rafmacalaba/datause-encoder-data huggingface dataset:
lyzed using United States data (Antr`as, Fort and Tintelnot, 2017; Goldberg and Reed, 2023). To the extent that import monopolization raises costs of input procurement in global value chains, it may impede countries’ efforts to diversify exports away from natural resources.

We provide evidence suggesting trade policy is a channel for import monopolization in natural resource-dependent economies. Figure 1b shows the commodity export share is positively associated with higher import tariffs. We also find the commodity export share is associated with greater use of non-tariff measures (NTMs) such as quotas, that limit entry into imports.

Trade protection increases import market concentration through direct and indirect mechanisms. The direct mechanism is that trade protection, ceteris paribus, both reduces demand and raises the cost of importing, which could lead firms to exit and increase concentration. Our evidence is consistent with this mechanism: imported product markets with higher tariffs are more concentrated, conditional on country-year and product-year fixed effects. The country-year fixed effects rule out this evidence being driven by differences in national market size or institutions, and the product-year fixed effects rule out this evidence being driven by differences in the global product market, like technology or the product’s share in the import basket of a typical developing country.<sup>3</sup> The indirect mechanism is tariff avoidance. Certain importers may be able to avoid paying tariffs, either by securing legal exemptions or by illegal tariff evasion. They thus achieve an asymmetric cost advantage that allows them to sell imports more cheaply than firms that cannot avoid tariffs, expanding their market share and increasing market concentration. The seminal literature on rent-seeking suggested as much, arguing tariff avoidance could explain the persistence of high tariffs, as elites that enjoy the advantage of avoidance are a constituency in favor of tariffs remaining high (Tullock, 1967; Krueger, 1974).

> 3Our data, which begin in 1997, succeed major tariff reforms in most countries, so we are unable to exploit within country-product variation in trade policy that would allow inclusion of country-product fixed effects in this analysis.

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# **Table 10: Fertility and Risky Behaviors, Among Girls Aged 12-17 at Baseline**

## **OLS estimates, standard errors in parentheses, p-values in braces**

||**Condom use:**<br>**Often/Always**|**Other**<br>**Contraceptive**|**Frequency**<br>**of Sex**|**Frequency of**<br>**Unprotected Sex**|**Unwanted**<br>**Sex**|**Transactional**<br>**Sex**|**Pregnancy**|**Out-of-Wedlock**<br>**Pregnancy**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Disruption Effect**|-.003|-.120*|.868|1.99***|.011|.023|.107***|.072*|
||(.015)|(.070)|(.678)|(.734)|(.037)|(.027)|(.036)|(.039)|
|**Treatment Effect in High Disruption**|.013|.102|-1.01*|-1.13*|-.003|-.024|-.043|-.075**|
||(.016)|(.083)|(.560)|(.738)|(.035)|(.028)|(.039)|(.038)|
|**Treatment Effect in Low Disruption**|-.005|-.010|.320|.377|-.004|.013|.006|-.010|
||(.011)|(.039)|(.520)|(.399)|(.020)|(.012)|(.020)|(.017)|
|**Difference [β3, p-value]**|{.333}|{.221}|{.082}|{.066}|{.976}|{.237}|{.258}|{.122}|
|**Control Mean at BL**|.062|.404|5.25|3.41|.067|.030|.115|.091|
|**Observations**|1,456|1,456|1,390|1,046|1,869|1,869|2,382|2,382|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. The Other Contraceptive category includes all methods other than condoms. Frequency of sex is measured over a 30 day period. Frequency of unprotected sex is defined as intercourse frequency for respondents that do not use any form of contraceptive, and equal to zero for those that do (to provide conservative estimates, respondents that ever use condoms are assumed to engage in protected sex). Unwanted Sex is defined as, "Been involved in any sexual intercourse that you were not willing to do". Transactional Sex is defined as, "Receiving anything such as money, gifts, help with school work or something else in exchange for sexual intercourse". Columns 1 through 4 do not control for baseline outcome values, while all other specifications do. Columns 7 and 8 control for any pregnancy at baseline (in or out-of-wedlock). The main results are estimated with an ANCOVA specification. Control variables for all specifications include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
the intersection between conflict and gender dynamics, there is scant research on non‐combatant adolescent girls. This study contributes to this literature by offering one of the first efforts to empirically quantify the impact of violence and conflict on educational attainment, labor market behavior, and social empowerment for non‐combatant adolescent girls.<sup>2</sup> This study utilizes survey data emerging from a World Bank‐administered pilot project in South Sudan to contribute to the existing conflict and gender literature on several fronts. First and foremost, it uses a cluster‐level difference‐in‐difference analysis to identify the impact of the conflict in South Sudan on girls aged 15‐24. Given the high levels of mobility in South Sudan, these surveys are repeated cross‐ sections. Second, the study contributes new knowledge on the impact of conflict on welfare, poverty, and aspirations by offering one of the first analyses of data on adolescent girls, a generally under‐ researched demographic. Finally, this research contributes to a growing body of evidence examining the impacts of earlier‐life environment on later life outcomes, and is closely related to a large body of literature on subjective perceptions of well‐being linked to significant and potentially traumatic life events.

