rafmacalaba/datause-encoder-data huggingface dataset:
The other side of the same coin is that, because household and firm dependence on natural resources differ, policy changes that limit access to overused natural resources will have greater effect on some firms and households than others. This can create a quandary for policy makers when a large number of dependent households are poor. In such cases, even though in the long run it is in the interest of the poor who depend on ecological services to preserve them, taking steps to better safeguard natural resources can, in the short run, adversely impact the livelihoods of those least able to adjust. By way of example, the authors of the South Africa study argue that, while sugar firms are geographically diversified in South Africa, smallholder producers are not and would be hard hit by a policy that more appropriately prices scarce water resources in the Incomati River Basin. Similarly, the study from India notes that the poor without land and resources who harvest wild shrimp larvae in the Sunderbans also depend most on access to estuarine resources.

# **5. How can policy condition outcomes?**

As discussed, the consequences of trade reform for specific ecologies and for the poor can be driven by a combination of international and national market outcomes and also by decisions taken by local communities, firms and households. Indeed, it is this large set of determinants and the complex way in which they interact that makes outcomes hard to predict. Even so, lessons drawn from the case studies indicate that predicting the outcomes from trade policy changes is less crucial than putting in place safeguards that protect vulnerable ecologies and households from a wide range of adverse outcomes and finding policies that broaden participation in economic opportunities.

As the previous discussion suggests, understanding how the physical features of the studies’ ecologies combine with market incentives and local livelihoods to affect poverty and the environment is crucial for the formulation of policy. At the same time, not all determinants are mutable and responsive to the instruments available to policy makers. In this section we focus on four areas suggested by the case studies whereby policy can influence poverty and environmental outcomes related to trade. These focus on (1) how market institutions determine the range of new economic opportunities stemming from trade reform; (2) how government programs that provide public goods can complement economic opportunities arising from trade and lower the hurdles that preclude participation by the poor; (3) the role institutions can play in building knowledge about important natural 24
# **I. INTRODUCTION**

This paper follows a strand of the literature that debates that sustained economic growth is the primary source of poverty alleviation (World Bank 2016, and World Bank; International Monetary Fund 2016, and World Bank 2018). In addition, this research focuses on potential future pathways in wealth inequality based on the idea that, in multiple countries, economic growth has played a vital role in the lower end of the income distribution diminishing poverty rates (Dollar, Kleineberg,  and Kraay 2016) and in increasing wealth at the higher end especially for the very rich  (Campos‐Vazquez, Chavez, and Esquivel 2017). Also, our study aims to highlight the recent historical role of both the natural resource sector and the speed of economic growth to predict future changes in poverty and income distribution.

The contribution of this paper to the literature on poverty and income distribution is threefold. First, our Monte Carlo simulation procedure offers an alternative method of estimating the dynamics of the income distribution in a simple setup, expanding the work of World Bank (2014) by modeling of the randomness of the growth rate of the Gini coefficient and its interaction with mean income growth. At the country level, the simulations assume independent movements of the growth rates of both average income and inequality according to historical and country category benchmarks. The second contribution of this paper is that it provides estimates of the certainty of future poverty and some aspects of the income distribution by 2030 for some country categories. The standard deviation of the Monte Carlo simulated projections accounts for the uncertainty of the poverty headcount, inequality and shared prosperity measures. The third contribution of this study intersects the discussion on the position of natural resource-rich countries regarding their effects on long-term growth (Havranek, Horvath, and Zeynalov 2016) versus the position of more-diversified economies, and the significant role that the speed of economic growth across countries plays in poverty alleviation (Ravallion 2013), income inequality (Ravallion 2018), and changes in shared prosperity measures (Dollar, Kleineberg, and Kraay 2014).

