rafmacalaba/data-use-mentions-extended huggingface dataset:
Survey of 2015 we obtain estimates using the different considered models. The target parameters for this simulation are mean welfare and the Foster et al. (1984) - FGT class of decomposable poverty measures for municipalities present in the census. The true values of the target parameters at the municipal level are based on the census data.

As a first step, we compare results using the three transformations discussed in the previous section. Figures 12 and 13 show box plots of empirical absolute relative bias and MSE of CensusEB estimates of poverty rates based on model (1) under each transformation. The Box-Cox and log-shift transformation yield estimates that are not only less biased, but also results with a lower empirical design MSE. As can be seen in the results from the first 3 columns in Table 2 the log-shift transformation yields aggregate results somewhat preferable to the Box-Cox. Additionally, in Table 2 it is quite clear that the natural logarithm may actually result in aggregate results that present considerably smaller gains over direct ones and thus model and residual checks should always be done to ensure adequate transformations are used to avoid such an outcome. The rest of the discussion will focus on estimates obtained from the log-shift transformation applied to the two-stage samples.<sup>38</sup>

> 36The removal is done sequentially starting with the least significant covariate, then the model is fit again without the covariate. The process is repeated until all covariates in the model are significant at the 1% level.

> 37 Box-Cox and log-shift transformations are available under R’s `sae` package as well as under Stata’s `sae` package. In the World Bank’s `PovMap` software, the only available transformation was natural logarithm. However, `PovMap` does allow for drawing residuals from the empirical distribution as well as from a Student’s _t_ distribution.

> 38Figures under other transformations are available upon request.

22
scarce remaining men (Heuveline & Poch, 2007). Countries may even experience factors that work in opposite directions, with varying effects over time. For example in Cambodia _during_ the Khmer Rouge regime child marriage declined due to high mortality among men, but _after_ the fall of the regime, there was a “rebound” increase in child marriage (Heuveline & Poch, 2007).

The mixed findings across country studies on the impact of conflict on child marriage could be the result of differing methods, specific contextual, temporal, or conflict differences, and/or data gaps. The current paper attempts to advance this literature by undertaking a multicountry study using consistent data and methods, substantively increasing our understanding of the relationship between conflict and child marriage.

# **3 Data**

In this section, we discuss data used to measure conflict, survey data used to measure child marriage, and data on geospatial boundaries. We matched conflict locations by merging together conflict measures with geospatial boundaries and household survey data. We then describe our key variables – namely the outcome variable (child marriage), our key covariate of conflict, and other controls.

# **_3.1 Conflict data_**

To measure conflict, we used the Uppsala Conflict Data Program (UCDP)<sup>4</sup> Georeferenced Event Dataset (GED) version 20.1 (Pettersson & Öberg, 2020; Sundberg & Melander, 2013),<sup>5</sup> which covered conflict events globally in 1989-2019. We discuss the variables used from the data in the key covariates section below.

> 4 UCPD captures conflict events, defined as “an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date” (Sundberg & Melander, 2013, p. 4).

> 5 <mark>UCPD data publicly available from</mark> <u><mark>https
- 65 -

# V. Performance

5.01 The .valuation of vocation&aly-specific education and training should ideally be carried out against a hierarchy of objectives. The ultimate objective is economic efficiency, measured<sup>by</sup> assessing the economic benefits of training (usually productivity<sup>as</sup> reflected in earnings). The next lower objective in the hierarchy<sup>is</sup> placement in employment, a necessary but not sufficient criterion for econ-omic efficiency. At this same level employer satisfaction with graduate performance provides a weaker but useful standard. Next comes course completion or graduation. This in turn rests on a number<sup>of</sup> internal efficiency measures, such as drop out and repeater rates. Enrollments as a proportion of capacity can serve as an initial indicator of institutional relevancy and efficiency. All of these measures can, and ideally should, be assessed against costs, yielding a range of efficiency and cost-effectiveness indices.

5.02 Fundamental to all of these measures, of course, is the actual establishment of a training institution. From the perspective of World Bank project investments this is an implementation criterion, one bound by the initiation and completion dates of a given project.

# ECONOMIC OUTCOMES

5.03 A review of Bank investments based primarily on document analysis is severely limited with respect to the indicators of investment effectiveness that can be used. Economic analyses of the outcomes of Bank investments are absent from project completion reports.

5.04 Reliable unit recurrent cost data is almost entirely lacking in project documentation. Recurrent costs were reported for 11 of the 76 projects in the sample (8 of 34 countries). The figures are most often developed by dividing gross recurrent cost budgets by enrollments across a set of institutions.

