rafmacalaba/datause-encoder-data huggingface dataset:
such ratio were Chile, Israel, Malaysia and Thailand. China figures, on the other hand, when one focuses instead on the growth of the FDI/GDP ratio. The countries with the largest annual increase in FDI outflows relative to GDP in 2011-2015, in declining order, were Hungary, South Africa, Chile and China. The countries with the largest annual increase in 2014-2015 so measured, again in descending order, are Hungary, Chile, Israel, Poland and China.<sup>5</sup>

**Table 2: Trends in the Magnitude and Volatility of Capital Inflows and Outflows**

||||1991-<br>1995|1996-<br>2000|2001-<br>2005|2006-<br>2010|2011-<br>2015|
|---|---|---|---|---|---|---|---|
|||Mean (quarterly average)|0.23|0.76|0.55|0.92|0.69|
|FDI|inflows|Standard deviation|0.15|0.50|0.38|0.59|0.41|
|||Coeff. of variation|0.61|0.71|0.70|0.57|0.56|
|||Mean (quarterly average)|0.01|0.04|0.07|0.29|0.20|
|FDI|outflows|Standard deviation|0.02|0.07|0.14|0.30|0.26|
|||Coeff. of variation|0.93|1.25|1.49|1.11|1.17|
|||Mean (quarterly average)|0.06|0.05|0.03|0.05|0.04|
|Portfolio<br>Equity|inflows|Standard deviation|0.10|0.12|0.09|0.21|0.14|
|||Coeff. of variation|1.35|1.56|2.21|1.99|2.79|
|||Mean (quarterly average)|0.00|0.00|0.01|0.04|0.01|
|Portfolio<br>Equity|outflows|Standard deviation|0.00|0.02|0.04|0.12|0.03|
|||Coeff. of variation|1.91|2.44|2.19|1.80|1.68|
|||Mean (quarterly average)|0.03|0.11|0.10|0.20|0.38|
|Portfolio Debt|inflows|Standard deviation|0.23|0.39|0.40|0.63|0.63|
|||Coeff. of variation|1.52|1.72|1.58|2.64|1.97|
|||Mean (quarterly average)|0.01|0.03|0.04|0.05|0.02|
|Portfolio Debt|outflows|Standard deviation|0.07|0.09|0.14|0.24|0.17|
|||Coeff. of variation|1.95|2.08|1.85|2.38|1.44|
|||Mean (quarterly average)|0.22|0.32|0.20|0.56|0.17|
|Other flows|inflows|Standard deviation|0.97|0.79|0.59|1.09|0.67|
|||Coeff. of variation|1.26|1.41|0.92|1.65|1.30|
|||Mean (quarterly average)|0.10|0.24|0.17|0.31|0.19|
|Other flows|outflows|Standard deviation|0.65|0.66|0.63|1.08|0.66|
|||Coeff. of variation|1.64|1.56|2.11|2.42|2.29|

_Notes:_ Mean, standard deviation and coefficient of variation are the median across all countries in the sample during respective time period. Coefficient of variation is standard deviation divided by mean. Data are quarterly from 1990 Q1 to 2015 Q4. All capital flows are expressed as % of annual trend GDP.

5 Note in addition that China is not included in most of our analysis because data on the composition of capital flows is incomplete.

9
recent years, government wages, when appropriately corrected for worker heterogeneity, were on par with, or higher than, private sector wages, especially for graduates.

> To the extent that globalization erodes labor market rents, the incentive to wait for "privileged" jobs, rather than taking readily available ones, could be reduced. However, this might be a long-term effect. In the short term, there is evidence that unemployment can go up, some times substantially.

> Figure 8 reports the unemployment rates of four successful globalizers during the

> first two decades of their reform process. The countries are Chile, Mauritius, Poland and

> Sri Lanka. Each of them can be considered a model in its own region. Needless to say,

> economic policies were not always ideal in all of them. Chile had a failed experience of exchange rate based stabilization, leading to a massive financial crisis. Mauritius relied on an export-processing zone regime that is not, theoretically, the most efficient way to

> integrate with the rest of the world. Poland and Sri Lanka maintained their protection of bloated and inefficient industries and state-owned enterprises. Still, on average the

> economic management of these countries is probably close to the best that can realistically be expected.

