rafmacalaba/datause-encoder-data huggingface dataset:
_of Economic Surveys_ 17(2): 115-156.

- Heckman, J.J., L.J. Lochner and P.E. Todd. 2006. “Earnings functions, rates of return and treatment effects: The Mincer equation and beyond,” in E. Hanushek and F. Welch, eds., _Handbook of the Economics of Education_ (Volume 1). Amsterdam: Elsevier: 307−458.

Hendricks, L. 2004. “A database of Mincerian earnings regressions.” Iowa State University.

- Kaboski, J.P. 2003. “Endogenous Mincerian Returns: Explaining Cross-Country Variation in the Returns to Schooling.” Ohio State University, Department of Economics.

- Kingsbury, I. 2018. “Making sense of low private returns in MENA: A human capital approach.” _International Journal of Educational Development_ 61: 173-183.

- Kugler, B. and G. Psacharopoulos. 1989. “Earnings and education in Argentina: an analysis of the 1985 Buenos Aires Household Survey.” _Economics of Education Review_ 8(4): 353-365.

- Layard, R. and G. Psacharopoulos, 1974. "The Screening Hypothesis and the Returns to Education" _Journal of Political Economy_ 82, no. 5: 985-98.

- López Bóo, F. 2010. “Returns to Education and Macroeconomic Shocks: Evidence from Argentina.” IZA Discussion Paper No. 4753.

Marshall, A., 1890. _Principles of Economics._ MacMillan _._

- Mincer, Jacob A., 1958.  “Investment in Human Capital and Personal Income Distribution”. _Journal of Political Economy_ 66(4): 281-302.

- Mincer, Jacob A., (1974), _Schooling, Experience, and Earnings._ National Bureau of Economic Research.

- Montenegro, C.E. and H.A. Patrinos. 2014. “Comparable estimates of returns to schooling around the world.” World Bank Policy Research Working Paper Series 7020.

- Munich, Daniel and George Psacharopoulos, 2018. “The external and non-market benefits of education: A review”.  EENEE, Analytical report No. 3/2017.

- Oreopoulos, P. 2006. “Estimating average and local average treatment effects of education when compulsory schooling laws really matter." _American Economic Review_ 96(1): 152−175.

- Patrinos, H.A. 2016. “Estimating the return to schooling using the Mincer equation.” _IZA World of Labor 2016_ .

Patrinos, H.A. 1995. “Education and Earnings Differentials.” World Bank (mimeo).

- Patrinos, H.A. and G. Psacharopoulos. 2010a. “Returns to Education in Developing Countries,” in P. Peterson, E. Baker and B. McGaw, eds., International Encyclopedia of Education

16
- 20 -

Tale.2: INCOME ELASTICITIES OF CONSUMPTION FOR C6TE D'IVOIRE A/

|Consumption category|Expenditure<br>share|Weighte|d inco|me elas|ticity:|v-|
|---|---|---|---|---|---|---|
|||1.5|2|3|4|5|
|Total expenditure (Gini|)<br>1|.32|.45|<br>.57|<br>.64|<br>.67|
|Casava|.036|.46|.54|<br>.64|<br>.7|.74|
|Rice|.04|.78|.87|<br>.95|<br>.98|<br>.99|
|Maize|.05|.42|.58|<br>.69|<br>.75|<br>.77|
|Millet|.001|.88|1.0|1.1|1.12|1.12|
|Bread|.016|.82|.88|<br>.95|<br>.98|1.0|
|Attieke|.018|.93|.99|1.05|1.07|1.09|
|Sugar<br>|.007|.59|.68|<br>.77|<br>.82|<br>.85|
|Cigarettes, tobacco,|||||||
|cola nuts|.011|.63|.65|<br>.68|<br>.70|<br>.72|
|Gasoline<br>|.04|1.71|1.58|<br>1.41|1.33|1.29|
|Public<br>transportation<br>|.03|1.13|1.12|<br>1.11|1.10|1.10|
|Telegraph-telephone|.003|2.25|1.96|<br>1.67|1.54|1.47|
|Alcoholic beverages|.017|1.17|1.14|<br>1.11|1.10|1.09|
|Electricity|.02|1.64|1.52|<br>1.41|1.36|1.32|
|Education expenses|.042|1.24|1.20|<br>1.16|1.14|1.13|
|Rent and dividends|.05|1.61|1.54|<br>1.45|1.39|1.35|
|Wages|.30|1.54|1.52|<br>1.42|1.37|1.34|

s' Income is defined as total expenditure plus consumption of home produced goods per capita.

Source: C6te d'Ivoire Living Standards Survey, World Bank, 1986.

