rafmacalaba/datause-encoder-data huggingface dataset:
# **III. Financial Services in LCR Trade Agreements**

## **_Overview of Preferential Trade Agreements in LCR_**

There are different types of preferential trade agreements based on their level of economic integration, ranging from FTAs to custom unions and common markets. These agreements all have in common the objective of reducing or eliminating most duties and other barriers to trade and investment among Members. However, compared to FTAs, Members to customs unions also adopt common external tariffs and more generally a common trade policy vis-à-vis non-Members, while Members to common markets allow for the free movement of all factors of production between them. The highest degree of economic integration is found in economic unions that, in addition to a common trade policy and the free movement of factors of production, aim for the unification of economic policies and the adoption of a common currency among participating members.

Since the 1990s, the world economy has witnessed an unprecedented proliferation of PTAs. The WTO expects the number of such agreements to soon reach 300 if all negotiations currently underway are concluded; this compares to only 50 such PTAs worldwide as recently as 1990<sup>32</sup> . Although trade rules on services are more recent, WTO notifications for services agreements have grown at a very fast pace. Almost every WTO Member is party to at least one PTA, while some regions – including LCR – have been particularly active in this regard. According to the Organization of American States’ (OAS) trade information database<sup>33</sup> , LCR countries have entered into 33 PTAs since 1994, primarily with trading partners within the Western Hemisphere, but also with the European Union and increasingly with countries in the Asia-Pacific region.

The entry into force of the NAFTA between Mexico, the US and Canada in 1994 is widely considered as a defining event in developing countries’ attempts to engage in PTAs with a view to securing greater access to key markets and consolidating recent domestic reforms. Two LCR countries in particular – Mexico and Chile – have been the initial driving forces behind the proliferation of PTAs in the region over the last decade, although other countries or sub-regions (e.g. Central America) have also recently joined the fray – see Table 3 below for a chronological list of all recent PTAs in LCR. PTAs concluded in the last decade have introduced important new features such as the treatment of intellectual property rights as well as trade and investment in services (including financial services), reflecting their rising importance in economic development and world trade. However, the co-existence and proliferation of different PTA types has led to concerns about an emerging “spaghetti bowl” that creates administrative complexity among trading partners due to different and overlapping commitments and

> 32 See Crawford J. and Fiorentino R. (2005), the World Bank (2005), and Roy M., Marchetti J. and Hoe Lim A. (September 2006) for a description of recent trends and potential drivers. 33 See http://www.sice.oas.org/tradee.asp

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estimate from cross-sectional data. The pooled cross section mixes the short-run and long-run responses to tariffs and also contains very little tariff variation, because most of the countries besides China in our sample face the same MFN rates. Our error-correction specification distinguishes between the short run and long run and mitigates some of this limited variation. It yields long-run elasticities of 8–10, which are larger than the cross-sectional estimates but still below those implied by the model. The model is calibrated to reproduce the errorcorrection estimates, so the difference between these estimates and the model’s true long-run elasticity implies uncertainty over trade policy makes recovering the model’s true elasticity This paper is related to the literature that studies the credibility of trade reforms. Early work focuses on the aggregate effects of temporary reforms (Calvo, 1987) or the credibility of reforms (Staiger and Tabellini, 1987; McLaren, 1997). More recent work uses firm-level and industry-level data to identify the effects of trade-policy uncertainty. This literature largely focuses on U.S. trade policy toward China. Handley and Lim ̃ao (2017) and Pierce and Schott (2016) leverage the well-defined nature of the uncertainty—the annual vote to retain China’s NTR status and the gap between the NTR tariffs and the fallback NNTR tariff—to measure the growth in trade that resulted from the elimination of uncertainty over future tariffs. Unlike these papers, we consider how these tariff gaps, which also capture the 1980 liberalization, influence trade dynamics in the lead-up to China receiving Permanent Normal Trade Relations (PNTR) status upon joining the WTO. Our paper also relates to several papers that use models to estimate the path of trade policy.<sup>5</sup> Handley and Lim ̃ao (2017) also use a dynamic exporting model and the evidence on a differential change in trade to estimate the probability of NNTR reversal. Unlike their work, we estimate a time-varying probability of U.S.-China tariffs over a longer interval. Complementary to our approach, Alessandria et al. (2019) estimate a time-varying probability of a change in U.S. trade policy from 1990 to 2005, but use within-year variation in trade flows and trade-policy risk. (2007) estimates a median trade elasticity from NAFTA of 6.9. Simonovska and Waugh (2014) estimate a trade elasticity of close to 4. Boehm et al. (2020) estimate a trade elasticity that ranges from 0.75 in the short run to about 2 in the long run.

