rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 35 7 7 35 30<br>30 8 Ea)<br>6 & 30<br>== 25 omen La Harareclinics and Ghitungiza 5 = 25 60 e2<br>& = ‘Wollagn from antenatal clinics 3 =] n<br>= 20 eet — Ghitungwiza 4 = 20 so &<br>= emearal pulation wanton ‘ol women In microbicidestud’ o aa 1 o<br>g 15 \ Racine “ig TF Harare a = 15 - Mankelan a ~ 40 8<br>a See NY t SeeoreremiayMe ° ra in c Tory 3 natal<br>a 10 enenal Popul = mat Kesey 2= 85 Wayman| Wayricinna = - 30 o<br>Women Maniclanal — o Harare at<br>5 In ganeral population mlHarare 1 Woman en antndata5 Sianclinics Harare Chintungwiza<br>Employocs ie pravention bl onarel women in microblclde shady _<br>voluMiary trom businesses In Hee and Chiturgwiza — | 10<br>0 counseling and testing study Workin trom “eee ontral women in microbirice study<br>1990 1995 2000 2005 2010, a Volunted}2000couneoling ared«2002 terting 2004iv = 2006, 2008 )= 2010s, a<br>sees Modelled incidence (%), adults 15-49, from Spectrum software<br>Prevalence (%), females 15-24 (source: national DHS/AIS reports)<br>aeee= Prevalence (%), females 15-24 (source: national ANC reports)<br>eee Prevalence (%), young females (source: published studies, see Annex)<br>=== Incidence rate (per 100 person years}, adults (source: published studies, see Annex)<br>eee ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013<br><!-- End of picture text -->
In contrast, workers in disadvantaged industries seem less sensitive to changes in foreign market access. Since international demand for their goods is generally low, better access to foreign markets will have less additional value for workers in these industries. As a consequence, market access is expected to play a less important role in the location decision of these workers.

Table 2: Market Access Impact by Sector

|Dependent|ln(m|igrants_ijst_/st|ayers_iist_)||
|---|---|---|---|---|
|variable:|(1)|(2)|(3)|(4)|
|_High: comparati_|_ve advantage industries_||||
|_ijst_<br>_MA_<br>∆<br>×<br>|2.949<sup>_a_</sup>|-0.018|||
|Agriculture|(0.435)|(0.298)|||
|_ijst_<br>_MA_<br>∆<br>×<br><sup>Food</sup>|1.377<sup>_a_</sup>|0.924<sup>_b_</sup>|||
||(0.451)|(0.424)|||
|_ijst_<br>_MA_<br>∆<br>×<br><sup>Wood</sup>|2.334<sup>_a_</sup>|1.474<sup>_a_</sup>|||
||(0.433)|(0.383)|||
|_ijst_<br>_MA_<br>∆<br>×<br><sup>Plastic</sup>|0.522<sup>_a_</sup>|0.234<sup>_c_</sup>|||
|& non-metallic|(0.136)|(0.134)|||
|_ijst_<br>_MA_<br>∆<br>×<br><sup>Basic</sup>|1.028<sup>_a_</sup>|0.529<sup>_a_</sup>|||
|metals|(0.197)|(0.183)|||
|(<br>_H_<br>β )<br>_ijst_<br>_MA_<br>∆<br>×<br>|||0.810<sup>_a_</sup>|0.829<sup>_a_</sup>|
|_Strong Adv_|||(0.150)|(0.152)|
|_Medium: no com_|_parative advantage_||||
|_ijst_<br>_MA_<br>∆<br>×<br><sup>Mining</sup>|<sup>1.062</sup><sup>_b_</sup>|0.915<sup>_a_</sup>|||
||(0.467)|(0.340)|||
|_ijst_<br>_MA_<br>∆<br>×<br>|2.008<sup>_a_</sup>|1.564<sup>_a_</sup>|||
|Textiles|(0.241)|(0.218)|||
|_ijst_<br>_MA_<br>∆<br>×<br>|0.440<sup>_b_</sup>|0.263|||
||(0.184)|(0.182)|||

21
their occupational attainment than non-migrants by way of taking advantage of the education received in contexts different from those of their place of origin.