Mass11#4/Welfare Dominance **_&_** the Design of Excise Taxation in the Cote D'1voire/Wlhirsk:ab/5/18/88
The boxes sent four message types; two related to electricity events (defined as changing status to “the electricity is now on” and “the electricity is now off”), and two scheduled messages. The devices sent a message each time the device detected an electricity outage, and another when the electricity turned back on. The devices were also programmed to send messages twice daily at pre-specified times, confirming the presence/absence of electricity at that time. The messages were received by a tablet device located in country. This device automatically saved all messages and uploaded them to a cloud server for processing. The resulting data provided the timestamp of each message and its content, which were then processed to generate a data file detailing the state of each monitoring box minuteby-minute (extrapolating from the content of SMS messages received).

# II.III – Comparing Summary Statistics

**Figure 3: Survey reported hours of electricity outages vs. automated reported outages**

<!-- Start of picture text --> Unconditional Conditional on an outage<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Overall SSB Overall Survey Conditional SSB Conditional Survey<br>2016w47 2016w51 2017w3 2017w7 2017w11 2017w15 2017w19 2017w23 2017w27 2017w31 2017w35 2017w39 2017w43 2017w47 2017w51 2018w3 2016w47 2016w51 2017w3 2017w7 2017w11 2017w15 2017w19 2017w23 2017w27 2017w31 2017w35 2017w39 2017w43 2017w47 2017w51 2018w3<br><!-- End of picture text --> Direct comparison of the survey vs. the box summary statistics reveals significant differences on average, due both to overreporting outages and underreporting at different times. Figure (4) reports the differences in the average hours of electricity outrages and the number of days (over the past 10) on which outages occurred. Respondents reported outages of slightly shorter duration than box-based estimates, but more noticeably underreported the number of days on which an outage occurred.

6
_Ingo Borchert and Mattia Di Ubaldo_ indicators. We use data on all services PTAs notified to the WTO, which are available from the novel World Bank Deep Trade Agreements 2.0 database (Mattoo et al., 2020). Crucially, these data contain detailed information on the structure and depth of services PTAs collected by Gootiiz et al. (2020). To the best of our knowledge, this is the first work exploiting such a rich data structure: this allows us to go beyond aggregate countrylevel effects of PTAs, and to study how different services sectors are affected by the PTA structure or certain configurations of services-liberalizing provisions.

Thus we make two principal contributions. First, we find that services PTAs are associated with significant and large increases in bilateral services trade, especially in regulation-intensive services such as financial and insurance services, business and professional services, and charges for intellectual property rights, respectively. But there are distinct differences across PTAs. We document that substantial trade-enhancing effects only emanate from deep trade agreements, and we trace the effects back to the existence of an ambitious agreement structure, meaningful disciplines and provisions that ensure accountability.

It is evident that PTA participation is not randomly distributed across country pairs; in particular, economies with certain features may self-select into signing services PTA. We address the potential endogeneity concerns by obtaining our results subject to a very demanding fixed effects structure and three-way clustering of standard errors.

Second, we also find that the presence of services PTAs affects the sourcing pattern of value added from partners that are embodied in a country’s exports. Economies tend to import relatively more foreign services value added from within services PTAs as compared to value added imported from non-PTA partners. Hence, in addition to direct trade effects, services PTAs are also conducive to increasing the contribution of foreign services 7
Policy Research Working Paper 9474

# **Abstract**

This paper documents some of the first estimates of the effect of the coronavirus pandemic on food security in a low- and middle-income country context. It combines nationally representative pre- pandemic household survey data with follow-up phone survey data from Mali and exploits sub- national variation in the intensity of pandemic-related disruptions between urban and rural areas. These disruptions stem from both government policies aiming to slow the spread of the virus and also individual behavior motivated by fear of contracting the virus. The paper finds evidence of increasing food insecurity in Mali associated with the pandemic. Difference-in-difference estimates show that moderate food insecurity increased by about 8 percentage points—a 33 percent increase—in urban areas compared with rural areas in Mali. The estimates are substantially larger than existing predictions of the average effect of the pandemic on food security globally and therefore highlights the critical importance of understanding effect heterogeneity.

This paper is a product of the Development Impact Evaluation Group, Development Economics in collaboration with the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gadjognon@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
To determine the sample size, we use several target variables: (i) nominal per capita expenditure; (ii) nominal total household expenditure; and (iii) poverty headcount ratios. We conducted an assessment of how the sample changes using these different targeted variables but our final estimates are based on total nominal household expenditure in order to be consistent with the sampling design of the HIES 2010.

