rafmacalaba/datause-encoder-data huggingface dataset:
mitigated by an exchange policy whereby the exchange rate was significantly devalued — from 368 to 915 pesos per US dollar— from the end of 1985 to the end of 1986.<sup>8</sup> At that time, the Mexican government realized that, given the significant trade liberalization process unilaterally undertaken, it was no longer reasonable to remain outside the General Agreement on Tariffs and Trade (GATT),<sup>9</sup> since this prevented the country from taking advantage of the commercial benefits granted to its Members through tariff preferences. Thus, Mexico joined the GATT in 1986.<sup>10</sup> Within the GATT, Mexico stated its commitment to further eliminate import licenses<sup>11</sup> and to reduce maximum tariff levels, but was authorized to put in place a system of antidumping and countervailing duties.

**Table 2 - Comparative Tariff Structures in Selected Years**

|**Concept**|**1982 **|**1985**|**1992 **|
|---|---|---|---|
|Maximum Tariff|100|100|20|
|Weighted Average Tariff|16.4|13.3|11.4|
|Tariff Dispersion|24.8|18.8|4.5|

Source: SECOFI, 1993.

# **2. Mexico’s First Antidumping Legislation and Its Adherence to the Antidumping Code as a Contracting Party**

In line with its decision to join the GATT, Mexico began the legal and institutional reforms required at the domestic level to support governmental actions adopted as a result of the new development model and its recent international commitments. In this context, in January 1986 the first antidumping legislation was enacted: the Law Regulating Article 131 of the Political Constitution of the United Mexican States on Matters of Foreign Trade (hereinafter, the Law Regulating Article 131). In November 1986, the Regulations against unfair trade practices in

> 8 Jaime Zabludovsky (1989), p. 15.

> 9 On November 26<sup>th</sup> 1985, the GATT Director-General was informed about the Mexican government’s decision to initiate the process of accession to the GATT, Foreign Trade Department (Gabinete de Comercio Exterior) (1986).

> 10 Mexico formally acceded to the GATT in August 1986. This accession, ratified by the Senate of the Republic in September 1986, came into force in November 1986.

3
**Table 2. Decentralization Indicators for Developing and Transition Economies**

||Tra<br>|nsition(199<br>|9)<br>|Dev<br>|eloping (1<br>|997)<br>|
|---|---|---|---|---|---|---|
||Average|Max|Min|Average|Max|Min|
|_Sub-national expenditures_|||||||
|As % ofGDP|10.8|20.4|5.8|7.4|18.3|0.8|
|As % ofpublic sectorexpenditures|22.3|38.8|7.3|23.3|45.2|3.5|
|Sub-nat’l education expenditures, as %<br>of public sector education<br>expenditures|55.9|91.4|0.2|49.8|97.5|0.2|
|Sub-nat’l health exp., as % of public<br>sector healthexpenditures|41.9|95.9|0.3|60.2|98.1|13.7|
|_Sub-national revenues_|||||||
|As % ofGDP|7.9|17.1|2.9|5.3|12.5|0.5|
|As % ofpublic sector revenues|18.4|36.0|5.6|16.6|39.8|2.2|
|_Fiscal transfers_|||||||
|As % ofsub-national revenues|24.0|50.4|4.1|42.2|80.8|5.0|
|_Sub-national autonomy_|||||||
|Taxautonomy|55.1|91.0|29.1|40.1|76.5|7.6|
|Expenditure autonomy|74.0|96.2|49.6|58.0|95.0|23.4|

Source: Author’s calculations based on IMF Government Finance Statistics, various years and World Bank Fiscal Indicators.

<u>(http://www1.worldbank.org/publicsector/decentralization/fiscalindicators.htm)</u> Many central governments play a larger direct role in service provision than the theory would recommend. For example, in India, Indonesia, South Africa and Mexico, the central government accounts for more than two-thirds of total expenditures. Even in countries where the de jure assignment of expenditures agrees with theoretical principles, practices can differ. Brazil and Pakistan are the cases in point. In Brazil, the central government has found it difficult to withdraw from some purely local functions such as public markets, local schools, and local bridges more than a decade after the adoption of the 1988 constitution. In Pakistan, central and provincial governments have a prominent role in local functions. In Mexico, the dominance of the central government results from both the direct assignment of functions to the federal level and the supposed inability of lower governments to assume delegated responsibilities. In China, Russia and other transition economies, state enterprises continue to have a role in local government functions. Their redistributive role associated with consumer and producer subsidies especially in the housing market is large and threatens the fiscal health of local governments.