<!-- Start of picture text --> IFgnre 0. [JmeaieoilloymEeam Ites in ccesfTunl 001bosiani r<br>25<br>20-<br>D15 -- CHILE<br>----- MAURITUS<br>E D - o \ t; tD \ . ' __----. POLAND<br>ER 10 - SRI LANKA<br>5 -<br>0<br>IN 4 Cb b<br>Year In the reform process<br><!-- End of picture text --> _Source:_ Constructed data gathered by Rama and Artecona (2002).

All four successful globalizers have in common a long period of high unemployment rates after the launching of the economic reforms. The precise year chosen to draw figure 8 is, admittedly, subject to controversy. For Chile and Poland, the year one in the figure is the end of the socialist regime, even if many important reforms happened subsequently. For Sri Lanka, the year one is 1977, when a large trade liberalization process was launched. For Mauritius, it is 1970, when the export-processing zone regime was introduced. Other dates would be defensible as well. When the initial year of the reforms is modified, it becomes unclear whether globalization actually led to 18
**Figure 4. Differences in Informality, LAC Countries and Chile**

<!-- Start of picture text --> A. Schneider Shadow Economy index B. Heritage Foundation Informal Market index<br>(% of GDP) (Higher,<br>60 6 more informality)<br>M ax HTI M ax<br>50 HTI 5<br>GTM HND HND<br>40 ARGBOLBRACOLCRIDOMECU GUY JAMMEXNICPANPERPRYSLVURYVEN 43 ARGBOLBRACOLCRIDOMECUGTMGUY JAMMEXNICPANPERPRYSLVURYVEN<br>30<br>C HL 2 C HL<br>20<br>1<br>Min<br>10 Min<br>C. Self Employment D. Non-contributor to Pension Scheme<br>(% of total employment) 120 M ax<br>M ax (% of labor force) HTI<br>50 HTI 100<br>GTM HND<br>40 GTMGUYHND PRY 80 BOL GUY NIC PRY<br>30 ARGBOLBRACOLCRIDOMECU JAMMEXNICPANPER SLV VEN 60 ARG BRACOLCRIDOMECU JAMMEX PANPER SLV VEN<br>URY 40 URY<br>20 C HL C HL<br>20<br>10 0<br>Min Min<br><!-- End of picture text --> Note: Presented are all predicted levels, which may be above/below the actual max/min values.

26
terms of observable characteristics. The results of the RAIPW are presented in Table 5.<sup>8</sup> A higher intensity of CBT sessions is not associated with statistically different impacts on socioemotional well-being or math test scores. Effects remain positive, but not statistically significant when we look at the PLANEA score, there is no clear pattern for the BarOn inventory, but all coefficients are not statistically significant.

**Table 4: ATE on Outcome Measures**

||(1)<br>PLANEA|(2)<br>BarOn|(3)<br>Math<br>score|(4)<br>Enrolled in<br>11th grade|(5)<br>Re-take<br>COMIPEMS|(6)<br>Student<br>Engagement|
|---|---|---|---|---|---|---|
|0 sessions|0.097<br>(0.077)|0.001<br>(0.065)|0.029<br>(0.139)|-0.090***<br>(0.016)|0.025**<br>(0.010)|-0.065***<br>(0.016)|
|1 to 4 sessions|0.143|-0.106|-0.061|0.074***|-0.038***|0.026|
||(0.088)|(0.073)|(0.154)|(0.025)|(0.014)|(0.024)|
|5 or more<br>sessions|0.148|0.062|0.001|0.190***|-0.068***|0.121***|
||(0.093)|(0.080)|(0.174)|(0.028)|(0.014)|(0.027)|
|Mean Dep.|||||||
|Control Group|0.008|0.023|5.648|0.522|0.099|0.604|
|Observations|1119|1282|1261|4632|4633|4633|

Notes: columns 1 to 4 show the ATE of PODER on two measures of socio-emotional skills (PLANEA and BarOn), the probability of being enrolled in 11<sup>th</sup> grade, the probability of re-taking COMIPEMS, respectively. The omitted category in this specification is represented by students who were assigned to the control group.

