rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: Non-linear controls**<br>Dependent variable: SBM Efficiency|**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|Volatility in Occupancy*Number of workers (logs)|-0.190***|-0.105***|-0.157**|
||(0.050)|(0.025)|(0.056)|
|Volatility in Occupancy|0.535***|0.580***|0.866***|
||(0.143)|(0.201)|(0.212)|
|Number of workers (logs)|0.113|-0.560***|-0.374|
||(0.070)|(0.123)|(0.218)|
|Central region Y:1 N:0|0.013|-0.027|-0.005|
||(0.086)|(0.064)|(0.108)|
|Eastern region Y:1 N:0|0.240***|0.216***|0.255***|
||(0.070)|(0.061)|(0.087)|
|Northern region Y:1 N:0|0.021|0.108|0.141|
||(0.116)|(0.093)|(0.130)|
|Sabah & Sarawak Y:1 N:0|0.122|0.161**|0.187*|
||(0.074)|(0.070)|(0.090)|
|Age of hotel (logs)|0.069*|0.065|0.088|
||(0.037)|(0.054)|(0.061)|
|Part of a chain Y:1 N:0|-0.170**|-0.291***|-0.338***|
||(0.063)|(0.096)|(0.114)|
|Women's ownership (proportion)|-0.646**|-0.528|-0.290|
||(0.283)|(0.318)|(0.340)|
|Finance major obstacle Y:1 N:0|0.013|0.089|0.080|
||(0.096)|(0.057)|(0.067)|
|Occupancy rate|0.118|0.303|0.339|
||(0.195)|(0.223)|(0.228)|
|Number of power outages|-0.010|0.006|-0.001|
||(0.023)|(0.017)|(0.021)|
|Largest owner is manager Y:1 N:0|0.016|0.066|0.012|
||(0.048)|(0.050)|(0.046)|
|Website Y:1 N:0|-0.062|-0.080|-0.110|
||(0.097)|(0.082)|(0.085)|
|Low competition Y:1 N:0|0.020|0.004|-0.019|
||(0.060)|(0.057)|(0.132)|
|Intermediate competition Y:1 N:0|0.064|0.049|0.449|
||(0.104)|(0.076)|(0.391)|
|Two-star hotel Y:1 N:0|-0.104|-0.022|0.176|
||(0.064)|(0.064)|(0.107)|
|Three-star hotel Y:1 N:0|-0.006|0.004|0.049|
||(0.060)|(0.047)|(0.117)|
|Four- or five-star hotel Y:1 N:0|0.147|0.003|0.062|
||(0.116)|(0.082)|(0.204)|
|Informal competition Y:1 N:0|0.023|0.071|0.096|

43
Table A1 (continuation) Poverty and Market Density: Household and District Controls

||Model 1|Model 2|Model 3|
|---|---|---|---|
|Access to credit||0_._005|0_._004|
|||(0_._07)|(0_._07)|
|Access to improved seeds||_−_0_._05|_−_0_._048|
|||(0_._058)|(0_._058)|
|Access to oxen||0_._518|0_._517|
|||(0_._122)<sup>_∗∗∗_</sup>|(0_._122)<sup>_∗∗∗_</sup>|
|Access to oxen rental||_−_0_._319|_−_0_._32|
|||(0_._105)<sup>_∗∗∗_</sup>|(0_._105)<sup>_∗∗∗_</sup>|
|Access to tractors||_−_0_._036|_−_0_._035|
|||(0_._068)|(0_._068)|
|Input market constraints||_−_0_._161|_−_0_._159|
|||(0_._106)|(0_._106)|
|Road constraints||_−_0_._391|_−_0_._385|
|||(0_._161)<sup>_∗∗_</sup>|(0_._160)<sup>_∗∗_</sup>|
|Disease constraints||_−_0_._178|_−_0_._18|
|||(0_._083)<sup>_∗∗_</sup>|(0_._084)<sup>_∗∗_</sup>|
|Security constraints||_−_0_._007|_−_0_._016|
|||(0_._308)|(0_._308)|
|Land constraints||_−_0_._193|_−_0_._196|
|||(0_._083)<sup>_∗∗_</sup>|(0_._083)<sup>_∗∗_</sup>|
|Credit constraints||_−_0_._014|_−_0_._017|
|||(0_._197)|(0_._196)|
|Land fertility constraints||_−_0_._384|_−_0_._382|
|||(0_._127)<sup>_∗∗∗_</sup>|(0_._128)<sup>_∗∗∗_</sup>|
|Access to veterinary services||0_._164|0_._164|
|||(0_._055)<sup>_∗∗∗_</sup>|(0_._055)<sup>_∗∗∗_</sup>|
|Land confict||0_._182|0_._183|
|||(0_._084)<sup>_∗∗_</sup>|(0_._084)<sup>_∗∗_</sup>|
|Land community||_−_0_._116|_−_0_._121|
|||(0_._063)<sup>_∗_</sup>|(0_._064)<sup>_∗_</sup>|
|Access to primary school||0_._004|0_._007|
|||(0_._061)|(0_._061)|
|Free medicine||_−_0_._116|_−_0_._115|
|||(0_._045)<sup>_∗∗_</sup>|(0_._044)<sup>_∗∗_</sup>|
|Election||_−_0_._144|_−_0_._141|
|||(0_._076)<sup>_∗_</sup>|(0_._075)<sup>_∗_</sup>|
|Access to water||0_._059|0_._058|
|||(0_._055)|(0_._055)|
|Public hospital||_−_0_._069|_−_0_._07|
|||(0_._092)|(0_._092)|
|Private hospital||_−_0_._124|_−_0_._125|
|||(0_._069)<sup>_∗_</sup>|(0_._069)<sup>_∗_</sup>|
|Observations|6734|6734|6734|
|R-squared|0_._11|0_._21|0_._21|