**Table 16. Standardized effects of the determinants of occupational attainment. Youths from 18-35** **<u>years of age of ENCEL 2017. Comparison by migratory status.</u>**

|Variables|Migrants<br>Effects|N<br>mig<br>Eff|o<br>rants<br>cts|Groups<br>Comparison|
|---|---|---|---|---|
|Social o<br>|rigin on occupationa|<br>l attainment|||
|Direct effects|-|-|||
|Indirect effects|**0.27**<br>*******|**0.21**|*******|*******|
|Via education|0.13<br>*|0.06|<br>*|**|
|Via education and cognitive abilities|0.01<br>ls|0.02|<br>**|**ns**|
|Via education and first employment|0.13<br>***|0.13|<br>***|**ns**|
|Total effect (direct + indirect)|**0.27**<br>*******|**0.21**|*******|*******|
|<br>Provider’s cogni<br>|tive abilities on occ|upational atta|inment||
|Direct effect|-|-|||
|Indirect effects|**0.01**<br>**ls**|**0.05**|*****|******|
|Via youth’s cognitive abilities|0.01<br>ls|0.05|<br>*|**|
|Total effect (direct + indirect)|**0.01**<br>**ls**|**0.05**|*****|******|
|<br>Youth’s cognit|ive abilities on occu|pational attai|nment||
|Direct effect|**0.04**<br>**ls**|**0.10**|******|*****|
|Indirect effects|-|-|||
|Total effect (direct + indirect)|**0.04**<br>**ls**|**0.10**|******|*****|
|<br>Educat|ion on occupational|attainment|||
|Direct effect|**0.23**<br>*****|**0.11**|*****|******|
|Indirect effects|**0.25**<br>*******|**0.27**|*******|**ns**|
|Via youth’s cognitive abilities|0.02 ls|0.04|**|**ns**|
|Via first employment|0.23 ***|0.22|***|**ns**|
|Total effect (direct + indirect)|**0.48**<br>*******|**0.38**|*******|*******|
|<br>First occu|<br> <br>pation to occupatio|<br>nal attainmen|<br>t||
|Direct effect|**0.35**<br>*******|**0.31**|*******|**ns**|
|Indirect effects|-|-|||
|Total effect(direct + indirect)|**0.35**<br>*******|**0.31**|*******|**ns**|

Levels of significance *** p<0.001, ** p<0.01, * p<0.05. Source: Own elaboration from ENCEL 2017 (n=3,310).

# **VII. Conclusions**

Throughout its history, PROSPERA has been consolidated as one of the central governmental interventions in the country directed at improving the quality of life of the population living in conditions of poverty. The program’s design has focused on two objectives, improving the current 50
52 PCADepth = », W,Provision, k=1 <!-- Start of picture text --> LO e ® rs ee e * . e<br>.<br>+ oe<br>° * e e s o**8ee ene ® ]<br>S 0 & 2 Sgeg8 i, *.<br>» 3°Ne'sihs° PY 4 e : e<br>N s ° oe ee<br>td] * e* e g “eeofegoo $of<br>® ¢ *eons o,3 8 ge*<br>_ e : eet a<br>ee r e et ee 8 o<br>< ° tof e 3 os<br>1960 1980 2000 2020<br>year<br>95% Cl ——— Fitted values<br>* PCA depth<br><!-- End of picture text -->
The petty cash and project advance data for 2018 is shown in the attached figure. The total amounts are a relatively modest percentage of the total disbursements. The petty cash process, when limited to small amounts on an overall basis and a low individual transaction threshold, is generally a good way for developing countries to provide easy access to small amounts of money to SUs like schools and health clinics and has a positive impact on service delivery.