5.05 These data, however, generally confirm that vocational secondary schools are more costly on an annual unit recurrent<sup>cost</sup> basis than general secondary schools, with ratios ranging from 1.
**Table 5** Impact of Treatments on Male SRH Perceptions <u>(Male</u> Data)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|SRH perceptions index|||||
|treatment x post|0.293***<br>(0.076)|-0.022<br>(0.106)|-0.000|2,314|
|treatment x post x goal|0.073<br>(0.140)||||
|Female friend somewhat or<br>very likely to have STI|||||
|treatment x post|0.140**<br>(0.058)|0.053<br>(0.074)|0.581|2,314|
|treatment x post x goal|0.001<br>(0.099)||||
|Over 15% of males have an STI|||||
|treatment x post|0.123*<br>(0.064)|-0.019<br>(0.072)|0.308|2,314|
|treatment x post x goal|0.070<br>(0.095)||||
|Girls have a right to ask to use<br>a condom|||||
|treatment x post|0.122*<br>(0.064)|-0.066<br
climatic stressors using the extensive data embedded in multi-topic and multi-purpose representative surveys should not be understated.

- We also highlighted that migration is not the only possible outcome of adverse weather events. From an empirical perspective, identifying people trapped in immobility in the aftermath of a shock is even more difficult than tracing induced mobility, especially in the absence of specific information on this provided by the affected household or individual.<sup>8</sup> While one can separately investigate effects on a welfare measure and, for example, indirectly argue that a reduction in consumption or assets, or an increase in liquidity constraints caused by a climatic factor may have prevented migration (see Hirvonen (2016) for an example of this approach), this may not necessarily have been the case, because the mediating role of the welfare measure is usually only indirectly and separately investigated. More generally, the empirical framework on the determinants of the “migration decision” could greatly benefit from the insights and models of the well-established literature on geographical and asset-based poverty traps (Barrett and Carter, 2013; Carter and Barrett, 2006; Carter et al., 2008; Jalan and Ravallion, 2002).

- <mark>Migration is not a random event, and weather- or climate-related migration is not an exception to this rule</mark> _<mark>.</mark>_ <mark>Self-selection comes into play when it comes to the decision to migrate or not (Carletto and De Brauw, 2012), be this decision due to climatic factors or not. Empirically, there are no clear prescriptions yet on how to address the self-selection-based endogeneity of migration,</mark><sup>9</sup> <mark>with the consequence that many research designs are neither robust nor reliable. Furthermore,</mark> since migration is intrinsically a selective process, any causal inference analysis needs to check for the determinants of migration and have access to the necessary information for the identification of

8 Incidentally,
et al. (2015) show that the quantity of water used is smaller compared to a conventional one. Based on the data for the 1995-2000 period on 81 countries, Lio and Liu (2006) find a significant positive relationship between the adoption of information and communication technology (ICT) and agricultural productivity. They also find that returns from ICT in agricultural production of the richer countries are about two times higher than those of the poorer countries.

# **5. Other Factors Driving Voluntary Investment on Green/Clean Technologies**

Unlike the general perception that the private sector does not have the interest to invest in new green/clean technologies because of their cost disadvantage as compared to conventional or non-green technologies, some empirical literature shows otherwise. Fama and French (2007) suggest that returns or payoffs are not the only criteria investors use to make decisions on green/clean investments. They argue that investors can buy assets as consumption goods rather than strictly based on returns or payoffs. Many information technology companies (e.g., Google, Facebook, and Twitter) think, during the initial public offerings, that the market for clean technologies will expand in the future even if they are expensive and not profitable for now. Ng and Zheng (2018) provide empirical evidence by comparing the investors’ demand between 99 green energy companies and 93 matching samples of non-green energy Fortune 500 firms. The results from their Capital Asset Pricing Model show that green energy portfolios perform comparably or better than a matching non-green energy portfolio. Contrary to the traditional perception that full filling the social responsibility is costly for firms, they find meeting the environmental objectives not affecting firms’ market performance.

Using sample data of firms from France, Germany, Italy, the Netherlands, and the UK during the 1985-2011 period, Colombelli et al. (2020) show a positive impact of innovation on the market value of firms. They also find that firms operating in sectors with a high propensity for green technologies yield a significant positive effect along with
a country _i_ and _j_ to construct an index of financial proximity (FP), such that: where here _Ci→j_ , _t_ refers to total cross-border claims from country _i_ to country _j_ .

