rafmacalaba/datause-encoder-data huggingface dataset:
AFTA and the USBTA, a study of the environmental effects of Vietnam’s experience with partial trade liberalization would nonetheless provide interesting insights about the trade-environment nexus in Vietnam.

A priori, the effect on the composition of production within Vietnam in response to trade liberalization is unclear. The composition of production will depend on how the supply costs of the producers in more polluting industries changes relative to those in less polluting industries as a result of trade liberalization. Based on 'traditional' factor endowments such as capital and labor, Vietnam's comparative advantage is in laborintensive production. If less strict environmental policies do influence production decisions, 'environment' can be considered a non-traditional factor of production, and Vietnam may have an advantage in pollution-intensive production. However, prior to trade liberalization measures that came into effect in the late 1990s, trade barriers and investment restrictions in manufacturing industries may have skewed the relative supply costs of producers and led producers to allocate resources into industries other than those dictated by traditional and non-traditional factor endowments.

Since 1997, however, effective rates of protection (ERP) have declined across all manufacturing industries<sup>7</sup> . As a result of increasing economic openness, domestic and foreign investors could invest in almost all manufacturing industries. As documented earlier in this paper, output and exports have increased during this period. This opening up of the economy through a reduction in trade restrictions and the selective removal of investment restrictions would influence the supply costs of producers leading to possible change in the composition of production and export.

We begin therefore by examining the degree to which the composition of Vietnam’s manufacturing output has shifted towards clean or dirty sectors, and how much of that shift can be explained by changes in the composition of exports.<sup>8</sup> Figures 3 and 4 show

> 7 P. Athukorala (2005).

> 8  In order to isolate this composition effect, we need a metric with which to label various industries are being relatively “clean” or “dirty.”  To that end, we rely on the World Bank’s “Industrial Pollution

7
# Table 2: Regression results from baseline specification

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TF|P Ackerberg_et al_.‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.357***||||||
||(0.000)||||||
|Entryt‐2||0.015|||‐0.085|‐0.080|
|||(0.835)|||(0.283)|(0.313)|
|Conductt‐2|||‐0.198***||‐0.217***|‐0.221***|
||||(0.000)||(0.000)|(0.000)|
|FDI Overallt‐2||||0.085*||‐0.074|
|||||(0.078)||(0.102)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4983642|5431331|5064175|5848487|4983642|4983292|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

30
Yang, Tracy and Reza Siregar. "An Empirical Examination of the Stock Market Returns in Selected Asia-Pacific Economies in the Pre- and Post-Financial Reform Period." Singapore: Institute of Southeast Asian Studies, February 2001.

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149
(finance, insurance) with manufacturing. Over 95% of the services in our employment data are local services (housing, local utilities, retail etc.), and these constitute our non‐tradable service sector.

The total value of trade – exports in particular – is relatively small compared to GDP, and notably so for some of the smaller countries in the region (Table 3). There are also large imbalances in terms of the trade levels, with the three Chinese regions and Kazakhstan having trade surpluses, and other countries having deficits (explained through the presence of large remittances and other transfers). Kyrgyzstan’s imports amount to more than 70% of its GDP.

Table 3: Exports and Imports relative to GDP (percent)

||Primary<br>exports|Manufacturing<br>exports|Primary<br>imports|Manufacturing<br>imports|
|---|---|---|---|---|
|China‐3|0|34|5|14|
|Kazakhstan|14|11|2|19|
|Kyrgyzstan|5|12|3|77|
|Pakistan|1|8|3|14|
|Tajikistan|5|7|4|48|
|Turkmenistan|17|2|0|13|
|Uzbekistan|2|9|1|13|

