rafmacalaba/datause-encoder-data huggingface dataset:
Table 8b: Impacts of PROSPERA on Type of High School All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 School Fixed Effects

||General<br>HS|Technical<br>HS|Technical/Pr<br>of|Private|
|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|
|PROSPERA beneficiary 2008|0.040***|-0.039***|0.001|-0.002|
||(0.005)|(0.005)|(0.002)|(0.002)|
|Female beneficiary|-0.006|0.007|0.001|-0.003|
||(0.007)|(0.007)|(0.002)|(0.002)|
|Female|-0.002|0.002|-0.004**|0.003**|
||(0.005)|(0.006)|(0.002)|(0.002)|
|Constant|0.392***|0.559***|0.021**|0.028***|
||(0.03)|(0.031)|(0.009)|(0.009)|
|Observations|68,871|68,871|68,871|68,871|
|R-squared|0.24|0.238|0.117|0.202|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 8c: Impacts of PROSPERA on Type of High School: 6<sup>th</sup> Graders in 2008 in Medium, High and Very Highly Marginalized Communities Nearest Neighbor Matching Models

|Variables|General|Technical|Technicalprofessional|Private|
|---|---|---|---|---|
||Propensity score matc|hing with exact|matching on age and state||
|Girls|0.0519***|-0.0317*|-0.0039|-0.0109*|
||(0.0163)|(0.0169)|(0.0045)|(0.005)|
|Observations|6,654|6,654|6,654|6,654|
|Boys|0.0895***|-0.01980*|0.0361|0.0017|
||(0.0214)|(0.0123)|(0.01862)|(0.0049)|
|Observations|4,853|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Finally, Tables 9a to 9c shows the difference-in-difference estimates of the impact of PROSPERA on ENLACE achievement tests for seventh grade, ninth grade and twelfth grade. For seventh and ninth grade, the coefficients report the increase in points achieved on the ENLACE exams due to the program. Because of the test score standardization, the estimates represent the impacts in standard deviations if one divides these coefficients by 100.

24
<mark>in other contexts, poverty predictions for refugees can be expanded at scale, with good prospects for the improvement of targeted programs.</mark> <mark>The poverty estimates used in this paper do not reflect the official poverty estimates monitored by the government and the international community. The interest of this paper is to test a cross-survey imputation methodology, and, for this purpose, we use a subsample of UNHCR refugee data that are not nationally representative of the refugee population in Chad. By contrast, the official poverty statistics require national consumption surveys conducted by the national statistical office with samples that are nationally representative. At the time of writing this paper, these national data were not available, but they are expected to be published before the end of 2020. This will provide another opportunity to validate the cross-survey imputation method proposed in this paper.</mark> <mark>The paper is organized as follows. Section II outlines the country context. Section III presents the data and analytical framework. T</mark> he estimation results are presented in section IV, and section V evaluates the <mark>targeting strategy</mark> used in Chad and our targeting method in light of the global experience. Section VI discusses the limitations of the study, and section VII concludes.

# **II. Country Context**

Chad is one of the poorest countries in the world. According to the latest national consumption survey, which was administered in 2011, 29% of the population falls below the food poverty line and 47% falls below the national poverty line (World Bank 2018). The past decade has been a decade of instability for Chad, with negative consequences on household well-being. Pe <mark>r capita gross domestic product decreased by 15% between 2015 and 2017, from an average of US$963 in 2015 to US$823 in 2017 (in 2010 purchasing power parity (PPP)). In terms of overall development, Chad is</mark> ranked 187 of 189 countries on the Human Development Index (World Bank 2019). Due to these challenges, Chad struggled to meet many of the Millennium Development Goals in 2015, and barring unforeseen economic growth or great increases in official development assistance, the country is not likely to meet many of the Sustainable Development Goals objectives set for 2030.

4
Myanmar, Papua New Guinea (PNG), and the Philippines, where a third or more of respondents working pre-pandemic were not working at the time of the survey (Figure 2). Most countries experienced periods of recovery in the latter half of 2020, when the rate of work stoppages declined. However, some countries like Mongolia and Myanmar faced renewed shocks due to a surge in COVID-19 cases late in 2020.

Figure 2. Work stoppages have varied across countries and periods Share of respondents working pre-pandemic who are not working Note: Work stopppages are defined as the share of respondents working pre-pandemic who were not working in the week preceding the survey.