# **Table 3: Central Involvement in Local Functions Remains Extensive**

15
- Myhr, A., C. Bjerkseter, A. Ågotnes and T.A. Nygaard (2014). Levelised cost of energy for offshore floating wind turbines in a life cycle perspective. Renewable Energy, Vol. 66, pp. 714–728.

- National Renewable Energy Laboratory (NREL) (2018). Simple Levelized Cost of Energy (LCOE) Calculator Documentation. Available at: https://www.nrel.gov/analysis/tech-lcoedocumentation.html (Accessed December 2019).

- Ouyang, X. and B. Lin (2014). Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China. Energy Policy 7 (70): 64-73.

- Partridge, I. (2018). Cost comparisons for wind and thermal power generation. Energy Policy, 112: 272-279.

- Shah, Sakhi and Morgan Bazilian (2020). LCOE and its Limitations. Memo, Energy for Growth Hub, <u>https://www.energyforgrowth.org/wp-content/uploads/2020/01/LCOE-and-itsLimitations.pdf.</u>

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36
||of shock impacts (e.g. economic and<br>welfare impacts)<br>• Detail and completeness of information<br>related to size and salience of shock<br>• No or limited ability to disaggregate<br>information<br>• Incomplete recording of uninsured<br>damages<br>• Lack of information to quantify impact<br>on (asset-)poor but highly vulnerable<br>population groups|idiosyncratic shocks as well as poor or<br>vulnerable population groups<br>• Information<br>collected<br>at<br>highly<br>disaggregated<br>level<br>but<br>can<br>be<br>aggregated<br>• Available information subject to survey<br>design|
|---|---|---|
|**Accuracy of data**<br>(Accounting biases)|• Regularly relying on one or few sources<br>per event and exposed to measurement<br>error therein<br>• Usually<br>based<br>on<br>approximations<br>without ability to quantify uncertainty or<br>accuracy|• Estimation based on (large) sample from<br>population of interest<br>• Explicit quantification of uncertainty in<br>estimates<br>• Subject to systematic (non-classical)<br>measurement error|
|**Comparability of**<br>**source data**|• Broad coverage across geographies and<br>time<br>• Exposed to idiosyncrasies in reporting<br>protocols between different data sources<br>without them typically being explicit|• Increased scope for harmonized data<br>collection across contexts<br>• Idiosyncrasies in data collection between<br>different microdata sources are explicit|
||**Geographical differences**||
|**Geographical**<br>**differences**|• Lower information density in LMICs<br>and lower ability to draw on ancillary<br>data sources for shock recording<br>• Systematic under-coverage of events and<br>marginalized<br>population<br>groups<br>in<br>LMICs<br>• Lower accuracy of data due to lower<br>density of (independent) sources of<br>information<br>• Underestimation of impacts in LMICs<br>due to higher share of uninsured<br>damages and low value of affected goods<br>and assets|• Potentially<br>greater<br>accuracy<br>of<br>microdata in HICs due to use of more<br>sophisticated measurement approaches<br>and higher levels of education among<br>respondents|

45
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1.25<br>— 12<br>= o. .<br>s 1.05 :<br>= ’<br>o1<br>— .<br>2 0.95 ee<br>£ 0.9<br>2<br><3<br>= 0.85<br>3 0.8<br>‘oO ~ io) oO _ —N foe) + a) ‘© Le co oO<br>co co ioe) ioe) oO oO oO on oO a oO oO oO<br>oa a oO oO Oo oO ion oO oO oO oO oO oO<br>— In(wage if skill>3)-In(wage if skill<3)<br>-------In(wage if schooling>12)-In(wage if schooling<=12)<br>Source: Author calculations from Quadros de Pessoal.<br><!-- End of picture text -->
Overall, we believe that our results show that letters are effective: they benefit job seekers and enhance firms’ screening ability. In particular, we find large employment gains for women, a group often excluded from informal referral networks. Reducing information asymmetries – through reference letters or other interventions – may thus improve equity by leveling the playing field for women in labor markets.

The remainder of the paper is structured as follows: Section 2 describes the study context and introduces a conceptual framework. Section 3 describes the data and experimental designs. We report and discuss the main empirical findings in Section 4, while Section 5 concludes.

# **2 Background and Conceptual Framework**

## **2.1 South Africa’s Labor Market**

The unemployment rate in South Africa is high (26.4%), especially for youths (36.9%) (StatsSA, 2015). The gender employment gap among black South Africans is substantial, despite the fact that black females are on average more educated than their male counterparts (Rospabe, 2001; Shepherd, 2008). One explanation is that firms appear to either underestimate or are more uncertain about the ability of female applicants (Malindi (2016)).

