rafmacalaba/datause-encoder-data huggingface dataset:
policy debates. In particular, we exploit a unique data set of 86 urban official poverty lines across 18 LAC countries,<sup>5</sup> as well as the recent 2011 PPPs from the ICP.

In doing so, we make an important contribution to the existing literature on international comparable poverty measurement at the regional level, which can be extrapolated to other regions in the world. In the particular case of LAC, this study can be considered the first one that explicitly documents the calculation of comparable regional extreme and moderate poverty headcounts. This paper also provides at least five additional elements that contribute to some extent to this literature. First, the paper includes a comprehensive list of all countries for which up-to-date data on urban official poverty lines are available (18 countries representing roughly 85.3 percent of LAC’s population in 2011 and almost all urban population of the region). As such, any existing bias in the estimation of the value of the regional poverty lines that results from excluding certain countries is likely to be relatively small.<sup>6</sup> Second, using up-to-date information from NSOs in LAC on poverty lines used to estimate official extreme and moderate poverty numbers in every country can be considered more transparent and easier to communicate to governments in the region. Third, the method proposed in this paper is relatively simple and easy to replicate, which is key to ensure credibility to the process (Ferreira et al. 2015). Fourth, this paper uses _subnational_ official poverty lines when available, which accounts for regional disparities in the standard of living within countries and allows for the replication of countries’ official poverty estimates. Finally, by using the most up-to-date poverty lines from NSOs in LAC, our approach is less sensitive to changes in Consumer Price Index (CPI) data (Chen and Ravallion 2010; Jolliffe and Prydz 2015).

Depending on the specification chosen, the paper estimates the set of extreme and moderate poverty lines to be approximately $2.5 to $3.2 and $5 to $6.8 per person per day at 2011 PPP values, respectively. We then apply these lines to the distribution of household per capita income, which is standardized under the SEDLAC project, a joint initiative of the World Bank and the Center for Distributional Labor and Social Studies (CEDLAS) at Universidad Nacional de La Plata (UNLP) in Argentina,<sup>7</sup> in all LAC countries for which microdata are available. Depending on the

> 5 The data set of poverty lines is available also for rural regions, summing up to 109 official poverty lines. However, the ICP-2011 round collected prices only for urban areas in most of the countries in LAC, making the use of rural poverty lines misleading.

> 6 Deaton (2010) argues that changes in the composition of the 15 countries used by Ravallion et al. (1991) result in significant changes in the value of the poverty line and the count of the poor worldwide (Jolliffe and Prydz 2015).

> 7 CEDLAS and World Bank (2015).

4
-4-

Table 1 Selected macroeconomic indicators<sup>(1974-83)</sup>

||1974|1975|1976|1977|1978|1979|1980|1981|1982|<br>1983|
|---|---|---|---|---|---|---|---|---|---|---|
|Real GDP growth rate(%)|5.7|-12.9|3.5|9.9|8.2|8.3|7.8|<br>5.5|-14.1|-0.7|
|Real effective exchange<br>rate (1980 = l00)*|83.6|113.4|103.0|101.7|115.4|115.2|100.0|86.0|98.2|117.5|
|Terms of trade<br>(1980 = 100)a|N.A.|N.A.|<br>N.A.|N.A.|<br>N.A.|99.4|100.0|84.3|80.4|85.8|
|Current account/GDP(%)<br>|-4.3|-11.8|2.0|-5.4|-7.6|-6.0|-7.1|-18.9|-14.7|<br>-7.1|
|Fiscal deficit(-)<br>or|||||||||||
|surplus/GDPb|-5.3|0.1|1.4|-1.1|-0.1|4.8|5.4|<br>2.6|-1.0|-2.6|
|M2/GDP(%)c|9.3|10.0|10.5|11.8|14.2|14.9|19.3|23.7|25.7|<br>19.3|
|Inflation (CPI)(%)|375.9|340.7|174.3|63.5|30.3|38.9|31.2|9.5|20.7<br>|<br>23.1<br> <br>|
|Unemployment<br>(%)d|N.A.|N.A.|<br>N.A.|11.8|14.2|13.6|10.4|11.3|19.6|<br>14.6|

# Source: Central Bank Bulletin unless otherwise specified.

- a. Source: Bank staff estimates.