Note: Estimates of household and district controls from the model of poverty on export market density. Instrumental variables. Standard errors within parenthesis ( _∗_ : significant at 10% level; _∗∗_ : significant at 5% level; _∗∗∗_ : significant at 1% level).

40
out of work: Challenges and Solutions around the Ninis in Latin America", The World Bank, Washington DC

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- Glewwe, Paul, and Michael Kremer. 2006. “Schools, Teachers, and Education Outcomes in Developing Countries,” in E. Hanushek and F. Welch, eds., Handbook of the Economics of Education: Volume 1. North Holland

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20
**Figure 4:** Treated and control groups

_Source_ : Authors’ calculations from CIMS (2020). Note: These shares represent the total number of beneficiaries and control households in the observation. Note that none of the estimations presented in the paper include all treated and control households in either rounds because of the structure of the survey and missing observations. The number of treated and control households for each outcome is presented in 2 to 4.

Among households included in the analysis, 52 percent of respondents worked in Round 1 and 60 percent in Round 2 (see Appendix 3). These statistics suggest that although the share of inactivity/unemployment is high in the sample of analysis, there are signs of employment stability following the onset of the COVID-19 crisis. The number of hours worked suggests that time-related under-employment was not a salient issue following COVID-19. Indeed, excluding those who worked zero hours, eligible households in Round 1 and Round 2 worked respectively 40 and 42 hours weekly. However, further explorations of the distribution of hours show a bimodal distribution in both Rounds 1 and 2 (see Appendix 5). Fourteen percent of the eligible households in Round 1 and 11 percent in Round 2 worked under 10 hours in the week prior to the survey.

18
would eliminate tariffs, quotas and other barriers to trade. Under PAFTA, all tariffs on goods of Arab origin were to be removed by January 1, 2005. Obstacles still hindering the full implementation of the great Arab FTA were to be cleared before 2010 and the freeing of service trade among Arab countries was expected to accelerate.<sup>13</sup> However, little is known about the extent to which the provisions of PAFTA and subsequent official declarations have been implemented.

**2.1 Goods – A largely completed agenda**

**Significant progress has been made in reducing barriers to trade in goods within PAFTA.** Over

the last decade, preferential liberalization under PAFTA and other PTAs has been complemented with reductions in MFN tariffs. As a result, the average uniform tariff equivalent of all tariffs (ad valorem and specific) for the region fell from 14.7 percent to 6.7 percent between 2002 and 2007. Despite mounting protectionist pressures during the global economic crisis, tariffs in the MENA region decreased by another 0.8 percentage points during 2008-09. The MENA region was actually the region where tariffs decreased the most during the financial crisis, especially on manufacturing goods (Figure 3). This improvement has been confirmed by private sector operators. In 2001, tariffs were ranked as one of the most important barriers to intra-regional trade in a survey of firms in the region (Zarrouk 2001). In a follow-up survey conducted in 2009, they were ranked last (Hoekman and Zarrouk 2009). A majority of respondent companies reported that tariffs on intra-regional trade have largely been removed, and that there has been a marked improvement in customs clearance-related procedures.

