rafmacalaba/datause-encoder-data huggingface dataset:
3 using a trade projection model developed jointly by the World Bank and UNCTAD (1989) and a gravity flow model developed for this project, we produce additional information about the likely impact on the developing economies of East Asia. East Asia was chosen for study due to its heavy reliance on the North American market, and the fact that the region produces a relatively wide range of labor-intensive manufactures that often face important North American trade barriers. High trade barriers are one precondition for NAFTA to divert third countries' exports.

# II. Quantitative Analysis of NAFTA's Third Country Effects

NAFTA's economic impacts on non-member countries broadly include: trade diversion, terms-of-trade changes, investment diversion, and the positive externalities associated with the "growth dividend." Several quantitative studies focusing on the NAFTA have appeared lately and they extend the findings of analyses produced during the debate on the United StatesCanada FTA.s This section summarizes the aggregate and sectoral level results derived from some of these models and also evaluates the fimdings of several studies that employed partial equilibrium trade models.

# A. Partial Equilibrium Analyses: The Aggreate Picture

Partial equilibrium analyses of the NAFTA suggest that it will not generate significant trade diversion from non-member countries. Laird (1990) for instance, employs 1983 5 Brown and Stem (1991) provide a useful review of stmdies which analyzed the impact of the Canada-US FTA. See Hufbaner and Shott (1993) for a summary of the potential effects of NAFTA. Section II here relies on Primo Braga (1992).
Figure 2. Comparison of GHG accumulation paths <!-- Start of picture text --> 6<br>5<br>4<br>3 Sc_1<br>Sc_2<br>2 Sc_3<br>1<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26<br>Time<br>Figure 3. Capital accumulation in the old and the new sector<br>45.00<br>40.00<br>35.00<br>30.00<br>25.00<br>Sc_1_K_1<br>20.00<br>Sc_3_K_1<br>15.00<br>Sc_3_K_2<br>10.00<br>5.00<br>0.00<br>1 3 5 7 9 11 13 15 17 19 21 23 25<br>Time<br>GHG stock<br>Capital stock<br><!-- End of picture text --> The carbon intensity of the old sector also plays an important role determining an optimal process of sectoral substitution. For Scenario 4 we assume _ε_ 1 =0.05, versus the value in previous scenarios of 0.02. With a more energy-intensive old sector, GHG accumulation also occurs more 11
94. Finally, we compare the actual distribution of spells with that generated by substituting high replacement unemployment insurance with low replacement unemployment insurance. That is, actual recipients are still entitled to the same number of unemployment insurance payments (in months), but they only receive low-replacement benefits, and nonrecipients remain so. The effects are qualitatively similar to the ones obtained in the previous examples: exit rates increase when we reduce the amount of the benefits (i.e. its generosity) and the distribution of spells shifts leftwards.

**Graph III**

<!-- Start of picture text --> Distribution of (predicted) unemployment spells 111)<br>Replacement ratios.<br>300OD<br>2.0 0<br>S -- ~~~~~~~~S<br>tO 3~,nonft 4to86 =Mhs 7to9rnofths lOtol2mono0is lt 2 yea. Momthn 2<br>yu,3<br>Duration of sil<br><!-- End of picture text -->

**Source:** Unemployment Registry

# **Exit-to-employment.**

95. As we mentioned above, there is substantial variation in the behavior of unemployment insurance recipients over time. In particular, although their unemployment spells are longer on average than those of non-recipients, they tend to abandon the Unemployment Registry faster than their counterparts once the benefits are exhausted. The question then arises as to what it means to leave the Registry, since exit does not necessarily imply employment. For instance, it could be the case that, once unemployment insurance is exhausted, workers have no further reason to remain registered. The policy implications associated with each scenario are fairly different, so we explore this issue further.

96. Using the Unemployment Registry, we estimate a probit model for the probability of exit-to-employment,<sup>2 5</sup> where employment means private sector work. As before, we first provide the baseline results, when no benefit-related variables are included in the regression (Table XIV). Women and married workers have a slightly higher probability of finding a job, and so do prime-aged and/or more educated individuals. These effects are fairly robust to different model specifications.

