rafmacalaba/data-use-mentions-extended huggingface dataset:
associated with the dependence on agricultural export growth as well as political events. For example, in 1956 a new constitution was drawn up and in 1963 Guatemala was preparing to enter into the Central American Common Market (MCCA).<sup>5</sup> At first sight, Figure 1 suggests that the civil war’s guerilla activities — starting around 1960 — appeared to have an impact only on short-run growth. However, the later growth accounting exercise suggests that, indeed, the trend growth of total factor productivity (TFP) was negatively affected by the civil strife from the beginning.

**Figure 1. Guatemala: Economic Growth, Social Conflict and Politics, 1951-2003** (growth rates in percent)

<!-- Start of picture text --> 10<br>1963: Central American Common Market<br>(MCCA) and coup d‘état<br>8<br>1956: New constitution Since 1977: Explosion of the internal<br>conflict into a full-scale civil war<br>6<br> = Presidential elections<br>4<br>1967: Counterinsurgency<br>guerilla operations<br>2<br>1975: Second oil crisis<br>1954: Military-backed coup d‘état<br>1985: Transition to civilian rule<br>0<br>1964-1965: Urban<br>1957: Murder of C. Armas guerilla activities<br>-2<br>1962: Student revolts “Jornadas<br>1960: Beginning of the civil strife de Marzo y Abril” 1982: Peak of the internal military<br>conflict under General R. Montt<br>-4<br>50 55 60 65 70 75 80 85 90 95 00<br><!-- End of picture text --> _Source:_ Author’s elaboration based on data from Banco de Guatemala. Data for 2002 (2003) is GDP growth preliminary (estimated). Historical events
people remained off‐grid in 2016. Data from the 2017 census and utility connections lead to an estimate that is almost 10 times as high: almost 50 million people (36 percent of the population). Based on the more conservative figure of 5 million, connecting the entire off‐grid population would raise income by $565 million a year. Based on the higher figure, the annual income gain could reach $5.7 billion.

Improving the reliability of electricity supply would add to these gains. Anecdotal evidence suggests that power cuts have been reduced over the past few years, thanks to additions of generation capacity and low global oil prices. Lacking official estimates of load‐shedding hours, 12
<br>Replacement Offer in low-income<br>households in Australia.|Most households achieved substantial energy<br>savings from the replacement program. The average<br>annual energy cost savings of refrigerator<br>replacements is 53%. However, the energy savings<br>could be compromised if the size of the new<br>refrigerator were much larger than the old unit.|
|Timilsina (2021)|Economic analysis of distributed PV<br>(DPV) in the residential,<br>commercial, and industrial sectors<br>using a model that allows electricity<br>exchange between the consumers<br>and the national electricity utility|<br>The benefits of DPV to the consumers are more<br>than 1.5 times as high as their costs|
|Baek et al. (2020)|<br>An hourly load profile simulation is<br>used for solar-plus-storage<br>applications for three types of<br>buildings: secondary school,<br>supermarket, and a typical single-<br>family home in Waukegan, Illinois.|In most cases, investing in solar PV resulted in<br>significant electricity bill savings for consumers<br>that paid back the initial capital cost of the system<br>in 11–12 years. Most homes and businesses in the<br>Waukegan area in Illinois could cut their electricity<br>bills by more than half.|
|Chesser et al.<br>(2019)|<br>A Logit regression model is used<br>based on 2010 household budget<br>survey of Ireland.|<br>Households that have adopted the micro- renewable<br>energy systems do not result in a reduction of<br>electricity use, rather it increases electricity use.|
|Inoue and<br>Matsumoto (2019)|<br>A conditional demand analysis is<br>used based
rate. Therefore, poverty accuracy and leakage will approximately be equal to the household poverty rate used to determine beneficiaries.