Source: HFPS; Round 2 is used for Myanmar May-Jun 2020; Round 3 is used for Vietnam Jul-Sep 2020 Differences across countries in mobility restrictions have had implications for the distributional impact of the pandemic on employment in the EAP.<sup>16</sup> Across and within countries, periods of lower mobility have generally been associated with higher average work stoppages (Figure 3A, Appendix Table 1).<sup>17</sup> Moreover, lower mobility, either due to continuing or sudden lockdown measures, has been associated with widespread employment impacts across the welfare distribution (Figure 3B, Appendix Table 1). In other words, during these periods, workers were similarly likely to stop working, irrespective of their position in the welfare distribution. Figure 3B shows that times of tighter _(de facto)_ mobility restrictions were associated with a more uniform distribution in work stoppages across welfare groups: The top 20 to bottom 20 ratio in work stoppages was closer to one (denoted by the horizontal dotted line) during times of low mobility, indicating a similar likelihood to stop working.<sup>18</sup> As mobility restrictions were relaxed, businesses and workplaces reopened, and economies recovered, poorer workers – often employed in less secure, informal jobs – were less likely to return to work. In figure

> 16 Results shown in this note are unconditional averages unless otherwise noted. Controlling for survey period or respondent characteristics such as being the household head, age, and gender, do not qualitatively change results. Greater susceptibility to seasonal labor among poorer workers may also be a concern when looking at employment patterns throughout the course of the year. Data show that while seasonality is indeed a commonly cited reason for being out of work, it does not explain differences that can be observed across the welfare distribution.

> 17 Among the countries considered, PNG appears as an exception. Despite having relatively normal internal mobility, work stoppages in the country are unusually high, likely associated with agricultural seasonality (World Bank and UNICEF 2021).

> 18 Vietnam represents an outlier with work stoppages being generally low, but among those that stopped working, wealthier workers were more likely to be affected. This can be explained by two factors which are discussed in detail below: 1) higher participation among poorer quintiles in agriculture; and 2) higher likelihood of poorer households to live in rural areas.

11
22

# **Appendix: Comparability issues, poverty lines, and regional panel**

The various rounds of the HES from 1983 to 1996 provide comparable data, at least much more so than many surveys available in other countries. Yet, the 1995-96 survey differs in some respects from previous surveys. Hence there are a few comparability issues, mainly in terms of the sampling frame and the expansion factors, the diary for food consumption, and the standard errors of poverty measures.

The first comparability issue relates to the expansion factors. The sampling frame for the 1995-96 HES consists of 14 strata corresponding to the Standard Metropolitan Areas (SMAs), other urban areas, and rural areas of the five administrative divisions of Bangladesh, as described in Table A2.

Accordingly, 14 expansion factors were computed by the BBS for the 1995-96 survey (last column of Table A2). In previous years, there were strata for the SMAs, urban municipalities, other urban areas, and rural areas for each of four divisions. While a corresponding number of expansion factors should have been provided, the BBS used only two expansion factors for these years, one urban and one rural.

Because welfare measures and probabilities of being selected vary between geographical areas, using two expansion factors only generatesd bias in the estimates of poverty for previous years. The problem is not too serious for rural areas. As can be seen from Table A2, the probability that a household will be selected in the various rural strata are similar. In 1995-96, four out of five rural expansion factors belong to the interval [3702, 3916]. But this is not true for urban areas. Highly populated urban areas such as the Dhaka and Chittagong SMAs, which are under-represented in the sample, have higher standards of living. Hence using aggregate urban expansion factors would increase urban (and national) poverty measures since the population share of dense and well-off areas would be under-estimated.

To provide a consistent set of expansion factors matching the HES sampling frame for the survey years prior to 1995-96, estimates of the number of households in each of the strata for each of the survey years would be required. This detailed information is not easily available for all strata because the structure of the sampling frame changed in 1995-96 versus previous years. For example, all non-SMA
In Table 6, one can see that the _MPe_ is highest in LI countries (0.022), which is more than double the rate in HI (0.009). UMI and LMI countries are in‐between (0.011 and 0.015 respectively). As such a 5ppt � immediate increase in θ� for LI countries will raise growth by 0.022 � 5ppts=11ppts, which is similar to the boost to growth in the first period in Graph 7. For HI countries, in contrast, a 5ppt increase in efficiency would raise growth by a much lower 5ppts (0.009 x 5ppts). The _MPe_ is inversely proportional to the _efficient public capital‐to‐output ratio_ , is increasing in the usefulness of public capital ( φ ) and also increasing in the public investment to output ratio ( I��⁄Y� ).

