rafmacalaba/datause-encoder-data huggingface dataset:
time lights data is obtained from Beyer, Jain and Sinha (2021). The authors obtained raw night-time lights data from VIIRS-DNB Cloud Free Monthly Composites (version 1) and corrected the raw data for outlier observations (averaging cells overtime and clustering areas based on the intensity of night-time lights). These corrections follow the approach advocated by Elvidge, et al. (2017). Values of night-time lights are reported in nanowatts per square kilometer. We collapse the monthly nighttime-lights aggregates from Beyer, Jain and Sinha (2021) to yearly levels before evaluating growth rates.

Nielsen’s surveys track sales of consumer goods through retail store level surveys, covering a network of mom-and-pop stores as well as modern retail stores in 52 cities and 2700 villages across India. The instrument collects quantities, prices and sale values of both branded and non-branded items. We use estimates of quarterly growth in store-level sale values from publicly available sources<sup>32</sup> . The quarterly growth values are aggregated at the yearly level by taking simple averages, see Figure 25.

Both night-time lights and Nielsen’s store-level surveys indicate welfare indicators peaked in 2017 and 2018. This period coincides with rapid rate of poverty reduction in our analysis. The sources also suggest a slowdown in 2016 and 2019 which further supports our finding that the rate of poverty reduction peaked in 2017-2018 and moderated in 2019.

# **6.3 A rise in urban poverty in 2016 followed by a rapid rise in consumption in 2017**

**Consumption growth trends from the IHDS-3 can help validate a break in poverty trends around 2016** . The break in poverty reduction around 2016 coincided with a rise in urban poverty in that year. Household consumption strongly rebounded thereafter. Households interviewed by the IHDS in February to April 2017 reported a negligible rise in consumption since 2011-12. In contrast, household consumption for interviews conducted between May to July 2017 is 5 percent higher than 2011 on an annualized basis. Consumption of the first cohort of households was plausibly affected by the demonetization of currency notes in November 2016 followed by rapid growth in consumption as the economy was remonetized. We observe similar trends in our analysis albeit with smaller magnitudes. Consumption growth for the first cohort of

32 List of all sources: http://bsmedia.business-standard.com/ ~~m~~ edia/bs/img/article/2016-08/ 09/full/1470687448-3888.jpg, https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/indiaFMCG-growth-snapshot-q3-2018.pdf, https://images.assettype.com/afaqs/2020-01/200d87dc-162d41ae-8fde-299faec4927f/Q4 ~~2~~ 019 ~~F~~ MCG ~~F~~ inal ~~D~~ eck.pdf. Quantity growth for 4<sup>th</sup> quarter of 2016 was not available online. 2015-16 references the period starting the third quarter of CY2015 to the second quarter CY2016.

55
_18_ _Price Support at Any Price?_ Table 3.2 gives basic characteristics of the impact of various paths of EU prices targeting on the agricultural sector.

Tab.3.2 Targeting EU prices Simulation experiments: POLICY EFFECTS

||Fast p<br>(EU prices|ath<br>by 2000)|Fast path<br>reduc<br>(EU pr. by 20<br>|/output<br>tion<br>00, quotas)<br>|Last moment<br>(50%EU p<br>201<br>|integration<br>rices by<br>0)<br>|
|---|---|---|---|---|---|---|
||2000|2010|2000[|2010|2000|2010|
|Income Relation|||||||
|(farmer's income/wage)<br>index, 91 level=100|142.9|**172.4**|**134.1**|**133.9**|**108.3**|**131.6**|
|index, base sol.=100|131.9|155.6|123.8|120.3|100.0|118.7|
|Number of farmers|||||||
|in millions|3.085|2.780|3.084|2.651|2.894|2.416|
|index, base sol.=100|106.6|115.1|106.6|109.7|100.0|100-0|
|Employment in<br>agriculture as % of total||**18.1%**||**17.3%**||15.8%|
|Average farm area|||||||
|<br>in hectares|6.7|7.4|6.7|7.8|7.2|8.6|
|index, base sol.= 100|93.8|86.9|93.8|91.1|100.0|100.0|
|Output per farmer|||||||
|<br>index, 91 level=100|126.3|216.4|118.4|164.1|131.9|184.6|
|index, base sol. = 100|95.7|117.2|89.7|88.9|100.0|100.0|