Table 4. Evaluation of a National Random Sample (Uniform Allocation Strategy for Baseline of Comparison)

||**Poverty Line/ PMT**<br>|**Cutoff**<br>||
|---|---|---|---|
|**Evaluation Criteria**|**Bottom 20%**|**Bottom 40%**|**Bottom 60%**|
|Povertyheadcount(observed)|20.00%|40.02%|60.00%|
|Householdpovertyrate(observed)|19.08%|37.20%|55.50%|
|Household poverty rate (predicted)|19.42%|37.31%|55.53%|
|Total accuracy|68.64%|53.35%|50.62%|
|Povertyaccuracy|18.69%|37.44%|55.54%|
|Undercoverage|81.31%|62.56%|44.46%|
|Leakage|19.59%|37.23%|55.52%|

_Notes:_ The predicted household poverty rate is determined by randomly selecting a proportion of households equal to the observed household poverty rate.

Table 5 presents targeting results based on the Sudan national PMT. Here program beneficiaries are determined by the PMT and a PMT cutoff score based on the observed household consumption that identifies the bottom percent of the distribution. We see that choosing this poverty line often underpredicts true poverty at low levels and overpredicts at higher levels. However, compared to randomly accepting beneficiaries, we see that poverty accuracy and leakage are significantly improved. For example, poverty accuracy with the PMT increases by over 15 to 30 percentage points depending on the targeted population living in poverty, with larger increases in
on infant mortality in India, Pullabhotla (2018) suggests that an increase of five upwind fires in a given year increases the infant mortality rate by approximately 10%.

The availability of satellite measures of emissions due specifically to biomass burning enables me to directly examine how the increase in agricultural fires caused by MNREGA translates to an increase in the emissions of three precursor pollutants of PM10 and PM2 _._ 5: black carbon, organic carbon, and sulfur dioxide (SO2). I use data from the MERRA-2 satellite platform that measures the monthly emissions rates from biomass burning of these three pollutants by district to determine the impact of MNREGA on emissions. To do so I replace agricultural fires ( _Fimy_ ) as the outcome of 21
ES SS ES SS SEnTSE RmSe eens<br>26<br>3 3<br>2 ~ ee Peee ee ee eee<br>2o 5 2 +- Sees =a «6s sete stehaiahalahalseelaiaiaiabaiehelsaalataieiaiataia<br>Lka yi Ff — | |<br>a<br>1 a “" 8 & 8<br>2 & %& 8&8 & & 8<br>Momentary Average Interruption Frequency Index (MAIFI) is measured in units of monetary<br>power interruptions (1 minute or less than 5 minutes) per customer. [tis usually measured<br>over the course of a year<br><!-- End of picture text -->
This working paper presents the results of a quantitative analysis focused on the influence of direct multilateral financial support on PPP contract cancellation. The World Bank’s Private Participation in Infrastructure (PPI) Database<sup>3</sup> defines canceled projects as those “from which the private sector has exited in one of the following ways: selling or transferring its economic interest back to the government before fulfilling the contract terms; removing all management and personnel from the concern; or ceasing operation, service provision, or construction for 15 percent or more of the license or concession period, following the revocation of the license or repudiation of the contract.” Multi-level probabilistic models and matching estimators across a large data set of PPPs are applied to examine the relationship between MLS and rates of contract cancellation. Results show that PPP projects that benefit from MLS have lower cancellation rates.

# **_Multilateral Support for Infrastructure PPPs_**

Following the definition adopted by the PPI Database, a project is considered to have multilateral support when it receives financial support including lending, equity contributions, or issuances of financial guarantee products. Upstream policy support, project preparation assistance, and other kinds of technical assistance not linked to a financial commitment on the part of the multilateral do not meet this definition of MLS, though these kinds of support are likely to play a role in the closure and successful application of infrastructure PPP.

The propensity of a PPP to receive MLS is hypothesized to be contingent on a number of project characteristics, market conditions, and strategies of multilaterals, reflecting both “donor interest” and “recipient need” models of aid flows (Basìlio, 2014). The population and level of development within a country may affect MLS lending generally, and for infrastructure PPPs specifically. Other analyses of multilateral aid suggest that multilateral financial flows are biased towards less populous countries with lower per capita GDPs (Basìlio, 2014; Neumayer, 2003).

Sector and region are also included to account for the organization of multilaterals’ lending programs, which
reasons. First, there is no evidence of strategic relocation of ex-combatants. The registers include data on village of origin and return. Comparing these for each of the ex-combatants, we find that around 88% have come back to their colline of origin, and 92% to their home municipality. Formally, our analysis of the relative influence of unobservables compared to observables suggests that our results are not driven by unobservable characteristics, such as the drivers of relocation (table S.6 in the appendix).