97. When we consider benefit entitlement, workers who receive unemployment insurance are more likely to find a job in the private sector than those who do not (about a nine

> 25 Only exit to employment in the private sector was considered as a successful exit. Qualitatively and quantitatively equivalent results are obtained when exit to both employment in the private sector and employment in a public employment program are considered instead.

26
**Figure 2. GDP growth versus service value added growth, 2000-07.**

<!-- Start of picture text --> 0 5 10<br>Service output growth<br>Note: black - all countries, red - EU-15, blue - NMS, green - Accession<br>6<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors’ calculations.

To better understand the link between services and growth in Europe we will look closer into the following questions: (1) Is there a growing importance of services in growth? (2) Is productivity of services increasing? (3) Is there an increasing tradability of services? (4) Is Europe‘s service exports evolving from traditional to modern? (5) Does Europe have specialization in service exports? (6) What are the trends in terms of direction of Europe‘s service exports?

# **2.1 The growing importance of services for general growth**

The sectoral contribution of services to GDP growth has risen for most countries in the world and the services share in global production has increased considerably. Looking specifically at EU-15, we see that it has a very high level of service domination in recent growth (82 percent), similar to USA and other advanced countries (see Figure 3, and Figures A7-9 for specific countries). As shown earlier, growth in services is somewhat lower than expected, even when taking the relatively low GDP growth in EU-15 into account, but the share of services in GDP is so significant that it is still the dominating sector for overall growth. Only with the exception of Finland and Germany, growth in EU-15 is increasingly service dominated when comparing the two periods, 1995-99 and 2000-08. Expectedly, growth in NMS and ACC is less service driven but again, the trend is an increasing dominance of services for growth – the contribution rose from 50 to 60 percent in NMS and 49 to 64 percent in ACC. Maybe more surprisingly, ACC is – albeit only slightly - more services driven than NMS. However, the next question is what type of services Europe and the different country groups are producing and in particular exporting. We will see that the type of service growth in ACC is very different.

5
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Financing Mali’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Mali needs to expand its infrastructure assets in key areas (table 17). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

**Table 17. Illustrative investment targets for infrastructure in Mali**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Fiber optic links to neighboring capitals and submarine<br>cable|Universal access to GSM signal<br>and public broadband facilities|
|Power|284 MW new generation*<br>0 MW interconnectors*|Electricity coverage of 39%<br>(100% urban and 7% rural)|
|Transport|Regional connectivity by good quality 2-lane paved<br>road|Rural network gives access to 14% agricultural<br>production|
||National connectivity by good quality 1-lane paved<br>road|Urban population within 500-meter paved road<br>Clear sector rehabilitation backlog|
|WSS|n.a.|MDG targets for water and sanitation|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. * Assuming trade stagnation scenario.

Meeting these illustrative infrastructure targets for Mali would cost around $1 billion per year through 2015. Capital expenditure would account for 65 percent of this requirement. The highest annual price tag is associated with the water and power sectors, each of which requires around $300 million. The transport and ICT sectors are also in need of significant funding—around $236 million and $178 million per year, respectively. The water sector spending is associated with meeting the MDG target for water and sanitation. The power sector spending is associated with providing 284 megawatts of new generation capacity to meet demand over the next decade and boosting electrification from 13 percent to 39 percent (table 18).