The analysis builds on two rounds of survey data that were collected for an impact evaluation of an adolescent girls program. The first round of data was collected between August and October 2010 and the second round of data was collected between January and February 2015. The two surveys measure the same indicators except that the endline survey has an additional module on conflict exposure. We use the data from the conflict exposure module to obtain self‐reported measures of cluster‐level exposure to the conflict and examine the impact of conflict‐related victimization on adolescent girls. For robustness, we also use external data on conflict events to examine the impact of the conflict exogenously. This analysis tests the hypothesis that girls exposed to the conflict had statistically different welfare outcomes than girls who were not exposed to the conflict.

The remainder of the paper is organized according to the following sections: Section 2 details literature on micro‐level conflict studies and the impact of conflict on women; Section 3 provides a description of the data, followed by a description of the conflict data and how conflict treatment

> 2 In this conflict, adolescent girls and young women did not constitute a significant number of participating combatants.

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# 2.1. Labor Market **ParticiRation**

What influences women's participation in the labor market? Do women differ from men in responding to labor market opportunities? Does humar, capital (for instance, education) help women more than men to participate in the wage sector? Do women face different constraints? Identifying these factors will help policymakers promote the rarticipation of women in the labor market.

The decision to join the labor market, given **the** constraints, is based on an individual's income-leisure trade-off. A household model framework can help identify the constraints that affect an individual's allocation of time (Becker 1965). This model identifies those individual characteristics, such as education and experience, household characteristics, including landholding and unearned income, and market conditions, such as wa-es, that influencze an indi.'idual's allocation of time. Thus the time allocated to different activities, including leisure, can be drawn as function of individual, household, and market characteristics. The tine allocation data can produce a discrete choice structure of whether to participate in the wage market. The decision can be estimated using a orobability functioin independently for males and females as follows: where: Y3(j-m,f) are binary dependent variables with 1 if jth individual participates in the wage labor market and 0 otherwise; X is a vector of individual characteristics that influences an individual's time allocation; Z is a vector of household and market factors which also explains why an individual participates in the labor market; r is the vector of coefficients
information from OpenStreetMaps (OSM 2021). We overlay the digital subway maps on our 25 km grid and compute the total length of subway lines in each grid cell and year. Both subway age and line length measures are highly right-skewed, so we apply the inverse hyperbolic sine transformation prior to estimation.<sup>6</sup>

# **2.3 Accounting for Wind Displacement of CO2 Emissions**

Space-based observations detect higher CO2 concentrations over emissions sources because atmospheric diffusion is not instantaneous. As the prevailing winds displace emissions from their sources, deviations from background concentrations persist for some time. City-level or plant-level estimates have commonly employed measures of wind direction to model these effects (Nassar et al. 2017; Wu et al. 2020; Ye et al. 2020). We replicate this exercise at global scale, using ERA5 monthly wind direction data for all grid cells in the database (Hersbach et al. 2019). For emissions from each grid cell, we determine the wind-directed path across neighboring cells. We compute monthly wind bearings at 0.25<sup>◦</sup> resolution from 10-m u and v components and then resample to our 25 km grid.<sup>7</sup> Wind paths are calculated in sequence. For each origin cell (A) in the sequence, the destination cell (B) is determined by the wind bearing in cell A. Using each grid cell as a source, we determine the sequential path across nearby cells through 20 iterations.

Theory provides no guidance on local atmospheric persistence as wind displacement proceeds, so we address the issue empirically. In preliminary regression experiments, we perform a grid search across two variables. The first is the duration decay function, modeled as the inverse of the iteration sequence number raised to a power that varies in increments of 0.1 between 0 and 2.0. The second is the number of iterations, which varies from 1 to 20. Our grid search yields best fits for decay and iteration parameters of 1.0 and 10, respectively.