Second, when the proportion of CNDD+ demobilized soldiers is included in the analysis of Verwimp and Bundervoet (2009), the coefficient associated with these spillovers is positive and equal to 0.01 ( _p_ -value = .04) without province fixed effects and 0.006 ( _p_ -value = .13) with fixed effects (available upon request). Its size is hard to compare with that of our estimates as their sample is different. In 2007, they did not go to two of our three provinces (Bubanza and Bujumbura Mairie) because the situation was deemed too volatile. Moreover, we do not use the exact same specification and indicators. Nevertheless, this suggests that collines hosting numerous CNDD+ ex-combatants were following a positive trend between 1998 and 2007.

25
also shows a causal link between malnutrition, as proxied by height, and success in school (see Glewwe and Miguel, 2008, for a review) and in the labor market (Strauss and Thomas, 1998). In short, child height-for-age is a good indicator of adult human capital.<sup>3</sup> But what sets our approach apart is that we analyze a long-term measure of nutritional status (height-for-age) in combination with a short-term measure (weight-for-age) for the same sample of children.

Like school attendance in Jacoby and Skoufias (1997), a child’s weight-for-age should be sensitive to the _current_ economic conditions of the household (see also Foster, 1995). This insight along with a key observation about the timing of rainfall shocks allows us to extract compelling evidence of an income effect from anthropometric data. Given its hilly terrain that makes large-scale irrigation facilities infeasible, Nepali agriculture is highly dependent on rainfall, roughly 80% of which falls in the summer monsoon. All the major staples (paddy, maize, and millet) are cultivated during this season. This stark seasonality of agricultural production means that the realization of a shock to income in our setting takes place with a lag relative to the realization of rainfall; that is, income is only obtained once crops have been harvested and, possibly, sold. The influence of rainfall on _disease environment_ , by contrast, is largely contemporaneous. Excessive rainfall in poor countries is linked to higher incidence of diarrheal disease and, indirectly, to an increase in vector-borne diseases. Episodes of acute illness induced by high rainfall can lower a child’s capacity to take in and retain essential nutrients from food. Thus, weight changes observed around the time of a monsoon rainfall shock but before the associated harvest should mainly reflect the change in disease environment whereas weight changes observed postharvest should reflect the combined effects of income and the _lagged_ effects of the disease environment that prevailed during the most recent monsoon.<sup>4</sup>

>
# **References**

- Abate, G. T., A. de Brauw, K. Hirvonen, and A. Wolle (2023). Measuring consumption over the phone: Evidence from a survey experiment in urban ethiopia. _Journal of Development Economics 161_ , 103026.

- Abay, K. A. (2020). Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs. _Agricultural Economics 51_ (3), 323–41.

- Abay, K. A., G. T. Abate, C. B. Barrett, and T. Bernard (2019). Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity relationship in agriculture. _Journal of Development Economics 139_ , 171–84.

- Abay, K. A., L. E. Bevis, and C. B. Barrett (2021). Measurement error mechanisms matter: Agricultural intensification with farmer misperceptions and misreporting. _American Journal of Agricultural Economics 103_ (2), 498–522.

- Alix-Garcia, J. and D. L. Millimet (2023). Remotely incorrect? accounting for nonclassical measurement error in satellite data on deforestation. _Journal of the Association of Environmental and Resrouce Economists 10_ (5), 1335–67.

- Arag ́on, F. M., F. Oteiza, and J. Pablo Rud (2021). Climate change and agriculture: Subsistence farmers’ response to extreme heat. _American Economic Journal: Economic Policy 13_ (1), 1–35.

- Barrios, S., L. Bertinelli, and E. Strobl (2010). Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy. _Review of Economics and Statistics 92_ (2), 350–66.