_Source: Model POLAGR, simulation Jan.1995_ The path of reduction in the number of farmers (27% reduction in the base run), remains the same in the LAST MOMENT INTEGRATION experiments, but slows down considerably in both FAST PATH and FAST PATH/OUTPUT REDUCTION experiments (the number of farmers in 2010 is, respectively, 15% and 10% above the base run). Consequently, the average farm area grows only to 7.4-7.8 ha (in the base run 8.6 ha). An artificially supported high income relation creates a major disincentive for the labor mobility in agriculture, discouraging farmers from searching for a job outside farming.

The output per farmer value increases considerably in the FAST PATH (a supply response, leading to production surpluses and unbearable costs), and falls in the FAST PATH/OUTPUT REDUCTION experiment (10% below the base run). This means that both FAST PATH experiments lead to undesirable effects: either a slowdown in reduction in the number of farmers is accoinpanied by huge production surpluses, or by a significant fall of productivity. It means, that restructuring of Polish agriculture is considerably slowed down by both FAST PATH experiments - a technical reduction of policy costs through quantity controls makes the policy feasible from the financial (and GATT) point of view, but does not eliminate a general harmful impact on structural changes.
_κ_ = ( _r_ + _δ_ ) _/_ (1 + _r_ ), which ensures that a default-free bond (with the same coupon structure as our sovereign bonds) trades at a price of 1 _/_ (1 + _r_ ). The price floor _<u>q</u>_ is set to 70% of the risk-free price, which is never binding in the simulations.

**Table 5:** Benchmark parameter values.

|Borrower’s risk aversion|_γ_|2.0|Standard|
|---|---|---|---|
|Risk-free rate|_r_|0.04|Standard|
|Discount factor|_β_|0.90|Standard|
|Income autocorrelation coefcient|_ρ_|0.6|Estimated|
|Standard deviation of innovations|_σν_|0.03|Estimated|
|Probability exclusion ends|_θ_|0.33|Mean exclusion = 3 years|
|Debt duration|_δ_|0.31|Debt duration = 5 years|
|Bond coupon|_κ_|(_r_+_δ_)_/_(1 +_r_)|Risk-free bond price = 1_/_(1 +_r_)|
|Price foor|_q_|0_._7_/_(1 +_r_)|Never binding|
|Lender’s risk aversion|_γl_|2.0|Aguiar et al. (2016)|
|Lender’s wealth|_W_|2.5|Aguiar et al. (2016)|
|Hidden debt price|_qh_|1_/_(1 +_r_)|Normalization|
|Hidden debt recovery|_χ_|1.0|Normalization|
|Mean of _ε_|_με_|1%|Our dataset|
|Standard deviation of _ε_|_σε_|2%|Our dataset|
|Income cost of defaulting|_λ_0|0.07|Avg. market debt = 26%|
|Income cost of defaulting|_λ_1|1.75|Avg. spread = 3.0%|
|Monitoring fee|_f_|0.03%|Freq. of monitoring = 7.1%|
|Recovery rate parameter|_α_|0.15|Mean recovery rate = 55%|

_Notes:_ Mean and standard deviation of _ε_ and the monitoring fee are expressed in percent of GDP.

The second part of Table 5 details our parametrization of the lender’s side of the model and the properties of hidden debt. We follow Aguiar et al. (2016) in setting the risk aversion coefficient of lenders to the same value as that for the borrower ( _γl_ = _γ_ = 2). The lender’s wealth _W_ is set to 2.5 (i.e., 250% of the mean income in the borrowing country), which is in line with the parameter value used in Aguiar et al. (2016). The price of hidden debt _qh_ is normalized to the risk-free price and the effective recovery rate on hidden debt _χ_ is normalized to one. The mean and variance of innovations to hidden debt are set using the values found in our dataset.<sup>37</sup> The last part of Table 5 has the remaining parameters. We calibrate the default cost parameters ( _λ_ 0 and _λ_ 1), the monitoring fee ( _f_ ), and recovery rate parameter ( _<u>α</u>_ ~~)~~ to target four moments from the data: (i) an average debt-to-GDP ratio of 26%, (ii) a mean spread of 3.0%, (iii) a frequency of monitoring of 7.1%, and (iv) a recovery rate of 55%. The first three moments are computed directly from our dataset. The recovery rate is taken from Graf von Luckner et al. (2023) and corresponds to the mean recovery rate obtained by bondholders in the modern era of sovereign bond financing. We solve the model using value function iteration and interpolation (Hatchondo et al., 2010).

