rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Despite progress in recent decades, a substantial fraction of children in developing countries attain little schooling, and many adults lack skills that are valued in the labor market. For this reason, policy-makers and academics continue to search for programs and policies that can raise educational attainment and learning in poor countries.

A number of interventions have recently been shown to increase school enrollment and attendance in some settings. These include deworming for school-aged children (Miguel and Kremer 2004); school construction (Duflo 2001); the provision of additional teachers (Banerjee et al. 2005); vouchers for private schooling (Angrist et al. 2002; Angrist, Bettinger, and Kremer 2005); and conditional cash transfers—transfers that are made to poor households, conditional on them keeping their children enrolled in school and attending regularly (see the review by Fiszbein and Schady 2009). Much less is known about how to improve learning outcomes (rather than school enrollment) for poor children in developing countries.

In principle, since there is a strong cross-sectional association between schooling attained and test scores in data from many countries (Filmer, Pritchett, and Hassan 2006), one might expect that programs that increase school enrollment and attendance would also improve learning outcomes. However, this may not occur for a variety of reasons. With the influx of new students, classrooms may become too crowded for much learning to take place. Children who are brought into school are often poorer, and education systems in developing countries may cater to the elites (as suggested by Duflo, Dupas, and Kremer 2008). There may also be selection on expected returns, so that those children who stand to benefit the most from education are already in school, while those brought into school by an intervention that increases enrollment are “marginal” children for whom the returns to schooling are low (related work includes Card 1999; Heckman, Urzua, and Vytlacil 2006; Carneiro and Lee 2008); these children may not learn much while in school. More generally, the association between schooling attained and test performance may in part reflect omitted variables, rather than primarily a causal effect of schooling on learning.

2
# **Duration dependence and change-points in the likelihood of credit booms ending**

**Vitor Castro**<sup>a,*</sup>

University of Coimbra and NIPE

**Megumi Kubota**<sup>b</sup> The World Bank

**Keywords:** Credit booms, duration analysis, Weibull model, duration dependence, changepoints

**JEL Codes:** C41,E32, E51 **Sector Board:** EPOL

a,* Castro: University of Coimbra, Faculty of Economics, Av. Dias da Silva, 165, 3004-512 Coimbra, Portugal; University of Minho, Economic Policies Research Unit (NIPE), Campus of Gualtar, 4710-057 Braga, Portugal; <u>vcastro@fe.uc.pt; tel.: +351 239 790543; fax: +351 239 790514.</u><sup>b</sup> Kubota: The World Bank, Poverty and Economic Management, Macroeconomics 1 Unit, Europe and Central Asia (ECA). 1818 H Street, NW, Washington, DC 20433, USA; <u>mkubota@worldbank.org; tel: +1 202 473 4844. The views expressed in this</u> paper are those of the author and do not necessarily reflect those of the World Bank or its Board of Directors.
# The GATT as International Discipline Over Trade Restnctions: A Public Choice Approach

by J. M. Finger

# Table of Contents

|I.|The Logic of Negotiated Reductions||
|---|---|---|
|II.|The Logic of Trade Remedies|4|
|III.|The Codes Approach|15|
|IV.|Voluntary<br>Export Restraints|18|
|V.|Conclusions|21|
|Reiere|nces|23|
**Figure 13: Distribution of life satisfaction among formal and informal workers**

<!-- Start of picture text --> 100% 35% 70%<br>90%<br>80% 30% 65%<br>70%<br>25% 60%<br>60%<br>50% 20% 55%<br>40%<br>30% 15% 50%<br>20%<br>10% 45%<br>10%<br>0% 5% 40%<br>Formal Informal<br>Fully satisfied Rather satisfied 0% 35%<br>Both yes and no Less than satisfied<br>Formal Informal Formal Informal<br>Not at all satisfied<br>Source: Authors’ estimation using RLMS 2016.<br>Note: This figure shows the distribution of levels of life satisfaction for formal and informal workers. Central<br>panel defines life dissatisfaction as sum of categories less than satisfied and not-at-all satisfied. Left panel<br>defines life satisfaction as sum of categories fully satisfied and rather satisfied. Using RLMS, the authors<br>define informal employment as workers that are employed at an enterprise without a contract in their primary<br>job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a<br>secondary job or in no job but in irregular economic activities are not included. This RLMS-based measure is<br>henceforth referred to as “RLMS (primary only)”.<br>percentage satisfied with life<br>percentage dissatisfied with life<br><!-- End of picture text --> 39 Informal Employment and Worker’s Wellbeing in Russia / June 2019
**Table 3:  Climate Scenarios for Sri Lanka**

