rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4: Greenfield Investments Hold the Biggest Lure for the Private Sector in SAR** ( _Investment in Projects (US$ mil)_

<!-- Start of picture text --> 80000<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>Concession Divestiture Greenfield Management and lease contract<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Sources: World Bank Private Participation in Infrastructure Database Notes:_ Concessions: When a private entity takes over the management of a SOE for a given period during which it also assumes significant investment risk. Divestiture: When a private entity acquires a stake in a SOE. When the private entities completely acquire a SOE it is considered to be a full divestiture. A partial divestiture is when the government transfers part of the equity of a SOE to private entities. This does not guarantee private management. Greenfield Projects: When a private entity or a public-private joint venture builds and operates a new facility for the period specified in the project contract. The facility may return to the public sector at the end of the concession period. Management and lease contract: Under a management contract, government hires private entities to manage facilities while still bearing the operational risk. However under a lease contract, the government leases assets to private entities while the private operator bears the operational risk.

# **6. Prioritizing Infrastructure Investments in South Asia**

**The demand for infrastructure investment is enormous but the available financial resources are limited; therefore, prioritizing infrastructure investment needs is crucial.** Criteria used to prioritize infrastructure investment needs play a major role in mitigating the infrastructure gap. A few existing studies attempted to address this question (see e.g., Berechman and Paaswell, 2005; Karydas and Gifun, 2006), but the methodological framework they developed is narrow and can be applied only to rank infrastructure investment projects. This section aims to develop a methodology that would be helpful in prioritizing infrastructure needs in developing countries, particularly in South Asia.

## **6.1 Methodological Framework for Prioritization of Infrastructure Projects/Portfolios**

**How to prioritize investment projects or portfolios is a common question a government at any jurisdictional level asks.** This question is especially critical in developing countries, particularly in South Asia and Sub-Saharan Africa where demand for investment is huge and financial resources are limited. Without an appropriate methodological framework to assess investment projects, it is difficult to allocate funding across various investment projects. The methodological framework consists of three main steps: (i) identifying factors that affect infrastructure investment decisions, (ii) quantifying identified factors, and (iii) ranking the infrastructure projects.

14
difficulties or a like in the Southeast, and most 13 Southerners maybe leaving their<sup>region</sup> of origin for fear of crime.

13 The 1988 Federal Constitution established the universal right to social security and instituted<sup>special</sup> eligibility conditions for rural workers under the _Regime Geral da Previdencia_<sup>_Social_(RGPS),Brazil's</sup> public pension system for workers in the private sector. This right was officially extend to rural<sup>areasin</sup> 1993. Recent analysis based on the 1996-1997 _Pesquisa sobre Padroes de Vida_<sup>(PPV)survey,found that</sup> the proportion of rural households receiving pensions from public institutions averages<sup>30 percentin</sup> Brazil's poorer Northeast, and 24 percent in the Southeast. Delgado (1999),<sup>Beltraoet. al.(1999) and</sup> others find that the implementation of the 1988 eligibility and benefit criteria<sup>has been effectivein lowering</sup> the incidence of poverty among rural households in particular in the Northeast.

The increase of rural migration could be indicative of such a socioeconomic impacts<sup>of therecent pension</sup> reform.

36
share per household (denoted as C), in the HBS.<sup>8</sup> We then follow Rubin (1986) and concatenate the HBS and the EU-SILC.<sup>9</sup> To this end, we take advantage of overlapping variables X, which we harmonize previously and describe later in this paper. We decided to impute energy spending shares into the EUSILC, as the EU-SILC is the richer dataset and contains many variables we are ultimately interested in to describe the energy-poor population in Bulgaria.

_Table 1: Conceptualization of data fusion application in this paper (EU-SILC and HBS in Bulgaria)_

|EU-SILC|**Variables, which form part**|**EU-SILC with information**|
|---|---|---|
|(Imputation for expenditure|**of EU-SILC and HBS**|**on I**|
|C)|||
|**HBS with information on C**|Socio-economic information<br>(X)|HBS<br>(Imputation for income I)|

_Source: Own elaboration based on Bacher and Prander (2018)_

**Before applying multiple imputation techniques to the missing information on energy spending shares in the concatenated dataset, we address the conditional independence assumption.** As we do

not dispose of an auxiliary dataset or auxiliary information on the relationship between I and C, we follow Donatiello et al. (2016b) and identify matching variables that are highly correlated with our variables of interest, the energy expenditure share, later in this paper. We identify one highly 8 While we could also impute income data into the HBS (the approach in Column 3), we decide to do the reverse as the EUSILC gathers more extensive information on individual characteristics, household wellbeing, and social protection programs. EU-SILC data contains a richer set of information and is therefore more attractive as a recipient dataset. Nevertheless, the European Commission (2014) recommends using the larger dataset as the donor. We do not follow this approach here, as we are interested in conducting microsimulations based on EU-SILC related variables in future projects.