> economies, find an average duration of 4.77 years, with a standard deviation of 1.52 years. In a panel of 11 emerging economies, Bai et al. (2017) report an average debt duration of 6.7 years.

> 37We obtain _με_ of 1% and _σε_ of 2% after winsorizing our revelations data at the 1st and 99th percentiles.

31
# **Table 5: Testing for an economic gradient in subjective welfare**

|Regression coefficients on|Ordered|Probit|CHO|PIT|
|---|---|---|---|---|
|log expenditure per person|||||
||Coeff.|s.e.|Coeff.|s.e.|
|**Tajikistan**|||||
|_Pre-vignette_|0.630***|0.037|0.580***|0.033|
|_Post-vignette_|0.560***|0.037|0.513***|0.032|
|**Guatemala**|||||
|_Pre-vignette_|0.429***|0.087|0.390***|0.055|
|_Post-vignette_|0.444***|0.065|0.389***|0.053|
|**Tanzania**|||||
|_Pre-vignette_|0.419***|0.117|0.504***|0.085|
|_Post-vignette_|0.529***|0.105|0.560***|0.082|
# **Migration**

Migration is a coping mechanism for youth and generally for the population, in response to unemployment, poverty, and lack of opportunities. In Haiti, more than one out of five persons aged 15 or older were not born in the same region as that of their current residence. Table 4.9 shows that in the most populous region, West, 36.7 percent of the population was born elsewhere in Haiti. This shows that the lives of Haitian youth are heavily affected by migration and the capital city is in most cases the destination for domestic migrants.

|Table 4.9: Domestic Migration 2001(percent)<br><br>|
|---|
|Migrated<br>Notmigrated|
|Artibonite<br>7.6<br>92.4|
|Center<br>15.3<br>84.7|
|Grand-Anse<br>11.5<br>88.5|
|North<br>4.9<br>95.1|
|Northeast<br>23.6<br>76.4|
|Northwest<br>1.9<br>98.1|
|West<br>36.7<br>63.3|
|South<br>11.2<br>88.8|
|Southeast<br>8.3<br>91.7|
|Total<br>20.4<br>79.6|

_Source:_ Own calculations based on ECVH 2001.

Migrants in Haiti are mainly economic migrants, whom Chiswick (1999) describes as: “more able, ambitious, aggressive, entrepreneurial, or otherwise more favorably selected than similar individuals who remain in their place of origin,” i.e., they are positively self-selected. This hypothesis is supported by Figure 4.4, which shows higher average years of schooling among migrants aged 15 and older who left a specific region, compared to those who stayed behind.<sup>31</sup> Only the few who left the metropolitan area (which lies in West) are less educated than their peers. Yet their level of education is still higher than the average of other regions, giving them a competitive advantage by migrating. On average migrants have nearly three more years of completed education than nonmigrants.

> 31 Data did not allow an examination of age at the time of migration so it was not possible to calculate the proportion of youth migrating.

21
Table 13b: Impacts of PROSPERA on ENLACE tests 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 School Fixed Effects

|Variables|7th-6th score|9th-6th score|12th-6<sup>th</sup><br>level|
|---|---|---|---|
||_Panel A: Spanish_|||
|PROSPERA beneficiary 2008|9.893**|8.680*|-0.021|
||(4.364)|(4.748)|(0.036)|
|Female beneficiary|-5.553|-2.514|0.028|
||(5.926)|(6.416)|(0.047)|
|Female|10.006*|1.907|-0.011|
||(5.439)|(5.887)|(0.043)|
|Constant|-5.195|50.291**|0.670***|
||(20.881)|(22.769)|(0.176)|
|Observations|5,234|5,757|2,897|
|R-squared|0.411|0.546|0.315|
||_Panel B: Mathematics_|||
|PROSPERA beneficiary 2008|7.185|2.262|0.018|
||(4.439)|(5.807)|(0.035)|
|Female beneficiary|-3.579|1.045|-0.007|
||(6.028)|(7.848)|(0.046)|
|Female|0.025|-0.578|-0.111***|
||(5.533)|(7.201)|(0.042)|
|Constant|18.868|99.985***|0.690***|
||(21.239)|(27.847)|(0.172)|
|Observations|5,234|5,757|2,863|
|R-squared|0.499|0.583|0.383|

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.