**_Figure 3.  Changes in tariffs regions, 2008–09_**

<!-- Start of picture text --> Percent<br>1.5<br>7.0<br>1<br>0.5<br>0<br>East Asia & Pacific Europe & Central Asia Latin America & Middle East & North South Asia Sub-Saharan Africa<br>Caribbean Africa<br>-0.5<br>-1<br>Total trade Agriculture Manufacturing<br><!-- End of picture text --> Source: World Bank and UNCTAD staff estimates.

> 13 Kuwait Declaration of the Arab Economic Summit, January 19-20, 2009.

12
# Human Capital and Climate Change

Noam Angrist, Kevin Winseck, Harry Anthony Patrinos, and Joshua Graff Zivin<sup>∗</sup>

**JEL Codes:** D72, H41, I20, I28, P16, Q01, Q5

**Keywords:** Human Capital, Education, Climate Change, Compulsory Schooling Laws, Voting

> ∗Angrist: Oxford University, Youth Impact, World Bank (email: noam.angrist@bsg.ox.ac.uk); Winseck: University of California San Diego (email: kwinseck@ucsd.edu); Patrinos: World Bank (email: hpatrinos@worldbank.org); Graff Zivin: University of California San Diego, NBER (email: jgraffzivin@ucsd.edu).

> The authors thank Diego Ambasz and Javier Sanchez-Reaza for in-depth comments and expertise. Additional useful comments were provided by Rafael E. De Hoyos Navarro. The authors thank Domagoj Raˇci ́c for his assistance in coding green party data in Europe and Anshuman Gupta for his careful coding of compulsory schooling law data.
# **Climate Change, Conflict, and Cooperation: Global Analysis of the Resilience of International River Treaties to Increased Water Variability**<sup>*****</sup>

Shlomi Dinar<sup>†</sup> , David Katz<sup>‡</sup> , Lucia De Stefano<sup>§</sup> , Brian Blankespoor<sup>**</sup> JEL Classification: Q 25, Q 28, Q 54, Key Words: Water variability, climate change, international water treaties, water allocation mechanisms, institutional mechanisms, conflict, cooperation

> * This paper received support from the Research Support Budget of the World Bank.

> † Florida International University (dinars@fiu.edu) (corresponding author)

> ‡ University of Haifa (katzd@geo.haifa.ac.il)

> § Complutense University of Madrid (luciads@geo.ucm.es)

> ** Development Economics Research Group, World Bank (bblankespoor@worldbank.org)
# **Variables and Indices Composition**

Table A4: Indices composition

|Index|Variables|
|---|---|
|SRH behavior (15 yo+)|Ever had sex [yes = 1] (15 yo+) (Reserve)<br>Had transactional sex in the last 6 months [yes = 1] (15 yo+) (Reserve)<br>Was sexually active in the last 12 months [yes = 1] (15 yo+) (Reserve)<br>Used a condom in last sexual intercourse [yes = 1] (15 yo+)<br>Ever used a contraception to avoid pregnancy [yes = 1] (15 yo+)<br>|
||Engaged in any IGA (last 6 months) [yes = 1]<br>Total profts cash (last 30 days) (IHS)|
|IGA|Received income from IGA (last 30 days) [yes = 1]<br>Number of IGA adolescent has been engaged in (last 6 months)<br>Number of IGA adolescent took the initiative (last 6 months)<br>Worked in a nonagricultural IGA (last 6 months)[yes = 1]|
|SRH knowledge & attitude|Knows of at least one modern contraception method [yes = 1]<br>Has ever heard of HIV/AIDS [yes = 1] (15 yo+)<br>Agrees using a condom can reduce HIV transmission risk [yes = 1] (15 yo+)<br>Attitude index on maternal health care (15 yo+)<br>|
||Index gender equality (std)<br>Index gender equality based on conformity to traditional gender role beliefs (std)<br>Ideal age for a woman to marry|
|Gender attitudes|Ideal age for a man to marry<br>Finds at least one disadvantage for a girl to marry before 18 [yes=1]<br>Expected education for adolescent’s female children<br>Gap between the ideal level of education for her daughter(s) and son(s)|
|Aspirations|Adolescent’s ideal number of children<br>Occupation desired by adolescent is high or intermediate status [yes = 1]<br>Revenue adolescent wishes to make from desired occupation (IHS)|
|SES|Self esteem index<br>General efcacy index<br>Self-awareness index<br>Problem-solving index<br>Perseverance index<br>Index on ability to infuence others<br>Creativity index|
|Marriage and pregnancy|Adolescent ever been married [yes = 1] (Reserve)<br>Adolescent currently pregnant [yes = 1] (Reserve)<br>Adolescent ever been pregnant [yes = 1] (Reserve)|
||Experienced at least one type of emotional violence [yes = 1] (Reserve)|
|Violence (15 yo+)|Experienced at least one type of physical violence [yes = 1] (15 yo+) (Reserve)<br>Experienced at least one type of sexual violence [yes = 1] (15 yo+) (Reserve)|
|Decision-making individual|Decision-making index on individual issues (std)|
|Decision-making couples (married)|Decision-making index on couples issues (if married at baseline and midline) (std)|
|Finances|Borrowed money in the last 12 months [yes = 1]<br>Saved money in the last 12 months [yes = 1]|
|Nutrition|Adolescent’s dietary diversity score [0–1]<br>|
|Mental health|Overall, adolescent feels happy or very happy [yes = 1]<br>Mental health index|