_Figure 5 Advances as Percent of Total Disbursements_ <!-- Start of picture text --> 16.0<br>13.9<br> 14.0 12.7<br> 12.0 10.9<br> 10.0<br> 8.0<br> 6.0<br>3.3<br> 4.0 2.8 2.5<br> 2.0<br> ‐<br>%of total %of total %of total<br>Total Center Provinces<br> Petty Cash Project Advance<br>Percentage<br><!-- End of picture text -->

# _Source:_ Authors.

An analysis of the frequency distribution of project and program advances and of petty cash advances was therefore done (see Annex II for details). From it, the following is evident:

- 83% of the advances are below 100M Riels or US$25,000;

- 25% of the Petty Cash advances at Center are above 400M riel or US$100,000; and

- - At the provinces 95% are below 100M Riel or US$25,000.

Some petty cash transactions are quite large. Out of a total of 4,098 petty cash transactions in the first 4 months of 2018, only 100 transactions which are more than 400,000,000 Riel would constitute almost half of the total amount 199 billion i.e. 98 billion Riel. Out of these 10 transactions 27 take place at the center and 73 at the provinces. In 2017 a total of 11,775 petty cash transactions took place totaling to 506 billion Riel or about 2.55 % of the total amount disbursed. However, of these, 251 transactions were above 400 million Riel or $ 100,000 and totaled 205 billion Riel or 40% of the total. 242 of these high value transactions took place at the center and 9 at the . provinces 21
grant well-being because of two main threats to identification. First, as Table 3 highlights, RAMV migrants migrated earlier (by seven months, on average), meaning some differences could be due to longer assimilation time. Second, although PEP was introduced unexpectedly and RAMV was introduced earlier without any announcement or expectation that it would be used to provide benefits, registration in both was still voluntary. Hence, it is possible that RAMV and non-RAMV migrants were already different or that the decision to register was driven by unobservable characteristics correlated with refugee well-being.

The data in Table 3 to ease the concern that RAMV and non- helps first by demonstrating RAMV migrants were largely comparable across a range of pre-RAMV sociodemographic characteristics, including retrospective socioeconomic characteristics in Venezuela (prior to migrating) and factors that might be correlated with migrating to Colombia. However, the data on reasons for not registering in RAMV and PEP is suggestive of potential confounding factors (Tables E.1–E.2). For instance, the decision to not register in RAMV stemmed from lack of information, lack of proof of Venezuelan nationality, and inability to take time off from income-generating activities. The decision to not register in PEP also included lack of information and loss of proof of RAMV registration, which was sent to registered email accounts. To the extent that these characteristics might be correlated with the outcomes of interest and with unobserved factors such as connections to relevant networks and entrepreneurship, the simple Intent-to-Treat comparisons between RAMV and non-RAMV migrants would result in biased estimates of PEP’s true effects.

# **IV. B Identification strategy: Fuzzy regression discontinuity design**

To address these challenges, we base our empirical strategy to identify PEP’s causal effects on a fuzzy regression discontinuity design (RDD).<sup>12</sup> The fuzzy RDD exploits the discontinuity in the likelihood of receiving PEP based on the RAMV cutoff date. As described earlier, only migrants who had registered in RAMV were eligible to apply for PEP. The RAMV reg-

> 12The preanalysis plan proposed both the discontinuity design and reduced-form Intent-to-Treat and IV estimates. This article focuses on the RDD for brevity since it offers the strongest causal evidence. The ITT, however, is consistent with the descriptive analysis in Table 2.

17
Policy Research Working Paper 8169

# **Abstract**

This paper evaluates the effect of the Rural Capacity Building Project, which aimed at promoting growth by strengthening the agricultural service systems in Ethiopia and by making them more responsive to smallholders’ needs. The project intended to increase the outreach of agricultural extension services to help farmers become aware of and adopt economically viable and environmentally sustainable technologies and practices. The paper examines the impact of the Rural Capacity Building Project using panel data on 1,485 geographically dispersed households in project and control kebeles. The results show that the strengthening of extension services had a positive impact on economic participation in the household, land area cultivated, and adoption of marketable crops, suggesting that access to extension helped farmers switch to more commercial, market-oriented agriculture. In addition, and contrary to previous evidence from other countries, female-headed households seem to have benefited equally from the project. However, the project was not able to reduce the preexisting gender gap in agricultural outcomes.