The labor market in South Africa offers a context conducive to investigating the role of information asymmetries. Most of the unemployed did not complete secondary education (55%) and have no or limited work experience (50.6%), which leaves firms with very little information to screen job applicants. In addition, the quality of education is low on average and highly variable, which limits the use of educational credentials as signals for productivity (van der Berg, 2007). Finally, unemployment spells in weak labor markets are less indicative of job seekers’ ability (Kroft et al., 2013).

Information asymmetries affect how firms and workers are matched. Some large firms in South Africa administer aptitude tests as part of the hiring process. While these tests can increase aggregate productivity and labor demand by improving match quality (Mortensen and Pissarides, 1994; Pissarides, 1985), they have not been widely adopted. This may be due to firms having fewer incentives to test candidates for jobs where investment in training is limited and employment spells are brief (Autor and Scarborough, 5
the impact of trade facilitation on the extensive and intensive margins.<sup>3</sup> In order to implement the Heckman procedure on our sample, an identification variable influencing the probability of exporting but not the volume is required to comply with the exclusion restriction. We enlarge the coverage of HMR’s entry cost indicator, which reflects the fixed entry costs for a pair of trading countries, and use it to satisfy the exclusion restriction in the Heckman estimation method. The two-stage approach allows assessment of export performance along the intensive margin (export volume growth) and extensive margin (propensity of trade between two countries).

As a variation of our model, we incorporate interaction terms between our trade facilitation indicators and per capita GDP to analyze the differentiated impact of trade facilitation variables on exports by income cohorts. As there have been recent contributions to the econometric estimates of gravity models,<sup>4</sup> we check the robustness of our results to different estimation methods and restricting the sample to trade in different sectors.

We also deal with the potential endogeneity of our trade-facilitation indicators to trade flows in three ways. First, we employ a two-step procedure to isolate the part of each trade facilitation related indicator explained by income per capita and population, and use the unexplained residuals to proxy for the trade facilitation indicators. Second, the temporal variation of the trade facilitation indicators provides an additional way to deal with the endogeneity of trade facilitation indicators, as lagged variables can be used as instruments for contemporary values. Third, we follow Freund and Rocha (2010) and restrict exports to new products to deal with reverse causality of exports to trade facilitation, as trade in new goods cannot affect investment in ‘hard infrastructure’ or institutional reform. To take into account omitted multilateral resistance effects, we follow the procedure originally proposed by Baier and Bergstrand (2009) for one-stage gravity estimates, and extended by Behar et al. (2009) to a twostage sample selection model, which consists of correcting bilateral variables. Our results suggest

> 3 The first stage consists of a probit model determining the probability that a country pair engages in trade, whereas the second stage is a gravity regression augmented by a term correcting for selectivity that is computed from the first stage, the inverse Mills ratio.

> 4 See, for instance, Santos-Silva and Tenreyro (2006, 2008), Helpman, Melitz, and Rubinstein (2008), Baier and Bergstran (2009), and Martin and Pham (2008).

4
1 International migration is one of the most important factors affecting economic relations between developed and developing countries in the 21<sup>st</sup> Century. At the start of the century it was estimated that about 175 million people – roughly 3 percent of the world population – lived and worked outside the country of their birth (United Nations, 2002). The international remittances sent by these migrant workers to their households back home have a large and profound impact on the developing world. According to _Global Development Finance_ (World Bank, 2004), official international remittances sent home by migrant workers represent the second most important source of external funding in developing countries.<sup>1</sup> Official international remittances now total $75 billion per year and are about twice as large as the level of official aid-related inflows to developing countries.<sup>2</sup> Despite the ever-increasing size of official international remittances, relatively little attention has been paid to examining the economic impact of these transfers on households in developing countries. A host of key policy questions thus remain unanswered: What kind of migrants remit, and how much is remitted to various regions of the developing world? How do international remittances affect the welfare and poverty status of households in various countries? And how do remittance-receiving households consume and invest their remittance earnings?

This paper proposes to answer these, and similar, questions using a three-step approach. First, the paper builds a new data set of 115 developing countries. This data set includes all those developing countries which were reported by the International Monetary Fund (IMF) as having received official international remittances in the year 2003. The paper uses this data set to identify the regional distribution of remittance flows in the developing world, and to analyze the economic determinants of remittance flows. Second, the paper reviews findings from recent household-level research on how remittances affect welfare and poverty in various developing countries.<sup>3</sup> This section finds that the receipt of international remittances increases the level of household income and reduces the level and depth of poverty in the developing world. Third, the paper reviews findings from recent research on how remittance-receiving households consume and invest their remittance earnings. This section shows that households receiving international remittances spend less at the margin on consumption goods – like food –
**Figure 22a: Growth incidence curve (urban): Benchmark vs. crisis scenario in year 2**

