a research programme to study how to improve institutions for pro-poor growth in Africa and South-Asia. The views expressed here are not necessarily those of DFID. Acknowledgements: We acknowledge the enormous assistance of Charles Obiero and Samuel Nthenge of the Ministry of Education and Godfrey Ndeng’e and Samuel Kipruto of the Kenyan National Bureau of Statistics in assembling the data sources used here. We have benefited from comments from Gabriel Demombynes, Charles Kenny, Karega Mutahi, Lant Pritchett, Jakob Svensson and participants’ at iiG events in Nairobi (Institute for Economic Affairs), Kampala (Economic and Policy Research Council), Frankfurt (Goethe University) and Oxford (Centre for the Study of African Economies).

1
WPS 3296

# **Youth At Risk, Social Exclusion, and Intergenerational Poverty Dynamics: A New Survey Instrument with Application to Brazil**

## by

# **Dorte Verner**<sup>**1**</sup> **and Erik Alda**

## **Abstract**

This paper addresses the underlying causes of problems and risks faced by poor and excluded people, focusing on youth of 10-24 years of age. We develop a survey instrument that addresses poverty in broad sense including hunger, early pregnancy and fatherhood, violence, crime, drug use, low levels of social capital, and low educational attainment. The paper also sheds light on intergenerational transfer of risks that are considered to induce poverty, e.g. in early pregnancy and education attainment. We document simple findings based on the survey data gathered in three poor urban neighborhoods in Fortaleza in the Northeast Brazil. Our main findings show that: (i) poor youth are at considerable risk of growing up without their father—only 7 percent grow up with their father present in the household; (ii) the intergenerational transmission of low education attainment is at play, but it is diminishing; (iii) the risk of early pregnancy and fatherhood is large; 31 percent of the youth had their first child before age 16, triple that of the adult population; (iv) the risk of sexual abuse and violence within the household exists— 6 percent of the youth answered that they had their first sexual relationship with a family member and 13 percent grow up in household with violence; (v) the social capital levels are low—only five percent of the youth and 9 percent of the adults have measurable social capital; and (vi) the risk of growing up in a violent neighborhood is large—59 percent of the youth claim that they live in a violent neighborhood, 80 percent feel unsafe in their neighborhood and 50 percent feel unsafe at home.

## World Bank Policy Research Working Paper 3296, May 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 The authors would like to thank Josbertini Clementino, Egidio Guerra, Virginia Martins, and the members of MH2OCE and IPOC for their invaluable contribution to this project. We would also like to thank the people in the neighborhoods who helped elaborate this survey instrument which helps us to better understand the issues faced by youth and adults in poor urban neighborhoods.
production facilities, and how these decisions are influenced by improvements in infrastructure linking specific regions to market centers.

The empirical analysis focuses on Uganda for two main reasons. First, the country’s national economic policy calls for infrastructure improvements to accelerate national economic growth as well as develop a regionally balanced industrial landscape. Specifically, the National Industrial Policy identifies serious infrastructure shortfalls – particularly in electricity supply and transport as being binding constrains to growth, and also makes a case for infrastructure to support industrial parks in 21 towns throughout the country to create a national portfolio of industrial centers (GoU 2007). Identifying the implications of public investments in stimulating private investment in alternate locations can improve the sharpness of the infrastructure portfolio.

Second, the Ugandan national business registry provides spatially detailed information of firms providing detailed information on location and product lines of industrial firms in the country. The physical location of these firms is identified with considerable accuracy using GPS technologies. By combining economic analysis with geographically referenced data on placement of infrastructure (roads and electric grids), natural topography, as well as distribution of human capital across the country, we can concretely identify locations which generate the highest economic returns to public infrastructure investments.

Our empirical strategy is based on estimating models of firm location choice, and with analytic underpinnings based on the new economic geography literature that develops linkages between infrastructure development and agglomeration of economic activity.<sup>2</sup> In summary, if firms value scale economies from market access and external economies from agglomeration, then they are likely to concentrate production facilities. Infrastructure investments in these places can relieve congestion costs and attract further private investment. However, infrastructure investments may also try to promote spatial economic equity by improving access to remote areas. These investments may be

> 2 Baldwin et al (2005), Fujita et. al (1999), Puga (2002)