**Table 18. Indicative infrastructure spending needs in Mali, 2006–15**

<u>US$ million per year</u>

|Sector|Capital<br>expenditure|Operations and<br>maintenance|Total needs|
|---|---|---|---|
|Information and communication technologies|79|99|178|
|Power|220|82|302|
|Transport|144|92|236|
|Water supply and sanitation|229|82|311|
|Total|672|355|1027|

Sources: Mayer and others 2008; Rosnes and Vennemo 2009; Carruthers and others 2009; You and others 2009. Derived from models that are available online at http://www.infrastructureafrica.org/aicd/tools/models 41
**Table 41. Importance of IPA services: Most important IPA service**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.00482|0.000821|||-0.0379|-0.0177|||
||(0.0407)|(0.0393)|||(0.120)|(0.127)|||
|Export % of sales|-0.000178|-0.000248|-0.000109|-0.000194|-0.00213|-0.00131|-0.00172|-0.000963|
||(0.000543)|(0.000529)|(0.000546)|(0.000546)|(0.00162)|(0.00173)|(0.00168)|(0.00181)|
|Share of sourcing via imports|0.00116**|0.00105*|0.00106*|0.000959*|0.00385**|0.00342*|0.00370**|0.00326*|
||(0.000537)|(0.000536)|(0.000562)|(0.000556)|(0.00168)|(0.00179)|(0.00178)|(0.00187)|
|High-income source country|-0.130***|-0.0535|-0.125**|-0.0549|-0.407**|-0.156|-0.417**|-0.174|
||(0.0482)|(0.0494)|(0.0506)|(0.0508)|(0.179)|(0.198)|(0.188)|(0.201)|
|>250 employees|0.0430|0.0488|0.0365|0.0436|0.157|0.209|0.134|0.191|
||(0.0467)|(0.0464)|(0.0470)|(0.0462)|(0.148)|(0.156)|(0.151)|(0.157)|
|Cumulative inv. >US$10M|-0.0271|0.0270|-0.0332|0.0229|-0.0414|0.0659|-0.0708|0.0453|
||(0.0459)|(0.0445)|(0.0454)|(0.0445)|(0.138)|(0.145)|(0.141)|(0.148)|
|Years in country|-0.00105|-0.00237|-0.000781|-0.00232|-0.00478|-0.0103**|-0.00412|-0.0101**|
||(0.00165)|(0.00147)|(0.00168)|(0.00150)|(0.00492)|(0.00490)|(0.00498)|(0.00499)|
|Level of foreign ownership (%)|-0.00227***|-0.000340|-0.00201***|-0.000245|-0.00750***|-0.000967|-0.00708***|-0.000986|
||(0.000700)|(0.000745)|(0.000689)|(0.000741)|(0.00274)|(0.00301)|(0.00272)|(0.00301)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.091|0.035|0.105|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is a composite ordered categorical variable constructed from responses to the following series of questions: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Maximum score across all services]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

58
that the results from this robustness check are remarkably close to the results reported in Table 3.

# (5.4) Economic Signi...cance

The estimated e¤ects of rainfall variations on wage, and labor allocation between employment in own farming and hired labor in agriculture as reported in Table 3 provide strong evidence in favor of a model with signi...cant labor supply response in market activities as households reallocate labor from home production. The evidence presented in Table 2 also establishes clear and strong links between agricultural productivity as measured by rice yield per acre and irrigation on the one hand and rainfall variations on the other. The rainfall shocks thus can be interpreted as shocks to the productivity parameter � in our model. It is thus reasonable to interpret the results on the e¤ects of rainfall variations as capturing largely the e¤ects of agricultural productivity. As we noted in the empirical strategy section, our result can also be given an instrumental variables interpretation, which allows us to provide point estimates of the causal e¤ects of agricultural productivity increase on the agricultural labor market.

Using the coe¢ cient estimates in Tables 2 and 3, we compute the IV estimates of the impact of agricultural productivity on outcomes of our interest. The IV estimate for the e¤ects of agricultural productivity on the wages of daily labor implies that a one percent increase in agricultural productivity (crop yield per acre) increases the wage for unskilled agricultural labor by about 0.93 percent, which is a substantial impact. A one percent increase in the productivity increases share of people engaged in own farming by about 1.4 percent, while it reduces the hired daily labor in agriculture by 1.5 percent.<sup>24</sup> The estimate

> 24While household may also draw labor out of non-farm activities and into own farming in response to agricultural productivity shock, our results show that bulk of the reallocation happens within agricultural