||**CURRE**<br>**NT**|**CCSR**|**PCM**|**HAD3**|**CSIRO**|
|---|---|---|---|---|---|
|**TEMPERATU**<br>**RE**|(Celsius<br>)|_(Change in t_|_emperature (C))_|||
|Quarter 1 (FEB)|25.38|2.86|1.98|3.31|2.91|
|Quarter<br>2<br>(MAY)|27.18|2.65|1.58|3.41|2.96|
|Quarter<br>3<br>(SEPT)|26.23|2.61|1.43|3.33|2.74|
|Quarter<br>4<br>(NOV)|25.23|2.69|1.75|3.15|2.88|
|**Annual**<br>**Average**||2.70|1.69|3.30|2.87|
|**PRECIPITATI**<br>**ON**|(mm)|_(Change in p_|_recipitation (%))_|||
|Quarter 1 (FEB)|8.18|-33.99|72.98|-55.50|55.50|
|Quarter<br>2<br>(MAY)|20.83|-5.81|-33.22|-7.06|-9.70|
|Quarter<br>3<br>(SEPT)|17.91|109.00|39.14|27.97|31.71|
|Quarter<br>4<br>(NOV)|29.74|-42.80|48.66|-24.95|6.76|

Source: Mendelsohn (2003).

Note: Given our definition of the quarterly averages for each of the climate variables, the middle month in the quarterly average was used to model each of the climate scenarios.

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Table S1.8: EFFECTS ON INTERHOUSEHOLD LOANS

||Credit|received|Credi|t given|Net cre|dit received|
|---|---|---|---|---|---|---|
||(Yes=1)|(BDT)|(Yes=1)|(BDT)|(Yes=1)|(BDT)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|-0.020|-378.141*|0.046***|553.392***|-0.027|-954.619***|
||(0.019)|(207.860)|(0.006)|(80.279)|(0.018)|(263.890)|
|Mean in control|0.224|1130.131|0.028|224.281|0.218|905.850|
|Adjusted R-squared|0.096|0.034|0.021|0.012|0.088|0.012|
|Observations|13464|13464|13464|13464|13464|13464|
||**Pane**|**l B: Loans**|**within the**|**village**|||
|Treatment|-0.013|-90.552*|0.017***|192.035***|-0.014|-281.826***|
||(0.008)|(50.925)|(0.003)|(32.899)|(0.008)|(65.304)|
|Mean in control|0.068|237.230|0.013|74.133|0.068|163.096|
|Adjusted R-squared|0.026|0.012|0.010|0.008|0.025|0.007|
|Observations|13464|13464|13464|13464|13464|13464|
||**Panel**|**C: Loans o**|**utside the**|**village**|||
|Treatment|-0.011|-289.012*|0.031***|362.863***|-0.016|-675.220***|
||(0.015)|(163.245)|(0.004)|(52.772)|(0.014)|(203.455)|
|Mean in control|0.179|892.901|0.018|150.147|0.177|742.753|
|Adjusted R-squared|0.081|0.026|0.013|0.008|0.077|0.009|
|Observations|13464|13464|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). All monetary values are measured in Bangladeshi Takas, deflated to 2007 prices using the annual CPI index published by the Bank of Bangladesh. In 2007, 1USD=69BDT nominal and 1USD=18.5BDT at PPP. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in Panel A, column 1 is a dummy variable (DV) equal to 1 if the respondent’s household received any loans from another household during the 12 months preceding the survey; in column 2 it is the monetary value of loans received during the past 12 months; in column 3 it is a DV equal to 1 if the respondent’s household gave any loans to another household during the past 12 months; in column 4 it is the monetary value of all loans given by the respondent’s household during the last 12 months; in column 5 it is a DV equal to 1 if the respondent’s household was a net loan receiver in the past 12 months; in column 6 it is the monetary value of loans received minus loans given by the respondent’s household in the past 12 months. All dependent variables in Panel B (C) are identical to those in Panel A, except they refer to within (outside) village informal loans.

