rafmacalaba/datause-encoder-data huggingface dataset:
Operationally, we exploit a rich module on FAFH collected as part of the multi-year National Household Survey in Peru (ENAHO) to shed light on the extent to which welfare measures may differ based on whether FAFH accounted for. Peru is a relevant context to study this question since FAFH is fairly widespread and increasing. In 2013, the average Peruvian household spent about 28 percent of their food budget on FAFH.

To assess the impact on poverty measurement, we follow the official methodology adopted by the National Institute of Statistics and Informatics (INEI) and start with a scenario where FAFH is not accounted for. Then, we use this estimate as the benchmark over which the impact of including FAFH is assessed. Peru introduced a big methodological change in 2010, and therefore we use that year for our analysis. The definition of FAFH adopted by INEI includes all food _prepared outside_ the home. We estimate the effect of FAFH on the poverty rate, the poverty gap, and the severity of poverty. Then, to evaluate the effect on consumption inequality we compute the Gini coefficient based on the expenditure distribution with and without FAFH. Finally, we go beyond a summary welfare measure and analyze whether lack of accounting for FAFH changes our understanding and characterization of the poor population, i.e. we analyze how the profile of the poor changes once we take into account FAFH.

Our analysis indicates that failure to account for FAFH has significant and sizable effects on poverty and inequality indices and our understanding of poverty in general. First, proper accounting of FAFH results in extreme poverty rates that are 18 percent higher and moderate poverty rates that are 16 percent lower than the scenario without FAFH. The increase in the extreme poverty rate is driven by the fact that the cost of the food basket is higher when FAFH is taken into account (which raises the poverty line), an effect that outweighs the increase in measured consumption that results from accounting for FAFH (which increases household consumption). The increase in the cost of the food basket is explained by the higher per calorie costs derived from FAFH relative to food prepared at home.

Second, and by contrast, the moderate poverty rate falls because the increase in resources that come from accounting for FAFH consumption offsets the increase in the moderate poverty line. When we split FAFH into take-out food, food prepared and consumed outside by children, and food prepared and consumed outside by adults, we find that all these effects are driven by adult consumption outside the home. These effects are also consistent, in fact more pronounced, when we 5
**Table 7: Descriptive statistics of the variables included in the working dataset**

|**Variable**|**Mean**|**Standard Deviation**|**Minimum**<sup>**a**</sup>|**Maximum**|
|---|---|---|---|---|
|Scarcity1|5.056|27.965|0.000|<br>246.679|
|Scarcity2|177.383|1142.484|0.000|<br>10438.417|
|YrsDecentralization|2.711|7.547|0.000|<br>36.000|
|YrCreation|1985.229|18.486|1926.000|<br>2002.000|
|MainObj|1.008|0.692|0.000|<br>1.731|
|MinrObj|6.208|8.791|0.000|<br>33.972|
|SuccObj1|3.540|7.814|0.000|<br>63.510|
|SuccObj2|2.907|2.551|0.000|<br>7.555|
|GovrBdy|3.566|1.768|0.000|<br>5.000|
|MtdCreatn|1.193|0.903|0.000|<br>2.000|
|InstDismntld|0.627|0.837|0.000|<br>3.000|
|InstCreatd|1.542|1.262|0.000|<br>3.000|
|PltclCost|0.494|0.942|0.000|<br>5.000|
|FormsDisput1|2.241|1.265|0.000|<br>4.000|
|FormsDisput2|0.940|0.502|0.000|<br>2.000|
|TypesDisput|4.207|2.934|0.000|<br>9.000|
|Facilities|21.762|263.256|-1410.219|<br>1348.949|
|SectrComposit|1.257|0.793|0.000|<br>2.183|
|SectrUseShars|0.299|0.768|-0.425|<br>6.609|
|ShareSW|0.434|0.381|0.000|<br>1.000|
|PrblmsBfr|6.871|1.823|0.000|<br>9.750|
|PrblmsAftr|5.837|1.524|2.334|<br>8.967|
|IncrmntTasks|0.128|0.371|-2.671|<br>0.762|
|IncrmntImprv|0.104|0.518|-4.162|<br>0.728|
|ImprvRespons|1.466|3.639|-8.294|<br>9.217|
|ExistUsrGrp|0.871|0.783|0.000|<br>1.731|
|%UsrPay|2.084|13.990|0.000|<br>127.886|
|BgtPrCpta|0.292|2.658|0.000|<br>24.213|
|%BgtExtr|0.190|0.328|0.000|<br>1.000|
|%BgtBsn|0.356|0.391|0.000|<br>1.000|
|%BgtSrcs|-0.115|0.413|-0.713|<br>0.710|
|%Bgtspnt|0.287|0.474|-0.267|<br>1.370|
|InstCng|0.627|0.487|0.000|<br>1.000|

a PC variables may have negative minimum values due to the process of their estimation.