**_Transport:_** Our measure of bilateral trade costs between cells (including access points) is based on travel times from De Soyres et al. (2018), who calculate the travel time between various pairs of cities in Central Asia. They use the current transport infrastructure network to compute shipping times before the BRI, and enrich this with planned projects related to the BRI to compute a post‐BRI matrix of shipping times. They also compute a matrix of shipping times enhanced by additional reductions in border crossing times, resulting in three matrices covering 1,818 cities in 71 countries. We connect the main urban area in each cell of our study to the nearest city in their database, and calculate the optimal travel time, time�� , expressed in days, through this location to all other cells in the data.  This is done for three scenarios: pre the BRI, after the construction of infrastructure, and after the � additional reduction in border times.  We map transport times into transport costs τ�� according to functional form , τ��� � 1 � λ�time��� as in Alder (2017) and Roberts et al., (2012). Time is measured in days, and we set θ�0.75 reflecting similar assumptions elsewhere in the literature such as for rural goods in China in Roberts et al. (2012). λ<sup>�</sup> varies by sector, highest for non‐tradable services ( λ<sup>�</sup> �1.8� which are produced and sold predominantly in the same location, lowest for manufacturing (which, while heavy to transport, is less time sensitive than agriculture λ<sup>�</sup> �0.09� , and primary goods lying between the two ( λ<sup>�</sup> �0.18�.

For example, prior to the BRI, the shipment of goods between Urumqi in China and Almaty in Kazakhstan takes nearly 8 days, including border times, which is equivalent to an iceberg transport cost factor of 1.42 for manufacturing and 1.84 for primary goods. Shipment between Almaty and Astana, both in Kazakhstan, takes 1.1 days, equivalent to an iceberg transport cost factor of 1.09 for manufacturing and 1.19 for primary goods.

11
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Whether agricultural PAC money influenced electoral outcomes is left as an open issue deserving further research.

Returning to Figure 5.1 it should be noted that there are candidates on this top 20 Ag money list who are not members of Ag committee (Fazio, Edmondson, Bliley, Bonior and Anthony). However, they were all (with the possible exception of Anthony) from congressional districts with influential agricultural constituents (CA, OK, VA, MI). For example, Fazio’s district (CA, district 3) was among the 30 leading congressional districts by market value of agricultural products sold in 1997 (1997 Census of Agriculture), and Edmondson’s district was among the thirty leading Cattle and Calves districts Finally, contribution to Ag committee members, and membership on Ag committees are not independent of each other. The received wisdom in the literature on the political economy of agriculture policy is that representatives from districts with agriculturally influential constituencies seek out membership on agriculture committees and lobby hard for positions of influence on its subcommittees. Consequently, they are in position to influence agriculture policy especially during the five-yearly Farm Bill legislations. Their influential positions endear them to PAC influence. Ag PAC contributions and the ability to influence agriculture policy are therefore simultaneously determined.

Figures 5.2–5.5 depict the top-20 Ag PAC list from the four election cycles between 1993 and 2000. They reinforce the foregoing observations. In each of these cycles, giving to members of the House agriculture committee is highlighted. And candidates who appear in the list but are not members of the agriculture committee belong to districts with agriculturally influential constituencies.

An interesting pattern emerges when comparing the combination of party with committee membership across the five election cycles. A natural experiment that the cycles provide is the switch from Democratic to Republican majority in 1995 and thereafter. Whereas, the top-20 recipients who were agriculture committee members comprise largely Democrats during the 1991-92 and 1993-94 congresses, they were mainly Republicans in the three later congresses. This pattern reinforces the view that Ag PAC money was not party driven (or ideological) but rather sought influence.

26
incidence of civil conflicts are indirect, occurring either through the intervention variable or its interaction with RELPOL. These results suggest that unlike in non-MENA, where conflicts are mostly about a public prize linked to ethnic polarization, in MENA they are mostly about a public prize linked to sectarian polarization.