> 5Alessandria et al. (2017) estimate the expected path of inward and outward trade policy for China from macroeconomic time series. Ruhl (2011) estimates the probability of ending the ban on Canadian beef after an outbreak of “mad cow disease.”

6
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Table 2.2 (continued) <!-- Start of picture text --> Liabilities:<br>l * Extensive provisions, in which all conceivable occurrences are provided for.<br>l * In general: Each railway is responsible for its own losses regardless of fault.<br>l * Labor claims to be borne by each party for its own employees, except: Employees hired by<br>Railway A for the benefit of Railway B will be covered by Railway B.<br>Arbitration Clause<br>Terms of Agreement; Termination; Event of Abandomnent:<br>| * Term is set at 30 years in the example.<br>* | One year's notice is required for termination by Railway B.<br>l * Railway A can abandon the line; Railway B would then have an option to purchase.<br>Successors and Assigns<br>Source: Norfolk & Western Railway Company, sample trackage rights agreement with general<br>conditions [no bibliographic data available].<br><!-- End of picture text -->
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Table 4: Average characteristics of students attending different types of secondary schools, Brazil 1988

|||School|type||
|---|---|---|---|---|
|Student characteristic|Federal<br>technica|l SENAI|Teacher<br>training|General<br> <br>secondary|
|Sex (% female)|26.0|6.8|96.1|58.7|
|Age in years|18.5|19.4|18.9|18.7|
|SES (factor score)|.18|.19|-.29|.02|
|Family size|4.7|4.2|4.4|4.5|
|Hours working weekly|5.8|8.6|8.0|14.5|
|Sample size|192<br>|118|309|1992|

17. To take into account these background differences between students attending different schools, we entered five student characteristic variables into the model: social class background, sex, age, family size, and number of hours per week that a student worked. All background characteristics were allowed to vary within schools, but a random effect was observed for SES only. The other four variables were "fixed" and the residual variances set to zero. Because general secondary and teacher training schools are found in both the public and private sector, we also included a dummy variable to test for sector effect on achievement.

18. Average achievement. The results of these analyses are presented in Table 5 (general secondary schools and public schools are the two omitted categories); since we are modelling the within-school SES-achievement relationship, the school average SES is entered into the achievement equation as a control variable. The results show that older students performed less well on both mathematics and Portuguese tests than did younger students, and girls outperformed boys in Portuguese, but boys outperformed girls in mathematics. Students who worked less outperformed those than students who
**<u>Table 5: Outcome Variables by Treatment Category</u>**