29
# **Table A11: Two Stage Least Squares Regressions, 2006**

Financial Vulnerability Regressions (Marginal Effects)

|||**finburd**|**en**<br>|||**finvul**|||**arrea**|**rs1**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0473|-0.0426|0.08|-0.03|0.0860 ***|-0.1305 **|0.16|2.46 **|0.0020|0.18|
|Estonia|-0.2231 **|0.2851 **|0.10|-2.65 ***|-0.0889 **|0.0815|0.11|-1.86 *|-0.1507 **|0.15|
|Hungary|0.0107|-0.0120|0.07|0.12|-0.2481 *|0.0677|0.10|-1.90 *|-0.0803|0.07|
|Latvia|-0.2277 *|-0.0216|0.07|-1.23|0.0155|0.0749|0.09|-0.54|-0.2912 **|0.11|
|Lithuania|-0.3614 **|-0.1271|0.09|-0.87|0.0342|0.0426|0.09|-0.04|-0.0054|0.09|
|Poland|-0.4520 ***|0.0260|0.07|-3.40 ***|-0.2721 **|0.0477 ***|0.10|-2.59 ***|0.1028|0.07|
|Slovak Republic|0.0789 **|-0.3338 **|0.05|2.06 **|0.0257|-0.0488|0.06|0.45|0.0890|0.07|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.0046|0.0081|0.05|-0.06|0.0100|0.0446 **|0.16|-1.27|0.0341 ***|0.14|
|Belgium|0.1484 ***|0.0379|0.04|0.93|-0.0050|0.0391 ***|0.16|-1.77 *|0.0257|0.13|
|Cyprus|0.1165|-0.1261 *|0.08|1.42|0.1402|-0.0747|0.10|2.04 **|0.2389 ***|0.08|
|Denmark|-0.0369 ***||0.07||-0.0590 ***||0.12||-0.0089|0.13|
|Finland|0.0755 **|0.0270|0.04|0.62|0.0276|0.0693 ***|0.16|-1.08|0.0204|0.12|
|France|-0.0547|0.1391 **|0.03|-1.69 *|-0.0413|0.0862 ***|0.14|-2.89 ***|0.0381 **|0.08|
|Greece|-0.3330 *|0.1004 **|0.08|-2.25 **|-0.8502 **|0.2246 **|0.14|-2.65 ***|-0.0025|0.20|
|Ireland|-0.0679|0.1150 ***|0.09|-2.31 **|-0.0703|0.0820 ***|0.18|-2.80 ***|0.0829 **|0.15|
|Italy|0.2246 ***|0.0876|0.04|1.23|-0.0753 **|0.1245 ***|0.10|-3.35 ***|-0.2033 ***|0.08|
|Luxembourg|0.1810 ***|-0.0393|0.06|2.58 ***|0.0162|0.0169 *|0.14|-0.03|0.0136 *|0.22|
|Netherlands|-0.1489 **||0.05||-0.0920 ***||0.10||-0.0446 *|0.10|
|Portugal|0.0400|-0.1067 *|0.08|2.23 **|-0.0044|0.0934 ***|0.05|-2.28 **|-0.0097|0.07|
|Spain|0.1129|-0.4729 **|0.05|3.02 ***|-0.0336|0.0572|0.06|-1.36|-0.0377|0.07|
|Sweden|0.1316|0.0492 ***|0.05|0.76|0.0502|0.0780 ***|0.10|-0.24|0.1074 **|0.12|
|United Kingdom|0.1580 ***|0.0711 ***|0.05|2.68 ***|0.0488 ***|0.0672 ***|0.15|-1.03|0.0043|0.11|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