**Table 8. Two-Stages Linear Probability Model Panel 1965-2005**

||**LPM**||**TSLPM**||
|---|---|---|---|---|
||**Non-MENA Pane**|**l**|**MENA Panel**||
||Eq. (8.1)<br>(2)|1st stage<br>Eq. (9.1)<br>(3)|1st stage<br>Eq. (9.2)<br>(4)|2nd stage<br>Eq. (8.1)<br>(5)|
|**Dep. Variable**|PRIO1000|Int_nh|Int_nh x<br>RELPOL|PRIO1000|
|LGDPC|-0.054<sup>***</sup>|-0.26|-0.03|0.029|
|LPOP|0.023<sup>***</sup>|-0.09|-0.08<sup>**</sup>|-0.012|
|PRIMEXP|0.048|1.18<sup>***</sup>|0.05|-0.21|
|DEMOCRACY|0.016|0.09|-0.11|-0.27<sup>**</sup>|
|MOUNTAINS|0.001|0.01|0.002<sup>***</sup>|0.005<sup>**</sup>|
|NONCONT|0.084<sup>**</sup>||||
|ETHPOL|0.182<sup>***</sup>|-0.18|0.08|-0.031|
|RELPOL|-0.055|-2.82|-1.69|-0.064|
|TOTBORD||-0.000|-0.000||
|TOTBORD<sup>x</sup>RELPOL||0.0003<sup>**</sup>|0.0003<sup>***</sup>||
|LAAM|0.022||||
|SAFRICA|0.020||||
|ASIAE|-0.017||||
|LGDPxRELPOL||0.18|-0.03||
|PRIMEXPxRELPOL||-1.34<sup>*</sup>|0.30||
|DEMOCRACYxRELPOL||0.51|0.59<sup>*</sup>||
|MOUNTINS*RELPOL||-0.04<sup>***</sup>|-0.03<sup>***</sup>||
|LPOPxRELPOL||0.11|0.12||
|Int_nh|-0.141<sup>**</sup>|||-0.125|
|Int_nh<sup>x</sup>RELPOL|0.424<sup>***</sup>|||1.044<sup>***</sup>|
|Constant|0.008|1.94|1.40<sup>***</sup>|0.010|
|N|824|122|122|122|
|CenteredR<sup>2</sup>||||0.181|
|Durbin-Wu-Hausman Endogeneity<br>2<br>|0.597|4.505<sup>***</sup>|||
|test|||||
|F test of excluded instruments||11.03<sup>***(15%</sup><br>bias)|23.76<sup>***(5%</sup><br>bias)||
|Stock-Wright test for the joint significance<br>ofallexcludedinstruments||||33.48<sup>***</sup>|
|HansenJtest||||5.361|

Notes:<sup>***(15% bias)</sup> and<sup>***(5% bias)</sup> indicate that we can reject the null hypothesis of weak instrument at the 1% significance level, using Stock and Yogo (2005) critical values, allowing respectively for 15% and 5% bias of maximal TSLP relative to the LPM estimator.

37
In order to get the iterative scheme started, we would of course need suitable initial estimates for _p_ ( ̄ _εa|e_ ̄ _a_ ) and _p_ ( _ua|e_ ̄ _a_ ). Note that even without any prior knowledge about the distributions for _ua_ and _εah_ , we should be able to obtain a reasonable estimate of the distribution for _ε_ ̄ _a_ by appealing to the Central Limit Theorem (CLT). Under the assumption that _εah_ are independend across households, we have that the distribution of _ε_ ̄ _a_ tends to a normal distribution with mean zero and variance equal to _σε_<sup>2</sup><sup>_/na_.Inatypicalhouseholdincomesurvey,</sup><sup>_na_willbeintherange</sup> of 10 to 100, which is sufficiently large for the CLT to take effect. The corollaries below derive the initial estimates for _p_ ( ̄ _εa|e_ ̄ _a_ ) and _p_ ( _ua|e_ ̄ _a_ ) under the assumption of normal distributed _ε_ ̄ _a_ (with known variance).