The last somewhat surprising result is because an increase in efficiency θ�� only affects new investment. Intuitively, in countries with low public investment rates, an increase in the efficiency of new public investment will only have a small short‐run effect on the average efficiency of installed capital – and hence on growth ‐ because the new efficient public capital is only a small fraction of the total capital stock. In these countries, a permanent increase in the efficiency of public investment will still boost output, but it will just take more time for these gains to materialize.

**<u>Graph 7: Incremental Output Growth from a 5‐ppt. Increase in the Efficiency of Public Investment in the LTGM‐PC (Congestion</u>** , ζ�1 **)**

<!-- Start of picture text --> HI UMI<br>% %<br>0.15 0.15<br>Initial θ: 0.87 Initial θ: 0.75<br>Scenario, θ N in 2017 and onwards: 0.92 Scenario, θ N in 2017 and onwards: 0.80<br>θ in 2035: 0.90 θ in 2035: 0.78<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Congestion, φ = 0.17 Congestion, φ = 0.10 Congestion, φ = 0.17 Congestion, φ = 0.10<br>LMI LI<br>% %<br>0.15 0.15<br>Initial θ: 0.73 Initial θ: 0.59<br>Scenario, θ N in 2017 and onwards: 0.78 Scenario, θ N in 2017 and onwards: 0.64<br>0.10 θ in 2035: 0.76 0.10 θ in 2035: 0.63<br>0.05 0.05<br>0.00 0.00<br>Congestion, φ = 0.17 Congestion, φ = 0.10 Congestion, φ = 0.17 Congestion, φ = 0.10<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> 30
<!-- Start of picture text --> Total number of Female refugees Total No. of male refugees<br>500000<br>400000<br>300000<br>200000<br>100000<br>0<br>Adjumani Arua Hoima Isingiro Kampala Kamwenge Kanungu Kiryandongo Kisoro Kitgum Kyegegwa Masindi Moyo Yumbe<br><!-- End of picture text --> Figure 4: Aggregate distribution of refugees (disaggregated by sex) in Uganda _(Source: Authors’ illustration using study dataset)_ Uganda’s refugee management approach, backed by its Refugees Act of 2006 and Refugees Regulations of 2010, is among the most progressive refugee approaches in the world (World Bank, 2019). Enshrined in the country’s refugee regulatory framework are important principles and freedoms which are protective of the refugees hosted within the country. Firstly, refugees are not settled in camps but mostly live in refugee settlements (Kreibaum, 2016; UNDP, 2017). Refugee settlements, according to Jacobsen (2001), are expanses of land segregated purposely to host refugees for protracted periods of time. In contrast with camps therefore, refugee settlements are usually characterized by relatively more permanent housing structures, they are planned for population growth and land for farming is provided to support refugees in attaining self-sufficiency, among other differences (Jacobsen, 2001). Refugees in Uganda also have the freedom of movement and association, a right to find or establish jobs/employment, a right to access social services including education and health, and a right to own property and access land, among others (Betts et al., 2017; Betts et al., 2019; World Bank, 2019). As recommended by the 1999 refugees Self-Reliance Strategy (SRS), refugees are supposed to be given seeds and land to encourage farming, among other things (Betts et al., 2017; Betts et al., 2019). The refugees SRS was formalized through the Office of the Prime Minister (OPM) and in collaboration with UNHCR (WHO, 2018; Betts et al., 2019). The aim of the refugees SRS was obviously to promote the self-reliance of the refugees. According to UNHCR (2005), “Selfreliance is the social and economic ability of an individual, a household or a community to meet essential needs (including protection, food, water, shelter, personal safety, health and education) in a sustainable manner and with dignity” (p.1). Therefore, the vision with this SRS was the progressive reduction in the need for humanitarian aid for refugee assistance in Uganda 7
**Table 6. Intention-to-treat effects on formalization, banking, bookkeeping and innovation**