Uncertainty in poverty projections is a topic that has been long debated in the literature. In many cases, researchers study the uncertainty of ex-ante poverty outcomes via scenario analysis. For instance, Ravallion (2013) finds that alternative scenarios could lead to very different poverty reduction outcomes. A “pessimistic” scenario could see 1,000 million people lifted out of poverty Page **2** of **45**
information on the functional form of the distance decay function of waves and it was adapted from available sources for mangroves and salt marshes.

The other major limitation of this approach is that the potential for migration is only the first step towards understanding whether mangroves will actually migrate or not. Mangroves are already under severe pressure from conversion for aquaculture and tourism, overcutting, pollution, and other factors. Mangroves have been lost in many areas and are severely degraded in others. Many mangrove forests may not survive to 2100, regardless of the impact of climate change. For those forests that do survive, demographic, economic and other factors may block migration, even where the ecological conditions would make it possible. Coastal areas are the most densely populated parts of the globe, with many large, rapidly expanding urban areas; competition for space is fierce. Also, many of the rural poor live in the low‐ elevation coastal zone (Barbier 2015). Therefore, preserving and cultivating mangroves as a source of coastal defense will require addressing competing land uses, which is beyond the scope of this report.

# **7. Concluding Remarks**

There has been an increased recognition that mangroves can be successfully used either alone or in combination with built infrastructure to provide coastal protection (Narayan et al., 2016).<sup>18</sup> Greater awareness of the role of mangroves in coastal protection as part of a multi‐ dimensional strategy for climate change adaptation has led to large‐scale programs to rehabilitate and replant mangroves in countries like Vietnam and the Philippines as well as small programs in many other countries (Beck et al., 2015). Mangroves may be particularly effective in rural areas where populations are widely dispersed and the construction of hard infrastructures like seawalls may not be economically feasible over long coastlines. A review of 53 nature‐based defense projects (including 12 mangrove projects), found that mangroves could be 2 to 6 times less expensive than the commonly used alternative, submerged breakwaters, for relatively low waves (Narayan et al, 2016).

> 18 For example, mangroves planted in front of an embankment can provide additional protection and reduce the necessary height of the embankment as well as its maintenance costs (Tri et al. 1998).

26
However, official curricular policies among contemporary nation-states are more than superficial reflections of curricular standards imposed by extemal agencies. There is growing evidence that the curricular policies articulated by national educational leaders are both reflective of and influenced by an emerging global educational culture (see Meyer, Benavot and Kamens, forthcoming). At the very least, it is interesting to examine whether national standards of curricular policy have long-tern effects on important societal changes such as economic growth, and we shall turn to this issue presently.

# Research Design and Methodoogy

This quantitative cross-national study attempts to establish causal relafions between variable features of social units (in this case, nations) using a non-experimental research design. The defining feature of this type of comparative research is its use of attributes of macrosocial units in the explanatory statements (Ragin, 1987). As in previous cross-national analyses of economic growth, this study uses aggregate, national level data. The unit of analysis is countries; the sample size depends on the data coverage of the variables included in the models.

This study employs a multiple regression methodology using panel data, the advantages of which have been discussed by Hannan (1979) and Tuma and Hannan (1984). Panel regression analyses incorporate both variation between nation-states and variation over time. They basically involve regressing the dependent variable at a latter time point (2) on the same variable measured at an earlier time point (tl) and on a series of independent variables also measured at the earlier time point (tI). In the present study, a 25-year panel design was used in which the level of economic development at 1985 was regressed on itself at 1960 (the "lagged dependent variable"), on measures of instructional time devoted to different school subjects at 1960, and on several control variables also measured about 1960.3 Analyses were performed separately for all nations and for a smaller sample of only developing nations in the Third World to check for differential effects. Since data coverage on most of the above measures (except for curricular emphasis) is quite extensive, the "sample" of countries from 3. A shorter panel design in which fifteen year lagged effects were examined produced results that are nearly identical to those reported in the paper.