_Note:_ AIDS = acquired immunodeficiency syndrome; HIV = human immunodeficiency virus; IGA = income-generating activity; IHS = inverse hyperbolic sine; SES = socio-emotional skills; SRH = sexual and reproductive health; std = standard deviation; yo = years old.

37
households, 100% of the cooking in urban households and 7% of the cooking in rural households and 100% electrification, and electrifying 18% of the total passenger kilometers by 2025, capacity requirements (under the 7.2% GDP growth scenario) will be 6,621MW, 9,987MW, 15,731MW, 23,049MW and 36,489MW.

**Figure 7. Hydropower potential vs. capacity required to meet domestic demand in Nepal, Bhutan and Afghanistan (MW)**<sup>**5**</sup>

# **7 (a) Nepal**

<!-- Start of picture text --> 90000<br>80000<br>Technical potential (83,000 MW) WECS‐BAU<br>70000<br>WECS ‐R<br>60000<br>WECS‐H<br>50000<br>40000 Economic potential (43,000 MW) WECS‐RP<br>30000<br>WECS‐HP<br>20000<br>NEA REV<br>10000<br>0 Timilsina<br>2020 2025 2030 2035 2040 and Toman<br><!-- End of picture text --> _Note and source: ‘WECS-BAU’ refers to electricity capacity requirement forecast made by the Water and Energy Commission Secretariat WECS of Nepal to meet domestic demand under the Business as Usual scenario. ‘WECS-R’ and ‘WECS-H’ refer to the capacity requirement forecasts made by WECS under the reference economic growth (7.2%) and high economic growth (9.2%) scenarios, respectively. ‘WECS-RP’ and ‘WECS-HP’ refer to capacity requirement forecasts made by WECS with government’s policy interventions for substitution of cooking, heating and transportation fuels with electricity under the reference and high economic growth scenarios. All these forecasts are from WECS (2017). ‘NEA’ refers to capacity requirement forecasts based on electricity load forecasts made by the Nepal Electricity Authority (NEA) in 2015 (NEA, 2016). ‘Timilsina and Toman’ refers to load projections used in Timilsina and Toman (2016) study._

> _5_ The generation requirement forecasts presented here, especially for Afghanistan and Bhutan are not the precise ones, very simplistic assumptions are made to extrapolate the load forecasts where needed and to calculate required capacity from the load forecasts. They should be used as indicative numbers and should not be interpreted as ‘World Bank’ electricity forecasts for the corresponding countries.

14
# **3. Description of Data Sources**

This evaluation of the VL program benefits from a wealth of data sources available in Peru. The data used in this analysis come from four main sources: (1) information on the geographic allocation of VL expenditures; (2) national household living standard surveys; (3) national demographic and health surveys; and (4) the Public Expenditure Tracking Survey (PETS).

## _VL Expenditures_

The _Vaso de Leche_ (VL) Program has maintained monthly records of expenditures allocated to each administrative (department, province and district) region in Peru since 1994. We use this information, along with district population sizes from the 1993 census, to determine real annual total and per capita VL expenditures in each of the recipient districts for the years 1994 to 2000. It is worth noting that allocations made to the district VL committees do not translate fully into benefits to recipients. But, given the small-scale leakages at least to the committee level found in the PETS study, we are confident that they represent a reasonably accurate proxy for the value of benefits available to the district residents.

## _Living Standard Surveys_

Two sources of household living standard surveys were available for this study. The first is the _Encuesta Nacional de Hogares_ (ENAHO), collected by the _Instituto Nacional de Estadistica e Infor7natica_ (INEI) in 1998, 1999, and 2000. These nationally representative surveys of over 6,500 households (2,000 for the 2000 survey) were carried out quarterly, with each quarter's survey focusing on a different theme. We concentrate on the second quarter module which focuses on social services, and include information on participation in the VL program. Household income information is also available for each module.