In the control group, we observe very little correlation between the measures of socioemotional well-being and the results on the math assessment. Therefore, any significant effect of CBT-based sessions on the math results would be concerning for the validity of our identification strategy. Reassuringly, both for students who attended 1 to 4 sessions and those who attended 5 or more, we find a null effect on the math score (column 3). Individuals, originally assigned to the treatment group, who attended 0 sessions displayed a 9-percentage point reduction in the probability of enrolling in 11<sup>th</sup> grade. Some of these individuals had quit school in order to re-enter the COMIPEMS assignment, as we observe that those who had 0 sessions in either the 10 or 20 session treatment group were 2.5 percentage points more likely to try being re-assigned to a different academic option through the COMIPEMS system. Individuals who had attended between 1 and 4 counselling sessions displayed a 7.4 percentage point increase in the probability of enrolling in 11<sup>th</sup> grade, while among those who had attended at least 5 sessions there is a 19 percentage points increase in the probability of enrolling (column 4). Conversely, we observe a reduction in the probability of re-entering the COMIPEMS assignment system: there is a 3.8 percentage points reduction among those who had attended between 1 and 4 sessions, and a 6.8 percentage points reduction among those with 5 or more sessions (column 5). When we consider the net effect, as

> 8 Alternative matching strategies produce similar coefficients. Results are available upon request.

19
important overview of education in colonial Africa. He points out that in colonial Africa, as in Europe centuries earlier, popular or mass education was the purview of Christian churches. He charts broad directions in education for British, French and Belgian colonies, noting the long-term influence missionaries had on education in British colonies, and Roman Catholic supervision of schools in Belgian Congo as an arm of the state. The French system, especially in French West Africa, was more independent of mission control, and was characterized in Hailey‘s opinion ―by a greater precision of objective and unity of method.‖ Differences between French and British educational systems reflected different ideologies of rule, and the French acknowledgement that the African colonies were an integral part of France (Hailey (1957: 1135)).

The British deferred responsibility in education to the missionaries, and absorbed the products of missionary schools in the lower echelons of the colonial bureaucracy. This left room for missionary assertion, and when the British set up an Advisory Committee on Education in the Colonies in 1924, it was even in response to missionary prompting that the colonial office should take more interest in African education (Hailey (1957: 1166)). This was the context in which a rather minor American organization, the Phelps Stokes Fund, came to wield enormous influence on educational policy in British Africa. The Phelps Stokes Fund commissioned two surveys of African education, the first in 1920 and the second in 1923, building off its growing reputation in American Negro Education. Berman points out how the Fund succeeded in ―perpetuating the belief that the educational methods which had been hammered out for the freed slaves in the post-bellum period were particularly relevant for black Africans in the 20<sup>th</sup> century‖ (Berman (1971: 132)). The Fund emphasized the utility of industrial and agricultural aspects of education in America versus ―academic‖ schools, and suggested a similar approach for Africa. The British Colonial Office needed a policy on education in Africa but had little relevant experience to draw on, and so turned to the Fund and its apostolic educational director Thomas Jesse Jones. Jones would author two reports on Africa for the Colonial Office, his commitment to agricultural and technical training providing the British with a pedagogy, ―but also with a creed which would complement Whitehall‘s political philosophy of Indirect Rule‖ (Berman (1971: 134-5)). Jones brought the model of the Hampton and Tuskegee schools to Africa, and pioneered the establishment of Jeanes schools.

But American influence manifested itself in another form, primarily in South Africa, where the American Board of Foreign Missions had been active since the 1850s. Through its 26
25 to point out what are likely to be some of the more important concerns, in the present context. A key question has always been how well the methodology approximates the distribution of the value of the benefits. In the health sector, "need" as mt3asured by reported illness, is often juxtaposed with treatments received to serve as the underlying yardstick against whiuh to judge equity of access and intrinsic value of benefits. Yet, basing medical need on whether the household reports a member being sick in the prior week seems to be rather unsatisfactory. This tells us nothing about the severity of the illness. It might not be unreasonable to assume that poor households tend to ignore illnesses (out of necessity) more than rich ones. Chernikovsky and Meesook (1986) also speculate that access to health services influences the reporting of illness in that the likelihood of being treated encourages recognition of a poor health condition. In either case we have a biased assessment of the degree of need which, in turn, impairs our ability to assess how equitable the distribution of health expenditures is. The probable direction of the bias in recall will be to underestimate the need of the poor. In some cases there may be other indicators we can use. For example, Chernikovsky and Meesook look specifically at pregnant women. Within this relatively homogeneous subgroup, they look at the determinants of whether care was received and wh&t kind. Unfortunately,-the 1987 SUSENAS does not contain detailed data on pregnancies.

