rafmacalaba/data-use-mentions-extended huggingface dataset:
- **_Umbrella groups, networks, and coalitions_** ‐these involve alliances between groups with common interests or activities. Often they are driven by key individuals within community organizations and NGOs or by external actors such as international NGOs, FBOs, or donors. Coalitions tend to focus on advocacy while networks tend to focus on information sharing, coordination, and support to member organizations, although the distinction is not always clear cut.   Additionally, networks are increasingly involved in the disbursement of funds to support community‐level initiatives.

- **_Formal partnerships_** ‐these include formal agreements between community organizations and government or other organizations for the delivery of specific services. Formal service‐delivery agreements are mostly initiated by external actors including government agencies, international NGOs, and donors. There agreements may be the result of proposals from CBOs approved for funding.

- **_Referral relationships_** ‐community organizations may have links with services without any formal partnership agreement, for example, with facilities that provide HIV counseling and testing or with education or social welfare services. Such links are initiated both by community organizations and by external actors.

- **_Coordination mechanisms_** ‐these include local AIDS councils or forums. Coordination mechanisms are usually established by government, although there are examples of mechanisms initiated by NGO umbrella groups, coalitions, and networks.

- **_Informal collaboration_** ‐many community organizations have informal links with other organizations on an _ad hoc_ basis.

# **Box 9: Partnerships and collaboration in South Africa**

A community audit in South Africa (Birdsall and Kelly 2005a) found that 88% of CBOs and NGOs and 50% of FBOs network collaborated with other organizations, but few had formal partnerships. The Departments of Health and Social Development use CBOs and NGOs as service‐delivery partners; service‐level agreements are one of the clearest examples of formal partnerships. Another interface between community organizations and government is local planning and coordination of the AIDS response, although formal coordination is limited in
the GIS road network and the 93 nodes, we used the shortest path routing algorithm to compute number of kilometers, travel time, and total road user cost for each origin-destination pair (or route).

_Table 5. Terrain Classes and Percentage of Land Area Peru_

|**Class**|**Description**|**Peru**|
|---|---|---|
|**1**|Plains|0.11|
|**2**|Mid-altitude plains|0.00|
|**3**|High-altitude plains|0.00|
|**4**|Lowlands|10.92|
|**5**|Rugged lowlands|17.41|
|**6**|Platforms (very low plateaus)|1.48|
|**7**|Low plateaus|0.05|
|**8**|Mid-altitude plateaus|0.00|
|**9**|High plateaus|0.72|
|**10**|Very high plateaus|0.14|
|**11**|Hills|20.83|
|**12**|Low mountains|7.44|
|**13**|Mid-altitude mountains|10.37|
|**14**|High mountains|16.81|
|**15**|Very high mountains|13.70|

_Source:_ Authors’ calculations.

# 6.2. GIS Data Limitations

A number of limitations associated with the input data sets and the methods used in this analysis should be taken into account in the interpretation of results.

_Scale of roads_ . The scale of the source data (that is, the level of detail) for roads is unknown, but there are obvious differences between the countries and in comparison with large-scale data sets such as Open Street Map. The
- We scale the carbon input to energy production to match the carbon intensity of GDP: 0.46 kg CO2 per 2015 $ of GDP, averaged over 2000-2022, from World Development Indicators of the World Bank.

- The value of total energy consumption as a share of GDP is 7.1%, calculated from GLORIA database.

- The constant USD price of carbon is restricted to attain primary fossil energy supply expenditure-GDP ratio of 3.9%, from GLORIA database.

- The ratio of total imports to GDP is around 26%, calculated from GLORIA database.

Note that the steady state values of a few important variables have a direct equivalent in the data and have been calibrated accordingly. These values can be set directly and do not require another parameter to be restricted. They are reported in Tables B2 and B3.