9 Both Rubin (1986) and Renssen (1998) recommend concatenations in settings of complex survey designs.

10
# **Table A2**

Summary statistics, by paved road at baseline

||No Road|Paved Road|Total|
|---|---|---|---|
|Primary school|0.691|0.866|0.784|
||(0.462)|(0.341)|(0.412)|
|Medical center|0.184|0.437|0.318|
||(0.387)|(0.496)|(0.466)|
|Electrifed|0.257|0.554|0.415|
||(0.437)|(0.497)|(0.493)|
|Crop land irrigated share|0.344|0.455|0.404|
||(0.359)|(0.381)|(0.375)|
|Literate share|0.427|0.499|0.465|
||(0.184)|(0.153)|(0.172)|
|Scheduled caste share|0.157|0.184|0.172|
||(0.215)|(0.193)|(0.204)|
|Distance from nearest town (in km)|28.4|20.0|23.9|
||(29.6)|(20.7)|(25.6)|
|Population|730.6|1708.2|1249.8|
||(933.3)|(2312.9)|(1867.6)|
|Number of villages|276678|313426|590104|

Notes: This table presents means and standard deviations of baseline variables and outcomes for all villages in India. The first column presents summary statistics for villages without a paved road in the 2001 Population Census, the second column for villages with a paved road, and the third column for the pooled sample.

47
Figure O5: Effects of SNAP-benefit changes on prices and real sales (a) Log price index on fitted log benefits per (b) Log real sales on fitted log benefits per population population <!-- Start of picture text --> 9.66 9.68 9.7 9.72 9.74 9.76 9.66 9.68 9.7 9.72 9.74 9.76<br>First stage fitted values First stage fitted values<br>.106<br>13.845<br>.104<br>13.84<br>.102<br>13.835<br>Log price index .1 Log real sales<br>13.83<br>.098<br>13.825<br>.096<br><!-- End of picture text --> Notes: This figure plots the log price index against the fitted values of log benefits per population as predicted by the IV. Both variables are residualized by regressing on a set of controls, store fixed effects, and period fixed effects. For each store-year-month observation, the first-stage fitted values are calculated and grouped into 50 quantiles. The x-axis displays the mean of the residualized first-stage fitted values in each quantile. The y-axis shows the mean of the residualized log price index in each quantile. The line of best fit is obtained from the regression using all observations in each sample. The slopes are 0.082 (0.023) and 0.00597 (0.109), respectively.

Figure O6: Changes in SNAP benefits per population against changes in synthetic benefits per population, Farm Bill and ARRA <!-- Start of picture text --> (a) Farm Bill (b) ARRA<br>25 NY 25 MO<br>DC MA SD WY<br>IN WI<br>NH<br>20 IL ID DE CT OK MNUT MAKSNE<br>15 UTCOCA GA TNAZTXRIMTMDOKMNSCSDNCIAKSWVNVFLARMIPAALNDWA MS NENJMONM WI VA OR VTME 20 CO CT IL VTMT NJAZMENDID GAMDWAPAIATNOR ALDEMI NV LANCSCNHNM KY FL WVARVA MS<br>10 WY OH NYCA DCINRI<br>KY<br>5 15 TX<br>5 10 15 20 16 17 18 19 20<br>% change in synthetic benefits per population % change in synthetic benefits per population<br>Fitted values % change in benefits per population Fitted values % change in benefits per population<br><!-- End of picture text --> Notes: This figure plots the changes in SNAP benefits per population against changes in synthetic benefits per population during the Farm Bill and the ARRA. The slopes are 0.43 (0.27) and 1.48*** (0.28), respectively. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Ohio and Louisiana are dropped during the Farm Bill due to disaster-relief events.

87
# 4 **. Causes and Determinants of Crime and Violence: Empirical Evidence**

A number of typologies exist to categorize different types of violence. The public health approach often systematizes violence according to characteristics of those committing the violent act, i.e. self-directed, interpersonal or collective violence.<sup>60</sup> Rozenthal (1998) suggests a division into “political, delinquency and interpersonal violence” and, in a similar vein, Chernick (1998) suggests “political, criminal and social violence”.<sup>61</sup> Moser and Shrader (1999) point out that these typologies lack conceptual congruency and propose the categories political, economic, and social violence, each identified in terms of the type of power that consciously or unconsciously violence is used to gain or maintain.<sup>62</sup> In a later paper the same author proposes political, institutional, economic, and social violence, each defined in terms of motivation for the physical act that consciously or unconsciously is used to gain power.<sup>63</sup> As these examples show, constructing clear-cut categories is difficult for such a multi-facetted phenomenon and most categories will overlap somewhat. The number of typologies also reflects the fact that the field is compartmentalized between different academic approaches, the dominant ones being public health and psychology.