Using these parameters, we incorporate wind displacement effects as follows. For each year and month, we use our industrial and fire emissions data to compute total CO2 emissions separately for industry and fires in each grid cell. We route these emissions across nearby cells through 10 sequential iterations, identifying the destination cells by iteration. Once this process is complete for all cells in the grid, we proceed cell-by-cell. For each cell, we add across observations for displaced CO2 from every other cell, with separate totals by iteration step. We weight these totals for industry and fire CO2 by the inverse iteration step number (which incorporates the decay function). Next, we add across the weighted totals to obtain the overall decay-weighted totals for wind-displaced CO2 in each cell. These are the variables _DI_ and _DF_ in the econometric model (equation [1]).

> 6 We use the IHST rather than the logarithmic transformation because most of the observations in the data set are zeros (see Burbidge, Magee and Robb (1988) and Layton (2001)).

> 7 The bearing calculation formula can be viewed at https://www.movable-type.co.uk/scripts/latlong.html.

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as OECD countries have often utilized nontariff barriers (NTBs) against more competitive imports. 2/ On the positive side, industrial country governments have provided _substantial incentives for the development of high technology industries (like aerospace) where a comparative advantage can be maintained or increased.

As might be expected from the importance of such issues, the policy debate has stimulated considerable empirical research on the concept of comparative advantage. A major innovation was Balassa's (1965) development of the "revealed" comparative advantage (RCA) model and subsequent extension of the approach (Balassa (1978)) to account for a "stages" approach to industrialization. 3/ The "revealed" comparative advantage model was employed in studies by UNIDO (1985) (1987) that attempted to identify industries experiencing major shifts in established patterns of production and trade. An alternative empirical approach for measurement of comparative advantage was tested by the United States National Bureau of Economic Research (NBER) that employed relative shares of labor and capital used in the production of

- .2/ Documentary evidence on the increased use of nontariff barriers in sectors with long-term structural adjustment problems can be found in Yeats (1979) or Laird and Yeats (1988). The latter study examined trends in nontariff barrier use in major OECD countries over the 1966-1986 period and concluded that the value of trade affected by NTBs such as quantitative restrictions, variable import levies and other import charges (excluding tariffs) import licensing and different product standards or regulations more than doubled over this interval (from 25 per cent in 1966 to about 58 per cent in 1986). The increase was even mo•e dramatic in sectors like textiles, clothing and steel.

- 3/ Country i's "revealed" comparative advantage in product j is defined as the ratio of the share of product j in i's exports to the share of j in world trade. If this index takes a value greater than unity the country is considered to have a "revealed" comparative advantage in the product while a value below unity indicates a comparative disadvantage. See Balassa (1965) or UNIDO (1985) for a discussion of the basic properties of the RCA model.
areas may lack the institutional frameworks necessary for coordinated conservation activities, leading to unchecked exploitation of natural resources and increased threats to biodiversity. In some cases, these regions may be rich in biodiversity, but their legal ambiguity complicates the establishment of conservation policies and enforcement mechanisms.

Fragile and Conflict-Affected Situations (FCSs) are also included due to the significant impact that political instability, civil unrest, and war can have on environmental protection. Biodiversity conservation in FCSs is particularly challenging, as the breakdown of governance structures often leads to deforestation, poaching, illegal mining, and other forms of environmental degradation. Moreover, these regions frequently lack the resources to prioritize environmental protection amid immediate humanitarian and security concerns, further exacerbating threats to biodiversity.

Additional elements for our analysis are provided by terrestrial and marine water bodies that cross national boundaries. Their ecosystems are critical for biodiversity, but are often excluded from shared arrangements for water resource management. Even in cases where ecosystems are included, their protection depends upon monitoring and enforcement provisions that require sustained support.

Our terrestrial analysis focuses on transboundary river basins, which are particularly vulnerable to unsustainable water management practices, pollution, and habitat loss. Managing transboundary river basins requires international cooperation to ensure the long-term health of their ecosystems. Conflicting national interests, such as competing demands for water, can complicate conservation efforts, making these regions particularly important to study.