# **B.4 Estimation strategy**

In this subsection, we discuss our Bayesian estimation strategy together with the results. The basic idea of Bayesian statistics comes from the Bayes’ rule: where _θ_ is the vector of parameters, _p_ ( _θ|_ **y** ) is the probability distribution of parameters given data (posterior distribution), _p_ ( **y** _|θ_ ) is the probability distribution of data given parameters (likelihood), _P_ ( _θ_ ) is the prior distribution of _θ_ and _p_ ( **y** ) is the marginal distribution of data. We are interested in marginal posterior distribution of model parameters; however it is not always possible to analytically compute marginal posterior distributions due to complex integral calculations.

Bayesians, therefore, have developed numerical methods that allow us to approximate marginal posterior distributions of parameters. The basic idea is to draw from some other distribution (proposal or importance distributions) and find moments of simulated data, which are going to converge to moments of posterior distribution under specific conditions (Herbst and Schorfheide 2015).

We use Metropolis Hastings (MH) algorithm, which is part of MCMC family.
6 (2) cov[Yk, F(Y)] Rk cov[Yk, F(Yk )] Equation (2) shows that the effect of source k income on overall income inequality can be broken down into three components: (a) the share of income component k in total income (captured by the term Sk); (b) the inequality within the sample of income from source k (as measured by Gk); (c) the correlation between source k income and total income (as measured by Rk).

Using this decomposition, it is possible to identify how much of overall income inequality is due to a particular income source. Assuming that additional increments of an income source are distributed in the same manner as the original units, it is also possible to use this decomposition to ask whether an income source is inequalityincreasing or inequality-decreasing on the basis of whether or not an enlarged share of that income source leads to an increase or decrease in overall income inequality. On the basis of equation (2): where gk is the relative concentration coefficient of income source k in overall inequality.

From equation (3) it follows that income source k is inequality-increasing or inequality-decreasing according to whether gk is greater than or less than unity.'<sup>0</sup> II a. Data Sets for Egypt and Jordan
to contribute to this literature, this paper report findings from an impact evaluation of Bangladesh’s e- procurement system. It poses the research question: Does electronic procurement, in particular electronic tendering and contract award, improve value for money (i.e. economy), access to public tenders and administrative efficiency? If yes, under which conditions?

6. When the Government of Bangladesh (GOB) introduced e-procurement in 2011, the country provided both a promising and challenging context. Procurement-related violence frequently made the headlines of newspapers, as local “strongmen” connected to insider firms systematically used armed gangs to physically hinder competing bidders from submitting paper-based bids.6 Part of e-procurement’s promise was that it could cut through such practices and drastically increase access to public tenders. At the same time, limited administrative capacity and high staff turnover made introducing e-procurement challenging (World Bank, 2002).

7. Crucially to our claim of novel insights, the scope of e-procurement in Bangladesh goes beyond most if not all such systems in low and middle-income countries. It digitizes the entire contracting process, from advertisement to contract award, including buyer-bidder communication, bidder registration, bid submission, and bid evaluation, with no parallel “paper trail”. Hence, it is a fully functional transactional system. By contrast, most other e-procurement systems only provide for online advertisement and submission of tenders, but the “back-end” of tender evaluation and award remains largely paper-based (OECD, 2016).

8. This impact evaluation focuses on the adoption of e-procurement by the three government agencies that are jointly responsible for most7 of Bangladesh’s public works procurement: the Local Government Engineering Department (LGED), the Roads and Highways Department (RHD), and the Bangladesh Water Development Board (BWDB). These three agencies were also the first to introduce e-procurement in Bangladesh, starting in FY2012-13.

9. Our primary data source is administrative data on public procurement. We obtained detailed electronic procurement data for all electronic tenders of BWDB, LGED
interpreted as the effects of exchanging a day in the 18–21<sup>◦</sup> C reference bin with a day in the other bins.

# **4. Results**

# **_4.1. Effects of temperature on poverty_**

We start examining the effects of temperature change on poverty using the country-level analysis (Panel A) and subnational level analysis (Panel B) in Table 1. For the country analysis, we aggregate our newly constructed GSAP database weighted by the subnational population. We analyze three poverty indicators using the daily poverty lines of $1.90, $3.20, and $5.50. For each outcome, we present the results of the fixed-effects panel model in Columns (1), (3), (5), followed by the results of the long differences model in Columns (2), (4), (6). In both panels, the results are strongly statistically significant and confirm the negative effects of higher temperature on poverty for all the three different poverty lines.