**Foreign Direct Investments.** Data from the UNCTAD’s Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows, outflows, inward stock and outward stock by region and economy.<sup>13</sup> We use the inflows and outflows in order to construct a bilateral financial integration (FI) controls, such that:

**Sectoral composition.** If shocks have a sectoral component, then two countries that tend to specialized over time in the same sectors could have an increase in business cycle comovements over and beyond any trade effects. in order to account for such a mechanism, we create an index of “ _proximity in sectoral composition_ ” based on the World Development Indicators. We use the share in value added of main sectors: service and agricultural sectors and we decompose manufacturing sectors into 7 main sub-sectors.<sup>14</sup> We then compute the following index:

where _k_ refers to a particular sector. Pairs of country with very similar sectoral composition have an index close to 1, while countries that completely specialize in different sectors would have an index of 0.

**Third country index.** We finally attempt to control for third country effect. This is motivated by the fact that two countries trading mostly with similar partners are likely to comove. We

> 13Data are in principle collected from national sources. In order to cover the entire world, where data are not available from national sources, data from partner countries (mirror data) as well as from other international organizations have also been used. Data can be downloaded on the UNCTAD website.

> 14This includes textile, industry, machinery, chemical, high-tech, food and tabacco, other. Data are available here: `https://databank
17More details on the PNDT Act are available in Retherford and Roy (2003) and Visaria (2005).

18Since the state of Sikkim changed its border during the period of analysis, we exclude it from our sample.

19Women who do not survive to the date of interview, for whatever reason, are not in the NFHS data, and nor are their children. Sex-selective abortion in the post-ultrasound period may elevate maternal mortality 7
deciles, which includes the middle class and rich households at almost 3%.<sup>6</sup>

## **4.1. Access Patterns in Latin America: The Situation as of 2012**

> 5 We often take the semantic shortcut of referring to households “in a given quantile” when talking of those below a given threshold (e.g., in quantile 1 for those below the first quantile threshold) or between two successive ones (e.g., in quantile 5 for those between the quantiles 4 and 5 thresholds).

> 6 Household surveys of the type used here typically do not include very rich individuals because of random sampling, non-response, or large under-reporting. This may lead to the figure for the growth of income of the top decile being lower than the actual one, but it is inconsequential for our purpose.

6
Relying on Generator<br>Energy|0.005|0.050|0.027|0.120|0.349|0.235|

Notes: This table compares variables common to both the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone. The 2014 HBS is the last known population estimates of these variables for Yemen. Consistent with reports of humanitarian and news agencies on the ground in Yemen, the WFP estimates report significantly worse welfare statistics and access to basic services relative to the2014 HBS before the conflict.

37
from air pollution.<sup>7</sup> Note that this measure will be inclusive of intra-annual adaptation measures taken to reduce the impact of air pollution on economic activity (e.g. shifting work from high pollution to low pollution days). Crucially, to the extent that air pollution has long-term effects on GDP growth (i.e. if it reduces future growth by reducing human capital formation in school aged children today) we will not detect these effects with our approach.

# **3 Data**

## **3.1 GDP data**

We compile real district GDP using officially published reports by state governments and CEIC (a data platform that compiles data from various official sources). The series is

> 7Note, we are not estimating the effect of pollution on long-run average growth rates, rather our outcome is the year-to-year change in GDP in percentage terms.

7
associated with slightly higher shares in agricultural employment but lower shares in services for districts whose population is close to a main city (on average 8 km away). The relation is opposite for districts from the second quintile, with a population on average 27 km away from a main city. Combined investments are associated with lower agricultural employment share and higher service employment share. The relation between structural change and infrastructure investments seems to be the strongest for the most isolated districts.

# **3.2 Instrumental Variables**

In this section, we use an instrumental variables identification strategy to deal with the potential endogeneity in the placement of the infrastructure. We instrument both roads and electricity, and the interaction terms.<sup>18</sup>

## **3.2.1 Method**

We instrument electrification and access to a paved road. Regarding electrification, the instrumental variable relies on four assumptions. First, electricity generation must be connected to demand, which comes mostly from the main cities. Second, the sources of energy generation that are identified are the main sources of electricity generation. Third, the locations of the supply sources are exogenous to economic geographic development. Finally, the locations between the generation sources and the main demand centers are more likely to be electrified.

We identified two sources of energy generation that can be used for the IV strategy: dams for hydroelectricity, wind farms, gas and geothermal production stations. The main sources of energy supply are hydropower in Cameroon, and gas in Nigeria. Similar to Moneke (2020), we develop an IV which yields a hypothetical electrification status based on a location’s proximity to a straight line corridor from electricity generators to the main cities. First, we identify the locations of the electricity generators using two databases, one on dams opening year and another on power plant locations (Platts database). For Cameroon, we use the geolocalized database including all dams in Africa and their year of opening. For Nigeria, we use the
disproportionately relevant to the country’s exports over the ensuing two decades. Given the importer-industry-year fixed effects, this selection must also be disproportionate for the country compared to other nations. Important tests against this concern include the exclusions of candidate industries in Table S8a and the robustness checks on spatial distributions in Table S7a.