Another weakness of the methodology relates to the fact that all facilities dispensing a certain type of service (e.g. primary schools) are treated identically (e.g. urban and rural primary schools). Yet, by all accounts, differential service quality is an important characteristic of the provision of health and education services in Indonesia. This is relevant in allocating government subsidies in that the per unit cost of a low quality service will generally not equal that of a high quality one. (It may in fact be higher if, for example, low quality is the result of the costs of reaching the area in which the service is located. But, this may be the result of low public expenditures in the past and introduces the problems of what time
"improper practices") is perfectly measured, it may nevertheless be a noisy indicator of graft if there are differences across countries in what "improper practices" are considered to be.

We assume that the disturbance term has zero mean, E[Eo,k)1=O; has the same variance across countries within a given indicator but a different variance across indicators, E[gj,k)<sup>2</sup> ]=ao(k)<sup>2</sup> ; and is independent across indicators and countries, E[E(,k)gj',k')]=O if j#j' or k•k'. The variance of the error term can be interpreted as a measure of how informative indicator k is about go), and is likely to vary across indicators. The assumption that the errors are independent across indicators is a strong one, but unfortunately one that is also difficult to relax. Intuitively, without this assumption we cannot identify whether the correlation of scores between two indicators is due to their common component of governance go), or whether it simply reflects the correlation of errors. In contrast, this identifying assumption maintains that all of the correlation of scores across indicators is attributable to their common estimate of govemance. We will consider the consequences of relaxing this assumption in the following section. For now, we simply note that this identifying assumption corresponds to a "best case" scenario regarding the precision of govemance aggregates, since it assumes that each indicator provides independent information on a particular aspect of governance. As a result, we are if anything likely to overstate the precision with which governance is measured.

The parameters c(k) and P(k) map unobserved govemance into the observed data. Although all of our indicators (after rescaling) are nominally in the same units and are measured on a scale from zero to one, there are nevertheless three reasons why these parameters may differ across indicators. First, not all indicators use the entire range of possible scores. For example, although WDR measures perceptions of graft on a scale from one to six, the lowest observed score is only 2.36. This suggests that a(k) on this indicator may be greater than that of an indicator such as EIU which uses the full range of possible scores. Second, a given indicator might be 'easy" ("tough") relative to other indicators in the sense that it tends to overestimate (underestimate) a particular aspect of governance in countries where it is in fact low (high). This would be reflected in a relatively high (low) value of a(k) on that indicator. Third, consider a nonrepresentative indicator that covers a set of countries in which the average level of a 10
in column 6 that the coefficient for the deepest agreement in our sample (the European Union) is 0.97. Additionally, we find the expected negative and significant impact of tariffs on trade which is not captured in regressions with international flows only. Unfortunately, due to data limitations on product level output, we limit our analysis to international trade when we study the effects of trade diversion.

<u>Table 5. PPML Regression: Trade Creation Internal Flows</u>

||Depth PPM<br>|L Internal Flo<br>|ws<br>|||||
|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Trade|(2)<br>Trade|(3)<br>Trade|(4)<br>Trade|(5)<br>Trade|(6)<br>Trade|(7)<br>Trade|
|Depth LE|0.849***||||1.023***|0.972***|0.996***|
||(0.045)||||(0.070)|(0.132)|(0.130)|
|Depth All||0.722***<br>(0.041)||||||
|Depth Core LE|||0.555***<br>(0.033)|||||
|Depth Core All||||0.483***<br>(0.032)||||
|old PTAs|||||0.282***|0.267***|0.261***|
||||||(0.061)|(0.067)|(0.066)|
|PTA||||||0.026|-0.031|
|||||||(0.055)|(0.055)|
|ln(1+TTRI)|||||||-2.131***<br>(0.551)|
|N|116,134|116,134|116,134|116,134|116,134|116,134|97,825|
|Exp.-Year FE|yes|yes|yes|yes|yes|yes|yes|
|Imp.-Year FE|yes|yes|yes|yes|yes|yes|yes|
|Exp.-Imp. FE|yes|yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

To better understand the impact, and to quantify the effect, of additional provisions in trade agreements, we consider three agreements that are characterized by different levels of depth. First, we calculate the trade impact of the Peru-Chile FTA, a relatively shallow agreement signed in 2009, which includes 11 legally enforceable provisions. Second, we calculate the trade impact of the United States-Korea Free Trade Agreement (KORUS FTA) signed in 2007, an agreement with a medium level of depth which includes 15 provisions. Third, we estimate the impact of the EU which comprises eight agreements, Treaty of Rome and successive enlargements, which cover 43 legally enforceable provisions.<sup>32</sup>

> 32 Details on the policy areas covered by the Peru-Chile FTA, KORUS FTA and the EU Treaties are in Table A4 in the Appendix.