||Formal<br>|ization|Ban<br>|king|Bookk<br>|eeping|Innov<br>|ation|
|---|---|---|---|---|---|---|---|---|
||All|Existing|All|Existing|All|Existing|All|Existing|
|Post|||||||||
|Cash|0.078**|0.090**|0.217***|0.204***|0.102**|0.123**|0.182**|0.182**|
||(0.031)|(0.031)|(0.046)|(0.047)|(0.044)|(0.046)|(0.059)|(0.075)|
|Matching|0.035|0.036|0.044|0.032|0.006|0.022|0.151*|0.160*|
||(0.027)|(0.024)|(0.048)|(0.051)|(0.030)|(0.039)|(0.067)|(0.078)|
|ANCOVA|||||||||
|Cash|0.103**|0.103**|0.209***|0.198***|0.131**|0.131**|0.196*|0.196*|
||(0.033)|(0.033)|(0.043)|(0.046)|(0.046)|(0.046)|(0.087)|(0.087)|
|Matching|0.052*|0.052*|0.034|0.020|0.026|0.026|0.124|0.124|
||(0.024)|(0.024)|(0.046)|(0.052)|(0.038)|(0.038)|(0.079)|(0.079)|
|Control group mean|0.066|0.064|0.619|0.627|0.292|0.282|−0.123|−0.121|
|Standard deviation|0.248|0.244|0.486|0.484|0.455|0.451|0.844|0.853|
|S.E. clustered|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Block fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2 (Post)|0.142|0.145|0.168|0.172|0.157|0.151|0.085|0.089|
|R2 (ANCOVA)|0.218|0.218|0.312|0.308|0.173|0.173|0.127|0.127|
|N (Post)|1,042|969|1,107|970|1,042|969|1,009|937|
|N(ANCOVA)|969|969|1,107|970|969|969|913|913|

_Note:_ ***p < .01, **p < .05, *p < .1. Formalization, banking, and bookkeeping are dummies, and each equals one if the firm is formalized, owns a financial account, or keeps books on sales and purchases. Innovation is an index, that is, the first component from a principal component analysis applied on 15 dummies each taking the value 1 if the firm introduced either of the 15 listed innovations. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head, and whether a member in the owner household is a community leader. The control group mean refers to the follow-up survey. The detailed regression results are provided in Table A.8 and Table A.9 in the appendix.

24
<mark>Yoshida (2016): "A global count of the extreme poor in 2012: data issues, methodology and initial results."</mark> _<mark>Journal of Economic Inequality</mark>_ <mark>,</mark> **<mark>14</mark>** <mark>(2): 141‐172.</mark>

- Ferreira, Francisco H.G. and Jeremie Gignoux (2011): "The Measurement of Inequality of Opportunity: Theory and an application to Latin America”, _Review of Income and Wealth,_ **57** (4): 622‐657.

- <mark>Ferreira, Francisco H.G. and Vito Peragine (2016): “Individual Responsibility and Equality of Opportunity.” Chapter 25 in M. Adler and M. Fleurbaey (eds),</mark> _<mark>The Oxford Handbook of Well‐ Being and Public Policy</mark>_ <mark>. New York: Oxford University Press.</mark>

<mark>Fields, Gary S. (2000): “Income Mobility: Concepts and Measures” in N. Birdsall and C. Graham (eds.)</mark> _<mark>New Markets, New Opportunities? Economic and Social Mobility in a Changing World</mark>_ <mark>. Washington, DC: Brookings Institution and Carnegie Endowment Press.</mark> Fiszbein, Ariel, Norbert Schady, Francisco H.G. Ferreira, Margaret Grosh, Niall Keleher, Pedro Olinto and Emmanuel Skoufias (2009): _Conditional Cash Transfers. Reducing Present and Future Poverty._ Washington, DC: The World Bank.

- Foster, James, Joel Greer and Erik Thorbecke (1984): “A class of decomposable poverty measures.” _Econometrica,_ **52** (3): 761‐66.

- Gertler, Paul J., James Heckman, Rodrigo Pinto and Sally Grantham McGregor (2014): “Labor market returns to an early childhood stimulation intervention in Jamaica.” _Science_ , **344** : 998‐1001.