This paper is a product of the Office of the Chief Economist, Africa Region the Gender Cross Cutting Solution Area. 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 jvaillant@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
results and generally found serious shorter-term impacts as national income decreases, an increase in the fiscal deficit as tax revenue falls, and an increase in the trade deficit as exports fall and imports increase. Substantial longer term impacts on development prospects, perpetual external and fiscal imbalances due to increased debt service payments post-disaster and spending requirements, and negative effects on income distribution were also found (ECLAC and IDB, 2000; Otero and Marti, 1995). They generally hold that the significance of the impact depends on the size of the disasters, the size of the economy and the prevailing economic conditions (Otero and Marti, 1995). Benson (1997a,b,c) and Benson and Clay (1998, 2000, 2001) produced a number of case studies on Fiji, Vietnam, the Philippines, and Dominica. The timeframe of this analysis was mainly short-term, i.e. the period up to one year after a disaster. They detected severe negative economic impacts, with agriculture being hit most strongly, an exacerbation of inequalities, and reinforcement of poverty, however also finding it difficult to isolate disaster impacts on economic variables from other impacts. Murlidharan and Shah (2001) by means of a regression analysis analyzed a large data set of 52 catastrophes in 32 developed and developing countries with a the short-term focus (year before event compared to year of event). They found catastrophes for all country income groups to affect short-term growth very significantly. In the medium-term (average of two preceding years compared to average of event and two following years), the effect on growth was still significant. Over time, they detected impact on economic growth to subside. They also discovered associations between disasters and the growth of external debt, the budget deficit and inflation. Crowards (1999 discussed in Charveriat, 2000) examined the impacts of 22 hurricane events in borrowing member countries of the Caribbean Development Bank and found that GDP growth slowed by 3% points on average post-event, but rebounded due to the increase in investment the following year. He also detected large variations around averages.<sup>2</sup> Charveriat (2000) for most cases in her disaster sample identified a typical pattern of GDP with a decrease in the year of an event and a recuperation of the growth rate in the following two years due to high investment into fixed capital. She detected the scale of short-term impacts to depend on the loss-to-GDP-ratio and whether the event was localized or country-wide. For high-

> 2  This study could not be obtained and we rely on Charveriat (2000) as a secondary source.