|||**No**|**ne**|||**With ce**|**rtificate**||
|---|---|---|---|---|---|---|---|---|
||**1999**|**2002 **|**2004 **|**2007 **|**1999**|**2002 **|**2004 **|**2007 **|
|**Expectations of land redistribution in**<br>|**the next fi**<br>|**ve years an**<br>|**d of confli**<br>|**ct**<br>|||||
|Expect change in holdings|0.75|0.62|0.38|0.39|0.78|0.64|0.38|0.24|
|Expect an increase in holdings|0.55|0.45|0.19|0.11|0.55|0.36|0.18|0.04|
|Expect a decrease in holdings|0.20|0.17|0.19|0.28|0.23|0.27|0.19|0.19|
|Concerned about land conflict (p)||||0.20||||0.14|
|**Land related investment over the last**|**12 months**|**(plot level**|**)**||||||
|Repaired structure / built new one|||0.36|0.24|||0.12|0.25|
|Number of hours spent|||8.22|5.51|||2.26|4.38|
|Constructed new structure|||0.10|0.08|||0.07|0.10|
|Plot has conservation structure|||0.44|0.34|||0.22|0.32|
|**Participation in land rental market**|||||||||
|Rent-out land|0.24|0.21|0.29|0.34|0.17|0.15|0.26|0.33|
|Area rented out (ha)|0.20|0.21|0.28|0.34|0.14|0.16|0.32|0.45|
|Rent-in land|0.37|0.26|0.20|0.36|0.49|0.31|0.36|0.45|
|Area rented in(ha)|0.25|0.19|0.15|0.29|0.29|0.19|0.27|0.44|
|Number of households|462|463|475|477|229|233|347|356|
|Number ofplots|||2284|2415|||1886|2086|

**Source:** Own computation from AAU/Gothenburg/WB Survey. Empty cells imply that no data was available. Note: Except where indicated in the table, all variables are at the household level.

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# **Table 12: Results with Core and Non-core Expenditures**

# **(Dynamic Panel - GMM)**

## **_Dependent variable: Growth rate of GDP per capita_**

||Fast growing<br>countries<br>**(1)**|Comparison<br>group<br>**(2)**|Whole sample<br>pooled<br>**(3)**|Whole sample<br>with dummies<br>**(4)**|
|---|---|---|---|---|
|Openness|0.048|0.134|0.043|0.042|
|(% of GDP)|(1.665)*|~~(~~1.13)|~~(~~1.164)|~~(~~0.914)|
|Tax revenue|0.007|1.307|0.385|1.051|
|(% of GDP)|~~(~~0.014)|~~(~~0.87)|~~(~~0.483)|~~(~~0.903)|
|Other revenue|-0.039|2.156|0.548|1.158|
|(% of GDP)|(-0.069)|~~(~~0.868)|~~(~~0.696)|~~(~~1.026)|
|Core expenditure (-1)|1.102|-0.226|0.803|...|
|(% of GDP)|(1.591)*|(-0.261)|~~(~~1.162)||
|DFAST*Core expenditure (-1)|...|...|...|3.198|
|(% of GDP)||||~~(~~1.392)|
|DCOMP*Core expenditure (-1)|...|...|...|0.287|
|(% of GDP)||||~~(~~0.292)|
|Non-core expenditure (-1)|-0.996|-0.938|-1.591|-2.211|
|(% of GDP)|(-1.184)|(-0.9)|(-1.915)*|(-1.872)*|
|Budget surplus|0.000|-1.018|-0.358|-0.971|
|(% of GDP)|~~(~~0)|(-0.97)|(-0.803)|(-1.242)|
|Inflation - consumer price index|-0.354<br>(-3.391)***|-0.124<br>(-3.437)***|-0.110<br>(-3.461)***|-0.116<br>(-2.883)***|
|Growth rate of GDP per capita (-1)|-0.067<br>(-0.352)|-0.518<br>(-2.352)**|-0.385<br>(-1.991)**|-0.332<br>(-1.341)|
|No. of observations|97|169|266|266|
|Overidentification statistics|3.52|11.101|11.55|6.04|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. DFAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. Overidentification statistics is for instrumental variables where H0: there is no overidentification problem. We fail to reject in each case.

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WPS3163 1

# **Achieving the Millennium Development Goals**

## **The Role of Infrastructure**

Danny Leipziger, Marianne Fay, Quentin Wodon and Tito Yepes<sup>1</sup>

### <u>Abstract</u>

This paper provides an empirical analysis of the determinants of three child health outcomes related to the Millennium Development Goals: the infant mortality rate, the child mortality rate, and the prevalence of malnutrition. Using data from Demographic and Health Surveys, the paper goes beyond traditional cross-country regressions by exploiting the variability in outcomes and explanatory variables observed within countries between asset quintiles. The paper shows the relationships existing between the prevalence of diseases (diarrhea and malnutrition) and mortality. The findings suggest that apart from traditional variables (income, assets, education and direct health interventions), better access to basic infrastructure services has an important role to play in improving child health outcomes. The analysis of interaction effects between interventions also suggests the importance of combining interventions in order to meet the Millenium Development Goals.