51
# Appendix Table 14: Household tasks

||I prepare<br>food<br>(planter)|I prepare<br>food<br>(spouse)|<br>I prepare<br>food -<br>both<br>reporting<br>(planter)|I prepare<br>food -<br>both<br>reporting<br>(spouse)|I clean<br>and do<br>laundry<br>(planter)|I clean<br>and do<br>laundry<br>(spouse)|I clean<br>and do<br>laundry -<br>both<br>reporting<br>(planter)|<br> <br>I clean<br>and do<br>laundry -<br>both<br>reporting<br>(spouse)|I take<br>care of<br>kids<br>(planter)|<br>I take<br>care of<br>kids(spou<br>se)|I take<br>care of<br>kids -<br>both<br>reporting<br>(planter)|<br>I take<br>care of<br>kids -<br>both<br>reporting<br>(spouse)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|0.14*|-0.03|0.09|-0.03|0.08|-0.01|0.02|-0.01|0.15*|-0.02|0.08|-0.02|
||(0.07)|(0.06)|(0.07)|(0.06)|(0.07)|(0.06)|(0.06)|(0.06)|(0.09)|(0.09)|(0.10)|(0.09)|
|Couples' Training|-0.12|0.06|-0.15**|0.06|-0.09|0.06|-0.10|0.06|-0.12|0.15|-0.16|0.15|
||(0.08)|(0.06)|(0.07)|(0.06)|(0.07)|(0.06)|(0.07)|(0.06)|(0.10)|(0.12)|(0.11)|(0.11)|
|Seedling Subsidy + Couples'<br>Training (beta)|0.02|0.03|-0.06|0.03|-0.01|0.05|-0.08|0.05|0.02|0.13|-0.09|0.12|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|<br>0.75|0.52|0.30|0.56|0.84|0.36|0.16|0.39|0.76|0.12|0.28|0.14|
|Observations|1384|1038|1035|1035|1386|1038|1035|1035|1384|1038|1035|1035|
|Mean for control group|0.70|3.73|0.54|3.73|0.72|3.71|0.56|3.71|1.19|3.08|1.10|3.08|
|Mean for individual training|0.84|3.72|0.63|3.72|0.81|3.72|0.58|3.72|1.32|3.08|1.16|3.08|

Robust and clustered standard errors in parentheses.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

4
<!-- Start of picture text --> Employed baseline: out of labor force afterwards<br>16<br>14<br>14<br>Pal<br>E<br>E<br>=]<br>oD 12<br>oD<br>o<br>fs]- a 4 ° ah)<br>Fs A ° $ 028<br>isZ + 088<br>08 ¢ O77<br>@ Pre-covid cohort<br>06 $ 058 @ Covid. cohort<br>4 months later" 8 months later*** 12 months later"** 16 months later"**<br><!-- End of picture text -->
22 <!-- Start of picture text --> APPAREL GROUP NO. 5 (TROUSERS)<br>Pie** Poo UVai Xm US Ostar.<br>1<br>0 Il/ s 7 , & & 10 1 1<br>UNMD STA<br>Figuse 3(v<br><!-- End of picture text --> <!-- Start of picture text --> APPAREL GROUP NO. 7 (WOMEN'S COATS)<br>Pries P t Unit 4 U S DooX<br><!-- End of picture text --> <!-- Start of picture text --> 1961 _ IS<br><!-- End of picture text --> <!-- Start of picture text --> S *S~~~~~~2<br>K<br>IJ<br>110 140 4 W|<br>UNRIO l AIU<br>Pigure 3(vii)<br><!-- End of picture text -->

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<!-- Start of picture text --> rigure 3(viii)<br><!-- End of picture text -->
policies) we see no evidence of treatment interaction by per capita consumption levels, education, or gender (Table A5).<sup>17</sup>

# **Heterogeneity by partisan alignment of central and local government**

A final dimension of heterogeneity relates to voters’ ability to correctly attribute credit for policies and programs in contexts of divided government and/or federalism. Following an earlier literature on retrospective voting (Powell and Whitten, 1993), several studies have focused on the partisanship of elected officials at central and regional or local levels as a potential modifier of citizen responsiveness to government actions. Divided control of government between different parties can affect citizen response, because citizens may not completely understand which party deserves credit for the program, either because of they have limited information levels about policy details in general, or because policy responsibility is genuinely shared by multiple actors in a political system. For example, in Pop-Eleches and Pop-Eleches (2012), voters changed their opinion of local government (which implemented the program) but not of the responsible national politicians or the central government (which started and funded the program).<sup>18</sup> The opposite pattern is present in Brazil, where Zucco (2013) finds that voters rewarded the president, but not legislative candi17We can also compare the effect of the 2008 Under 5 catch up ITN campaign to the 2010 universal coverage campaign. Like the 2010 campaign, the 2008 net distribution campaign also was contemporaneous with the a round of the National Panel Survey (the 2008-2009 round), meaning that the same RD identification strategy can be implemented, although the first stage treatment is notably smaller. The increase in respondents living in a household where someone used a free net the previous night increases by 13 percentage points rather than 44 percentage points in 2010.The increase is 22 percentage points when restricting analysis to households with children under 5, who were the target of the campaign. Interestingly, however, there is _no_ political benefit to politicians in the aftermath of this campaign. There is no treatment effect whether we test this for the whole sample, or when we restrict the regression to households that had at least one child under 5 in 2008, or with a continuous measure of approval as the outcome variable, or when including controls or varying the bandwidth. However in addition to the weaker first stage, this comparison is not as well powered as that of 2010, since only 13 percent of respondents were interviewed after the campaign, compared to 49 percent after the 2010 campaign.

