rafmacalaba/data-use-mentions-extended huggingface dataset:
. 2020). The methodology and data sources used in this study offer several benefits over traditional approaches to project evaluation. Specifically, our evidence is obtained from data that are (a) nationally representative, (b) collected independently of the studied project, and (c) able to control for improvements in the considered outcomes that might have occurred even without the project. The second aspect (b) matters, because using data that are unrelated to the evaluated projects minimizes the risk of researcher demand bias (e.g., the risk that beneficiaries of a project provide more favorable responses when they are interviewed by someone who is associated with the project). In addition, the employed DiD method can separate observed changes in the outcomes of beneficiaries that were due to the project from changes that would have also occurred in the absence of the project. Moreover, existing research on EE interventions tends to focus on supply-side solutions (Creutzig et al. 2018) whereas the project studied here represents a combination of demand- and supply-side measures.

Our findings also relate to studies that argue that there exists a link between EE and energy access (Can et al. 2018; Dagnachew et al. 2018). Specifically, these studies argue that improvements in EE can act like an increase in energy generation capacity which helps to facilitate the expansion of electricity access, especially in areas where the bottleneck to wider electricity access is not the cost of connecting more clients to the grid, but limited electricity generation capacity. The results we obtain from the project in Malawi are consistent with this view and the existence of synergies between EE and energy access.

Finally, the paper contributes to the growing literature that uses remote-sensing data from satellites to measure economic and energy-related outcomes (Henderson et al 2012; <mark>Shi et al. 2018; Falcheta et al 2019)</mark> . Such data have been shown to give rise to valid indicators of energy production, energy consumption, and carbon emissions (see Jasiński 2019 and the
medical sectors; other countries acted only on one side, either restricting trade or liberalizing it. Although many countries resorted to trade policy instruments, it is noteworthy that numerous countries did not resort to trade policy tools at all.

- There is also substantial heterogeneity across countries in the types of trade instruments used and the extent and speed with which crisis-motivated trade measures were removed.

The patterns of observed trade policy activism in response to the COVID-19 pandemic raise many questions regarding the determinants and underlying political economy of trade policy. We have suggested several areas for research using the data set and hope the data collection exercise will stimulate such research. Understanding the motives underlying choices in the design and implementation of trade policy responses to the pandemic, reflected in the substantial cross-country heterogeneity observed in the data, is one area. Another concerns quantification of the effects of different trade policy strategies, both for the world as a whole and for the countries concerned. The preliminary analysis reported above suggests that export bans increased international prices of critical medical goods, especially for PPE. A third policy research question the data can help address revolves around the broader question of the value of trade agreements. To what extent did countries comply with basic provisions of trade agreements calling for transparency, and for trade restrictions in response to emergencies to be temporary and necessary? These are just some of the research questions suggested by the stylized facts revealed by the data set. No doubt there are many others, including questions that are not directly related to trade.

# **References**

Baldwin, R. and S. Evenett (eds). 2020. _COVID-19 and Trade Policy: Why Turning Inward Won't Work_ , VoxEU.org eBook, CEPR Press. At: https://voxeu.org/content/covid-19-and-trade-policy-whyturning-inward-won-t-work

- Bown, C. 2020a. “Trump's trade policy is hampering the US fight against COVID-19,” March 13. https://www.piie.com/blogs/trade-and-investment-policy-watch/trumps-trade-policy-hampering-us-fightagainst-covid-19

- Bown,
# **Box 5: Regional perspectives**

<u>Eastern Europe and Central Asia</u><sup>13</sup>

# **Key characteristics and trends in public debt and financial sector exposures**

Albania (80.6% of GDP), Ukraine (71.9% of GDP) and Kyrgyz Republic (60.5% of GDP) had the highest public debt levels in ECA no EU. Bulgaria (78.9% of GDP), Croatia (76.5% of GDP), Romania (49.4% of GDP), and the Czech Republic (45.4% of GDP) had significant shares of foreign currency debt. All four countries are not part of the euro area and the major share of their foreign currency issuances are denominated in euro. A large part of public debt in Armenia (2017), Azerbaijan (2016), Georgia (2018), and Ukraine (2017) is medium‐ to long‐ term maturity and denominated in foreign currency, while in Kazakhstan, public debt appears to be denominated in equal share of both local and foreign currency and in the Russian Federation the majority of the public debt is in local currency denominated.

According to 2016 Fitch data, Albania and Romania had over 20% of bank assets invested in government securities. In addition to the direct exposure, Romanian banks are indirectly exposed to the sovereign via their mortgage loan portfolio, as the majority of these loans are under the _Prima Casa_ program, where the states guarantees half of the mortgage. From 2012 to 2016 the share of sovereign debt in total bank assets increased by more than 10 percentage points in Ukraine and Serbia. This is partly explained by the financial crisis experienced in both countries, resulting in bank recapitalization with government bonds and greater investments in government assets as a risk mitigation technique given widespread non‐performing loan problems.