Our marine analysis focuses on transboundary overlaps in Exclusive Economic Zones (EEZs), which are formally managed by marine joint regimes with specific arrangements for the exploration and exploitation of natural marine resources (Flanders Marine Institute, 2019 and sourced from the United Nations' <u>DOALOS repository). Like transboundary river basin</u> arrangements, these regimes can be inconsistent in their attention to biodiversity management, particularly for migratory marine species. Cooperative governance in these joint regime areas is critical for ensuring that marine resources are used sustainably while safeguarding the biodiversity that exists in shared marine ecosystems.

By overlaying these critical geopolitical boundaries onto our species occurrence maps, we aim to identify areas where conservation efforts may be more difficult due to political, legal, or social challenges. These areas are often at the intersection of biodiversity hotspots and governance weaknesses, making them crucial for targeted conservation actions and policy interventions.

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In the Bank's latest governance and anticorruption strategy, this definition has persisted almost unchanged, with governance defined as: _"...the manner in which public officials and institutions acquire and exercise the authority to shape public policy and provide public goods and services"._<sup>_6_</sup> In our own work on aggregate governance indicators that we discuss further below, we defined governance drawing on existing definitions as: _"...the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them."_<sup>_7_</sup> While the many existing definitions of governance cover a broad range of issues, one should not conclude that there is a total lack of definitional consensus in this area. Most definitions of governance agree on the importance of a capable state operating under the rule of law. Interestingly, comparing the last three definitions provided above, the one substantive difference has to do with the explicit degree of emphasis on the role of democratic accountability of governments to their citizens. And even these narrower definitions remain sufficiently broad that there is scope for a wide diversity of empirical measures of various dimensions of good governance.

The gravity of the issues dealt with in these various definitions of governance suggests that measurement in this area is important. While less so nowadays, in recent years there has however been considerable debate as to whether such broad notions of governance can in fact be usefully measured. Here we make a simple and fairly uncontroversial observation: there are many possible indicators that can shed light on various dimensions of governance. However, given the breadth of the concepts, and in many cases their inherent unobservability, no one indicator, or combination of indicators, can provide a completely reliable measure of any of these dimensions of governance. Rather, it is useful to think of the various specific indicators that we discuss below as all

> 5 World Bank (1992)

> 6 World Bank (2007), p. i, para. 3.

> 7 Kaufmann, Kraay, and Zoido-Lobatón (1999), p.1.

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over-sample large establishments. Sample sizes for enterprise surveys range from 250-1,500 firms by country.

The Enterprise Survey data provide information about innovation in developed and developing countries. It allows us to study the effect of knowledge spillovers in countries that have the most to gain from the knowledge of others. The other advantage is that it gives us the possibility to examine the existence of international knowledge spillovers on a wider base. This is in stark contrast with previous works, which explore the issue using two-country studies. In addition, the surveys collect information on more than one type of innovation, which allows us to compare the economic impact of spillovers across different dimensions of product innovation. The resulting data set is a cross-section of 17,271 firms, from 72 countries from 2003-2006.

Tables 5 and 6 show the share of firms engaged in quality and variety innovations across regions and industries. A large proportion of firms report quality and variety innovations. However, there is substantial heterogeneity across regions. The Sub-Saharan Africa and Latin America and Caribbean regions have the largest propensities to introduce new products, 52% and 56%, respectively. While the Middle East and North Africa zone has the lowest one, 16%. Europe and Central Asia and the Pacific areas have the highest share of firms innovating to improve the quality of existing products, 60%. While the Latin America and Caribbean region registers the lowest one, 33%.

Industries such as Textiles, Beverage, Auto and auto components, and Other transports show propensities to up-grade the quality of existing products that exceed 66%. While the Food and Other manufactures industries display the lowest shares, 38% and 17%, respectively. There is not much variation in the percentage of firms that perform variety-innovations across sectors. The lowest percentage is registered for the Paper industry, 30%, while the highest share is reported for the Food sector. Finally, Table 7 presents summary statistics of the main variables.

# **6 Results**

As mentioned, we report results on patents and product innovation separately. The following subsection focuses on patents at the country level, followed by the discussion of the results on product innovation by firms.

## **6.1 Patents**

Tables 8 and 9 present Negative Binomial estimates of the patent function with country fixed effects as in Allison and Waterman (2002), which is a superior estimator than the FE estimator proposed by Hall et al. (1984).<sup>2</sup> In both tables, the specification test for over-dispersion indicates that we can

> 2In Hall et al. (1984), fixed effects are included in the form of the over-time average number of patent counts (by country). However, this approach does not control for the indirect effect of country-specific effects through the over-dispersion parameter. See Allison and Waterman (2002) for details.

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