Public health specialists and psychologists have tended to focus their efforts on research into the root causes of violence. They emphasize that violent behavior is learned and that it is affected by biological and environmental factors alike. As already mentioned, there is a significant relationship between exposure to violence or victimization as a child and a later propensity for violent conduct. But in addition to that, violence is the result of a complex interplay of individual, relationship, social, cultural and environmental factors. The most widely used model to conceptualize the various influences on violence is the so-called ecological model (see figure below), which identifies four levels of influence on violent behavior. These are individual factors (such as biological and demographic factors, educational attainment, substance abuse, etc.), relationship factors (e.g. relations with peers, partners, family), community factors, (i.e. the context into which social relationships are embedded, such as schools and neighborhoods) and societal factors (such as cultural norms, such as norms that entrench male dominance or support the use of excessive force by police).<sup>64</sup>

> 60 World Health Organization (2002). 61 Rozenthal (1998), Chernick (1998) quoted in Moser and Shrader (1999). 62 Moser and Shrader (1999).

> 63 Moser (2004).

> 64 World Health Organization (2002).

11
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Country-year fixed effects control for any national-level time-varying economic conditions. In the context of the model, the relevant factors are prices in the primate city. Empirically, they also include the level of industrialization, oil production, and terms of trade, as well as policies, including gasoline subsidies and preferential trade pacts with developed countries like the American Growth and Opportunity Act (AGOA) and the European Union’s Everything but Arms (EbA) regulation. They also control for global macroeconomic fluctuations, including commodity prices, as well as differences across satellites in the lights data. City fixed effects control for initial size and all other fixed city characteristics. City-specific time trends allow each city to be on its own growth path.

The identifying assumption for _β_ is thus that there is no other time-varying withincountry variation net of linear growth correlated with network distance to the primate times the change in oil price that affects city growth, or more specifically, In specification checks below, distances to other large cities are tested in combination with distance to the primate to determine whether it is actually the cost to the primate city that matters, as opposed to other correlated transport costs.

In order for these regressions to pick up these effects, it must be the case that transport costs substantially affect contemporaneous economic activity, and that oil prices affect contemporaneous transport costs. On the first point, Gollin and Rogerson (2010) find that in Uganda, internal transport costs for crops can easily exceed their farmgate price. It is hard to imagine that this does not affect cropping decisions. Using national trade flows data, Lim ̃ao and Venables (2001) find that transport costs affect international trade substantially, with an elasticity of around _−_ 3. The World Bank Enterprise Surveys of establishments ask respondents whether “transportation of goods, supplies, and inputs...present any obstacle to the current operations of your establishment?” In the most recent (2006–2009) round, in all 15 countries studied, over half of respondents said that transportation was an obstacle, and in 11 countries, at least a quarter said that it was a major or very severe problem.<sup>33</sup> On the second point, Teravaninthorn and Raballand (2009) report a breakdown of transport costs for truckers and trucking companies along several international corridors from a port to the capital city of a landlocked country. Because these are international journeys, I expect costs to be somewhat higher than for those journeys that remain in

> 33Data are available from http://www.enterprisesurveys.org/. Accessed 22 January 2010.

14
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **5 Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **5.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in the treated states. Treated households report 10.637.2 log points lower expenses on all kinds of transport relative to control households after the program was implemented (Figure C1(a)). We also find that the share of transport expense in total expenditure decreases by 0.3-0.6 percentage points (Figure C1(b)). Consistently, we find a reduction in the expenditure on bus/train/ferries category (BTF) 9
The positive impact of trade preferences in Table 2 could be driven by unobserved omitted variables at the exporting country-product-year level, such as domestic sector-specific policy reforms. If this were the case, one would expect to observe a significant increase in apparel exports by AGOA beneficiaries to destination markets besides the US. The negative apparel coefficient in columns (2) and (4) of Table 3 offers evidence against this concern. But in order to systematically account for potential omitted variable biases, we estimate a different type of specification where we pool across the US and the sum of EU and ROW markets such that for each country-HS 6-digit product-year there are two observations. The specification is given by:<sup>38</sup> where all subscripts and variables are defined as earlier and _d_ is a destination market, the US or the sum of EU and ROW. The differences relative to Eq. (1) are in the additional destination dimension of the data used and the fixed effects included: country-HS 6-digit product-destination δcpd, country-destination-year δcdt, and HS 6-digit product-destination-year δpdt. All coefficients

> 38 This specification draws on that proposed by Defever and Ornelas (2015) to study third-market effects of the end of the MFA on Chinese apparel exporters.

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