8
**Table S13:** Program impacts on marriage market outcomes (husband attitudes towards women's empowerment)

|**Panel A: Baseline Dropouts**|Attitudes<br>Towards<br>Women's<br>Empowerment<br>Super Index<br>(Standardized)|Index of<br>Preferences for<br>Children's<br>Education<br>(Standardized)|Index of<br>Attitudes<br>Towards Wife's<br>Autonomy<br>(Standardized)<br>Two Years A|Index of<br>Attitudes<br>Towards (non)-<br>Abuse<br>(Standardized)<br>fter Program|Index of Wife's<br>Divorce<br>Prospects<br>(Standardized)|Desired<br>Fertility|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|=1 if Conditional Schoolgirl|0.145|-0.000|0.189|0.162*|-0.162|-0.161|
||(0.100)|(0.117)|(0.129)|(0.091)|(0.129)|(0.138)|
|Mean in Control Group|0.000|0.000|0.000|0.000|0.000|3.649|
|Sample Size|326|326|326|325|325|324|
|**Panel B: Baseline Schoolgirls**|||||||
|=1 if Conditional Schoolgirl|0.069|0.013|0.125|0.123|-0.048|0.050|
||(0.108)|(0.095)|(0.117)|(0.103)|(0.167)|(0.118)|
|=1 if Unconditional Schoolgirl|0.254|-0.315*|0.462|0.175|0.171|-0.066|
||(0.199)|(0.183)|(0.389)|(0.109)|(0.123)|(0.201)|
|p-value UCT vs. CCT|0.374|0.078|0.392|0.683|0.252|0.586|
|p-value Treatment|0.414|0.196|0.325|0.208|0.336|0.837|
|Mean in Control Group|0.000|0.000|0.000|0.000|0.000|3.194|
|Sample Size|543|543|543|543|542|541|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. All variables are constructed so that higher values are better. The husband gender empowerment super index is a standardized index of the other variables in this table. Additional detail on the construction of the indices can be found in in Appendix A and Appendix B. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to husbands of respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
The assessment of _policy implementation_ shows less rigidity than the law currently allows, getting a benchmarking score of Emerging (Table 3). At the level of sub-indicator, municipallevel authorities seem to have more say over teacher hiring and firing than what the law allows, leading to a _score of 2.7 that could be called Emerging_ , which is more autonomous than the Emerging score given to policy intent. The same pattern is observed in the other two sub indicators, where principals report more involvement by municipal-level authorities than the law requires. In summary, personnel management is more flexible than what the law allows, and that flexibility is significant because parents and schools may use it for designing better teacher incentives.

|**Table 3: Thailand: Policy Intent and Implementation Scores for Indicator of Personnel**<br>**Management**|
|---|

|**Sub Indicator**|**Intent**<br>**Score**|**Implementation**<br>**Score**|**Intent**|**Implementation**|
|---|---|---|---|---|
|**2.1 School autonomy in teacher**<br>**appointment and deployment**<br>**decisions.**ESAappoint and deploy<br>teachers under union or civil service<br>agreements.|**2.0**|**2.8**|Emerging<br>|Established<br>|
|**2.2 School Council’s role in teacher**<br>**tenure or transfer.**Councils are<br>consulted and exert influence on transfer<br>requests.|**1.0**|**2.3**|Latent<br>|Emerging<br>|
|**2.3 Autonomy in the hiring and**<br>**firing of principals.**ESA has<br>autonomyinprincipal hiringand firing.|**1.0**|**2.9**|Latent<br>|Established<br>|
|**Overall**|**1.3**|**2.7**|Latent<br>|Emerging<br>|

Source: World Bank 2013 and school survey data. The differences between the intent and implementation scores are statistically significant at the 0.001 level.

# **4.3 Participation of the School Council in School Finance**

The participation of the council in school administration is very important because it is where parents can exercise their real power as clients of the education system. If the council has to cosign payments, then it has purchasing power automatically. The use of an operational manual is extremely important in this area, since it allows Council members to adequately monitor school management performance, help the principal with cash flow decisions, and become a catalyst for seeking additional funds from the community. The use of detailed operation manuals by the School Council is also a good vehicle for increased accountability and for the institutionalization of autonomy.

Policy intent for this indicator covers five sub-areas regarding the legal authority of the School Council:

- a. Participation in the preparation of the school budget

- b. Approval of the school budget

- c. Use of a manual outlining the participation of the School Council in school finances

13
effects are driven by the highway corridor regions rather than the hinterlands; forest cover diverges between the places close to the GQ and NS-EW, but not in those far away.