25
# **1. Introduction**

Scientists are confident now that the “global average net effect of climate since 1750 has been one of warming” (IPCC, 2007:3), and that “[A]t continental, regional and ocean basin scales, numerous long-term changes in climate have been observed. These include changes in arctic temperatures and ice, widespread changes in precipitation amounts, ocean salinity, wind patterns and aspects of extreme weather including droughts, heavy precipitation, heat waves and the intensity of tropical cyclones” (IPCC, 2007:7).

The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) suggests that “Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising of global average sea levels. The 100-year linear trend (1906-2005) of 0.74 [0.56 to 0.92] °C is larger than the corresponding trend of 0.6 [0.4 to 0.8] °C (1901-2000) given in the Third Assessment Report” (IPCC, 2007:1-10).

These higher world temperatures are expected to increase the hydrological cycle activity, leading to a change in precipitation patterns and increase in evapotranspiration. More specifically, climate change is expected to increase heat, reduce/increase precipitation, and also increase water supply variability both intra- and inter-annually. “There is high confidence that by mid-century, annual river runoff and water availability are projected to increase at high latitudes (and in some tropical wet areas) and decrease in some dry regions in the mid-latitudes and tropics. There is also high confidence that many semi-arid areas (e.g. Mediterranean basin, western United States, southern Africa and northeast Brazil) will suffer a decrease in water resources due to climate change” (IPCC 2007b:8). The Fourth Assessment Report further verifies the findings from the Third Assessment Report that states: “One major implication of climate change for agreements between competing users (within a region or upstream versus downstream) is that allocating rights in absolute terms may lead to further disputes in years to come when the total absolute amount of water available may be different.” (IPCC, 2001: Section 4.7.3).

Some studies assert that climate change can lead to conflict between states who share international bodies of water following the likely dwindling water supplies (Gleditsch et al 2
# Figure 1: Trends in Risks From Extreme Events, 1960-2000

(Source: CRED: EM-DAT)

- 1a: Risk of Death From Flooding (per Million – Log Scale)

<!-- Start of picture text --> 10.00<br>1.00<br>0.10<br>1960 1970 1980 1990 2000<br><!-- End of picture text -->

- 1b:  Risk of Being Affected by Flooding (Per Million – Log Scale)

<!-- Start of picture text --> 100,000.00<br>10,000.00<br>1,000.00<br>100.00<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> 37
infrastructure. Not surprisingly, a large body of recent literature is devoted to evaluating the impacts of transport investment using various innovative modeling and empirical strategies. However, much of the existing literature models transport improvement as a reduction in travel time or distance while taking the transport prices as given. Recently there is increasing realization in the literature mostly on international shipping that transport services are subject to thick market and network externalities which make transport costs themselves endogenous to trade flows. In this paper, we use half a million actual shipment data from a trucking portal in India to provide evidence on how trucking costs depend on route characteristics and affect the intensive and extensive margins of trade/shipment flow. The empirical analysis using the prepandemic period data confirms the presence of thick market externality along a route and spillovers across routes due to network externality, both of which confer advantages to origins and destinations with larger market sizes. These findings suggest that returns to the construction of a road would depend on not just the reduction in travel time but also the characteristics of the places that it is connecting. This may explain why studies find large benefits from the construction of the Golden Quadrilateral that connected major cities in India (Ghani et al, 2016; Asturias, Garcia-Santana and Ramos, 2018) but at best modest returns from India’s vast rural road construction projects (Asher and Novosad, 2020).

We utilize exogenous variations in value-added tax on gasoline across states to provide a causal estimate of the elasticity of shipment flow with respect to trucking cost. Using the VAT rate along the next probable route as an instrument while controlling for VAT along its own route, we find that a 1 percent increase in trucking unit cost reduces trade flow by 2.8-3.9 percent which is larger in magnitude than estimates from international shipping data by Brancaccio et al (elasticity=1.03) and but similar to Wong (2018) (elasticity=2.8-3.7). The implied elasticity of substitution derived from the above estimate of trade elasticity is about 21-29, confirming the a priori expectation that this elasticity should be higher in poorer countries where consumers tend to be more sensitive to prices (Duranton, 2014).

In the absence of data on the universe of trucking transactions, we developed an empirical methodology to define the extensive/spatial margin of trade by using observable route characteristics to predict the 23