> 18Note however that the effect was driven by local governments who were aligned with the coalition in power at the national level.

21
issuers (Table 4, Panel A). Specifically, the number of annual issuers was about 125 percent higher in 2008-16 when compared to 1990-98. In contrast, the number of annual issuers in international markets increased only 11 percent over the sample period.<sup>12</sup> The estimates also show that the median firm issuing in domestic bond markets was smaller in 2008-16 relative to previous periods (Table 4, Panel B). The size of the typical issuer in domestic markets per economy-industry declined by around 30 percent in bond markets during 2008-16, whereas the size of international issuers remained constant over the same period.<sup>13</sup> For a broader perspective, we compare the use of domestic and international bond, equity, and syndicated loan markets across firms of different size. We classify issuers into ten deciles by size and examine the share of firms, per decile, issuing in different markets during 1990-2016. The results show that, whereas the relatively smaller issuers use almost entirely domestic equity and bond markets, larger firms tend to use a wider set of instruments issued in different locations (Figure 4). In the first decile (the smallest issuers), 97 percent of the firms were domestic equity and/or domestic bond issuers, whereas only 7 percent of those firms issued in other markets. Relatively larger firms also used domestic capital markets, but they raised capital across markets in a more balanced manner.<sup>14</sup> Because smaller firms are typically younger, this pattern of financing across firm size is consistent with a pecking order that suggests that firms use domestic markets first and, then, access international and syndicated loan markets at a later stage, when they become larger.<sup>15</sup>

> 12 Wald tests of equality of coefficients confirmed that the differences in trends between the numbers of domestic and international issuers in Table 4, Panel A are statistically significant at the 1 percent level. These unreported tests were computed within each of the subsamples of economies. 13 For robustness, we run regressions using total assets of listed firms (instead of our proxy based on issuance size) and find an even larger decline in the size of domestic bond issuers between 1990-98 and 2008-16 (Appendix Table 2). 14 The fact that the sum of different types of issuers is close to 100 percent in the first decile means that most of the smallest issuing firms raised funds in only one (domestic) market. The sum of these percentages does not need to be 100 percent because firms in each decile can issue in more than one market and thus belong to more than one group of issuers. 15 There is a high correlation between firm size (in terms of assets) and age. Using Worldscope data on listed East Asian firms, a 1 percent increase in age is associated with a 0.5 percent increase in size.

16
16 curative care are not possible; and of course there<sup>are economies of scope in</sup> the combined provision of preventive and curative<sup>care.20</sup> In addition, it can be argued that all individuals benefit from the "option<sup>value, of availability</sup> of curative care, whether they ever actually use<sup>it or not. In the end,</sup> though in many settings it is possible to say that<sup>"too much' is spent on</sup> high-cost hospital care and "too little" on largely<sup>preventive basic care, the</sup> existing literature is virtually silent on the<sup>appropriate balance and</sup> criteria for determining that balance.

Some insight on this issue is likely to come from the small but<sup>growing</sup> literature on the cost-effectiveness of health interventions in industrialized countries. Insights from these include (World Bank, 1990;<sup>Barnum, 1990):</sup>

- Most primary prevention activities (such as health promotion campaigns against smoking, alcohol abuse and for exercise and use of motorcycle helmets) are "good buys";

- Some secondary prevention activities (such as cervical cancer screening and physical screening for breast cancer) are "good buys"; but many common secondary prevention activities (such as hypertension screening programs) are not cost-effective;

- Many treatment procedures are not cost effective, but some (including pacemaker implantation, hip replacements and treating of breast and cervical cancer, and even some coronary<sup>bypass</sup> surgery) are cost-effective.

The limitations of this literature are still very great: the literature has primarily been devoted to assessing the cost-effectiveness of medical procedures, has only been applied in industrialized countries and is still fraught with fundamental methodological and empirical problems.<sup>Research in</sup> this area is vital, both tv clarify the outstanding questions<sup>and, prior to</sup> this, to force policymakers to ask the right questions and think correctly.