JEL categories: I12, O10, H42 Keywords: Millennium Development Goals, Infant mortality, Malnutrition, Infrastructure

### World Bank Policy Research Working Paper 3163, November 2003

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

**<u>Corresponding author:</u> Marianne Fay World Bank, 1818 H Street Washington, DC 20433 Email:** **<u>mfay@worldbank.org</u>**

> 1 This paper was prepared in the Finance, Private Sector and Infrastructure Department of the Latin America and the Caribbean Vice Presidency at the World Bank as part of a research program on MDGs and Infrastructure.
# _Unorganized Sector: National Sample Statistics_

We employ plant-level data from the years 1994, 2000 and 2005 to study the impact of highway infrastructure investments on the unorganized sector of Indian manufacturing. Establishments in the unorganized sector in India are unregistered, do not pay taxes, and are generally outside the purview of the state, so this group closely parallels common discussions and definitions of the informal sector. Our data are taken from the National Sample Statistics (NSS). Many of the properties of this dataset resemble the ASI, and the development steps are closely related, so we do not repeat them. Unlike organized manufacturing, however, we are not able to calculate TFP measures due to lack of reliable data on asset formation in the unorganized sector. On the other hand, the NSS allows us to examine differences in male- versus femaleowned businesses that we describe briefly later. Our core sample for the unorganized sector contains 363 districts.

# _The GQ Highway Upgrades_<sup>_3_</sup>

Road transport is the principal mode of movement of goods and people in India, accounting for 65% of freight movement and 80% of passenger traffic. While national highways like the GQ network constitute about 1.7% of the road network, they carry more than 40% of the total traffic volume. India launched its National Highways Development Project (NHDP) in 2001 to improve its transportation infrastructure. This project, the largest highway project ever undertaken by India, aimed at improving the GQ network, the North-South and East-West Corridors, Port Connectivity, and other projects in several phases. The total length of national highways planned to be upgraded (i.e., strengthened and expanded to four lanes) under the NHDP was 13,494 km; the NHDP also sought to build 1,500 km of new expressways with six or more lanes and 1,000 km of other new national highways, including road connectivity to the major ports in the country. Thus, in a majority of cases, the NHDP sought to upgrade a basic infrastructure that existed, rather than build infrastructure where none previously existed. The GQ program in particular sought to upgrade highways to international standards of four- or six-

> 3 This section is an abbreviated introduction, and we refer readers to Ghani et al. (2012a) for greater details about the GQ structure and the other highway systems in India.

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# **7 Implied effect on women’s excess mortality**

Next, I turn to calculating the implied impact of son preference on mortality among adult women from the observed sex ratios for first births in the data.<sup>49</sup> The observed sex ratio ( _so_ ) can be expressed as: _so_ = 1 _−ms_ (1 _<u>n−sn</u>_ )<sup>;where</sup><sup>_sn_isthebiologicallynormalsexratio,and</sup><sup>_m_isthemortality</sup> rate among women with first-born girls due to son preference (i.e., with no son preference: _m_ = 0 and _so_ = _sn_ ). Therefore, _m_ = _sos_ (1 _<u>o−−ssnn</u>_ )<sup>.Table16reportstheobservedsexratiosbyagegroupin</sup> the entire sample and the subsample of uneducated women. Figure 12 shows the implied values of _m_ (for each age group) among women with first-born girls assuming a normal sex ratio of 0.514. As expected, the inferred mortality rates increase with age, ranging from 2.2 to 8.4% for women aged 30 to 49 (3.7 to 13% among uneducated women).