# **V.B.1 Mechanisms for Highway Effects**

We consider four possible mechanisms for the forest cover loss caused by India’s major highway networks: (i) increased demand for timber products by firms due to local growth; (ii) increased demand for firewood due to shifts in household fuel consumption; (iii) expansion of agriculture into previously forested lands; and (iv) clearing of trees for settlements and industry. This section presents suggestive evidence that the deforestation along India’s major highways is predominantly caused by increased logging driven by local timber demand.

To identify potential mechanisms, we use the regression specification used to identify effects of highways on forest cover (Equation 5), with data from the economic and population censuses which were undertaken in various periods between 1990 and 2013. Because the results above suggest forest cover changes within 100km of the highway network, we use 100km distance bands and define 200-300km as the omitted distance band.<sup>23</sup> The years in the sample are determined by census availability.

We first look at changes in employment in the list of industries that are directly downstream from timber harvesting (described in Section III). Panel A of Figure 5 shows results from a regression of log employment in major wood-consuming sectors on the usual set of year-distance-band fixed effects. We graph the point estimates on the 0-100km coefficient in each year that the Economic Census is available ( _i.e._ the coefficients _β_ 0 _−_ 100 _km,_ 1990, _β_ 0 _−_ 100 _km,_ 1998, _β_ 0 _−_ 100 _km,_ 2005, and _β_ 0 _−_ 100 _km,_ 2013 from Equation 5). These estimates can be interpreted as the difference in residual log employment between the 0-100km distance band and the 200-300km distance band, after controlling for state-year fixed effects and the controls described above. In 1990 and 1998 (before the GQ was begun), there is no significant difference between areas close to the highway corridor and areas that are far from it, nor is there a significant trend. By 2005, we see a 5% increase in employment in wood-consuming

> 23We find virtually identical results when we use 50km distance bins.

24
**Table 9:** Household Member Health

|**Outcome**|**_Sp_**<br>**Control**<br>**Mean**|**_ouse_**<br>**Estimated**<br>**Impact**|**_G_**<br>**Control**<br>**Mean**|**_irls_**<br>**Estimated**<br>**Impact**|**_B_**<br>**Control**<br>**Mean**|**_oys_**<br>**Estimated**<br>**Impact**|
|---|---|---|---|---|---|---|
|Sick in last 30 days|0.27|-0.01|0.15|-0.04<sup>_∗∗_</sup>|0.13|-0.02|
|||(0.02)||(0.02)||(0.01)|
|Days sick in last 30 days<sup>_⋄_</sup>|2.24|-0.03|0.61|-0.14<sup>_∗_</sup>|0.60|-0.09|
|||(0.20)||(0.07)||(0.07)|
|Observations|1|988|1|284|1|451|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

25
31

# **Table 5. Economic Growth and the Role of Aid, RER Misalignment, and Financial Development in the World**

_Cross-country panel data consisting of non-overlapping 5-year averages spanning 19702004_ _Dependent variable: Growth rate of real GDP per capita Estimation method: GMM-IV system estimator_

||**[1]**|**[2]**|**[3]**|
|---|---|---|---|
|_Control Variables_||||
|Initial GDP per Capita|-0.0050**|-0.0037**|-0.0069**|
|(in logs)|(3.14)|(2.02)|(6.10)|
|Initial GDP per Capita|-0.1300**|-0.1404**|-0.1415**|
|Cyclical Component|(5.83)|(8.22)|(8.98)|
|Inflation|-0.0146**|-0.0123**|-0.0128**|
|(Log of [inflation+100])|(9.26)|(7.65)|(12.89)|
|Government Expenditures/GDP|-0.0185**|-0.0164**|-0.0138**|
|(in logs)|(4.66)|(4.14)|(4.37)|
|Human Capital Investment|0.0235**|0.0171**|0.0228**|
|(secondary enrollment, in logs)|(7.74)|(5.20)|(10.04)|
|Rule of Law|0.0128**|0.0100**|0.0116**|
|(from ICRG, 0-6)|(9.12)|(5.57)|(8.32)|
|Trade Openness|0.0197**|0.0143**|0.0137**|
|(trade volume/GDP, in logs)|(6.93)|(5.47)|(6.45)|
|_Aid, RERMIS, and Financial Development_||||
|Official Development Assistance/GDP|0.0017**|0.0008**|0.0010**|
|(beginning of period)|(6.76)|(2.31)|(3.63)|
|Official Development Assistance/GDP squared|0.0000**|0.0000*|0.0000**|
|(beginning of period)|(3.58)|(1.78)|(2.17)|
|RER Misalignment|-0.0117**|-0.1236**|-0.1044**|
|(% difference b/w log REER and its equilibrium)|(4.53)|(5.68)|(5.03)|
|Peace Onset|0.0077|0.0008||
||(1.48)|(0.12)||
|Post-conflict 1|0.0164**|0.0042||
||(4.82)|(0.82)||
|Post-conflict 2|0.0213**|-0.0073||
||(3.31)|(0.75)||
|Financial Development|-0.0005|0.0042**|0.0036**|
|(Liquid liabilities / GDP,in logs)|(1.35)|(4.70)|(4.32)|
economies of scale, which lower extraction costs, or in oligopolistic industries, where various forms of collusion can increase prices.