Some banking sectors also face substantial exposure to sovereign risks through state‐owned enterprises (SOEs)
in Sudan, in 2010 in Azerbaijan, and in 1996-1998 in Angola.

To examine the relationship between oil and gas discoveries and arms imports I combine data on oil and gas discoveries from Horn (2011), updated by the World Bank, with arms imports data from the Stockholm International Peace Research Institute (SIPRI), as well as from the United Nations (COMTRADE). I estimate a panel covering 120 developing countries over 1990-2017, i.e. the post-Cold War years which also include the latest oil price boom that peaked in 2011 (Figure 4). I find that in the five years following a giant oil 4
period, a fraction of the old capital depreciates. Population and productivity growth are exogenous drivers of the model’s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to growth of the population aged 15-64 years. Productivity growth is modeled as exogenous and factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model. To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and export so that the goods and services markets clear. Second, total investment equals total saving, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.

The model is calibrated with GTAP version 7.0 data. However, not all data needed for the model are available in the GTAP database. For example, biofuels are not a proper sector in the original GTAP 7.0; therefore, we modified the database in a way that allowed us to introduce biofuels sectors in our CGE model. For this purpose, we collected detailed information on production, consumption and trade, a total of seven new biofuel sectors, which have been created by splitting existing GTAP sectors. The land data are also based on the GTAP 7.0 database.

# **3. Impacts on Intensities and Absolute Emissions**

The study simulates various level of carbon tax ranging from US$10 to US$250 per ton of carbon dioxide. Per ton of carbon, the range corresponds to US$37 to US$917, the upper range is indeed a very high level of carbon tax. This section presents key results from simulations of the model.

# **3.1 Impacts on emission intensities**

Figure 5 illustrates
are some systematic differences: the 535-household sample is wealthier, which is consistent with households in the subsample being required to own at least one phone. For instance, while 88% of non-ultra-poor households in the TUP survey own at least one phone, only 72% of ultra-poor households own at least one phone.

**Comparing Survey-Based Measures of Well-Being and Deprivation** As shown in Table 1 and Figure S3, the two survey-based measures of well-being are only weakly correlated. In the full sample, the correlation between the asset index and consumption is just 0.37; in the matched subsample, the correlation is 0.34. These modest correlations may be due in part to the fact that, as discussed above, the consumption data capture short- and medium-term deprivation, whereas the asset index is a better indicator of long-term wealth. Measurement error may also weaken these empirical correlations.

Also notable is the weak relationship between the two survey-based measures of deprivation and the ground truth ultra-poor designation: while the ultra-poor population makes up 27% of the overall sub-sample, less than half of the ultra-poor fall into the bottom 27% of the sample by wealth index or consumption. These differences may be partly attributable to measurement error, but they surely also arise from the fact that they are conceptually distinct constructs: while the consumption and asset indices focus primarily on economic flows and stocks, respectively, the ultra-poor designation was designed to be more holistic and multidimensional, informed in part by community perceptions of vulnerability (Sen, 1992; Alkire et al., 2015).

The fact that the ultra-poor designation is not strongly correlated with the survey measures of consumption and wealth has important implications for the targeting analysis presented below. In particular, it suggests – and our later results affirm – that a policy targeted solely on assets or consumption data will do a poor job of differentiating between ultra-poor and non-ultra-poor. The relatively weak correlation between consumption and the asset index also hints at a later finding that targeting based on a combination of the two
economic development. Reported monetary damages of disasters are higher in richer economies, while human costs are usually lower. As a consequence, this is likely to lead to biased estimates. Second, the paper provides a theoretical explanation of the obtained results by looking at the propagation of the shock that affects agricultural productivity. I add Gallic and Vermandel’s (2020) agriculture sector framework to an otherwise standard Real Business Cycle (RBC) model (Kydland and Prescott, 1982; King et al., 1988; King and Rebelo, 1999), extend it with a more detailed fiscal sector, and calibrate the model to resemble the economy of Bangladesh - a country particularly sensitive to frequent weather-related natural disasters.

# **3 The Empirical Framework**

## **3.1 Data**

– The main variable in the analyses the measure of disaster intensity, comes from the ifo GAME database (Felbermayr and Groeschl, 2014). It is based on primary geophysical 4
|51.9|7.7|10.6|14.4|
|United States|35.4|40.9|46.5|46.7|49.7|53.4|41.4|48.4|54.7|
|Average|24.2|28.2|32.1|35.7|38.4|41.3|18.5|22.8|28.1|

Note: Global output growth factor, global interest rate factor, and global inflation factor are extracted from detrended national industrial production growth rates, short-term interest rates (1<sup>st</sup> differenced), and inflation rates in G7 countries, respectively, using a dynamic factor model.