The second source is the 1994 and 1997 _Encuesta Nacional de Hogares sobre Medicion de Niveles de Vida_ (ENNIV) surveys, collected by the _Instituto Cuanto._ These are nationally representative surveys of more than 3,500 households that collect multiple indicators of household and individual well-being (e.g., education, housing, health, economic activity, consumption and assets). The 1994 ENNIV includes information on VL participation by the household, while the 1997 data also includes estimates of the values of the transfers made to the household. Anthropometric measurements of heights and weights of young children were also recorded in the 1997 ENNIV survey.

Alderman and others (1995) an expectation of targeting all consumption of a good to one individual within a household unit is not easily reconciled with any standard household model.

_3_
Table A.4: Educational outcomes of Albanian immigrants relative to Greek natives in 2001

|Dep. Var:|PrimaryE|ducation|Secondary|Education|Tertiary|Education|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Albanian, adult when arrived|-0.032***|-0.032***|-0.224***|-0.224***|-0.124***|-0.124***|
||(0.001)|(0.001)|(0.003)|(0.003)|(0.003)|(0.003)|
|Albanian, below 12 when arrived|-0.020***||-0.253***||-0.181***||
||(0.002)||(0.006)||(0.008)||
|Albanian, 6-12 when arrived||-0.024***||-0.259***||-0.179***|
|||(0.002)||(0.007)||(0.010)|
|Albanian, below 5 when arrived||-0.014***||-0.242***||-0.189***|
|||(0.003)||(0.010)||(0.016)|
|R2|0.00|0.00|0.10|0.10|0.01|0.01|
|N|558,222|558,222|538,130|538,130|468,366|468,366|
|Average outcome among Greek natives|0.984|0.984|0.718|0.718|0.192|0.192|
|Age andgender FE|Y|Y|Y|Y|Y|Y|

**Notes** The table presents regression estimates measuring the educational gap between Albanian immigrants and Greek natives. The sample consists of 16-60 individuals in the 2001 Greek census who are either native-born or born in Albania (26,681). Albanian immigrants who arrived when adults are 19,879 individuals who report to have migrated when they were older than 20. There are 4,188 and 2,614 Albanian immigrants who arrived at an age between 6 and 12 and below 5. The sample is further resstricted to migrants who have eat least spend 5 years in Greece. The outcome tertiatry educaion is only defined for indivdiuals older than 25. All regressions estimates include both gender and age fixed-effects Notes: *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; standard errors are robust to heteroskedasticity. Source: Author’s elaboration on the 2001 Greek census.

35
## **2.6 Solving the Model**

In this section, the solution method is presented for, first, deriving the optimal share of oil revenue that should be invested (and consumed); secondly, the method to solve the problem about which allocation is equitable will then be presented. Throughout, the section will refer to the technical appendix for derivations.

The share of oil revenue that should be invested ( γ ) in order for the economy to reach it’s efficient level can simply be derived by equating, as in (21), the decentralized capital stock with the golden rule capital stock, since they both depend on γ . This is two equations with two unknowns to be solved for the optimal investment share, which therefore depends on all the economy’s characteristics and can be derived as in (22), where **x** = [ α , _n_ , _g_ , δ , τ , λ , β , γ , _q_ , θ , κ ] . The model is calibrated with the values displayed in Table 1, which are believed to be realistic to fit Brazil. The following section of this paper will describe our findings. Private savings include both household and firms” saving, while public saving is negative in Brazil (Figure 2). This reveals that there is a major for corporate saving decisions and fluctuations in determining the saving rate in Brazil.

**<u>Figure 2. The Composition of Saving in Brazil is Driven by Corporate Saving</u>**

<!-- Start of picture text --> 20%<br>16.8%<br>15%<br>10%<br>5.1%<br>5%<br>0%<br>Corporate Saving Families saving Public Saving<br>-5% -3.0%<br><!-- End of picture text --> Source: IPEA 2006 (National Institute for Applied Economic Research).

# **3 Macroeconomic Effects of Rising Oil Revenue in Brazil**

When oil revenue increases there is scope for higher investment — and perhaps higher consumption if the increase in oil revenue is large enough. As a baseline, it assumed that the government is not investing the oil revenue but is, instead, using it for consumption by transferring it to the living generations. Workers will save a certain share of the transfer they receive but retirees will consume the 15