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# **Abstract**

The authors use comparable micro level panel data for 14 countries and a set of identically specified empirical models to investigate the relationship between exports and productivity. The overall results are in line with the big picture that is by now familiar from the literature: Exporters are more productive than non-exporters when observed and unobserved heterogeneity are controlled for, and these exporter productivity premia tend to increase with the share of exports in total sales; there is strong evidence in favour of self-selection of more productive firms into export markets, but nearly no evidence in favour of the learning-by-exporting hypothesis. The authors document that the exporter premia differ considerably across countries in identically specified empirical models. In a meta-analysis of their results the authors find that countries that are more open and have more effective government report higher productivity premia. However, the level of development per se does not appear to be an explanation for the observed crosscountry differences.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the links between trade and productivity. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at afernandes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
-16-

authorities did not relV on relative prices for allocating resources; they instead used the State Plan's material product targets to allocate resources. However, the Plan's targets usually resulted in the households having to save more than thev would have wanted because aggregate demand for goods exceeded aggregate supply. The small number of assets compelled households to invest their savings in these assets. Thev were the oniV ones available and they offered the highest real retum, which, in turn, resulted from the authorities price control policy.

Bulgaria's high involuntary household saving rate is evidenced when compared to that of other markEt countries (see Table 8). In 1985-90, on average, Bulgarian households' saved about 10 percent of their disposable income, of which thev invested more than two thirds in financial assets. Although international comparisons of household saving rates are risky because of the particular conditions of the Bulgarian economy, they provide an idea of the relative size if compared to high and low saving countries. While Bulgaria's household saving rates are similar to those experienced bV high savers, such as Japan and Germany in the late-1 980s, thev are very high when compared to the low saving rates of US households.<sup>9</sup> Table 8: Household Savings Patio 1/ (In Percent)

||. __...........<br>1985<br>.....<br>...<br>.........|._. .......<br>1986<br>..<br>.|..............<br>1987<br>.<br>....<br>........|.....<br>1988<br>....|......<br>..<br>1989<br>..|<br>......<br>1990<br>....|
|---|---|---|---|---|---|---|
|Bulgaria:|9.4|8.2|9.2|11.1|9.9|16.1|
|<br>in Housing Assets<br>|3.2|2.5|2.6|2.6|2.3|2.1|
|inFinancial Assets|6.2|5.7|6.6|8.5|7.5|14.0|
|MEMO:|||||||
|Japan|16.0|16.4|15.1|14.8|15.3|n.a.|
|<br>U.S.<br>|4.5|4.3|3.3|4.3|5.6<br>|n.a.|
|Germany<br>........<br>...............<br>_*_...............|11.4<br>.................................|12.2|12.3|12.6<br>......<br>.|12.2<br>..........|n.a.<br>.......<br>.... .....................**.**........|

Source: Data provided by Bulgarian authorities; and OECD (1M90).

Note: (1) Households savings as ratio of disposable income.

Interest rates helped to channel the involuntary savings into the financial sVstem by offering attractive,yields relative to alternative savings outlets until 1986. Until 1986, interest rates on domestic financial assets were attractive compared to what a saver could obtain abroad in a deposit denominated in foreign exchange. Also until 1988, real interest rates on financial assets were only slightiv negative and did not discourage savers from depositing their savings in the 91 The involuntary high households savings rates would be more evident if Bulgaria's househoids saving retes are adjusted for life-cycle effects, such as are the provision for re tirement and housing. In Bulgaria. unlike market economies, the provisions for retirement and housing are made by the State and, thus households do not need to save for theba motives.
# **Table 1. Share (percentage) of employment/work rates with added definition not detected in 3-month definition, conditional on employment in a project, by project characteristics, and country, women aged 15-64**

||**Tunisia**|**Egypt,**<br>**Arab**<br>**Rep.**|
|---|---|---|
|**Additional employment market definition**|||
|**Non-farm enterprise in or out of an establishment**|||
|All outside|18.9|12.0|
|Inside (one or more)|11.7|16.4|
|Total|15.0|14.0|
|**Livestock size**|||
|All small/medium|55.7|48.3|
|Large (some or all)|52.5|26.4|
|Total|55.2|38.6|
|**Crop land area**|||
|Below median||18.7|
|Median or above||16.2|
|Total||17.0|
|**Additional work extended definition**|||
|**Livestock size**|||
|All small/medium|34.5|10.4|
|Large (some or all)|22.7|9.7|
|Total|31.1|10.2|
|**Crop land area**|||
|Below median||10.8|
|Median or above||3.7|
|Total||6.7|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: Restricted to participants in the project type.

Market livestock work tends to be particularly underreported (55% of additional market employment was not reported as 3-month market employment in Tunisia and 39% in Egypt). There were meaningful differences only in Egypt in detecting market employment by livestock size, with those with only small/medium livestock particularly under-reported (48% of additional market employment was not reported as 3-month market employment, compared to 26% for large livestock).

Differences by crop land area are only available in Egypt. While 17% of additional market employment was not reported as 3-month market employment for those participating in market 14