rafmacalaba/datause-encoder-data huggingface dataset:
energy policy in the country. However, from 2015 onwards momentum towards sustainable cost recovery reforms has been created.

In fact, tariffs have been very low in relation to costs over most of the period covered by this paper, with the exception of the years from 2015. As a result, the economy and society have enjoyed a massive subsidy flow over many years, supporting disposable incomes and employment, and postponing the need to painfully achieve international competitiveness. And in consequence, the power sector has been deprived of self-generated resources, so has needed very extensive propping up.

This policy combination is now starting to change in favor of actual cost recovery, and the relevant institutions may perhaps be adapting to make that changes at last. Time will tell, and much will depend on the political will and ability to ride out the adjustment process, and society’s will and ability to endure it.

It is very important to note in the data given below, that the most recent years are not reflected, although that is where the recent policy changes have been mostly happening, and the large effects on electricity of underpricing from and underpayment to the natural gas sector are not really captured either. But we do still get a sense of what the performance has been, albeit a partial one. In short, what we see is tariffs well below costs (even on a financial basis without accounting for the economic cost of underpricing natural gas), and a high degree of cross-subsidies between customer classes.

The analysis of cost recovery focuses on the data for the holding company- EEHC. The Quasi fiscal deficit (QFD) measures the revenue gap for a utility by measuring the difference between what an ideal utility with no underpricing, no under collection and technical losses within the 5 percent level would earn and what the real utility actually collects as revenues. The QFD can be divided into three main components: revenue gap attributable to under collections, underpricing, and excessive losses. For Egypt given the structure of the sector, it makes more sense to look at the accounts of the holding company – EEHC.

Complete data are available for only four years 2011-2014 to calculate the QFD (figure 8). There is clearly a trend of rising revenue gap for EEHC during this period with the absolute QFD rising from just over $3 billion in 2011 to over $6 billion by 2014. In terms of GDP the QFD rose alarmingly during this period from 1.3 percent to 2.2 percent of the GDP. Over 79 percent of the QFD is attributable to underpricing, a theme that comes up repeatedly during this study. Tariffs have been kept below costs pretty much the entirety of the time period from the 1970s to 2014.

21
experiential questions can help to measure psychological stress over the future of food security. Qualitative research on the impact of the food price crisis (along with the fuel and financial crises) between 2008 and 2011 found high levels of stress and anxiety over the ability to provide adequate food for the family was reported in nearly all interviewed communities from a study of 17 countries (Heltberg and others, 2012).

Within the medical health literature, it is reasonably well accepted that seemingly subjective attributes such as stress and anxiety can have harmful physical effects. Rozanski and others (1999, p. 2192) note that “an extensive recent literature now establishes that psychosocial factors contribute significantly to the pathogenesis of CAD” [coronary artery disease]. Examples from this literature document that psychological stress is associated with increased cardiovascular risk factors, such as hypertension and insulin resistance, and with outcomes such as ischaemia, arrhythmia, and pump failure (see Brotman and others 2007, and references therein).

It may be reasonable to assert, as Headey and Ecker (2012) do, that objective measures of nutritional outcomes should be of paramount importance in the context of poor countries. This though presumes an ability to precisely measure adverse effects of food insecurity in a timely manner. We believe there are a couple of reasons to be cautious in this view that objective measures of food security are of greater importance in poor countries. The first is simply about timing. Worrying about the future ability to meet food needs may be a useful early indicator of food insecurity leading to adverse nutritional outcomes at a later point in time. Objective measures of adverse outcomes from food insecurity may only be measurable after the onset of food insecurity. Attention to the need for early warning signs can aid rapid policy responses and potentially blunt the adverse consequences of food insecurity. A second reason is that the adverse physical effects of anxiety and stress are likely 20
Policy Research Working Paper 9475