- b. Consolidated central government deficit or<sup>surplus.</sup>

- c. N2 = Ml + Dp, where Ml = Money in circulation<sup>plus drawable money of private sector.</sup> Dp = Time deposits at more than 30 days of private<sup>sector, excluding time savings.</sup>

- d. Source: National Institute of Statistics.
43

**<u>Table 5. Fund Size and Coverage of Existing DIS, 2010</u>**

|||Potential depos|it liabilities, 2010|||Abilit|y to pay, 2010||
|---|---|---|---|---|---|---|---|---|
|Country|Total deposits(US$bn)|Eligible deposits(US$bn)|Covered deposits(US$bn)|Size of the DIS fund(US$bn)|Public debt(as%of GDP)1|/<br>GDP(US$bn)|Total deposits / GDP<br>Si|ze of DIS fund / Covered deposits|
|Argentina|95||27.6|1.21|49.2|367.6|25.8|4.4|
|Australia|1336|1269.2|815|0|20.5|1247.2|107.1|0|
|Austria|25||6.5|0|72.3|378.4|6.6|0|
|Belgium|704.1|366||1.18|95.7|472.5|149.0|0.32 3/|
|Brazil|933|721.2|205.3|12.73|65.0|2142.9|43.5|6.2|
|Bulgaria|31.9|26.7|17.3|0.51|14.9|47.8|66.7|2.94|
|Canada|1803|1153.9|631.1|2.02|83.1|1614.1|111.7|0.32|
|Cyprus|42.4|76.5|32.6|0.04|61.3|23.1|183.5|0.11|
|Czech Republic|129|114.4|65.9|0.54|37.9|198.5|65.0|0.82|
|Denmark|302.5||100.9|0.73|42.7|313.1|96.6|0.73|
|Estonia|10.8|8.1|5|0.2|6.7|19.1|56.5|4.1|
|Finland|142|138.3|60.3|0.81|48.7|237.1|59.9|1.34|
|France|1742|1602.6|1167.1|2.45|82.4|2569.8|67.8|0.21|
|Germany|3395|1358||5.09|82.5|3310.6|102.5|0.37 2/|
|Greece|371.4|284.4|177.8|2.33|148.3|294.8|126.0|1.31|
|Hong Kong|877|859.5|175.4|0.18|35.5|228.7|383.5|0.1|
|Hungary|88.4|65.3|34.3|0.38|82.1|128.0|69.1|1.11|
|India|1166|1107.7|384.8|5.39|67.5|1711.0|68.1|1.4|
|Indonesia|279|251.1|170.2|2.04|26.1|709.5|39.3|1.2|
|Ireland|268.7|||0.98|91.2|209.8|128.1||
|Italy|2050|922.5|635.5|0|119.3|2059.2|99.6|0|
|Japan|11101|9990.9|7881.7|3.15|216.0|5495.4|202.0|0.04|
|Korea|951|646.7|256.8|4.13|33.4|1014.9|93.7|1.61|
|Latvia|20.3|16.5|6.5|0.18|39.7|24.1|84.2|2.82|
|Luxembourg|866.3|130.9|18.3|0|19.5|53.0|1634.5|0|
|Malta|58.3|10.7|8.5|0.01|66.0|8.4|694.0|0.13|
|Mexico|178|178|103.2|0.52|42.2|1046.7|17.0|0.5|
|Netherlands|1202|709.2|577|0|63.4|778.6|154.4|0|
|Portugal|272.1|200.9||1.99|94.0|229.4|118.6|0.99 2/|
|Romania|93.7|41.6|26.8|0.33|31.1|164.8|56.9|1.23|
|Russia|692|325.2|221.4|3.99|11.0|1524.9|45.4|1.8|
|Singapore|456|319.2|86.6|0.11|98.5|231.7|196.8|0.13|
|Slovak Republic|51.6|26.5|25.7|0.04|41.0|87.4|59.0|0.14|
|Slovenia|30.6|24.4|12.7|0|38.7|47.1|65.0|0|
|Spain|1963|1276|922.6|3.41|61.7|1387.4|141.5|0.37|
|Sweden|587.7|336.7|86.3|2.75|39.4|463.1|126.9|3.18|
|Switzerland|1481|1081.1|355.4|0|48.5|549.1|269.7|0|
|Turkey|399|235.4|99.8|5.4|42.3|731.1|54.6|5.41|
|United Kingdom|3183.2|1419.3||0|78.5|2296.9|138.6|0|
|United States|7888|7888|6231.5|-7.48|94.8|14958.3|52.7|-0.12|

