rafmacalaba/datause-encoder-data huggingface dataset:
approach of Guriev et al. (2021) and focus on districts that experienced sharp increases in mobile coverage. Specifically, I consider the case where mobile coverage increased by more than 50 percentage points in a year. In principle, districts can only experience such an event only once, assuming that coverage doesn’t fall substantially.<sup>18</sup> About 15,303 districts in 99 countries in the sample experienced such a sharp increase in mobile coverage at some point during the study period. Focusing on this sample, I estimate an event study using the De Chaisemartin and D’Haultfoeuille (2020b) estimator that is robust to heterogeneous treatment.<sup>19</sup> Results are plotted in Figure 4. Still, the results do not show the absence of pre-trends, as the growth in nightlight intensity in treated districts appears to be decreasing from periods 5 to 3 prior to the treatment. The post-treatment estimates, however, confirm the findings in Table 1 of a positive association between mobile connectivity and economic activities. Overall, the results from the TWFE estimator show a strong association between mobile connectivity and local economic growth. These effects are statistically and economically significant. In spite of this, the estimates are unlikely to be causal due to the plausibly endogenous rollout of the mobile network expansion: telecom companies are more likely to deploy network coverage to areas with high economic activity and potentially high demand relative to places with low potential demand. The absence of parallel trends as shown in the event study analysis confirms this assertion.

# **3.2 IV Regression**

To address the issue of causality, I use an IV strategy that exploits plausibly exogenous variations in the diffusion of mobile network coverage in other countries in the same region induced by the harmonization of telecom policies within the region as an instrument. This instrument follows the approach of Acemoglu et al. (2019) and Acemoglu et al. (2021) who exploit regional waves in democratization as instrument for democracy in estimating the effect of democracy on economic growth and citizens’ support for democratic institutions, respectively.

Two main factors motivate this instrument. The first relates to the regionalization of telecom policies. In many regions around the world, regional economic blocs (unions) have an association of national telecom regulators or regional telecom regulators that set guidelines on telecom policies in member countries. A good example is the European Union, where the European Commission through its Body of European Regulators for Electronic Communications

> 18Decline in coverage can occur when the rate of population increases at a faster rate than coverage.

> 19Notice that here, a district is considered treated in year _t_ if the coverage rate increased by more the 50% and 0 otherwise. Hence, the De Chaisemartin and D’Haultfoeuille (2020b) estimator that is robust to heterogeneous treatment effect with a binary treatment variable is used.

11
variance matrix of the data moments.<sup>8</sup> We compute this variance matrix by bootstrapping with 1000 repetitions.

We estimate the model parameters using data from 2006 to 2012 for the set of moments for which the model has predictions on, and that are observed in the data.<sup>9</sup> The first set of moments that we match consists of the rates of successful hijacks over time, determined in the model by equation (1). Even though the identification of the structural parameters is jointly determined by the entire set of targeted moments, the success rates in the model over time are a direct function of the parameters governing the probability of success given by equation (1).

The next set of moments used in the estimation corresponds to the bi-annual average number of pirate attacks and the annual average size of ships attacked, normalized to its 2006 mean. In the model, these moments are obtained from the nested problem that pirates solve in order to decide whether to enter and if so, the size of the vessel to target. The number of pirate attacks at every year is determined by the mass of pirates with ability above the cutoff _<u>a</u>_ <u>(</u> _δi, n_<sup>_t−_1</sup> _, ν_<sup>_t_</sup> _, κ_<sup>_t_</sup> ). Therefore, matching the number of attacks over time allows to discipline the parameters ruling the measure of the set of entrants and the entry cost function outlined in equation (6). As the Euler Equation shows, the decision on vessel size is influenced by the parameters that define the learning curve of pirates: even though it is costlier to attack larger ships, by doing so pirates can raise their ability in subsequent years. Hence, the normalized annual averages of size of ships attacked can help pin down the parameters governing the learning law of motion in equation (5).

We also match the annual averages of the size of ships successfully hijacked (normalized to the average size of ships attacked in 2006), which are determined by the prob-

> 8Altonji and Segal (1996) show that using the optimal weighting matrix introduces significant small sample bias. We follow Blundell, Pistaferri and Preston (2008), and use a Diagonally Weighted Minimum Distance approach for our weighting matrix. Under this approach, W is set to the diagonal elements of the optimal weighting matrix, while the terms outside the main diagonal are set to zero.