30
|**Ta**|**ble 8: All IPAs wit**<br>|**h Legally Enf**<br>|**orceable IPR**<br>|**s Provisions (**<br>|**Bilateral Trad**<br>|**e, 100 IPAs)**<br>|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Low-IP|(2)<br>AI|(3)<br>BIO|(4)<br>CHEM|(5)<br>ICT|(6)<br>MED|(7)<br>PT|(8)<br>Other|
|**Exports**<br>log(GDPi)|0.381***<br>(0.076)||||||||
|Sector × log(GDPi)||0.451***|0.184*|0.306***|0.192**|0.481***|0.380***|0.293***|
|||(0.092)|(0.106)|(0.099)|(0.097)|(0.094)|(0.127)|(0.079)|
|Sector × LI × IPAi|–0.485**|0.597***|–1.762***|–0.634*|3.175***|0.095|0.586*|0.709***|
||(0.213)|(0.228)|(0.463)|(0.368)|(0.316)|(0.259)|(0.319)|(0.252)|
|Sector × LMI × IPAi|–0.235**|0.707|0.487|0.500|0.102|0.216|0.907**|0.334|
||(0.112)|(0.435)|(0.785)|(0.352)|(0.499)|(0.546)|(0.425)|(0.374)|
|Sector × UMI × IPAi|0.144|–0.450|1.022**|0.278|–0.841|0.063|–0.803*|–0.192|
||(0.150)|(0.387)|(0.403)|(0.322)|(0.594)|(0.507)|(0.421)|(0.148)|
|Sector × HI × IPAi|0.262*|0.382**|1.853***|0.575***|–0.249|0.696***|0.066|–0.042|
||(0.148)|(0.190)|(0.231)|(0.207)|(0.214)|(0.215)|(0.297)|(0.151)|
|**Imports**|||||||||
|log(GDPj)|0.527***<br>(0.046)||||||||
|Sector × log(GDPj)||0.125***|0.143*|0.027|–0.080|0.110***|0.069|0.025|
|||(0.042)|(0.079)|(0.040)|(0.070)|(0.027)|(0.056)|(0.057)|
|Sector × LI × IPAj|0.051|0.044|–0.769*|–0.224|1.107***|0.079|0.094|0.146|
||(0.189)|(0.117)|(0.429)|(0.194)|(0.288)|(0.188)|(0.156)|(0.198)|
|Sector × LMI × IPAj|–0.150*|–0.214**|–0.303|–0.165|–0.010|–0.328**|–0.234**|–0.232**|
||(0.083)|(0.088)|(0.375)|(0.164)|(0.184)|(0.158)|(0.103)|(0.096)|
|Sector × UMI × IPAj|<br>–0.209|<br>–0.208*|<br>–0.009|<br>0.152|<br>–0.066|<br>–0.159|<br>–0.067|<br>–0.095|
||(0.143)|(0.114)|(0.168)|(0.197)|(0.260)|(0.147)|(0.125)|(0.109)|
|Sector × HI × IPAj|<br>–0.099|<br>0.242*|<br>0.872***|<br>0.307*|<br>–0.161|<br>0.241**|<br>0.355**|<br>0.233|
||(0.218)|(0.139)|(0.278)|(0.164)|(0.237)|(0.112)|(0.157)|(0.163)|
|Observations||||||||915,312|
|Number of country pairs||||||||24,098|
|Pseudo_R_<sup>2</sup>||||||||0.948|

_Notes_ : Robust standard errors two-way clustered by exporter and importer in parentheses. Includes exporter- and importer-specific time trends, exporter- and importer group-sector year fixed effects, and bilateral pair fixed effects. TRIPS compliance by country included not reported. Observations at 5-year intervals: 1995, 2000, ... 2015. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

28
The relation involves average, not marginal productivity of capital, because natural disasters do not destroy the least productive assets first, but affect random sectors and random portions of the existing capital (Hallegatte and Vogt‐Schilb 2016). Note that if the productivity of the affected capital was equal to the marginal productivity of capital, that is ��� , then the loss of consumption would simply be equal to the loss of capital (and would thus be independent of the reconstruction duration).