We use three accuracy statistics to assess the performance of the sample: (i) RSEs of less than 10 percent from the mean nominal per capita expenditure for each district and division (urban/rural), as well as for each quarter at the national level; (ii) RSEs of less than 10 percent from the mean nominal household expenditure for each district and division (urban/rural), and for each quarter at the national level; (iii) SEs of less than 5 percent of the poverty headcount ratio for each district and division (urban/rural), and for each quarter at the national level.

Given that the sample for the HIES 2016/17 was designed to provide reliable poverty and welfare statistics at three different levels, accuracy in the estimation of statistics for all levels cannot be obtained simultaneously without compromising on the cost of the survey (i.e. on the sample size). The final allocation of the sample was selected to minimize the sampling errors at the district level, produces reliable and unbiased estimates of poverty for each quarter, and achieves the level of precision of poverty statistics for most of the sampling strata.

For the HIES 2016/17, BBS decided to prioritize the production of reliable annual districtlevel poverty and welfare statistics over annual division-level statistics for rural, urban, and CC. This decision stems from the fact that district-level statistics correspond to clear administrative policy making units and estimation of reliable statistics at the district level is in general more challenging. As a result, we first compute the optimal allocation of the sample at the district level and then modify it slightly for cases in which poverty and welfare estimates at the other two levels are not reliable. After these modifications, the final proposed sample successfully achieves an acceptable level of accuracy for poverty and welfare statistics at the – three levels annual district level, quarterly national level, and annual division rural level. Even after these modifications, however, there is some suggestive evidence that poverty and welfare estimates for some of the urban strata in the divisions of Chittagong and Mymensingh 38
Policy Research Working Paper 7012

# **Abstract**

There is a consensus on the need for Thailand to reform its education system to be able to compete with other high performing countries in the region. In terms of learning outcomes, the most recent evidence from the Programme for International Student Assessment shows little improvement over time. This paper uses the World Bank’s Systems Approach for Better Education Results (SABER) approach in Thailand to contrast policy intent and policy implementation in school autonomy and accountability. The policy implementation data were obtained from a survey of school principals of the schools that participated in the Programme for International Student Assessment and merged the data sets. First, the study analyzes the gap between policy intent and policy implementation. Then it examines the effect of the gaps on various schooling outcomes while controlling for covariates. The analysis finds significant differences between the Systems Approach for Better Education Results indicators of policy intent and policy implementation in all areas assessed by the indicators. Schools in Thailand exercise more flexibility in their personnel management in practice than what is intended by policy; student assessments need to address issues of content, reliability, and validity and school accountability needs to improve the interpretation of student assessments to make schools more accountable. There is a positive association between the Programme for International Student Assessment scores and school autonomy and accountability.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hpatrinos@ 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
a) <!-- Start of picture text --> Food Crops (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>Range of all sample paths<br>average<br>10% quantile<br>12 50% quantile<br>2 90% quantile<br>Stochastic-optimistic<br>Stochastic-pessimistic<br>11<br>1<br>10<br>0<br>9<br>-1<br>8<br>-2<br>Deterministic-baseline<br>7 Deterministic-optimistic<br>Deterministic-pessimistic<br>-3<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>b)<br>Biofuels (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>20<br>Deterministic-baseline Range of all sample paths<br>35 Deterministic-optimistic average<br>Deterministic-pessimistic 10% quantile<br>15 50% quantile<br>90% quantile<br>30 Stochastic-optimistic<br>Stochastic-pessimistic<br>10<br>25<br>5<br>20<br>0<br>15<br>-5<br>10<br>-10<br>5<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>c)<br>2G Biofuels (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>1600<br>Deterministic-baseline Range of all sample paths<br>Deterministic-optimistic 200 average<br>1400 Deterministic-pessimistic 10% quantile<br>150 50% quantile<br>90% quantile<br>1200 100 Stochastic-optimistic<br>Stochastic-pessimistic<br>50<br>1000<br>0<br>800<br>-50<br>600 -100<br>-150<br>400<br>-200<br>200 -250<br>-300<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion tons billion tons<br>million tonnes of oil equivalent million tonnes of oil equivalent<br>million tonnes of oil equivalent million tonnes of oil equivalent<br><!-- End of picture text --> 21
**Table 7: Severe dry events in Tanzania. The decadal occurrence of 1-in-10-year dry events in Tanzania is reported as the mean of the decadal occurrences of the individual climate model realizations. The number of years in which Tanzania experiences a 1-in-10-year dry event but another country does not is reported as a percentage of the decadal occurrences in Tanzania.**