**Figure 22b: Growth incidence curve (rural): Benchmark vs. crisis scenario in year 2**

<!-- Start of picture text --> 0 10<br>-2<br>5<br>-4<br>0<br>-6<br>-8 -5<br>-10<br>-10<br>-12<br>-15<br>-14<br>-16 -20<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Annual growth rate, % Annual growth rate, %<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011

# **5.4 Simulation of the 2008/09 crisis**

In addition to the crisis scenario outlined above, an alternative crisis scenario has been formulated that assumes conditions similar to those in 2008/09. The 2008 financial crisis did not have a similarly devastating effect on the Uruguayan economy as the Argentine crisis of 2001/02; nevertheless growth declined substantially. The manufacturing sector was affected due to its strong linkages with MERCOSUR and particularly Brazil (see also Estrades and LLambí, 2013).

Similarly to the previous analysis, two scenarios are considered. The benchmark scenario is the same as that in the previous section, while under the crisis scenario, a recession similar to that experienced in 2008/09 is simulated: a real GDP growth rate of 1.5 percent in year 1 followed by a mild increase of 2.5 percent in year 2. A particularly sharp decline is assumed for manufacturing, which is immediately affected by the downturn. Overall inflation is projected to remain in the current levels (8.0 percent and 7.5 percent in year 1 and year 2, respectively), but it is mainly explained by a relatively high increase in food prices. Food price inflation is expected to increase to 9.8 percent in year 1 and slightly decline in year 2 to 9.5 percent. Population and labor market projections are very similar to those for the previous scenarios.

Poverty only increases slightly under this crisis scenario (Figure 23). Similar to the previous simulation, poverty declines from 13.7 in 2011 to 11.7 percent in year 2 under the benchmark scenario. Assuming a crisis similar to 2008/09, poverty rates increase to 13.5 in year 1 followed by a slight decline to 12.9 in year 2. This increase represents a poverty rate that is on average 1.1 percentage points higher than it would have been in the absence of the simulated macroeconomic shocks, but still lower than the 2011 levels. Moreover, the simulated poverty rate for year 2 under a crisis similar to that of 2008/09 is 2.3 percentage points lower than that simulated under a crisis similar to the Argentine crisis. In addition, this crisis scenario has an small effect on income inequality (Figure 24).

20
Country Risk Guide (ICRG) – an index including GDP per capita, real GDP growth, annual inflation rate, budget balance and current account balance as components.

The CPIA is available for the period 1995‐2015, but we discard the first three years and use only 1998‐2015, on account of significant changes in the rating system that were introduced in 1998. Specifically, the ratings were from 1 to 5 up to 1997 but from 1 to 6 thereafter, and rescaling the earlier ratings would be arbitrary. There have been further changes in the CPIA since 1998 but most of these had to do with additional questions about the social sectors and fiduciary matters, which increased the total number of questions to 20 for a time, before returning to the current 16 questions. However, during this period of change the questions for Clusters A and B did not change and hence we can use these with some confidence.

It is desirable to have a second dependent variable drawn from a source unrelated to the World Bank. This would have the benefit of avoiding the accusation of bias which might be applied to the CPIA: for the personnel who develop the CPIA overlap with, or may have influence over, the personnel who run development policy operations. A task manager of a SAL/DPF may be tempted to upgrade the relevant parts of the CPIA. Another potential source of bias is that for the poorer countries, whose funding is from the International Development Association (IDA), the country allocation rises with the CPIA by formula, so that an ambitious country director may be tempted to push up the CPIA in order to garner more funds, which would then be likely to be applied to policy lending.

Our second dependent variable is the Economic Freedom Index of the Heritage Foundation (see <u>https://www.heritage.org/index/about), which cannot be impugned for the</u> above‐cited biases. The Index measures economic freedom based on twelve quantitative and qualitative factors, grouped into four broad categories:<sup>13</sup>

1.  Rule of Law (property rights, government integrity, judicial effectiveness)

2. Government Size (government spending, tax burden, fiscal health) 3. Regulatory Efficiency (business freedom, labor freedom, monetary freedom) 4. Open Markets (trade freedom, investment freedom, financial freedom) Each of the twelve economic freedoms is graded on a scale of 0 to 100. A country’s overall score is derived by averaging these twelve economic freedoms, with equal weight being given to each. We use a modified version of the Index excluding the parts on government integrity, judicial effectiveness and the tax burden, so as to ensure maximum correspondence in subject matter with that of Clusters A & B of the CPIA.

13 Some of the components do not measure the quality of policies per se, but rather policy outcomes. For instance, Government Size considers actual government spending, rather than the extent to which fiscal policies achieve stabilization and allocative efficiency.

7