11
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Table 3 also shows whether the subjects in the different groups changed their contribution level from the standard game to the agreement game. The Nepali data generally show small differences in contributions between the standard game and the agreement game, as already shown in Table 1, also when separated by voting groups. For each of the three groups, we test the hypothesis that subjects did not change their behavior between the two treatments. We find that we cannot reject this hypothesis at the 5% significance level in any of the three groups. It is important to note, however, that the contributions among the yesvoters were already very high in the standard game, which can help explain why they did not increase further in the agreement game.

In Ethiopia, the yes-voters in the groups that formed an agreement increased their contributions from 45.7% in the standard case to 53.7% in the agreement case, and this difference is significant (Wilcoxon sign rank test, p-value=0.004). The yes-voters in the groups that did not form an agreement also increased their contributions significantly from 43.5% in the standard game to 50.3% in the agreement game (p-value=0.054). In contrast, the no-voters did not change their behavior and contributed on average 36.0% in both games (pvalue=0.992).

**<u>Table 3.</u>** <u>Average contributions by voting decisions.</u>

|||Contributio<br>public goo|ns in standard<br>ds experiment|Contributi<br>public go|ons in agreement<br>ods experiment|
|---|---|---|---|---|---|
|Subject’s vote|Group agreement|Nepal|Ethiopia|Nepal|Ethiopia|
|Yes|Yes|66.9%|45.7%|67.1%|53.7%|
|Yes|No|66.7%|43.5%|69.4%|50.3%|
|No|No|54.6%|36.0%|50.3%|36.0%|

# _4.4 Determinants of contributions_

We have seen that yes-voters and no-voters differ in their contribution patterns in both the standard game and the agreement game. A somewhat surprising result is that the yes16
Voluntary Export Restraints and Resource Allocation in Exporting Countries*

# by

Jaime de Melo The World Bank and Centre for Economic Policy Research

# and

L. Alan Winters University of Wales, Bangor and Centre for Economic Policy Research

# Table of Contents

|1. Introduction|1|
|---|---|
|2. Resource Allocation Implications of a VER|2|
|3. Estimating the Reduction in Factor Demand:<br>Korean<br>Leather Footwear|11|
|4. Illustrative Welfare Calculations|19|
|5. Conclusions|22|
|Footnotes|24|
|References|26|
|Appendices|28|

* This research was financed by World Bank Research Project 672-40. We thank three referees, Wendy Takacs, Paul Brenton, Taeho Bark, participants at the STEP/CEPR Conference in Venice, May 1988 and at the European Research Workshop in International Trade in Bergen in July 1989 for helpful comments on an earlier draft. We thank Maria Ameal and Alexander Pfaff for logistic support.
13