**Corollary 6** _The probability density function of ε_ ̄ _a conditional on e_ ̄ _a, which we denote by p_ ( ̄ _εa|e_ ̄ _a_ ) _, is a normal-mixture with known parameters:_

̃ _where wi_ = _wi/_<sup>�</sup> _i_<sup>_w_ ̃</sup><sup>_iwith:_</sup> _where φ denotes the normal probability density function, and where πi, μi, and σi_<sup>2</sup><sup>_denotethe_</sup> _parameters associated with the normal-mixture distribution Fu._

**Corollary 7** _The probability density function of ua conditional on e_ ̄ _a, which we denote by p_ ( _ua|e_ ̄ _a_ ) _, is a normal-mixture with known parameters:_

_with γai_ = _σi_<sup>2</sup><sup>_/_(</sup><sup>_σ_</sup> _i_<sup>2+</sup><sup>_σ_</sup> _ε_<sup>2</sup><sup>_/na_)</sup><sup>_,andwhereαi_=</sup><sup>_α_ ̃</sup><sup>_i/_�</sup> _i_<sup>_α_ ̃</sup><sup>_iwith:_</sup> _where φ denotes the normal probability density function, and where πi, μi, and σi_<sup>2</sup><sup>_denotethe_</sup> _parameters associated with the normal-mixture distribution Fu._ The following propositions provide some properties of the estimators.

**Proposition 8** _At every iteration-k, the mean and variance for the probability density function g_ ˆ _ε_<sup>(</sup><sup>_k_)(</sup><sup>_ε_) = �</sup> _j_<sup>_λ_ˆ(</sup> _j_<sup>_k_)</sup><sup>_φ_(</sup><sup>_ε_; ˆ</sup><sup>_ν_</sup> _j_<sup>(</sup><sup>_k_)</sup> _,_ ˆ _ωj_<sup>2(</sup><sup>_k_)</sup> ) _solve:_

15
12 among the groups.

The DHS program again provides the necessary data in the form of life table measures of child mortality for five-year periods before each survey and the socioeconomic characteristics of the mothers at the time of the survey, as indicated by the mothers" educatlon (none, primary, secondary and higher) and place of residence (urban, rural). The basic approach is again to model the child mortality estimate for a period zero to four or five to nine years before the survey in terms of the corresponding estimate for the period 10 to 14 years before the survey, the number of years in the calendar period, 1980-84, and the number of years in the calendar period 1985-89. Two more variables are added in an attempt to capture crisis and adjustment effects: the number of years, up to a maximum of three, that a perlod included prior to effective adjustment (to capture crisis effects), and the number of years. up to a maximum of three, that a period included after effectLve adjustment (to capture adjustment effects). For this purpose, the date of effective adjustment has been taken as the beginning of the year in which real government expenditure began a sustained reduction in trend (Gallagher and Ogbu 1989; Table 11.3).

Because the sample size is small for disaggregate **d** ata, analysis has been limited to the measure of child mortality that maximizes person-years of exposure - - that is the probability of dying by aged **five,** Sqo. This measure is also the best available indicator of overall mortality in childhood. It has been modelled in two ways: as the actual level per 1,000 live births with the base level for the period 10 to 14 years before the survey included as an independent variable; and as the ratio of the actual level to the base level 10 to 14 years before the
3 Figum I _High Female_ **_Primary_** _School Enrollment_ <!-- Start of picture text -->
1.2<br>to  3<br>0.8<br>0.6<br>0.4<br>0.2<br>01<br>0  35 40 4550  5560  6570  7580<br>Oiginal (1965-69) fetale life expectancy<br>'Vote:  Raw data and fitted values based on  mgrsion  equation.<br><!-- End of picture text -->