52
movement of girls to be more restricted than that of boys. On the other hand, if the leading channel through which conflict decreases human capital accumulation is through direct engagement of children in fighting, then boys would be more negatively affected.

However, the general expectation that schooling should be disrupted in regions of more intense fighting may not be very well founded in Nepal. First, national trends do not indicate an increase in poverty coinciding with the conflict but a steady decrease in poverty instead (World Bank, 2005). Second, the insurgents have been reported to actively and coercively challenge traditions that tend to decrease educational attainment, namely discrimination against women, lower castes and indigenous ethnic groups (Hart, 2001; Lama-Tamang et al., 2003; Aguirre and Pietropaoli, 2008).

In Nepal, marriage is almost universal, arranged marriages are the norm, and early marriages are quite common, especially for girls. Nearly 80 percent of 20-year old women and of 25-year old men surveyed in the 2001 and 2006 Demographic and Health Surveys of Nepal were already married (Figure 1). Adverse economic conditions such as those potentially brought about by civil conflict and a potential shortage of husbands due to the fighting and migration are unlikely to affect the quasi-universality of marriage, but violent conflict could lead to an increase in the proportion of women who do not marry and are therefore likely to end up in submissive positions in the household of a relative. Younger brides are generally preferred by prospective grooms, so that the dowry attached to them is lower. Civil conflict may therefore also lead impoverished parents to favor early marriages. If the conflict affects female educational outcomes, it is also likely to have an impact on early marriage, as girls who leave school earlier are more likely to be married young. In addition, the strong focus on sexual purity, of unmarried girls especially, in most religious groups of Nepal, combined with the reported prevalence of rape during the conflict (Manchanda, 2004; Ariño, 2008), may have ruined
Table 1. East Road construction alternatives

||||Details|
|---|---|---|---|
||Scenario|Comine Sement||
|||g<br>Length(km)|Specifications|
||Status Quo|||
|0|(SQ)<br>(no upgrading)|41.533|Gravel surface with earth drains for all segments|
||Medium Desin|29.546|Gravel surface with earth drains for segments with <9% grade|
|1|g<br>(MD)|7.335<br>4.652|Bituminous sealed surface with lined drains for segments from 9-15% grade<br>Cement concrete surface with lined drains for segments with >15%grade|
||Hih Di|22.355|Gravel surface with earth drains for segments with <6% grade|
|2|g esgn<br>(HD)|14.526|Bituminous sealed surface with lined drains for segments from 6-15% grade|
|||4.652|Cement concrete surface with lined drains with >15%grade|
||Sealed Long,|25.34|Gravel surface with earth drains for shorter or lower-gradient segments|
|3|Steep Sections<br>(SLS)|16.193|Bituminous sealed surface with lined drains for long, steep sections where<br>road segments withgradients of 6% or above extend for 250 meters or more|

# B. Climate Events Likely in Malaita

Rainfall data specifically for Malaita are not currently available but, based on expert input, a set of probable hazard events was selected as a basis for modeling the expected effects on the current road and each of the proposed upgraded road alternatives over the 30-year lifespan of the road. These events include the following three heavy rainfall hazards with varying intensities.

- A high intensity rainfall with a return period<sup>34</sup
2019), an essential predictor of stunting later in childhood (Christian et al., 2013). However, to the best of our knowledge no evidence yet exists that links the impacts of deforestation to stunting rates.

In this paper, we contribute to this emerging literature by taking a closer look at the water-borne channels and the causal effects of forest loss on human health. Such an endeavor is challenging due to the multiple factors determining health outcomes and the degree of complementarity that must be accounted for by including socioeconomic, demographic, infrastructure, environmental, behavioral, and institutional factors (Pattanayak & Pfaff, 2009). We exploit the wealth of data available in the Demographic and Household Surveys (DHS), which includes not only contemporaneous health and socioeconomic outcomes, but also full birth histories for all live births that occur in the years before the survey. We restrict our sample to 0.7 million children from rural areas<sup>1</sup> in 46 countries from 2000-2020 for which we have spatial coordinates. This allows us to link the health outcomes with high-resolution deforestation data and study the effects of forest loss on the incidence of DD (by linking lags of forest loss to the survey year) as well as stunting (by linking lags of forest loss to the birth year).