To test the explanation that the effect of political instability on FDI varies with the type of investment, we assess the impact of political instability on both the level and composition of cross-border greenfield FDI flows. We define political instability as the propensity of a country to experience regime or government change; political, religious, and ethnic violence; as well as practices that have a detrimental effect on contracts, law and order, and the stability and efficiency of institutions. Shocks to political stability affect economic conditions and thereby affect expected rates of return as well as risk perceptions. We examine whether these affect investments in different sectors differentially. More specifically, we test the hypothesis that negative shocks to political stability have a stronger effect on investments in the non-resource tradable sectors than in the resource and non-tradable sectors. For this purpose, we assemble a quarterly panel data set of greenfield investments by destination, sector, and source in the Middle East and North Africa (MENA) from 2003 to 2012. The focus on greenfield FDI investments is motivated by the fact that these investments are the dominant mode of entry into MENA countries; these types of flows accounted for more than 80% of all FDI flows into developing MENA during the period of investigation and more than 95% of FDI flows into the developed Gulf economies. In addition, disaggregated data on FDI flows by source, sector, and destination are not available on a quarterly basis.

Whether, and if so, how the drivers of investments vary across sectors has important implications for countries development trajectories and consequently policy design. Many countries strive to attract “high-quality” FDI into (preferably high-tech) manufacturing in the belief that such investments have significant spillovers and will enable them to catch up to the 4
# **Table 5:  Education, Income and Pollution Exposure for Children**

|Adult Education Level<br>(Range: 0-4)|(1)<br>Cooking Area<br>PM10(ug/m<sup>3</sup>)|(2)<br>Living Area<br>PM10(ug/m<sup>3</sup>)|Hours<br>(Chi|(3)<br>Outside House<br>ldren 0-5)|
|---|---|---|---|---|
|Female|-20.305<br>(7.77)**|-14.404<br>(7.11)**||0.130<br>(0.69)|
|Male|-7.556<br>(3.36)**|-6.546<br>(3.76)**||-0.612<br>(3.85)**|
|Income Per Capita|-9.753|-4.752||0.033|
|($US Per Day)|(4.82)**|(3.03)**||(0.18)|
|Constant|298.616<br>(87.64)**|252.849<br>(95.78)**||5.145<br>(21.73)**|
|Observations|4,174|4,174||483|
|R-squared|0.07|0.06||0.04|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Education and household per capita income have the expected effects on determinants of indoor air pollution. All estimated effects are large, highly significant, and have the expected negative sign. Women’s education has a particularly large effect. Our results indicate that when men’s and women’s education levels jointly increase from 0 (no primary schooling) to 4 (post-secondary education), predicted PM10 in the cooking area decreases by about 110 ug/m<sup>3</sup> . This is a very large effect, since the average PM10 concentration for our 236 monitored households is 260 ug/m<sup>3</sup> . Each increase of $US 1.00/day is associated with a decline of 10 ug/m<sup>3</sup> , so the predicted reduction over the sample income range (less than $.50/day to $15.00/day) is approximately 150 ug/m<sup>3</sup> .

Although education and income strongly reduce average pollution, they do not seem to change children’s daily activity patterns in ways that reduce pollution exposure. Children’s hours spent outdoors are not significantly affected by women’s education or income per capita, and the measured 17