> 9The choice to start our estimation in 2006 despite having data going back to 2005 is motivated by the discussion of the political context in Somalia during this period discussed in Section 2: pirates faced disruptions from ICU operations on land until 2006. Ignoring these would then affect the identification of the model’s structural parameters. From 2006 onwards, as emphasized earlier, no such interferences took place as the ICU was removed from power and the transitional government did not have any power outside of the capital. Piracy was concentrated in the warlord-dominated Central and Northern part of the country, which enjoyed relative stability.

21
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59
# Table of Contents

|PM|
|---|

|I.|Introduction....................................|1|
|---|---|---|
|II.|Existing and Potential Arrangements ........................................|5|
|m.|The Relative Importance of Minilateral Arrangements .............................|7|
|**IV.**|The Managed Trade Alternative ..........................................|14|
|V.|Final Comments .................................................|20|
|Appe|ndix<br>Statistics on International Trade in High Technology ........................<br>Products|26|
# **1. Introduction**

Two phenomena have characterized the trade and trade policy landscape since the early 1990s. On the trade side, the rise of Global Value Chains (GVCs), i.e., the de-nationalization of production, has changed the nature of international trade. For instance, trade in parts and components increased by close to six times between 1990 and 2015 (more than other forms of trade, which increased by a factor of 4.5). On the policy side, Preferential Trade Agreements (PTAs) are increasing in number and deepening in content.<sup>3</sup> The number of PTAs surged from 50 in 1990 to close to 280 in 2015. New data collected by the World Bank document that these agreements are also “deepening” in the sense that they cover an expanding set of policy areas well beyond the traditional focus of PTAs, such as investment and competition policy.

The aim of this paper is to analyze the relationship between preferential trade agreements, particularly “deep” PTAs, and global value chains. Specifically, the goal is to answer seven policyrelevant questions:

- i) How have PTAs evolved over time?

- ii) Why do countries sign PTAs in a world with global value chains?

- iii) Do PTAs increase GVC integration?

- iv) Would the undoing of PTAs affect GVCs?

- v) How does the content of PTAs impact GVC trade?

- vi) How do GVCs affect the choice of PTA partners? vii) What is the outlook of the GVC-PTA relationship going forward?

This paper contributes to the large literature on preferential trade agreements (e.g. Limao, 2016) in different ways. First, based on new World Bank data, I document how PTAs have deepened over time and how this evolution is associated to the rise of GVCs. Second, I review the theoretical literature on the rationale for the GVC-PTA relationship and outline several avenues for future research. Third, I discuss recent empirical research suggesting that deep agreements boost GVC integration, and showing how this impact differs across country groups. Finally, I present a simple framework to help us think about the GVC-PTA relationship going forward.

While more work in this area is needed, several findings emerge from this review. New data on the content of trade agreements and on participation in global value chains point to a strong positive correlation, with deeper agreements associated to more intense GVC relationships. Economic theory identifies several reasons for this relationship, ranging from the need to internalize cross-border policy spillovers to the benefits of stronger commitments in policies that affect GVC participation. Econometric analysis confirms that deep PTAs boost participation in global value chains, suggesting that trade agreements can be an effective tool for policy makers to anchor national producers to global and regional production processes. Going forward, the

> 3 In this paper, the term “preferential trade agreements” will be used rather than the term “regional trade agreements (RTAs)” since some of these agreements are not necessarily between countries within the same region or in regional proximity.

2
the preparation or support necessary to complete the projects on time. Some determinants may improve one outcome but hurt others. For example, programs with a higher proportion of faculty working in industry have lower ETG but higher dropout rates. Such instructors may design engaging curricula (lowering ETG) but deviate students away from the program and into industry (raising dropout rates).

# **_Labor Market Outcomes_**

**Formal employment.** Teaching numerical competencies is associated with a 15-pp increase in formal employment, relative to an average formal employment of 59 percent (Figure 4 panel a; Table A12 column 1). While intrinsically valuable, these competencies may also capture related skills such as logical reasoning and critical thinking. Further, programs that provide remediation during the program also have higher formal employment—by providing a context for the remediation, they may be more effective at raising employability-related skills. Some LPS practices are associated with higher (4-9 pp) formal employment, such as running an employment center (9 pp), assigning staff to collect graduates’ employment data, and collecting data frequently on graduates’ employment or employers’ satisfaction with graduates. Further, graduates from programs with a higher proportion of experienced faculty or that have enough equipment for practice also have higher formal employment. On the other hand, formal employment is lower in programs with a higher proportion of young faculty, who may have little experience working in industry or teaching these programs.