- Blankespoor, B., T. Croft, T. Dontamsetti, B. Mayala, and S. Murray (2021). Spatial anonymization: Guidance note prepared for the Inter-Secretariat
**Figure 2:** Consort Diagram

<!-- Start of picture text --> 254 clinics in 4 districts<br>(Bo, Bombali, Tonkolili, and Kenema districts)<br>Baseline Survey: September 2011<br>508 communities, 2,540 households (HH)<br>Control Community Non-Financial<br>Monitoring (CM) Awards (NFA)<br>Clinics = 84 Clinics = 85 Clinics = 85<br>Endline HH = 1,680 Endline HH = 1,700 Endline HH = 1,700<br>Endline Survey: May – June 2013<br>508 communities, 5080 HH<br>Ebola Outbreak: May 2014 – March 2016<br><!-- End of picture text --> Figure 2: the samples and timing associated baseline and endline surveys, the randomization, and the Ebola crisis. An initial end to the outbreak was declared in November 2015; however, a few additional cases emerged and the country was finally deemed “Ebola free” in March 2016.

# **3.4 Integrity of the Experiment**

Table C.1 reports balance across pre-specified covariates. Most notably, we find that the number of injuries or illnesses reported is lower in both treatment arms relative to control, household size is slightly smaller, and there are fewer households reporting a recent childbirth in CM. If anything, we expect such imbalances make it harder to find effects on general and maternal utilization. We also find that NFA communities have better cell phone coverage and a higher level of educational attainment, are less likely to belong to the Temne ethic group and are less likely to believe what a doctor told them. As a robustness check, we include unbalanced variables at baseline as additional controls.

We report manipulation checks in Tables C.2 and C.3 based on survey responses. Over 85 percent of CM communities report a meeting held by one of our implementing partners to discuss working with the
poverty based on household per capita income at the individual level. Given that the main purpose of the SEDLAC project is to enhance cross-country comparability of household surveys, these

> Economic Research (IPEA, for its acronym in Portuguese) several years ago, which are updated on a yearly bases based on the CPI (IPEA 2014). 15  Cuba and the República Bolivariana de Venezuela are not included in this study due to the lack of microdata from SEDLAC project in both countries and the lack poverty lines in the case of Cuba. The estimation of the international poverty lines is robust to the inclusion of the República Bolivariana de Venezuela. 16  Bourguignon (2015) presents a detailed evaluation of the SEDLAC data set. SEDLAC is a harmonized database of LAC’s households’ surveys compiled by the poverty group at the World Bank in partnership with the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina.

8
>24</sup> The other countries of Central America spend between 3 and 4 percent of GDP on public education.<sup>25</sup> These shares are low especially for the middle income countries, and reflect the low levels of public spending and revenue collection (Figure 19).

Ánnual per student expenditure in higher education is low, except in Costa Rica. Even in Panama, per student expenditure is only about US $ 3000 (in 2005 US $ PPP); in El Salvador, Guatemala, Honduras and Nicaragua, it lies between $ 1000 and $ 2000. With these low levels of spending, the quality of tertiary education is likely to be very low. In Costa Rica, on the other hand, per student spending has been rapidly rising since 2005, going from $ 5500 to $ 7500 in just 4 years, but this rise may not indicative of improvements in quality.<sup>26</sup>

> 21 The findings of this review are presented in Tertiary Education for the Knowledge Society, published in September 2008. Background reports prepared by 21 countries, Reviews of Tertiary Education in 14 countries and other documents of the review are also available on the OECD website www.oecd.org/edu/tertiary/review.

> 22 Data on public spending on education, as noted in the ICEFI (2010) report, are of variable quality. In particular, there are anomalies in the data for Nicaragua, and differences with figures reported in Ministry of Education reports.

> 23 OECD Education at a Glance 2011.  Expenditure on educational institutions as a percentage of GDP , by source of fund and level of education (2008)

> 24 The high level of public spending on education in Honduras is largely due to the relatively high wages for school teachers.

> 25 Author’s calculations based on ICEFI, 2010.

> 26 Author’s calculations based on ICEFI, 2010. Faculty-student ratios, a possible measure of quality, have not declined in Costa Rican
of total trade costs with respect to each trade cost variable. Thus, a smaller coefficient on distance for online flows could simply signal that the bundle of online products has a lower elasticity of substitution than the offline bundle. To estimate these elasticities of substitution we assume that trade costs online and offline are gamma distributed with shape parameter _k_<sup>_f_</sup> , where _f_ is the type of flow, but an identical scale parameter. Then using existing estimates of the elasticity of substitution for aggregate trade flows, we can back up consistent estimates of the elasticity of substitution online and offline using the fact that the variance of a gamma distributed variable is proportional to the mean by a factor equal to the scale parameter. For a detailed description of the methodology to estimate the online and offline elasticities of substitution, see section 5.1.