Notes: Total deposits refers to total deposits held by banks in the country. Eligible deposits refers to deposits repayable by the deposit insurance scheme, before the level of coverage is applied. Covered deposits are obtained from eligible deposits when applying the level of coverage. 1/ General government gross debt (as % of GDP). Source: IMF World Economic Outlook April 2014. 2/ Size of DIS fund / Eligible deposits. Sources: European Commission, International Association for Deposit Insurers, Financial Stability Board (2010, 2012), FDIC, Laeven and Valencia (2012), IFS, IMF staff reports, and national deposit insurance agencies.
_Role of NGOs in Health Dellvezy Health Policy Study_ .. t _^Af&icX_ various policy opdons governments have employed in reladon to NGOs. Finally, It raises some as yet unanswered questions about environments condurive to NGO activity and contribudng to toles that NGOs can be encouraged to fulfill without sacrificing their very strength in the development process. This paper draws on limited information In several countries that have Insdtutionalized cooperative relationships betwen the government and NGO health services. It considers Malawi, Ghana, Kenya (to a limited extent) and Swaziland as case studies. While these are by no means the only countries where government-NGO cooperation has been established, limited documentation on NGO activides, particularly In the health field, has made it difficult to compose a general picture for all of Sub-Saharan Africa.

# History

4. The provision of modem health care In Africa remained largely in the hands of non-govemn.ental providers throughout the colonial period. Before the mid-twendeth century, the main NGOs providing health care in Africa were Christian missions which were the flt to exend health care services Into peripheral areas. After World War II, a new wave of secular NGOs sprang up (Abed and Chowdhury 1988) which, in tkldng social development through community empowerment as their mandate, saw health as a natural part of their responsibilides. Indigenous "grassroots" organizations-coopt ratives, and welfire and women's organizations-emerged largely in response to the wanlag of Idnship des that accompanied urbanization (Bratton 1989). In some cases, local organizations grew from the more traditional, tribal organizations. The church-affiliated organizations were and condnue to be the most numerous In Africa, although secular NGOs offerlng health care have proliferated. Both church-affiliated and secular NGOs, however, tend to have internadonal links and financial support, and are often staffed primarily by expatriates. National NGOs, although increasing appear to be less strong in Africa than in either Latin America or Asia.

5. The heterogeneous nature of NGOs makes it impossible to generalize about the nature of their activides. Discussion of NGOs tends **to** be characterized by a plethora of preconceived ideas or generalizadons, either by those who are convinced of the innovadveness, fei3dbility and appropriateness of NGO activities, or by opponents of NGOs who point to their limited scope or lack of documentation of activities or of accountability. Indeed, one Indication of the limited consensus about NGOs is that the very nature of the type of insdtutions at issue Is not agreed upon; the negative definition by ecus!on (nona.ovemmental) Is not Instructive in establishing posidve common characteristcs (Povler in Iinyanjul 1985).

6. A range of NGOs is active In the health field and can be differentiated according to a number of dichotomies, such as Internadonal/natdonal, religious/secular, research/action, community fundedVexternally funded4 etc. But to a certain extent, _all_ of the above are categories are cross-cutdng; Moreover, it is not possible to obtain clear indications
h ∑h=1 <mark>Wh Where p</mark> h<sup><mark>denotes the number of plots in household</mark></sup> <mark>h on which seasonal crops are grown, and w</mark> h<sup><mark>is the</mark></sup> <mark>final adjusted weight.</mark> <mark>The resulting weights were used to compute estimates in our analysis. Wherever population means are estimated, the standard errors provided with the estimate takes into account the clustered and stratified sampling design.</mark><sup>57</sup> <mark>All regression models with continuous variables as outcomes are estimated using ordinary least squares regression, while regressions with binary outcomes are estimated via maximum likelihood using a logistic regression model.</mark>

# **References**

1. CRED. _2022 Disasters in numbers_ . https://cred.be/sites/default/files/2022_EMDAT_report.pdf (2023).

2. Lörinc, M. & Hotový, O. _Global Catastrophe Recap. First Half of 2023_ .

https://www.aon.com/getmedia/760ea02e-ce76-4348-800a-cb8d99ca2a8f/20230720-1h-2023global-cat-recap.pdf (2023).

3. Paddison, L. Catastrophic drought that’s pushed millions into crisis made 100 times more likely by climate change, analysis finds. _CNN_ (2023).

4. Zachariah, M. _et al. Extreme heat in North America, Europe and China in July 2023 made much more_ _likely by climate change_ . http://spiral.imperial.ac.uk/handle/10044/1/105549 (2023) doi:10.25561/105549.

5. UN OCHA. _Horn of Africa Drought Regional Humanitarian Overview & Call to Action_ .

https://reliefweb.int/report/ethiopia/horn-africa-drought-regional-humanitarian-overview-callaction-revised-26-may-2023 (2023).