Yet, the estimates at the subnational-level analysis (Panel B) have stronger magnitudes than those derived from the country-level analysis (Panel A). The differences between these two sets of estimates are statistically significant, which is confirmed by the t-tests for equality of the estimated coefficients shown at the bottom of Table 1. This suggests that studies using spatial aggregation of data at the country level could mask the impacts of warmer temperature. This finding is also consistent with previous studies showing more pronounced effects of 11
al._ (2018), Sposi (2018) etc.). In a related paper, using an endogenous model of sectoral linkages, I study the distortions in intermediate markets in the same countries and find that changes in distortions can deliver quantitatively meaningful expansion of the industrial base (Sinha, 2018).<sup>10</sup> Comparing Latin American economies to Korea and Taiwan shows that the low growth of productivity in the region is pervasive across sectors and is pivotal in accounting for the difference in growth experience of the region compared to the Asian miracles since the 1960s. Nonetheless, differences in productivity growth in manufacturing and wholesale are especially relevant (Ung ̈or<sup> ̈</sup> , 2017). If the manufacturing sector in the Latin American economies was to experience the high growth in productivity as realized by Korea and Taiwan, the sector would have lost a larger share of employment during the period according to the price-effect mechanism. Still, economic growth would be higher in such a scenario. Thus, the pertinent issue regarding policy making is to identify if the present allocation truly reflects inefficiencies. An even more

> 10Using panel data from 28 countries in a recent paper, I also find that the elasticity of substitution across sectoral inputs to be less than one in each of the three broad sectors of the economy (Sinha, 2019). Hence, linkages respond to changes in relative prices driven by sector-based productivity growth as well.

19
_Ingo Borchert and Mattia Di Ubaldo_ Figure 5: Key provisions by type of agreement, across 131 Services PTAs <!-- Start of picture text --> Bilateral PTAs Plurilat PTAs<br>disputes_lib_stands_lib_ratothdip_lprothdip_ttrothdip_lcrothdip_eprstruc3s_lib_app2 drdis_ma2dis_mfndis_nts_pol_subsdr_mutrec2struc1struc2dis_ma1s_lib_app1roo_m3_v1dr_mutrec1<br>1<br>.8<br>.6<br>.4<br>Average Share<br>.2<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using WB-DTA 2.0 data. Note: graph includes 92 bilateral and 39 plurilateral agreements over the period 2000-2016.

31
tent with the reports of humanitarian and news agencies on the ground.22 Furthermore, the regions that the WFP survey identifies as receiving the most assistance roughly align with the population-level estimates of the prevalence of food assistance (e.g., Tandon and Vishwanath 2019).

And lastly, for the empirical analysis, it is important to verify that access to mobile phones and the likelihood of responding to the survey does not change in response to the shocks that are the focus of the analysis. Table 1 reports summary statistics of respondent characteristics and the number of active mobile phone numbers owned by the respondent before and after each shock in the affected regions. For each shock, there is little difference in each variable following the shock- the magnitude of each difference is small, and only one coefficient out of 16 estimated is marginally statistically significant. Furthermore, none of the differences between pre- and post-shock is jointly significant (p-values of 0.518, 0.769, 0.202, and 0.615 respectively ).23 Combined, there is little evidence that the shocks significantly affected the characteristics of those who responded to the WFP mobile phone survey.