34
# Annex 1. Timing of data collection for the 2018/19 EHCVM **Table 11. Timing of interviews in the EHCVM surveys**

|||||**2018**<br>|||||||**2019**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Jun**|**Jul**|**Aug**|**Sep**|**Oct**|**Nov**|**Dec**|**Jan**|**Feb**|**Mar**|**Apr**|**May**|**Jun**|**Jul**|
|**Burkina Faso**||||795|1,153|1,224|342||||259|1,889|1,221|133|
|**Mali**||||1|522|1,723|662|1|||932|1,777|958|24|
|**Niger**|||||1,022|1,291|667||||432|1,183|1,230|195|
|**Senegal**||||358|1,100|1,398|710||||917|1,541|1,067|62|
|**Chad**|340|1,421|1,311|634||||688|1,258|1,379|461||||

Source: EHCVM and World Bank estimates.

26
development. These findings are somewhat contradictory to the idea of a conflict trap in which lack of growth and conflict reinforce each other. A good development policy in this context is policy that helps avoid violent conflict.

The remaining sections build on these findings. In sections 4 and 5 we argue that the humanitarian crisis triggered by civil war could impact the recovery period in ways that are not measurable in standard panel regressions. Section 4 discusses the close relationship between violence and refugees. In the average civil war year, around 500,000 persons leave their country. This is an important finding as it gives an idea of the scale of the humanitarian crisis entailed by mass violence. In section 5 we use the findings of a relatively new literature to argue that long term labor productivity and, hence, recovery might be strongly affected through permanent losses in human capital due to exposure to conflict.

In section 6 we argue that if countries are known to revert to conflict this will hinder investment. We know from enterprise surveys that firms react strongly to political instability. The expectation of future instability will therefore be a critical element in their decision making. We show in this context that political inclusion and returning refugees can predict inflows of foreign investment in the critical phase after conflict. A causal link is harder to establish here but it seems reasonable to assume that commitments to political inclusion might play a key role in attracting investment.

Section 7 turns towards a detailed discussion of political exclusion as a factor of instability. We focus on a channel that has been proposed by the recent literature in this area. In absence of strong political institutions, political exclusion matters because it leads to an unequal distribution of economic resources. We find quite striking evidence for this. In addition, we find that formal institutions matter in how political power is reflected in economic well-being. These findings are very much in line with the idea that political institutions help avoid conflict because they prevent a “winner takes all” dynamic in which loss of political power leads to economic decline.

Section 8 concludes. In this section we present additional conclusions that arise from our findings and the related literature.

5
9 individual is 42.3 years and then starts decreasing. The turning point is similar for individuals in the private sector (at 40 years) and who are self-employed (at 43.8 years).

Education affects positively participation into public and private sectors and reduces the likelihood of participation in the informal sector. Workers with university education have a higher probability of joining the public sector as opposed to the formal private sector. The older a worker which serves as a proxy for more work experience, the higher the probability of joining each sector. Females are less likely to participate in the formal sector and are more likely to be in the informal sector.

Having a parent in the public sector raised the probability of finding employment in the public sector and reduce the probability of being in the informal sector. Income effects on participation, measured by unearned household income is statistically significant and negative as expected for public and private sector workers since higher non-labor income would reduce the need to seek formal employment.