# **Abstract**

This paper assesses the impact of Ethiopia’s flagship social protection program, the Productive Safety Net Program on the adverse impacts of the COVID-19 pandemic on the food and nutrition security of households, mothers, and children. The analysis uses pre-pandemic, in-person household survey data and a post-pandemic phone survey. Two-thirds of the respondents reported that their incomes had fallen after the pandemic began, and almost half reported that their ability to satisfy their food needs had worsened. Employing a household fixed effects difference-in-difference approach, the study finds that household food insecurity increased by 11.7 percentage points and the size of the food gap by 0.47 months in the aftermath of the onset of the pandemic. Participation in the Productive Safety Net Program offsets virtually all of this adverse change—the likelihood of becoming food insecure increased by only 2.4 percentage points for Productive Safety Net Program households and the duration of the food gap increased by only 0.13 month. The protective role of the program is greater for poorer households and those living in remote areas. The results are robust to various definitions of program participation, different estimators, and different ways of accounting for the non-randomness of mobile phone ownership. Productive Safety Net Program participants were less likely to reduce expenditures on health and education by 7.7 percentage points and less likely to reduce expenditures on agricultural inputs by 13 percentage points. By contrast, mothers’ and children’s diets changed little, despite some changes in the composition of diets, with consumption of animal source foods declining significantly.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at ktafere@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
# COMPOSITION OF GOVERNMENT<sup>INVESTMENT</sup>

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Boano, C., Zetter, R. and Morris, T. (2007) _Environmentally Displaced People: Understanding the Linkages between Environmental Change, Livelihoods and Forced Migration_ . A policy briefing by the Refugee Studies Centre, University of Oxford.

Brown, O. (2008) _Migration and Climate Change_ . IOM Migration Research Series, no. 31. International Organisation for Migration, Geneva.

Brown, R. (1997) Estimating remittance functions for Pacific Island migrants. _World Development_ 25(4): 613-626.

Buhaug, H., Gleditsch N. and Thiesen, O. (2008) _Implications of Climate Change for Armed Conflict_ . Social Dimensions of Climate Change, The World Bank Group, Washington DC.

Burton, I., Kates, R.W. and White, G.F. (1978) _The Environment as Hazard_ . Oxford University Press, New York.

Byravan, S. and Rajan, S. (2005) Immigration could ease climate change impact. _Nature_ 434: 435.

Byravan, S. and Rajan, S. (2006) Providing new homes for climate change exiles. _Climate Policy_ 6: 247-252.

Carr, E. (2005) Placing the environment in migration: environment, economy, and power in Ghana’s Central Region. _Environment and Planning_ 37: 925-946.

Castles, S. (2002) Environmental change and forced migration: making sense of the debate. _New Issues in Refugee Research,_ Working Paper No.70. University of Oxford Refugee Studies Centre, UNHCR Evaluation and Policy Analysis Unit, Geneva.

Castles, S. and Miller, M. (2003) _The Age of Migration: International Population Movements in the Modern World_ (3<sup>rd</sup> ed.) The Guilford Press, New York, London.