6. Visualising India’s record-breaking rainfall. _The Economist_ (2023).

7. United Nations Office For Disaster Risk. _Global Assessment Report On Disaster Risk Reduction 2022 -_ _Our World At Risk: Transforming Governance For A Resilient Future_ . (UN, 2022).

16
with a di↵erent working status in the third year. The idea is that the outcome in the second year should not be impacted by a future (third year) working event. If this were so, then the assumption of identical trend for treatment and comparison groups would be invalid. Therefore, we estimate specification (1) for the students that: (i) are in the 3 Period sample (i.e. appear in three consecutive years); and (ii) have the same working status history in the first two years in the sample.

Though our empirical strategy allows us to control for all time-invariant individual and family characteristics there may still be some time-variant characteristics that are important. For example, it is likely that some transitions into and out of child labor are correlated with idiosyncratic transitory shocks at the household level and, if so, there may be a direct e↵ect of such shocks on learning.<sup>15</sup> In this case our estimates would be biased upward as we would attribute to child labor the direct e↵ect of the shock itself. We believe that it is likely that most of the impact of a shock that causes a child to enter the labor market is through the interference that the labor itself has on the time allocation of the student leading to less studying, fatigue, etc. Direct e↵ects from, for example, the additional stress of an employment or health shock in the household, would likely be small relative to the disruption the child experiences from working itself.

Nevertheless, we are able to perform a robustness check to see if such transitory shocks are biasing our estimates by including a control for the employment status of the father. Since we do not have this information from 2008 we estimate our benchmark model on a sample from 2007, 2009 and 2010 for observations that include the father’s employment status with and without the father’s employment status included as a control.

Moreover, other sources of shocks may be both correlated to child labor transitions and

> 15For instance Duryea et al. (2007) finds that household economic shocks are important determinants of transit into and out of child labor in metropolitan Brazil.

17
<!-- Start of picture text --> FigureDifferTherePercent01023-10atai f arerencef5.4ceom1438in BGHQinobservations.PSGHQreportedW vesin uponTransition1-16ThetheMeantransitioni equalfromfromtoEmpl Figure 5.4 -1Employmentand thementstd.errto toUnemploymentequalsUnemployment.10<br>Difference in GHQ in Transitions from Employment to Unemployment<br>Data from BHPS Waves 1-16<br>-10 0 10<br>Difference in GHQ reported upon the transition from Employment to Unemployment<br>There are 1438 observations. The Mean is equal to -1 and the std.err equals .10<br>30<br>20<br>Percent<br>10<br>0<br><!-- End of picture text -->

# **5.2 The role of social norms in duration**

This section tests whether the length of the unemployment spell is affected by the change in well-being reported when losing one’s job. A variable is created that calculates the reported change in happiness when the individual is made redundant. This variable is named ”Difference in happiness”. It stands for _Ve − Vu_ , which in the job search theory literature is the utility difference between being employed ( _Ve_ ), and unemployed, ( _Vu_ ). Even though information on 5700 unemployment spells are available, there are only 1400 observations for the reported changes in well-being. This is due to the fact that data for SWB is yearly, whereas the unemployment spells are coded monthly. The distribution of this variable is presented in figure 5.4.

The figure 5.4 has to be read as follows : 10% of the individuals report a drop of -1 in well-being, when losing their job. 31% report feeling no change in their well-being. As it is observable, a significant proportion of the individuals record feeling _happier_ when losing a job (almost 18%). The majority, however, reports feeling worse off (42%). The average _change_ in well-being is equal to _−_ 1, on a scale going from _−_ 12 to 12. This is a considerable drop, given that SWB is highly skewed to the top. .

To estimate the determinants of duration, two regression model are performed. The first is a standard OLS regression, and the second a proportional hazard model. The results from these models are shown in table 5.2. As shown before, there are large differences in duration, by gender, age, regions and educational achievement; so these are used as controls. The regional jobless rate is also likely to affect time spent looking for work: if more people compete to get jobs, average search duration should increase. As shown in the em24
Budjan, A., U. Pape, and L. Ralston. 2022. “Broken Promises: Evaluating an Incomplete Cash Transfer Program.” _Revise and resubmit at Journal of Public Economics_ . Available at: <u>https://bit.ly/3ddKHDb.</u>

- Buehren, N., M. Goldstein, S. Gulesci, M. Sulaiman, and V. Yam. 2017. “Evaluation of an Adolescent Development Program for Girls in Tanzania.” _World Bank Policy Research Working Paper_ 7961. World Bank: Washington, DC.

- Carlana, M. 2019. “Implicit Stereotypes: Evidence from Teachers' Gender Bias.” _Quarterly Journal of Economics_ 134(3): 1163–1224.