5
Table 4. Share of men and women reporting ownership of different assets

||||||||By work|status|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Full s<br>Men|ample<br>Women|Not<br>in an<br>Men|working<br>y sector<br>Women|Agric<br>Men|ulture<br>Women|NFE<br>Men|(own)<br>Women|NFE (s<br>Men|upport)<br>Women|Wa<br>Men|ge<br>Women|
|(A) Mobile phone|0.91***|0.77***|0.83***|0.67***|0.86***|0.73***|0.98***|0.89***|0.93|0.80|0.93***|0.85***|
|Exclusive|0.47***|0.40***|0.53***|0.37***|0.31*|0.25*|0.62|0.56|0.61|0.47|0.57|<br>0.56|
|Joint|0.44***|0.37***|0.31|0.31|0.55**|0.48**|0.36|0.34|0.32|0.33|0.35*|<br>0.29*|
|(B) Financial account|0.14**|0.12**|0.07|0.07|0.06|0.05|0.28**|0.17**|0.19***|0.04***|0.23|<br>0.26|
|(C) Motorized vehicle|0.68***|0.53***|0.44|0.37|0.69***|0.58***|0.93***|0.70***|0.64|0.60|0.57***|0.46***|
|(D) Livestock|0.39***|0.42***|0.16|0.18|0.68***|0.76***|0.11*|0.18*|0.17|0.06|0.13|<br>0.14|
|Exclusive|0.16***|0.21***|0.08|0.09|0.26***|0.35***|0.06|0.08|0.01|0.02|0.06|<br>0.04|
|Joint|0.23|0.23|0.09|0.09|0.44|0.45|0.06|0.10|0.16*|0.04*|0.08|<br>0.10|
|(E) Non-residential land|0.56|0.57|0.44|0.42|0.76|0.75|0.44|0.44|0.42*|0.62*|0.33**|<br>0.40**|
|Exclusive|0.17|0.18|0.18|0.15|0.16|0.17|0.12|0.12|0.17|0.24|0.16**|<br>0.22**|
|Joint|0.39|0.38|0.26|0.26|0.60|0.59|0.32|0.32|0.25|0.38|0.18|<br>0.18|
|(F) Residential land|0.75***|0.78***|0.67|0.72|0.86|0.89|0.66*|0.76*|0.77|0.63|0.33|<br>0.40|
|Exclusive|0.19***|0.22***|0.21|0.25|0.16|0.17|0.13**|0.20**|0.29|0.25|0.16*|<br>0.22*|
|Joint|0.55|0.55|0.46|0.47|0.71|0.72|0.54|0.57|0.47|0.39|0.18|<br>0.18|
|Observations|1,845|2,093|228|501|472|635|167|227|46|51|486|<br>359|

<u>Notes:</u> (1) Sample includes men and women self-reporting in the assets module, aged 18 and older. Labor categories were taken from reporting in the labor module.

(2) All estimates are weighted using the household sampling weight. Statistically significant differences between men and women within each category are indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10).

17
proposals for establishing minimum standards for Total Loss-Absorbing Capacity (TLAC) of G- SIBs.<sup>9</sup> The Basel Committee on Banking Supervision (BCBS) has focused initially on banks that are systemically important on a global scale (that is, the G-SIBs). The updated BCBS framework (2013) proposes an indicator-based measurement approach, with five groups of criteria: crossjurisdictional activity, size, interconnectedness, substitutability, and complexity. For each of these sets of criteria, supporting indicators have been defined (see table 3.1). The score for a particular indicator is expressed in terms of banks’ market share for the banks that have been included in the sample. Equal weights are applied to all five categories.

**Table 3.1. G-SIB Indicator-based Measurement Approach**

|**Category and weighting**|**Individual indicator**|**Indicator weighting**<br>**(percent)**|
|---|---|---|
|**Cross-jurisdictional activity (20%)**|Cross-jurisdictional claims|10.00|
||Cross-jurisdictional liabilities|10.00|
|**Size (20%)**|Total exposures as defined for use in the<br>Basel III leverage ratio|20.00|
|**Interconnectedness (20%)**|Intrafinancial system assets|6.67|
||Intrafinancial system liabilities|6.67|
||Securities outstanding|6.67|
|**Substitutability/financial**|Assets under custody|6.67|
|**institution infrastructure (20%)**|Payments activity|6.67|
||Underwritten transactions in debt and<br>equity markets|6.67|
|**Complexity (20%)**|Notional amount of over-the-counter<br>derivatives|6.67|
||Level 3 assets<sup>a</sup>|6.67|
||Tradingand available-for-sale securities|6.67|

_Source:_ BCBS 2013.

a. Level 3 assets are assets whose fair value cannot be assessed using observable measures.

Banks that have a score that exceeds a cutoff level set by the Committee will be classified as G- SIBs. Supervisory judgment may also be used to add banks with scores below the cutoff. As per the latest November 2013 update, a total of 29 G-SIBs have been identified. It is envisaged that the assessment will be conducted annually. These banks have been divided into five groups with additional capital requirements ranging from 1 percent to 3.5 percent of risk-weighted assets. The higher loss absorbency requirement will be phased in between January 1, 2016 and year-end 2018, along with the capital conservation and countercyclical buffers, becoming fully effective on January 1, 2019.