Figume 3. _Expected Ctange in Female Life ExPectancY Based on Initial Female Life Epectantcy and Level of Initial Female Pnrmwy School Enrolbnent_ <!-- Start of picture text --> 0.6<br>A A<br>A A<br>A5 0 045 0<br>A A l<br>0 ~~0<br>0.4 0<br>0<br>10.3<br>.M 0.2<br>0.1 0 L.owfemaleprnmnrv school enrolimem<br>AHighfenalepnmar.vschool enrollmena<br>0 I I I I I I I I I0I<br>35 40 45 50 55 60 65 70 75 80 85<br>Initial female life expectancyAe<br><!-- End of picture text --> Figure 2. _Low Female Primary Schol Enrollment_ <!-- Start of picture text --> 1 3.2<br>3.0 1<br>1 0.8<br>0.60<br>0.40<br>IL~ 0.2S<br>0<br>.7 30 33 3639 42 4548 5354<br>Original (1965-69) female life expectancy<br>Note: Raw data and fitted values based on regression equation.<br><!-- End of picture text --> Figure 4. _Age-Specific Fertility Patterns by Fertility Level_ <!-- Start of picture text --> 30<br>2 5<br>'<br>2<br>I N<br>t0<br>- TFR < 3.0<br>TF-41<br>TVR > 6.0<br>I I I 1<br>15-19 20-24 25-29 30-34 35-39 40-44 45-49<br><!-- End of picture text -->
# **China and the World Trading System**

_Aaditya Mattoo* and Arvind Subramanian**_ _Keywords_ : China, trade, multilateralism, WTO, Doha Agenda JEL codes: F1, F2, F5

*Development Economics Research Group, World Bank. Email: amattoo@worldbank.org.  **Peterson Institute for International Economics.  Email:  asubramanian@piie.com. The authors are grateful to Richard Baldwin, C. Fred Bergsten, Chad Bown, Bernard Hoekman, Gary Hufbauer, Pascal Lamy, Patrick Low, Will Martin, Zanny MintonBeddoes, and Martin Wolf for helpful discussions and useful comments, and, in particular, to an anonymous referee for detailed comments. The views expressed in the paper are those of the authors and should not be attributed to the World Bank, its Executive Directors or the countries they represent.
got subject to the influence of the economic performance of the US. When the interest rose in the US, the investment in Mexico was made less attractive and capital began flow out of Mexico. The $20 billion financial assistant from the US was a reward from the US in return for Mexico's weakening hostility.

## Reference:

1. World Bank. _Country Brief: Mexico._ March 6, 1996.

2. Economic Intelligence Unit. _Country Profile: Mexico._ London, United Kingdom. 1992/93, 1993/94, 1995/96, & 1996/97.

# **Peru**

## **A Glorious Ancient History**

Peru is the birthplace of the Inca Empire, one of the most important ancient civilization centers both in the pre-Hispanic Americas and in the World. The Spanish conqueror, Francisco Pizarro, arrived in 1532. Forty years after, the Inca Empire was finally defeated in 1572. Then the Spanish colonized Peru for more than two centuries. Peru declared independence from Spain in 1821. During the War of the Pacific over 1879-83, both Peru and Bolivia lost lands to Chile, and the mutual distrust among them remains toady.

## Economic Experiments (1950s-1990)

Peru has abundant natural resources, including mineral, fishing, hydrocarbon and human capital. Mining is import for balance of payment, which accounted for half of export.

Peru tried all kinds of economic polices for its development, first the laissez-faire policy in 1950s, and then import substitution and nationalization in 1960s and early 1970s. The rule of dictatorship dominated Peruvian politics. A democratic government led by President Fernando Belaunde Terry was elected and then overthrown by a coup in 1968. From 1968 to 1975, the military government started land reform and nationalized industries. Spending and foreign borrowing spiraled out of control. A bloodless coup installed another military regime and continued control till 1980. In the 1980 democratic election, Fernando Belaunde Terry was elected for his second term as the President. He started to reform the economy, but the debt crisis of 1982 forced the reforms into a pause. Garcia took the office in July 1985. Over 1985-90, Peru retreated to protectionism. Garcia promised to achieve high economic growth rate and to increase the living standard of the poor. He announced radical policies. He devalued the currency, raised wages, The unorthodox economic policy only led to short-lived growth in 1986-87 and then caused hyper-inflation. In 1987, the ill-judged move to nationalize banking system provoked protest and rallied opposition, over 1988-90 the economy contracted sharply. By 1990, the economy was on the verge of economic collapse. Rampant and terrorism grew out of the poor economic performance during the 1980s.

## **Fujimori's Reform (1990-1997)**

63