20 See Birdsall (1989) for discussion of this point in more detail.
investments. For portfolio investments, we use the Coordinated Portfolio Investment Survey (CPIS) from the International Monetary Fund (IMF) that measures the bilateral stock of portfolio assets, covering the period 2001-2014 for 75 source and 207 recipient economies. The data on cross-border syndicated loans are available at the transaction level and come from Thomson Reuters’ SDC Platinum database, covering the period 1996-2014. They comprise 111 source and 183 recipient economies. For FDI, we also use transaction-level data from two different sources. For M&As, we use Thomson Reuters’ SDC Platinum. For (announced) greenfield investments, we use the Financial Times’ fDi Markets. The M&A data span the 1990-2014 period and cover 139 source and 162 recipient economies. The greenfield investment data cover the 2003-2014 period, encompassing 157 source and 193 recipient economies.<sup>4</sup> The different datasets are not directly comparable in terms of their overall value of international investments. The CPIS annual data are on the _stock_ of bilateral investments, whereas all the other data are aggregated to bilateral annual _flows_ . In particular, we aggregate the transaction-level data to the bilateral level by summing the value of all transactions from a given source economy to a given recipient economy for every year in the sample. Because of their different nature, the value of portfolio investments is considerably larger than that of syndicated loans, M&As, and greenfield investments. Nonetheless, the overall patterns on their evolution across and within the different types of financial assets are still informative. The same applies to the comparisons of the number of connections of EAP with different regions and with itself.

To benchmark the extent of EAP’s financial integration with itself and other North and South economies, we estimate a gravity model including the following control variables: the GDP of the

> 4 North economies report consistently across the different databases, implying a good coverage of North-North and North-South connections. However, reporting of South-North and South-South transactions remains relatively sparse, arguably leading to an underestimation of the value of these connections. Because reporting by South economies varies across investment types, we also constructed graphs and computed regressions using a common sample of reporting economies. The results are qualitatively similar to the ones reported in the paper.

6
TABLE OF CONTENTS

|EXECUT|IVE SUMMARY<br>....................................<br>i|
|---|---|
|I.|INTRODUCTION<br>......................................<br>,<br>1|
|II.|THE CONTEXT OF RURAL EDUCATION ............................. 1|
||Institutional Environment ...................................<br>1<br>The School Environment .................................... 3|
|III.|RURAL SCHOOL SUPPORT SERVICES: EXPERIENCES AND LESSONS ...... 4|
||Limitations of Existing Systems .............................<br>4<br>Remodelling Rural School Support Services:<br>Bank Approaches ..........................................<br>5<br>Lessons<br>.....................................................<br>9|
|IV.|IMPROVING SUPPORT SERVICES FOR RURAL SCHOOLS .11|
||How to Deliver School Services .12<br>The Training and Visit System .13<br>The Relevance of the Training and Visit System<br>to Rural Education .16<br>Adapting T&V to Existing Institutional<br>Approaches<br>for Rural Education Extension Sou:ces .18|
|V.|CONCLUSIONS AND RECOMMENDATIONS<br>..<br>21|
|APPEND|ICES|
|Table<br>Table|1<br>................<br>24<br>2<br>................<br>27|
|Case S|ketch: Haiti ................<br>28|
|Bibli|ography ................<br>31|

The author wishes to acknowledge the support of<sup>Arturo Israel and</sup> Barbara Nunberg of the Public Sector Management<sup>and Private Sector</sup> Development Division for this work. Jacomina<sup>de Regt provided the</sup> information for the Haiti case sketch in Appendix<sup>3 and numerous project</sup> officers through the Human Resources Divisions across the Bank<sup>cooperated</sup> with interviews and bv Rrovidin co_ents on<sup>earlier drafts of the work.</sup>
# Figure 9. Vulnerabilities in EMDEs

# A. Debt

# B. Fiscal and current account balance

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>60 2018 2007 1.0 2018 2007<br>55 0.0<br>50<br>-1.0<br>45<br>40 -2.0<br>35 -3.0<br>30<br>-4.0<br>25<br>20 -5.0<br>Government debt Corporate debt External debt Structural primary Current account balance<br>balance<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. Unweighted averages of gross government debt for 146 EMDEs, nonfinancial corporate debt for 48 EMDEs, and total external debt for 61 EMDEs. B. Unweighted averages of the structural primary balance for 149 EMDEs, and current account balances for 143 EMDEs.