- Carlana, M., and E. La Ferrara. 2024. “Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic”. _Manuscript_ . Available at: http://michelacarlana.com/wp- <u>content/uploads/2024/03/TOP_CarlanaLaFerrara_2024.pdf</u>

- Carlana, M., E. La Ferrara, and P. Pinotti. 2022. “Goals and Gaps: Educational Careers of Immigrant Children.” _Econometrica_ 90(1): 1–29.

- Carlana, M., and M. Fort. 2022. “Hacking Gender Stereotypes: Girls’ Participation in Coding Clubs”. _AEA Papers and Proceedings_ 112: 583–87.

- Campante, F. R., and D. Chor. 2012. "Why Was the Arab World Poised for Revolution? Schooling, Economic Opportunities, and the Arab Spring." _Journal of Economic Perspectives_ 26 (2): 167–88.

- Cecchi, F., A. Garcia, R. Lensink, B. Wydick. 2022. “Aspirational hope, dairy farming practices, and milk production: Evidence from a randomized controlled trial in Bolivia”. _World Development_ 160: 106087.

- Chiapa, C., J. L. Garrido, and S. Prina. 2012. “The effect of social programs and exposure to professionals on the educational aspirations of the poor.” _Economics of Education Review_ 31(5): 778–798.

- Chong, A., I. Cohen, E. Field, E. Nakasone, and M. Torero. 2016. “Iron Deficiency and Schooling Attainment in Peru”. _American Economic Journal: Applied Economics_ 8(4): 222–255.

- Cools, A., R. Fernández, and E. Patacchini. 2022. “The Asymmetric Gender Effects of High Flyers.” _Labour Economics_ 79: 102287.

- Contreras Suarez, D., and L. Cameron. 2020. “Conditional Cash Transfers: Do They Result in More Patient Choices and Increased Educational Aspirations?” _Economic Development and Cultural Change_ 68(3): 729–761.

- Dalton, P. S., S. Ghosal, and A. Mani. 2016. “Poverty and aspirations failure.” _The Economic Journal_ 126(596): 165–188.

- Del Carpio, L., and M. Guadalupe. 2022. “More women in tech? Evidence from a field experiment addressing social identity.” _Management Science_ 68(5):3196-3218.

- Dinkelman, T., and C. Martínez. 2014. “Investing in schooling in Chile: The role of information about financial aid for higher education. _Review of Economics and Statistics_ 96(2): 244—57.

28
|Goto and<br>Sueyoshi<br>(2009)|parametric|Japan|annual<br>observations in 9<br>companies from<br>1983-2003|multi-product<br>translog cost<br>function,<br>random effects<br>maximum<br>likelihood<br>estimator|improvements in<br>productivity<br>growth after<br>deregulation|
|---|---|---|---|---|---|
|Pombo and<br>Taborda<br>(2006)|non-<br>parametric|Colombia|12 electricity<br>distribution<br>companies from<br>1985 to 2001|DEA|profitability,<br>partial input<br>productivity, and<br>output improved;<br>plant efficiency<br>and productivity<br>increased after the<br>reform|
|Zhao and<br>Ma (2013)|non-<br>parametric|China|balanced panel:<br>34 large power<br>plants for the<br>period 1997-2010|DEA|Operation<br>efficiency<br>improved on<br>average,<br>unbundling<br>boosted<br>productivity|

# **3.2. Macroeconomic Impacts of Electricity Sector Reforms**

In this section, we review the relevant literature analyzing the impacts of electricity sector reforms on macroeconomic indicators such as economic welfare, economic growth and poverty reduction. Electricity is one of the main inputs to economic growth especially in developing countries where economic development is constraint due to lack of infrastructure including reliable supply of electricity. Therefore, any programs and policies that relax the electricity supply constraints are expected to generate positive impacts on economic welfare and growth and also reduce poverty. In this section we present some evidence to support this argument.

## **_3.2.1. Impacts of reforms on economic welfare_**

The economy-wide welfare impacts of power sector reforms are reported by a few studies. Galal et al. (1994) that estimated the welfare impacts of the privatisation of the Chilean distribution and generation companies, is one of the first and most comprehensive studies assessing welfare implications of power sector reforms. The privatisation of the Chilean electricity companies (an electricity distribution and a power generation) led to a permanent gain in social welfare equivalent to 2.1% of 1986 sales value. However, the gains were achieved at a fiscal loss and two-thirds of the aggregate gains went to foreign shareholders. In Brazil, the privatization of the electricity distribution during the period 1995-2000 when 31