|**Sectors**|**Modes**|**Categories**|**Description of categories and measures**|**Measures per category**|
|---|---|---|---|---|
|||**Sector**<br>**Openness**|The entry questions in mode 4 focus on whether<br>local licensing is a requirement to provide the<br>services. If nationality is required to provide the<br>services or obtain a local license, the sector is<br>considered closed to foreign professionals. It is<br>possible for a country to allow professionals of<br>certain countries without restriction due to<br>regional trade agreements, while not allowing<br>people from other countries; in this case, the<br>comments provide details.|Nationality of host country required<br>Automatic recognition of foreign license<br>Foreign-licensed professionals eligible to practice subject to<br>conditions|
||**Mode 4:**<br>**Movement**<br>**of people**|**Licensing**|This category covers restrictions and<br>requirements for obtaining licenses to provide<br>the professional services. If a university degree<br>is required, we ask whether a foreign degree is<br>recognized; and if local university degree is<br>required, we ask whether it is a prohibitive<br>restriction. Similarly, we ask if previous work<br>experience is required and if that work<br>experience has to be locally obtained, which<br>could be a significant restriction.|Residency in the host country required<br>Education required<br>Education: Foreign degree recognized<br>Education: Type required<br>Education: Number of years required<br>Training or work experience required<br>Training or work experience: Foreign experience recognized<br>Training or work experience: Number of years required<br>Passing a professional exam in the host country required|
|||**Types of**<br>**entry**|These types of entry are found in GATS<br>schedules and regional trade agreements. The<br>definition of each type is found in Annex Table<br>2.|Types of entry available<br>Intra-corporate transferee (ICT) allowed<br>Service-supplying employee (SSE) of a firm based abroad allowed<br>Independent professional as employee of a local firm (IPE) allowed<br>Independent professional as contractual service provider (IPC)<br>allowed|
|||**Entry**<br>**conditions**|All measures covered under this category are<br>applicable to all types of entry: intra-corporate<br>transferees (ICT), service supplying employees<br>(SSE), or independent professionals (IPE and<br>IPC). These categories are commonly found in<br>GATS schedules and regional trade agreements.<br>The definitions of labor market and economic<br>needs test are provided in the Annex Table 2.|Quota for foreigners: ICT, SSE, IPE, IPC<br>Labor market test: ICT, SSE, IPE, IPC<br>Economic needs test required: ICT, SSE, IPE, IPC<br>Minimum wage or wage parity requirement: ICT, SSE, IPE, IPC<br>Limits on the length of stay initially allowed: ICT, SSE, IPE, IPC<br>Duration of stay initially allowed: ICT, SSE, IPE, IPC<br>Extension of stay permitted: ICT, SSE, IPE, IPC|
The Moroccan export sector grew after trade liberalization. Total exports increased 307 percent from 1993, with a decrease in 2020 due to the COVID-19 pandemic shocks (Figure 2.3). At the same time, the country’s trade balance (exports minus imports) has remained negative and decreasing (Figure 2.4), in part reflecting high dependence on fossil fuel imports.

# **Figure 2.3. Exports benefited from Figure 2.4. Trade balance remained openness negative**

Exports of goods and services, billions 2015 US$ <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

Trade balance, billions 2015 US$ <!-- Start of picture text --> 0<br>-4<br>-8<br>-12<br>-16<br>Source: Authors’ elaboration using data from<br>the World Development Indicators.<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br><!-- End of picture text --> One reason why the EU agreement was so important is that Morocco’s trade, in terms of partners, concentrates on Europe. In 2018, nearly 70 percent of Moroccan exports went to high-income countries. Its two main partners are France and Spain, accounting for 22 and 24 percent of total exports, respectively.

Morocco also exports a limited, but increasing, range of products. Between 2001 and 2019, Morocco increased the number of products it exported from 2,704 to 3,129 (WITS, 2022), which is both higher than neighbors the Arab Republic of Egypt and Tunisia and about 2/3 the level of the United States. Most importantly for this study, however, is that, at the same time, trade patterns shifted from female labor-intensive (e.g., apparel manufacturing) to male capital-intensive industries (e.g., machinery and transportation). In 2000, apparel exports represented nearly 40 percent of Moroccan total exports; by 2020, however, apparel’s share fell to 10.8 percent. In the same period, exports of machinery and electronics almost doubled from 10.6 percent to 18.3 percent, chemicals increased from 11.8 percent to 17.5 percent. Further, transportation equipment, including automotive products and parts, experienced an impressive increase from 0.5 percent to 16.5 percent of total exports.

Evidence from developing countries suggests that shifting trade patterns can affect some workers disproportionally depending on a sector’s labor intensity. Apparel, for instance, is typically characterized by a workforce that is more than 50% female. As described above, machinery and electronics, transportation, and chemicals have surpassed apparel in terms 4 | P a g e
Next, we examine heterogeneity in the impact of AGOA suspension across different product categories: agriculture, fuel and minerals, manufacturing (excluding textiles and clothing), and textiles and clothing. To investigate the heterogeneous effects for these product categories, the triple interaction term is further interacted with a dummy for each category. As reported in table 6, the impact of AGOA suspension is negative and statistically significant for textiles and clothing, but insignificant for the other product categories. The drop in exports of textiles and clothing due to AGOA suspension is substantial at 88 percent. Additionally, the extensive margin of trade in this product subcategories is also adversely affected due to AGOA suspension; the probability of exporting textiles and clothing decreases by 9.4 percentage points.