# **Section 4. Correlation between School Attendance and Violence in the District of Residence**

Using the WFP survey, we first analyze the correlation between poor school attendance and violence in the district of residence. Specifically, we estimate the following specification: (1) _Poor School Attendhgdmy_ = _ψh_ + _τmy_ + _ρg_ + _βV iolenc_ _~~e~~ Severitygdmy_ + _αXhgdmy_ + _igdmy_ where h denotes household, g denotes governorate, d denotes district, m denotes month, and y denotes year; _Poor_ _~~S~~ chool_ _~~A~~ ttend_ denotes an indicator equaling one if not all school-aged children attended school in the past month; _X_ denotes a vector of household control variables24; and _V iolence_ _~~S~~ everity_ denotes a number of measures of severity used in different empirical specifications, including an
the autos of borrowers who failed to repay their loans, they could not resell these repossessed cars without court approval. As a result, the time from repossession of a car to its resale by the bank averaged more than two years. After the reform the process of reselling a repossessed car became simpler and faster. Borrowers and creditors engaged in direct relationships and courts played a signicant role only when borrowers explicitly requested it. The law thus avoided unnecessary trials, reduced the reliance on courts and increased the enforceability of auto loan contracts.

Assunçao et al. (2014) use micro-level data from one of the largest banks in Brazil to provide direct evidence on the consequences of the reform. They show that the reform resulted in larger loans with lower spreads and longer maturities. It also expanded credit

> 27See Djankov et al. (2007).

25
# **1. Introduction**

Fragile and conflict-affected states (FCS)<sup>2</sup> face daunting challenges for development. The international community has been increasing its focus on FCS and has provided them with significant amounts of aid. Recently, FCS are receiving 30 percent of total Official Development Assistance (ODA)<sup>3</sup> . However, the increasing attention and resources have not yet translated into significantly improved development results. One and a half billion people live in FCS and they remain the group furthest away from achieving the Millennium Development Goals (MDGs)<sup>4</sup> . The contrast between the increasing focus/aid and development results leads to a critical question: Is aid effective enough to address the development challenges in FCS?

The importance of aid effectiveness in FCS is emphasized in ̳A New Deal for engagement in fragile states‘, endorsed by more than 40 partner countries and development partners since it was officially introduced at the 4<sup>th</sup> High Level Forum (HLF-4) on Aid Effectiveness in Busan, Korea in November 2011. While the relationship between aid and development is not straightforward, the improvement of aid effectiveness – defined as the management and delivery of aid – is rationally linked to improved development results<sup>5</sup> .

Identifying key aid effectiveness challenges is the critical first step for both FCS (recipients of aid) and development partners (providers of aid). This is the first of two main purposes of this paper. The second is an attempt to identify good performance examples in this area, as learning from each other‘s experiences is an important and practical step to improve aid effectiveness.

In order to serve for these purposes, this paper relies on quantitative results generated through the Survey on Monitoring the Paris Declaration (the PD survey) given the survey‘s three key advantages. First, it has comprehensive country coverage with 78 countries participating in the latest survey round
0.7<br>0.6<br>0.5 FD<br>FIA<br>0.4<br>FID<br>0.3<br>FIE<br>0.2<br>FII<br>0.1<br>0<br>Financial development indexes<br><!-- End of picture text -->

# _Source_ : Data from IMF (2017).

_Note_ : FD: Financial Development Index; FIA: Financial Institutions Access Index; FID: Financial Institutions Depth Index; FIE: Financial Institutions Efficiency Index; FII: Financial Institutions Index.

9
capita income.<sup>6</sup> Figure 5.2 presents a comparable graph for household income quartiles. Figures :i. and 5.2 clearly show that children in poor districts and poor households lag behind in the years of schooling they attain for any given age. The differences across quartiles increase with age, ,o that by age sixteen there is almost a two-year difference between children in the poorest and richest districts, and almost a one-year difference between those in the richest and poorest **iir** scome quartiles.

A host of factors probably contribute to differences in the educational attainment of children, including differences in the education of other household members, income,

> 6 INEI combined information from the 1993 population census and a household survey conducted in I1995 to estimate mean district per capita income. This is done by first, using the household survey to regress income per capita on a set of variables common to the census and the survey, and second, predicting mean income per capita for each district by applying the coefficients from the regression to the census (see INEI, 1996, and Hentschel et. al. for a discussion of the methodology).