Our results suggest that for an estimate of the aggregate elasticity of substitution of 5 (see Eaton and Kortum 2012), the online elasticity of substitution equals 4.5, whereas the offline elasticity of substitution equals 5.6. This last estimate is quite close to the Broda and Weinstein (2006) median estimate of 5.9 in our bundle of HS-6 digit goods. Moreover, while the online and offline elasticities of substitution are statistically different from zero at the 5 percent level, they are not statistically different from each other. This comforts us in our matching of online and offline products, and suggests that statistical differences in the estimated coefficients of the gravity equation will be due to the contribution of these variables to trade costs, rather than to differences in the elasticity of substitution.

Our eBay data also include data on average bilateral ad-valorem shipping costs. While we do not have an equivalent for bilateral offline flows, in the case of US imports we do have data on freight and insurance costs from USITC. When plotting these costs against distance (see Figure 4) we find that for both online and offline flows, shipping costs are uncorrelated with distance, even
<!-- Start of picture text --> 2 6 22 32 35<br>Income aggregates - High ome oer middle income wa Lower middle Qome Qw income<br>;<br>East Asia & Pacific~ (Ope,2 cring,TR10 11 ° ° “Philippines3336Qionesia ° Timeteste50<br>Mongolia<br>17 45<br>South Asia- Sri Qoka ° sees PakRian<br>Sub-Saharan Africa-. senSai17 *Cotstivore”22 er eS er er ee oe Bundi56<br>5 7 12 46<br>Middle East & North Africa- kuwait-O- © * O—-aeria - ° ° y oO<br>West Bank and Gaza emen, Rep.<br>568 11 20 27<br>Europe & Central Asia- Macedonia HYP SeaKhstan “Georgia C28ekistan® TajfPetan<br>Moldova<br>2 6 13 46<br>Latin; America; & Caribbean-,Q-; PaiQusy cee eee eee Gua@nala<br>0 10 20 30 40 50<br>Stunting prevalence (% under-5)<br>© High performer 9° Other<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Notes: Countries within each region with the highest & lowest stunting rates as well as those who have achieved more than 6 percent per year reductions in stunting (high performers) are labeled. Data correspond to the year with the most recently available stunting estimate for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2002 (Romania) and the most recent estimates are from 2016.
1. **Introduction**

An efficient selection of investment projects is essential for sustained economic growth. In centrally planned economies this selection process is biased towards large-scale investment projects and disregards private incentives. Whereas in a centrally planned economy the decision process about investment can be characterized as top-down, in the (ideal-type) market economy the opposite holds. Investment is carried out by the individual firm and the firm itself decides whether to invest or not. In general the firm is not able or willing to finance the entire investment with internal funds. Hence, banks and capital markets are called upon to provide external funds and thereby take part in the selection process of investment projects.

Given the economic importance of investment, it is clear that in the transition process of the formerly socialist countries in Central and Eastern Europe (CEE) the introduction of market incentives is crucial. One way to stimulate this is to have a financial system that allocates financial resources efficiently (Berglof and Roland, 1998, p. 18). It is therefore relevant to know whether the reforms have been successful (yet) in this respect. Roughly speaking two methods can be distinguished for the investigation of the success of the process of transition to market economy. The first is to conduct a survey among managers of industrial firms and financial institutions. Such a survey can investigate whether or not managers in transition economies still behave in line with the incentives typical of the centrally planned economy or whether they have changed their modes of behaviour and act nowadays more in accordance with the incentives of market economy. Examples of these survey studies are Pinto, Belka and Krajewski (1993) and Pinto and van Wijnbergen (1994).

The second approach for investigating the relationship between investment and finance in the transition process is to estimate an investment function. These studies use firm-level data to investigate whether investment is financially or liquidity constrained and whether the empirical relationship between investment and liquidity constraints corresponds with that found in similar studies of market economies. Examples of this type of studies are Lizal