With respect to the technology trends of US cities being endogenous, the robustness checks consider a variety of techniques, including the stringent restrictions around technology trends built upon Anglo-Saxon inventors and the work they cite. Another test utilizes the ethnic patenting growth in the United States to formulate an additional control against reverse causality. This control is calculated as the patenting growth within each industry in the United States by members of the focal ethnic community. Thus, estimations consider the technology transfer instrument Mit<sup>koutlinedinthemaintextanditsimpactforproductivitygrowthandtrade</sup> after controlling for the direct growth of patenting by industry of ethnic communities. The control is calculated across the ethnic groups discussed for ethnic patenting data, with the same control applied to countries within an ethnicity (e.g., the growth in US patenting by ethnic Hispanic inventors for computers is used as a control with both Mexico’s and Chile’s computer productivity and exports). As some countries do not map to an identifiable ethnic group with the name matching approach, the sample is reduced to 73,545 observations. For this sample, the weighted and unweighted instrumented elasticities before the ethnic patenting control is introduced are 1.32 (0.54) and 2.21 (0.92), respectively. With the control, these instrumented elasticities are very similar at 1.40 (0.64) and 2.32 (1.37), respectively. These too suggest the technology transfer is happening over and above direct immigrant contributions. Earlier versions of this paper consider direct ethnic patenting and exports more extensively. The early work also evaluates the role of sector reallocation from agriculture, the empirical contrast of the technology states in the exporter and the importer,
17 expect, adjust the efficiency scores to take into account the effects of the exogenous constraints. This ex post regression approach has in any case been criticized on the grounds that it makes no allowance for the way the scores are generated (cf. e.g. Burgess 2006).

# **Methods**

A more fundamental objection to the post-hoc analysis of the influence of exogenous constraints on efficiency scores is that such constraints ought to be allowed for _during_ the computation of the efficiency scores. In our hybrid approach we do this by allowing different groups of units (defined in terms of constraints) to have different frontiers.<sup>15</sup> This allows for considerable flexibility—much more than would be obtained than by, for example, including a constraint variable in a stochastic frontier. Our approach allows the location and shape of the frontier to vary across the groups. For each group, having generated the frontier, we compute an inefficiency score relative to the relevant frontier.

# **Empirical example**

We illustrate the idea of allowing for exogenous constraints using the California schools data. We stratify school districts by poverty, distinguishing between districts in the bottom half and top half of the poverty headcount distribution. The rationale is that pupils from more affluent families may face more favorable home environments than pupils from poor families: the time available for homework may be greater; the degree of parental input and oversight may be greater; the pupils may come to school better nourished; and so on. School inputs may be comparable, but home inputs that are beyond the control of the school may be smaller in schools in poorer catchment areas.

The school district estimates of school age students (aged 5 to 17) living in households under poverty were sourced from the U.S. Census Bureau‟s Small Area Income and Poverty Estimates (SAIPE).<sup>16</sup> According to the U.S. Census Bureau‟s website, the estimates were computed using
Germany, France, and the UK. The results remain essentially unchanged compared to baseline. An additional concern be that the results are driven the mass of may by elections over the past decade, and in particular following the Global Financial Crisis (GFC). In Column 7, we drop all elections after 2007 and test our main specification again. Even though this reduces our sample, the results remain robust and highly significant. The main findings are not driven by excessive volatility in international oil prices over the last decade. Nevertheless, we also find a significant negative relationship between oil price shocks and incumbency in the years since the GFC (available on request).

The main result is also robust and quantitatively similar to using the lagged change of the import commodity index, consisting of 45 import commodity prices (Column 8). A 1% increase in import commodity index in the previous year is associated with about 11 percentage point increase in the likelihood that the incumbent party will lose the election.

Finally, in Column 9 we show that a crude oil shock is not only more likely to cause electoral turnover, but it may also cause a reversal in political leaning. In particular, we use an indicator for different types of ideological transitions. A transition is defined as any instance where the incumbent and election winners have a different ideology (i.e. left-wing versus right-wing). The result in Column 9 shows that oil shocks are associated with a reversal in political orientation. A 1% increase in oil price shock leads to a six percentage points increase in the likelihood that the winning party belongs to the other end of the political spectrum. In other words, following an oil price increase, a left-leaning incumbent party is more likely to be replaced by a right-leaning party and vice versa. It is as if voters punish the incumbent party and would like a wholesale change in political orientations.

Oil price shocks reduce consumption growth, suggesting one potential mechanism through which oil shocks affect voting behavior. Columns (1)-