> 9 The Financial Stability Board (FSB) has issued in November 2014 policy proposals for public consultation consisting of a set of principles and a detailed term sheet on the adequacy of  the loss-absorbing and recapitalization capacity of G-SIBs, aimed at  providing home and host authorities with confidence that G-SIBs have sufficient capacity to absorb losses, both before and during resolution, and enabling resolution authorities to implement a resolution strategy that minimizes any impact on financial stability and ensures the continuity of critical economic functions.

11
about 2015 (led by a plummeting oil price). In response to slow growth, debt levels started rising again, reaching 49.0% by 2017. A large number of EMDEs saw their sovereign credit ratings downgraded since about 2016. South Africa lost its investment grade credit rating in early 2017. Fiscal buffers are depleted in many EMDEs, making them vulnerable to an economic downturn. Rebuilding those buffers is critical (World Bank, 2018), as is ensuring that the mirage of _the cycle is the trend_ does not undermine fiscal policy in the future. Great care is needed in producing economic forecasts, especially during commodity super cycles, paying close attention to distinguishing structural from cyclical factors.

**Figure 2: Public debt in advanced and emerging economies**

_(gross public debt in percent of GDP)_ <!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Advanced economies Emerging market and developing economies<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: IMF World Economic Outlook, April 2018.

This work builds previous research that looks at long cycles and fiscal policy (Strawczynski and Zeira, 2013) as well as commodity prices and fiscal policy (Cespedes and Velasco, 2013). The innovation of this paper is to understand the stance of EMDE countries using the combined _the cycle is the trend_ methodology while controlling for commodity price cycles. In addition, we argue that a fiscal stance need not be fixed over time (something that is commonly assumed). We extract time varying parameters that measure the fiscal stance over different periods.

In the next section we use the simple theoretical framework of Strawczynski and Zeira (2013) to illustrate how a structural shift in gross domestic product (GDP) translates into higher expenditures. If a cyclical uptick in growth is mistaken for an increase in trend growth, this increase in expenditure will, unintentionally, constitute pro-cyclical fiscal policy. We then turn to an empirical investigation of fiscal policy in EMDEs during the last 20 years, demonstrating that the latest commodity boom has indeed lengthened business cycles, posing one possible explanation for the pro-cyclical policy stance in our sample of EMDEs.

We then zoom into the South African experience, as the commodity boom had a particularly strong impact on lengthening the South African business cycle. We show how this supported overly optimistic growth forecasts, being one of the likely factors explaining an increase in expenditures and a reduction in fiscal buffers that limit South African policy space today.

# 2. A Simple Framework of the Government’s Fiscal Policy Stance

The model of Aguiar and Gopinath (2007) explicitly decomposes shocks into trends and transitory shocks and illustrates that it is optimal for governments to stimulate the economy and smooth out consumption if there is a negative transitory shock. Similarly, it is optimal for government to reduce 3
WPS3747 As with any such study, there are a number of important limitations to this work. First of all, one should keep in mind that our results are likely to underestimate the cost of temporary unemployment endured by displaced workers in some sectors – particularly in light of the fact that unemployment benefits are not available for most Indonesian workers. Of course, these costs could be mitigated by phasing the trade reforms in gradually. And they would be further diminished if these trade reforms raised the overall growth rate of the Indonesian economy. Such dynamic growth gains – fueled by increased productivity and investment – have been ignored here, but are addressed in the final three chapters of this volume.

18
<!-- Start of picture text --> SSA - Income Distribution - GIDD Projection for 2030<br>iQ<br>~S.<br>oe<br>N<br>oO<br>Ke) wo oO lo} lo}<br>N N © 2 2<br>oO —- N oO- lo}oO<br>Income per capita per day in PPP<br>—— 2007 —— BavU_lo —m— BaU_hi<br><!-- End of picture text -->