Then, to estimate the welfare impact of a macroeconomic loss in consumption, it is necessary to account for the distributional impacts of the disaster, that is whether it impacts poor or nonpoor people, and for the ability of the population to cope with the shock. Our framework is flexible regarding the definitions of “poor” and “non‐poor.”<sup>3</sup> In particular, one can decide to focus on extreme poverty by defining the poor following national or international poverty lines; through a Multidimensional Poverty Index; using a given fraction of the population (e.g., the bottom 20 or 40% of the income distribution); or using a broader definition of poor people that is relevant for disaster analysis (e.g., the population with no access to financial instruments, or the population that is “vulnerable” to falling back into poverty<sup>4</sup> ). In what follows, we define the poor as the people in the bottom 20% of the consumption distribution. All the calculations that are proposed in this section are nonetheless possible in a more complex framework with more than two categories.

# 2.2. Distribution of asset and consumption losses

The distribution of losses from a disaster depends on poverty, assets used, affected individuals and capital, and consumption losses, as each section of the framework below describes.

## 2.2.1. Income categories

In each country, there are � individuals, partitioned in �� poor people and �� non‐poor people. We call poverty headcount �� the share of poor people in the economy: The aggregate national consumption, income and assets are noted � , Y and � . The variables �� , �� , and � are the per capita consumption of poor, nonpoor and average individuals, respectively.

## 2.2.2. Affected and non‐affected people

To calculate the welfare impact of the disaster, we start by identifying the number of poor and non‐poor people that are directly affected by the shock, and the number of those that are not directly affected (but are affected by indirect impacts).

> 3 However, since the main difference between poor and non‐poor will be the marginal utility of income, monetary definitions of poverty appear more appropriate than non‐monetary definitions, in this very context.

> 4 See a discussion on the World Bank website: http://go.worldbank.org/R048B34JF0.

5
- 27 -

- (iii) The orderly and constructive integration of the Soviet Union and other CMEA nations into the world economy by co-financing of projects, and other joint activities, particularly in those regions where conflicts are being resolved, such as southern Africa and Southeast Asia, where they already have a large presence. As a prior step, if the Bretton Woods institutions were to open channels so as to acquire accurate data from the Soviets and East Europeans on their loans and other economic transactions in advance of reconstruction efforts this could strengthen their program planning and analysis and offer modest opportunities to develop practical ties with the Eastern bloc.

- (iv) Design long range approaches for supporting and sustaining more effective developing country participation in the new international regimes that will he requirad to alleviate and overcome future global security threats, especially such environmental concerns as the degradation of the world's vital atmospheric, Marine tropical, water, and biological resources.

# Peace. Reconciliation, and Reconstruction

72. In an address to a conference on the future of the World Bank, held in Washington in 1986, former German Chancellor Helmut Schmidt called upon the Bank to take the leadership in the reconstruction of war-torn Central America.<sup>4 2</sup> Given the complex local and international politics of that crisis, it seemed a bold suggestion. Yet, in light of the IBRD's initial mission in rebuilding Europe, it can not be deemed a novel one. The multilateral development banks could, if they chose, play such a role in several troubled regions of the developing world, particularly if, as Schmidt maintained, the Japanese and Europeans would give their political and economic support. Designing and carrying out such missions would, of course, require the cooperation of the United Nations, the appropriate regional coalitions such as SADCC or ASEAN, and ad hoc inter-governmental groups such as Contadora.

73. The key to such initiatives is "ripeness." As an analytic tool, the term can used to explain why some conflicts result in peace through compromise while others just lead to more

- 41/ For background see: Donald S. Zagoria, "Soviet-American Rivalry in Asia," in Korbonski and Fukuyama, op. cit. pp. 247-72; Gorbachev set a new tone of hope for improved relations with his July 28, 1986 Vladivostok speech on Soviet policy toward Asia.