50
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|**Table 4: Heterogeneous Impact on For**<br>_Dependent variables:_|**malizatio**<br>(1)|**n by Basel**<br>(2)|**ine Char**<br>(3)<br>**For**|**acteristic**<br>(4)<br>**malized: G**|**s**<br>(5)<br>**UFE data**|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||Operates<br>in||Doesn't<br>look like|<br>Index of<br>business|Does not<br>have|One visit<br>or fewer|Female<br>owner<br>|
||Female|Dantokpa||formal|size below|<br>secondary|<br>from tax|(sample|
|_Variable for heterogeneous analysis:_|owner|market|Trader|species|median|education|inspectors|restricted<sup>α</sup>)|
|_Impact in group [...] for heterogeneous variab_|_le=0_||||||||
|Group1|0.130***|0.102***|0.142***|0.119**|0.077**|0.150***|0.123**|0.175***|
||(0.035)|(0.026)|(0.033)|(0.054)|(0.032)|(0.035)|(0.050)|(0.045)|
|Group2|0.188***|0.151***|0.178***|0.207***|0.140***|0.182***|0.164***|0.228***|
||(0.024)|(0.016)|(0.022)|(0.031)|(0.020)|(0.021)|(0.030)|(0.031)|
|Group3|0.198***|0.174***|0.186***|0.197***|0.146***|0.222***|0.196***|0.209***|
||(0.021)|(0.014)|(0.019)|(0.027)|(0.018)|(0.018)|(0.026)|(0.027)|
|_Additional impact in group [...] for heterogen_|_eous varia_|_ble=1_|||||||
|Group1 x Heterogenous variable (int1)|‐0.064|‐0.047|‐0.099**|‐0.032|0.029|‐0.098**|‐0.039|‐0.078|
||(0.047)|(0.055)|(0.046)|(0.059)|(0.046)|(0.045)|(0.055)|(0.073)|
|Group2 x Heterogenous variable (int2)|‐0.091***|‐0.099***|‐0.087***|‐0.094***|<br>‐0.022|‐0.086***|‐0.042|‐0.108**|
||(0.030)|(0.035)|(0.029)|(0.034)|(0.028)|(0.026)|(0.032)|(0.047)|
|Group3 x Heterogenous variable (int3)|‐0.066**|‐0.079**|‐0.053**|‐0.049|0.022|‐0.112***|‐0.049*|‐0.049|
||(0.026)|(0.031)|(0.026)|(0.030)|(0.025)|(0.022)|(0.028)|(0.042)|
|Observations|3,596|3,596|3,596|3,596|3,596|3,596|3,596|1,619|
|R‐squared|0.398|0.398|0.398|0.398|0.396|0.405|0.397|0.404|
|Adjusted R‐squared|0.094|0.094|0.095|0.093|0.091|0.105|0.092|0.096|
|Mean heterogenous variable|0.629|0.217|0.550|0.818|0.500|0.591|0.804|0.415|
|Mean dep. var. in Control heterogenous=0|0.047|0.030|0.028|0.072|0.043|0.055|0.050|0.038|
|Mean dep. var. in Control heterogenous=1|0.021|0.034|0.033|0.022|0.018|0.014|0.026|0.013|
|_Pvalues of test: Heterogeneous=0_|||||||||
|Group1=Group2=Group3|0.158|0.012|0.403|0.334|0.110|0.071|0.343|0.584|
|Group1=Group2=Group=0|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Pvalues of test: Heterogeneous=1_|||||||||
|Group1+int1=Group2+int2=Group3+int3|0.020|0.303|0.004|0.010|0.017|0.157|0.024|0.347|
|Group1+int1=Group2+int2=Group3+int3=0<br>Note: Administrative data from GUFE and su<br>and interaction terms (treatment dummies X<br>each triplet). α : sample restricted to non‐tr|0.000<br>rvey data<br>variable<br>aders outs|0.008<br>March 2015.<br>for heterog<br>ide Tokpa|0.000<br>OLS regre<br>eneous an<br>market.**|0.000<br>ssion of th<br>alysis), co<br>*, **, * ind|0.000<br>e outcome<br>ntrolling fo<br>icate statis|0.000<br>variable on<br>r strata dum<br>tical signifi|0.000<br>treatment d<br>mies (dum<br>cance at 1, 5|0.000<br>ummies<br>mies for<br>and 10%.|

36
of this extension is not the main focus of this paper. The FSSP is still active in Punjab, and as of 2007, it covered 245,000 beneficiaries in middle school, which correspond to a take-up rate of 51 percent.<sup>10</sup> According to actual quarterly stipend data, the program spent around US$7.3 million in 2007 on stipends for grades 6 through 8.

A previous impact evaluation of the FSSP investigated the short-term impacts of the program on female enrollment. The evaluation uses difference-in-differences (DD) and Regression Discontinuity Design (RDD) models on provincial school censuses to estimate net growth in female enrollments in grades 6-8 (middle school) from 2003 (before the program started) to 2005 (two years into the program) and in stipend public schools relative to nonstipend public schools. Other models estimated compare the differences in female growth enrollment relative to male growth enrollment in a triple-difference framework. Even though the magnitude of program impact estimates varies across different models, this evaluation found that the FSSP increased the number of females enrolled in grades 6-8, on average, by 9-23 percentage points (Chaudhury and Parajuli, 2008).