|Country|2000s|2010s|2020s|2030s|2040s|2050s|2060s|2070s|2080s|
|---|---|---|---|---|---|---|---|---|---|
||<br>|**Number ofye**<br>|**ars in decade**<br>|**that benchma**<br>|**rk 1-in-10 dry**<br>|**event thresh**<br>|**old is exceed**<br>|**ed in Tanzania**<br>|<br>|
|Tanzania|0.5|0.59|0.77|0.45|0.64|0.55|0.36|0.36|0.32|
|||**Percentag **|**e ofyears tha**|**t trading part**|**ner has non-d**|**ry year when**|**Tanzania has**|**a dry year**||
|Australia|82.00|100.00|88.31|100.00|85.94|74.55|88.89|63.89|56.25|
|Canada|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|
|China|100.00|61.02|88.31|91.11|100.00|100.00|100.00|100.00|100.00|
|Ethiopia|64.00|61.02|76.62|100.00|100.00|81.82|88.89|75.00|100.00|
|India|90.00|93.22|88.31|100.00|92.19|100.00|100.00|100.00|100.00|
|Kenya|36.00|84.75|53.25|60.00|78.13|74.55|63.89|100.00|84.38|
|Madagascar|100.00|76.27|83.12|91.11|78.13|65.45|75.00|63.89|84.38|
|Malawi|90.00|69.49|83.12|71.11|64.06|58.18|88.89|50.00|71.88|
|Mexico|90.00|76.27|83.12|91.11|78.13|49.09|88.89|75.00|71.88|
|Mozambique|90.00|69.49|71.43|80.00|50.00|41.82|88.89|63.89|56.25|
|Russian Federation|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|
|South Africa|72.00|93.22|88.31|80.00|64.06|74.55|100.00|88.89|100.00|
|Sudan|100.00|84.75|100.00|100.00|85.94|81.82|88.89|75.00|100.00|
|Uganda|64.00|76.27|64.94|91.11|78.13|65.45|63.89|88.89|84.38|
|UK|64.00|84.75|94.81|100.00|85.94|81.82|88.89|88.89|71.88|
|USA|90.00|100.00|76.62|91.11|92.19|100.00|100.00|100.00|100.00|
|Zambia|82.00|69.49|64.94|71.11|56.25|58.18|88.89|75.00|56.25|
|Zimbabwe|82.00|93.22|83.12|80.00|56.25|74.55|100.00|63.89|71.88|

Source: Authors’ estimates from Meehl et al. (2007)
vide an explanation. The author identifies that treating vulnerable children—here, those with high violence—with similarly violent peers increases their stress levels and their probability of misbehaving at school, compared to treatment in a more violence-diverse group. Dinarte (2018) argues that exposure to risky environments usually increases individuals’ stress levels, either because they have to avoid danger or learn how to face it—for instance, by defending themselves.

In summary, these results on emotional regulation reinforce our previous conclusions on the ASP’s effects on misbehavior and attitudes. This low-intensity intervention improves noncognitive and cognitive outcomes of at-risk youth, and the effects are different according to individuals’ initial propensity for violence. For highly violent children, the impacts are mostly on absenteeism and academic performance. The intervention helps low-violence participants to regulate their emotions, which contributes to better attitudes and reduction of violent behaviors. Similar to our previous results, it is harder to change emotions in those who are more prone to violence. As a result, more intensive interventions may be more effective for these individuals.

Finally, an important contribution through these results is that the distribution of participants with unlike propensity for violence is relevant for this ASP and can increase its effectiveness. Specifically, segregation of participants might cause unintended neurophysiological effects on the highly violent children who are supposed to benefit most from these programs.

# **6. Learning versus Protection Mechanisms**

At least two mechanisms through this ASP may have changed behavioral and academic outcomes. First, students may have learned social skills and conflict management directly from the club curricula, through their interaction with other children, or both. We call this the _learning mechanism_ . Second, children may have reduced their violent behaviors because the ASP kept them busy and off the streets when they might otherwise be left alone and exposed to external risks (Gottfredson et al., 2004; Jacob and Lefgren, 2003; Newman et al., 2000). This will be the _protection mechanism_ or, as previous papers have called it, the “involuntary incapacitation” channel (Heller et al., 2017). Although this experimental design does not allow us to perfectly disentangle the two mechanisms, in this section we provide suggestive evidence that students are indeed learning social skills. Therefore, the first mechanism is more likely driving the effects.

First, we exploit the availability of baseline data on adult supervision after school hours to test for differences between both mechanisms. Our assumption is that treated students who reported being without adult supervision after school receive both effects from the intervention, and that the effects for students who are with an adult after school may be caused only by the learning 31