**Wages.** Higher wages (by 3-7 percent) accrue to graduates from programs with specific T&C practices such as teaching numerical competencies, providing remediation during the program _,_ granting credits for longer degrees, and relying heavily on the HEI’s perception of the labor market for curriculum updates (Figure 4 panel b; Table A12 column 2). Further, graduates from programs

26
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.11  Seats for intraregional travel within ECOWAS, BAG, and WAEMU countries**

<!-- Start of picture text --> a. ECOWAS b. Banjul Accord Group<br>10,000,000 2,500,000<br>8,000,000 2,000,000<br>6,000,000<br>1,500,000<br>4,000,000<br>1,000,000<br>2,000,000<br>500,000<br>0<br>0<br>c. West African Economic and Monetary Union<br>5,000,000<br>4,000,000<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br>Source: Derived from Bofinger 2009.<br><!-- End of picture text --> The WAEMU countries have sustained a continuing decline in seat capacity since the demise of their main carrier, Air Afrique. In 2001, the WAEMU market was double the size of the BAG market, with some 4 million seats. Air Afrique was the dominant carrier in the subregion, with a capacity of nearly 5 million seats. Following its collapse in 2001 there was a major vacuum in regional air transport capacity, and traffic never recovered, having declined to 3 million seats by 2007 (figure 2.11a).

By contrast, the BAG countries adjusted relatively rapidly to the collapse of their main carrier, Ghana Airways. As a result of their low per capita income and (in several cases) their recent experience of conflict, these countries tend to have relatively thin air traffic per capita, despite their high population densities. Up until 2001, Ghana Airways and, to a lesser extent, Nigeria Airways were dominant in the subregion, providing at least half of seat capacity. Following the collapse of both airlines, air connectivity dipped by about 20 percent between 2001 and 2004 but subsequently rebounded. By 2007 seat capacity had surpassed 2001 levels (figure 2.11b), driven mainly by the growth of Virgin Nigeria, flying between Nigeria and Ghana. In 2007, Virgin Nigeria had about 50 percent of the market in the regional community.

While countries in ECOWAS have fair international connectivity, domestic connectivity is very poor. More than half the countries in ECOWAS have no domestic connectivity at all (table 2.9). In a few cases, 36
-49 -

outdoor pollutants into the indoors. (The tight homes also may keep indoor pollutants indoors, however). Therefore, the estimated dose-response relationships between ambient air pollution and health, by necessity, incorporate the large proportion of time spent indoors by most people. This aspect will lead to a significant underassessment of dose when applying the existing dose-response functions to warm climates such as those in South and Southeast Asia, Latin America, and parts of South America. An underestimate is likely because residents in these climates will likely spend a greater portion of their time outdoors, both on a daily and an annual basis. In addition, indoor exposures in the homes in many of these countries appears to be quite high. If this is true, it is likely that a given level of air pollution, everything else constant, would generate much greater health effects in poorer, warmer countries.

Other disparities between countries may result in different quantitative impacts from exposure to ambient air. For example, some developing countries may have a greater proportion of individuals with chronic respiratory disease, and will not have the public warnings on high air pollution days that occur in the U.S. This could significantly increase the health impacts of a given level of air pollution. Simi!arly, deficiencies in vitamin C and E, likely to be more comunon in the developing world, reduce the host defense mechanism and may increase the effects of air pollution.

Only after epidemiologic smdies are completed in some of the developing countries, can the uncertainty about the impact of these factors be reduced. In the mean time, the suggested medtodology is to assume either that the demographic and health status profiles in the developing world are similar to those where the original studies were undertaken, or make some arbitrary adjustments to the doseresponse relationships. Of particular importance would be studies relating mortality, hospial visits, asthm and respiratory symptoms to both indoor and outdoor air pollution in Jakarta. A study design that involves the collection of daily counts of mortality or emergency room visits in state-owned facilities for the city as whole, would be appropriate for comparing air pollution effects with those developed from the U.S. This effort would require conscientious data collection and managennent, and quality-assured
MSCI weight during 2016-20.<sup>39</sup> By 2020, China accounted for approximately 40 percent of the MSCI Emerging Markets Index and less than 30 percent of the CPIS emerging market equity portfolios.<sup>40</sup>

# **5. Conclusion**

This paper showed that China’s post-2012 equity market internationalization benefits have been significant and spanned multiple years. At the firm level, those targeted by the internationalization events raised significantly more equity financing, increased their cash holdings, and invested more than other domestic firms. At the aggregate level, the internationalization process was associated with a significant fraction of equity raised and investment activities among all domestic listed firms in China between 2013 and 2020. Most of the rise in equity issuances by connected firms appeared to be primarily supported by domestic investors that increased their investments in those firms. Foreign entry only accelerated after China’s A shares were incorporated into the MSCI Emerging Markets Index in the late 2010s.