# **8 Conclusions**

This paper examines the phenomenon of excess mortality among women of reproductive and adult ages in India. It is the first to show that strong preferences for male children are partly driving female mortality in adulthood. In settings with son preference, there are two main ways for parents to ensure the desired sex composition of children. They can have repeated and closely spaced pregnancies until the ideal number of sons has been reached, a behavior medically known to increase the risk of maternal mortality and morbidity. Parents can also selectively abort female fetuses. In India, the latter appears to be more common among well-off, educated women, and the former among the poor and uneducated. Therefore, differential access to prenatal care as well as differences in initial health status between women from different socioeconomic strata can lead to differential mortality due to son preference.

I explore this issue by using three rounds of the National Family Health Survey for India. The main results can be summarized as follows. First, there are no systematic differences among women with a first-born girl or boy close to the time of birth. Second, women with first-born daughters are more likely to desire more children, to have performed an ultrasound test, to have had a terminated pregnancy, to have given birth to more children, and less likely to be sterilized. The effect on abortion is significantly larger for educated women (who also exhibit an attenuated effect on fertility), which confirms existing evidence that sex selection is more common among them. Third, women with a first-born girl are more likely to develop anemia more than a year _after_ birth. Fourth, differential incidence of anemia disappears in the subsample of older women. This age pattern of anemia is stronger for women with no education. Fifth, the share of living women with

> 49This approach is adapted from Abrevaya (2009) who estimates the prevalence of sex-selective abortions from the observed sex ratios.

27
standard (fixed-repayment) loan contract.

# **Theorem 1** ( **Townsend 1979** ) _S_ = [ _<u>y</u>_ , _r and_ ) _r_ ( _y_ ) = min( _y_ , _r_ ) _._

> In the optimum, the recipient specifies a threshold remittance _r_ such that she starts verification only if the migrant remits less than _r_ . In other words, the recipient specifies the smallest acceptable remittance level that does not lead to any scrutiny of the migrant’s income. The proof of this theorem can be found, for example, in Townsend (1979) or Gale and Hellwig (1985).

By theorem 1, the optimal remittance contract is fully characterized by the remittance  threshold. The optimal threshold _r_ solves the recipient’s optimization problem Prior to solving the problem and analyzing its properties, we impose The purpose of this assumption is to guarantee the existence and uniqueness of solution and to ensure the sufficiency of the first-order approach.

> _c_ 0 constraint so that the choice set of _r_ for the recipient is [ _<u>y</u>_ , _r_ ] . (Assume that _U M_ is not too large _c u_ to ensure that _r_ > _<u>y</u>_ holds). The solution to the unconstrained problem, denoted by _r_ , is given by the first-order condition

10
_28_ _WATER WHEN IT COUNTS_ growth, we expect that farmers will choose to use less water during early growth stages, allowing them to reallocate water to the later growth stages. If this hypothesis is correct, Since both treatments receive feedback, we start by using an event study design to draw comparisons in the incidence of water-related conflicts before and after the introduction of feedback (Figure 7). In the month immediately before the first distribution of feedback (December), the incidence of conflict was similar to that reported in the same month of the previous year. One month after farmers started receiving feedback (January), reports of conflict feel to zero and remained there from January to May. Over the dry period (July-November), conflicts are again reported, but their incidence remains substantially below that of the previous year. These findings are largely corroborated with our plotlevel measurements of water sufficiency (Figure 8). Overall, these results are consistent with the idea that farmers quickly began to use water more efficiently in accordance with the recommendations they received.

Figures 7 and 8 show that households were more likely to perceive water as sufficient and were consequently less likely to report conflicts over water in the months following the introduction of feedback than they were before feedback started. As Sections II.D and III suggest, the main gains from reallocation would come from reallocating water across planting stages. If farmers respond to reminders and there is any scope to increase watering, they should increase watering only in the most water-intensive (late) growth stages, when crops are not getting enough water. Instead, watering in the early growth stages should decline, as there is already more than sufficient water. This general pattern of reallocation is observed in Figure 9 among both treatment groups. In both groups, the distribution of water use in the first growth stage shifts left (or closer to requirements)