7
sup>20</sup> We conduct this exercise by using two measures of sovereign risk: sovereign ratings and 5-year CDS spreads.<sup>21</sup> Sovereign ratings reflect an independent assessment by a credit rating agency of the — creditworthiness of a country the level of credit risk associated with investing in its debt. We focus on the three main rating agencies — Standard and Poor’s, Moody’s, and Fitch — and source the rating data from Bloomberg.

The three rating agencies that we consider do not express their rating opinions with numerical scores. Ratings are instead communicated with alpha-numeric scores that range between AAA (the highest rating) and D/SD (meaning that a country is in default) for Standard & Poor’s and Fitch and between Aaa and C for Moody’s. For our empirical analysis, we convert the ratings into a

> 19 On the maintenance costs of public capital stock, see Kalaitzidakis and Kaylvitis (2005) and Kalyvitis and Vella (2015).

> 20 Most of the countries in the PIQ dataset are EMDEs. The only countries classified as advanced economies are Latvia and Lithuania. We do not include these two countries in the analysis of sovereign risk.

> 21 For robustness, we also check the results with EMBI spreads.

15
overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo‐localized household surveys (using the Demographic and Health Surveys<sup>8</sup> ) to assess the exposure of poor people to river floods relative to the exposure of non‐poor people.

Where neither study provides data, we use the average for countries which do have data weighted by their population (the average is 8%). This average is conservative, when compared with the few estimations found in published case studies ( **Table 3** ).

|Country|Hazard|Year|Poor|Nonpoor|Bias|Source|
|---|---|---|---|---|---|---|
|**Bangladesh**|Flood/cyclone|2009|25%|14%|56%|Akter & Mallick(2013)|
|**Mumbai**|Flood|2010|41%|24%|71%|Patankar and Patwardhan|
|**Kenya **|Flood|2012|99%|96%|1%|Opondo(2013)|
|**Guyana**|Flood|1997|40%|28%|18%|Pelling (1997)|
|**Vietnam**|Flood|2011|38%|29%|17%|Nguyen and James(2013)|

**Table 3: Exposure of poor and nonpoor people in selected case studies**

We assume there is no exposure bias for other hazards, due to the large scale of these events. This is a simplification considering for instance that local soil characteristics have an impact on earthquake vulnerability.

# 3.4.
, we measure employment as the number of active workers on December 31st of each reference year, and we include only establishments with positive employment in the sample. We also use the information on the 4-digit CNAE sector classification codes<sup>2</sup> and the legal nature of firms, which is a key variable in this study used to identify state-owned firms.

We also compute firm characteristics from worker-level information, for instance, we measure average firm wage, the share of college-educated workers, and firm-level innovation intensity, which is measured as the share of employment in technical and scientific occupations (PoTec), a proxy for R&D expenditure proposed by Ara ́ujo et al. (2009). We take advantage of the database panel structure to infer firm entry and exit based on the first and

> 2For multi-establishment firms, we consider the sector code associated with the largest establishment in terms of employment.

4
stocks in **(3.1)** above. But the conditions just mentioned under which historical flow accumulation would yield a good approximation to the value of the corresponding stocks are quite stringent. More specifically, correcting for the effects of unrequited capital transfers and debt reduction operations is relatively simple, as the relevant data is available from the **IMF** and the World Bank, but valuation effects are much more problematic. They arise from two main sources cross-exchange rate changes, whose effect depends on the currency composition of foreign assets and liabilities (generally unavailable from standard data sources), and changes in the secondarymarket price of assets **(e.g.,** equity prices in the case of portfolio investment, or market prices of developing-country debt). The latter valuation effects are even more difficult to estimate, as organized secondary markets often do not exist (particularly in developing countries).

Obviously, valuation problems could **be** easily overcome if information were available reporting asset stocks at current exchange rates and market prices. However, available stock information is limited to two main sources: (i) the foreign reserve data collected **by** the IMF's **IFS,** which value foreign exchange reserves at current exchange rates and have very broad coverage across countries and over time; and (ii) the external debt data compiled **by** the World

**13**