The importance of China in emerging market equity portfolios has grown gradually but could increase further. Market makers and authorities have integrated China progressively to minimize the potential for domestic disruptions caused by a surge in portfolio inflows and to avoid sudden large capital outflows from other emerging markets. As long as China’s weight in emerging market equity portfolios lags behind its weight in the MSCI Emerging Markets Index and investors follow the index, a catch-up in foreign investments could be expected. Moreover, China’s internationalization efforts expanded even further in 2023, when it connected more than 1,000

> 39 In addition, we simulate two alternative scenarios: if A shares were included in the index in 2018 with an inclusion factor of 100 percent and 0 percent. As would be expected, China’s actual weight in the index is in between the two counterfactual scenarios, suggesting that it may continue to rise if market makers increase the inclusion ratio.

> 40 Investments in China have surpassed investments in other emerging markets and have increased significantly since 2006. But the pace of growth has notably changed since 2017 (Figure 10, Panel B).

27
<mark>corresponding</mark> _<mark>aimag</mark>_ <mark>of a given zone. These values are simply added to the premium rates that come from step 4.</mark> <mark>Finally, the smoothed premium (equal to the pure premium from step 4 plus the catastrophe load) is weighted by a scale factor, determined at the</mark> _<mark>aimag</mark>_ <mark>level, such that the reinsurance premium rate is roughly identical in all three</mark> _<mark>aimags</mark>_ <mark>(about 38%). This last step aims to prevent participating insurance companies from targeting herders only located in the</mark> _<mark>aimag</mark>_ <mark>with the lowest reinsurance cost.</mark> <mark>Source: Authors.</mark>

**Table 5. BIP risk-loaded premium rates, weighted average by aimag**

||Goat|Horse|Sheep|Cattle|
|---|---|---|---|---|
|Uvs|||||
|Average|1.05%|1.50%|0.83%|1.50%|
|Min|0.62%|0.50%|0.50%|0.68%|
|Max|1.86%|3.22%|1.98%|2.48%|
|Bayankhongor|||||
|Average|2.33%|2.24%|2.59%|2.46%|
|Min|1.64%|1.50%|1.84%|2.07%|
|Max|2.76%|2.88%|2.99%|3.57%|
|Khentii|||||
|Average|1.20%|0.94%|1.07%|1.69%|
|Min|0.85%|0.55%|0.76%|1.20%|
|Max|1.74%|1.96%|1.53%|2.18%|

Source: authors.

The livestock loss exposure of the participating insurance companies is estimated under the following assumptions. Four insurance companies sell the IBLI product in the three pilot _aimags_ and the total livestock insurance penetration rate is assumed to be 10 percent. Three of them are concentrated in each of the three pilot _aimags_ , with a penetration rate of 4 percent in each _soum_ and each species. The fourth insurance company dominates the market and has a diversified portfolio with a penetration rate of 6 percent in the three _aimags_ for all species. This roughly replicates the current insurance market shares in Mongolia.

# _Herders_

Under such assumptions, historical data show that the probability of getting BIP payments in any given year is equal to 94% in Bayankhongor, 85% in Khentii and 70% in Uvs. The probability of receiving BIP payments higher than 10% of the risk-loaded premium is estimated at 61% in Bahnakhongor, 42% in Khentii and 27% in Uvs. Thus, there is a significant chance that some herders will get paid within the first year of implementation; such early payments would contribute to the credibility of the insurance program.

Herders subscribing only the DRP program have a lower probability of receiving any payments, as this program covers only extreme losses. The probability that DRP payments will be made in the pilot _aimags_ in any given year is estimated at 30%.

# _Insurance industry_

Under the assumption of 10% signup rates for BIP, the total premium volume (excluding operating costs) would be around $215,000. Since the reinsurance stop loss is 105 of total premium in the first year of implementation, the LIIP reserve would be about $276,000. The 23