Identifying health impacts of forest loss is challenging given a trade-off between health costs and economic benefits that may be associated with agricultural expansion or urbanization following forest loss. To address the potential bias from non-random forest loss and confounding trends, we examine a possible water pathway from deforestation to health outcomes by exploiting quasi-random variation in deforestation upstream to assess the impacts on waterborne disease outcomes for rural households downstream. This allows us to isolate a waterspecific channel through which forest loss impacts health. We also carry out a series of falsification and robustness checks to rule out spurious temporal or spatial correlation and that the observed health outcomes are caused by water- and not air- or vector-borne diseases.

We then replicate the global analysis for Peru
cross-country patent data.

Thus, the paper is related to the literature on innovation, productivity, and knowledge spillovers. Some contributions, including Bernstein and Nadiri (1989), Jaffe et al. (1993), Audrestsch and Feldman (1996), and Branstetter (2001), highlight that knowledge spillovers are partly localized because the ability to receive knowledge is influenced by the distance from the knowledge source. Others, such as Coe and Helpman (1995), Eaton and Kortum (1996), Xu and Wang (1999), VanPottelsberghe-De-La-Potterie and Lichtenberg (2001), Keller (2004), Javorcik (2004) and MacGarvie (2006) suggest instead that knowledge flows across countries embodied in international trade flows, either in intermediate input or equipment imports or in final goods.

The analysis of patents seems similar to research by Bottazzi and Peri (2007), who used cointegration techniques to test for a long-run relationship between a country’s R&D, its stock of ideas, and the stock of ideas from the rest of the world. They use as sample of fifteen OECD countries for the period 1973-1999. We depart from their analysis by testing directly the validity of the assumptions behind endogenous growth theories rather than its expected consequences. We also test for spillovers from patents stocks and R&D impact on new knowledge. Finally, we estimate panel-count-data models that allow us to deal with the integer nature of patent counts, and the 2
, with this effect stronger for those with more education. Friedson-Ridenour and Pierotti (2019) additionally suggest some married women limit how much they invest in their businesses to ensure continued support from their husbands and to be able to respond to household emergencies. Given concerns about survey fatigue and difficulties in getting entrepreneurs to answer, our long-term survey focused on business outcomes, and did not collect any information on household expenditure, household asset ownership, or children’s outcomes. We therefore can not look at this explanation directly, but in Table A10 examine this indirectly through exploring heterogeneity in impacts among women by baseline decision-making power and household structure. 85 percent of women say they have sole

> 4 Partial evidence against this explanation comes from seeing that for the subset of firms that recall 2019 profits, the 2021-2019 difference in profits in the control group is not statistically different between men and women for either the best month or worst month’s profits.

14
# **Primary School and Health Facility**

The final data source is the infrastructure survey. We are interested in service provision as experienced by the inhabitants of villages. We thus visited the primary school and health facility within a five-kilometer radius for each village, thus not necessarily the Tuungane-built facilities in treatment areas. Specifically, upon arrival in the village, the survey teams were tasked to visit the village chief to explain the data collection exercise and obtain approval. During this meeting, surveyors also asked the village chiefs about the name and location of the primary school and the health infrastructure that are used by the community. After obtaining this information, both facilities were visited for the infrastructure surveys. We instructed our surveyors not to visit the school or health facility if the facility was located more than five kilometers (about one hour walking distance) away. We thus do not measure the difference in quality provided between a Tuungane facility and a control facility. We compare the quality of nearby service provision for villagers in Tuungane and control areas. In other words, we are thus not interested in whether a Tuungane facility is better than a not-Tuungane facility. This study is interested in whether service provision has improved for people that live in Tuungane areas, compared that those that do not.

In total, 610 primary schools and 504 heath facilities were visited. Given that we visited 735 villages, this amounts to 83% and 68%, respectively. These numbers are 147 and 112 out of 180 for those villages that participated in the gender parity lottery. **Table A3** shows that there are no differences by Tuungane and gender parity treatment status. This is an important result in and of itself insofar that the nearby presence of hospitals and schools is an indicator for accessibility of service provision.

In sum, the data presented in this section suggest that the probability of attrition is similar across treatment conditions. Although unlikely, we acknowledge that those villages lost in treatment and control conditions may be different.

8
<br>percent<br>Global value chain participation,<br><!-- End of picture text --> Source: OECD; World Bank.

Note: Global value chain data are from the OECD-WTO TiVA database. The selected indicator is foreign value added as a percent of gross exports. Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; OECD = Organisation for Economic Co-operation and Development; TiVA = Trade in Value Added; WTO = World Trade Organization.

B. Low and high value chain participation are defined as below or above the sample average.

C.D. The sample only includes EMDEs with floating exchange rate regimes according to the IMF classification. D. Shock-specific pass-throughs are aggregated using shares of currency movements accounted for by each type of shock as weights.

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