# **4. Data**

This paper estimates the impact of the program with information at the school and household levels. The main data source for the first approach is the Punjab public school census collected annually in October from 2003 to 2009 by the provincial education department, including data from before and during the program implementation. It covers all public schools in Punjab and focuses on school information (including village name and date of establishment), infrastructure, teaching staff, enrollment, and some organizational indicators.<sup>11</sup> The enrollment data are reported by gender of the school and separated into different grade levels. Schools are also disaggregated by urban and rural areas. The total number of schools is 60,000-62,000 from 2003-04 to 200910, however the sub-sample for this analysis is a pool of cross-sectional data that includes

> 10 The take-up rate is difficult to calculate because eligibility to the program is determined by the grade, and not the age, of girls in stipend districts. For simplicity, this analysis estimates the take-up rate as the ratio of participants (245,000 girls covered by the program in 2007) relative to the sum of the actual and potential participants (those enrolled in grade 6-8 in private schools and those whose highest grade is 5-7 but are not enrolled at the time of the survey).

> 11 This survey is part of the institutional strengthening capacity effort within the PERSP and is used for monitoring resource allocation and performance in the sector. The survey does not cover private and religious schools in Punjab; a private school census was undertaken in 1999-2000, on the basis of which it was estimated that over one-quarter of all students were enrolled in private education.  A National Education Census, also conducted in 2006, enumerated all public and private schools.

8
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25
Sample Age eligible Age and Sisben eligible _Note:_ The table shows the results of a linear probability model on a dependent variable indicating the receipt of Colombia Mayor; fixed effects are omitted from the output. Standard errors are indicated in parentheses.

There is a certain contradiction in the results arising from the last two approaches. If the rollout, that is, the assigned number of beneficiaries, really evolved in a parallel fashion, one would expect to see wide variation in the prioritization score of the last person entering the program because there ought to be differences in the number and characteristics of eligible applicants. In our conversations with officials at the consorcio, we were told that the number of assigned beneficiaries is partially adjusted by the number of applicants a municipality is able to produce. This would explain the similarity in the minimum prioritization scores, but it should also result in more variation in the rollout. In our identification strategy presented in the next section, we partially assume that the official enrollment data do not properly represent actual enrollment levels, and we proceed to derive proxies for actual enrollment directly from the ENCV data.

# **6. Data and Empirical Strategy**

We employ the 2010–13 rounds of the yearly ENCV. The principal reason we use this particular data source is that it provides us with the richest data in terms of variables, sample size, and periodicity. The other frequent large-scale and nationally representative survey is the monthly Gran Encuesta Integral de Hogares (National Integrated Household Survey). While this survey would provide us with a much larger sample size and cover almost all the municipalities in the country, it lacks the breadth of observed variables available in the ENCV. Most importantly, it does not capture whether a household receives the Colombia Mayor benefit. Furthermore, the included variables do not allow for the construction of Sisben scores.

The ENCV sampled a total of 14,801 households in 2010, 25,364 in 2011, 21,383 in 2012, and 21,565 in 2013. The sampling scheme changed in 2011 and 2012, but was held constant in 2013. The main implication of the changes in the sampling scheme is that more municipalities are repeated in the last two rounds than in the prior ones. This, however, has no bearing on the validity of our empirical approach. We restrict our sample to households that have at least one eligible member according to the age and Sisben criteria—that is, with a sufficiently low Sisben score given the place of residence— and contain either a woman of at least 52 years of age or a man of at least 57 years of age. This yields a total of 15,955 households (2,507 in 2010, 5,618 in 2011, 4,120 in 2012, and 3,710 in 2013). The total 19
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