- Gertler, Paul.J., Sebastian W. Martinez and Marta Rubio‐Codina (2012): “Investing Cash Transfers to Raise Long‐Term Living Standards.” _American Economic Journal: Applied Economics_ , **4** (1): 164‐ 92.

Greenwald, Bruce C. and Joseph Stiglitz (1986): “Externalities in Economies with Imperfect Information and Incomplete Markets.” _Quarterly Journal of Economics_ , **101** (2): 229‐64.

- Grimm, Michael (2007): “Removing the Anonymity Axiom in Assessing Pro‐Poor Growth.” _Journal of Economic Inequality,_ **5** : 179‐97.

Heckman, James J. (2006): “Skill Formation and the Economics of Investing in Disadvantaged Children.” _Science_ , **312** : 1900‐1902.

- Heckman, James J. and Stefano Mosso (2014): “The Economics of Human Development and Social Mobility.” _<mark>Annual Review of Economics</mark>_ **<mark>6</mark>** <mark>: 689‐733.</mark>

Heckman, James J., Seong Hyeok Moon, Rodrigo Pinto, Peter A. Savelyev and Adam Yavitz (2010): "The rate of return to the High Scope Perry Preschool Program." _Journal of Public Economics_ , **94** (1‐ 2): 114‐128.

Heller, Sara B., Anuj K. Shah, Jonathan Guryan, Jens Ludwig, Sendhil Mullainathan, and Harold A. Pollack (2017): “Thinking, fast and slow? Some field experiments to reduce crime and dropout in Chicago.” _Quarterly Journal of Economics_ , **132** (1): 1‐54.

Hoynes, Hilary, Diane Whitmore Schanzenbach, and Douglas Almond. (2016): "Long‐Run Impacts of Childhood Access to the Safety Net." _American Economic Review_ , **106** (4): 903‐34.

20
Table 1: Sample sizes for the household surveys

||Rural|Urban|Total|
|---|---|---|---|
|Bolivia (2005)|1,750|2,335|4,085|
|Cambodia (2003)|11,990|2,994|14,984|
|Madagascar(2001)|2,036|3,039|5,075|
|Malawi(2004)|9,840|1,440|11,280|
|Nicaragua (2001)|1,790|2,211|4,001|
|Pakistan(1998-99)|10,254|5,909|16,163|
|Peru (2003)|3,638|1,378|5,016|
|Vietnam(1997-98)|4,269|1,730|5,999|
|Vietnam(2004)|6,938|2,250|9,188|
|Zambia (1998)|8,373|8,170|16,543|
|Total|60,878|31,456|92,334|

26
consumption during the Great Recession. This is an important control variable, and so we have also included it in some of the regressions.

|**Statistics**|**N**|**mean**|**sd**|**p10**|**p90**|
|---|---|---|---|---|---|
|Tradable employment/Population, 2007|3108|0.050|0.047|0.008|0.103|
|Supporting sector employment/Population, 2007|3093|0.012|0.017|0.004|0.023|
|Tradable employment/Employment, 2007|3102|0.145|0.107|0.030|0.288|
|Supporting sector employment/Employment, 2007|3146|0.035|0.037|0.012|0.064|
|Log change in tradable employment, 2007-2010|3013|-0.161|0.421|-0.580|0.186|
|Log change in supporting sector employment, 2007-2010|3131|-0.040|0.506|-0.600|0.505|
|Bartik|3128|-0.183|0.083|-0.282|-0.073|
|% White, 2007|3135|86.997|15.017|65.834|98.827|
|Median household income, 2007|3135|35597.430|9147.112|26312.470|46608.320|
|Median home value, 2007|3135|82862.160|45629.080|45377.850|127120.500|
|% Owner occupied, 2007|3135|74.063|7.541|64.320|81.818|
|% With less than high school diploma, 2007|3135|22.565|8.705|12.584|34.965|
|% With only high school diploma, 2007|3135|34.706|6.571|26.398|42.903|
|% With college education, 2007|3135|10.994|4.961|5.924|17.494|
|Unemployment rate, 2007|3135|5.820|2.730|2.998|9.071|
|Poverty rate, 2007|3135|14.154|6.454|7.261|22.605|
|% Urban, 2007|3135|39.318|30.881|0.000|84.608|
|Leverage, 2006|2219|1.573|0.584|0.971|2.366|

Table 4.1: Summary statistics 13