**Table 2 Djibouti: Public and Private Sector Participation Equation**

||<br>**Public**||<br>**Private For**|**mal**|<br>**Self-Emplo**|**yed**|
|---|---|---|---|---|---|---|
|**Variables**|**Marginal**<br>**Effect**|**Std.**<br>**Dev**|**Marginal**<br>**Effect**|**Std.**<br>**Dev**|**Marginal**<br>**Effect**|**Std.**<br>**Dev**|
|age|0.011 ***|(0.00)|0.004 ***|(0.00)|0.007 ***|(0.00)|
|age squared/100|-0.013 ***|(0.00)|-0.005 ***|(0.00)|-0.008 ***|(0.00)|
|female|-0.068 ***|(0.01)|-0.008 ***|(0.00)|0.009 ***|(0.00)|
|married|0.009 **|(0.00)|0.001|(0.00)|-0.009 ***|(0.00)|
|obtained certificate|0.048 ***|(0.02)|0.039 ***|(0.02)|||
|primary schooling|0.042 ***|(0.01)|0.014 ***|(0.00)|-0.009 **|(0.00)|
|<br>middle schooling|0.106 ***|(0.02)|0.020 ***|(0.01)|-0.014 **|(0.00)|
|secondary schooling|0.134 ***|(0.03)|0.028 ***|(0.01)|-0.004|(0.01)|
|vocation training|0.144 ***|(0.03)|0.016 **|(0.01)|-0.014|(0.01)|
|university|0.138 ***|(0.04)|0.023 ***|(0.01)|-0.022 **|(0.00)|
|parents work in public sector|0.010 **|(0.00)|-0.002|(0.00)|-0.011 **|(0.00)|
|parents work in private sector|-0.013 *|(0.01)|-0.003|(0.00)|-0.019 ***|(0.00)|
|ln_unearned income|-0.005 ***|(0.00)|-0.002 ***|(0.00)|-0.001|(0.00)|
|Pseudo R square|0.33||0.19||0.15||

_Note: standard errors are in brackets. *** denotes statistical significance at 1% level, ** at 5% and * at 10%._ _Source: Authors' estimates based on EDAM 1996._
apy. The idea is that, by providing relief from the day-to-day worries of budget constraints and some insurance against negative shocks, cash transfers may create the mental space needed to internalize the lessons and practice the behavioral skills obtained during therapy, thus increasing impacts. Our trial aimed to test this hypothesis, for which the evidence is mixed (Blattman et al., 2023; McKelway et al., 2023; Bossuroy et al., 2022; Haushofer et al., 2023b). Third, and finally, our trial aims to test the hypothesis that reducing depression during adolescence might translate into improved human capital accumulation. In the shortrun our primary and secondary outcomes focus on changes in aspirations, preferences, and expectations as mechanisms for investments in human capital, such as desired fertility, time preferences, educational aspirations, and ideal age of first pregnancy and marriage. In the medium-term, we examine actual changes in human capital including school enrollment and skill-based competencies, incidences of pregnancy and marriage, risky sexual behavior, and self-efficacy.

We find that 18%-28% of individuals in the control group (depending on the scale used to measure psychological distress) had recovered to have minimal depression approximately 12 months after the IPT-G intervention. Therapy significantly improved this primary outcome by more than 5 percentage points (pp) 12 months post therapy. However, these effects dissipated and were not statistically significant at the 24-month follow-up. We found no effects on secondary outcomes of psychological well-being, such as self-esteem, resilience, and locus of control at any of the follow-ups. It is important to note that the 14-week therapy intervention was conducted in late 2019, immediately prior to the emergence of the COVID-19 pandemic, meaning that the 12-month follow-up surveys were conducted at the height of the pandemic. While our results imply that IPT-G had some protective effect, it is plausible that the impacts of therapy may have been muted by the difficult conditions caused by the pandemic, including extensive school closures – Uganda had the longest school closures in the world at 22 months (Blanshe and Dahir, 2022) – and partial shutdown of the Ugandan economy.

Given the size of the impacts on mental health, we would expect limited changes in human capital accumulation. This is reflected in our findings at 12 months where we find mixed effects of IPT-G, with modest positive impacts on school enrollment, delayed marriage, desired fertility, and time preferences. While these effects are small (and generally do not survive multiple hypothesis testing), they are suggestive of the important role that improved mental health plays in broader well-being. By the 24-month follow-up, these impacts had also dissipated, with none surviving corrections for multiple hypothesis testing.

Surprisingly, the marginal effect of providing lump-sum cash transfers to IPT-G beneficiaries immediately following the completion of therapy was large and negative on mental 4
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8