**Table 6: AGOA suspension effect by product categories**

||lnExpo<br>|rts<br>|Export dummy|
|---|---|---|---|
||Coefficients|Marginal Effect||
||(1)|(2)|(3)|
|Suspended×Country×Product×||||
|Agriculture|0.824|120%|0.051*|
||(0.532)||(0.027)|
|Fuel and minerals|0.318|37%|0.005|
||(1.566)||(0.058)|
|Manufacturing|-0.010|-9%|-0.000|
||(0.139)||(0.007)|
|Textiles and clothing|-2.264**|-88%|-0.094**|
||(1.048)||(0.037)|
|Product-year FEs|Yes|Yes|Yes|
|Country-year FEs|Yes|Yes|Yes|
|Country-product FEs|Yes|Yes|Yes|
|Number of observations|584,434|584,434|584,434|

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

20
|Number of observations|25,541|25,541|19,485|6,056|23,852|23,852|18,296|5,556|
|---|---|---|---|---|---|---|---|---|
||**Pa**|**nel B: First st**|**age IV regre**|**ssion results**|||||
|Dependent variable: Ethnic Fractionalization|||||||||
|Origtime|0.204***|0.204***|0.196***|0.236***|0.202***|0.202***|0.194***|0.240***|
||(0.058)|(0.058)|(0.057)|(0.067)|(0.058)|(0.058)|(0.057)|(0.064)|
|Other controls (As above)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|F statistic|12.31***|12.31***|11.94***|12.64***|11.99***|11.99***|11.56***|14.09***|

Significance is denoted by *** (1%), ** (5%), * (10%). Robust Huber-White standard errors clustered on the country are in brackets. Instrumented values of Ethnic Fractionalization obtained from column 1 are used in columns 2. Similarly, instrumented values of Ethnic Fractionalization obtained in column 5 are used in columns 6.

63
<u>Table 5. Openness, Technological Innovation and the Demand for Skills.</u>

||<br>(1)|<br>(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Exporter|-0.003|0.003|-0.041|-0.050|-0.0653*|
||[0.0350]|[0.0360]|[0.0386]|[0.0386]|[0.0388]|
|Foreign Ownership|0.219***|0.249***|0.230***|0.216***|0.209***|
||[0.0393]|[0.0399]|[0.0411]|[0.0410]|[0.0414]|
|Technological Innnovation|0.318***|0.315***|0.282***|0.268***|0.265***|
||[0.0331]|[0.0333]|[0.0341]|[0.0342]|[0.0342]|
|Age Firm|-0.00350***|-0.00345***|-0.00555***|-0.00566***|-0.00555***|
||[0.00126]|[0.00127]|[0.00132]|[0.00131]|[0.00130]|
|Public Ownership|0.315***|0.320***|0.335***|0.327***|0.333***|
||[0.0487]|[0.0493]|[0.0534]|[0.0532]|[0.0534]|
|Managerial Education|-|-|-|0.709***|0.696***|
|||||[0.109]|[0.109]|
|Sales per employee|-|-|-|-|0.0223***<br>[0.00829]|
|Industry Fixed Effects?|Yes|No|No|No|No|
|Country Fixed Effects?|Yes|No|No|No|No|
|Country-Industry Fixed Effects?|No|Yes|No|No|No|
|Country-Industry-Size Fixed Effects?|No|No|Yes|Yes|Yes|
|Observations|6,072|6,072|6,072|6,072|5,976|
|Rsquared|0.19|0.20|0.27|0.27|0.28|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is the logarithm of the share of workforce with more than the secondary education. Columns (1) through (5) report different specifications. In addition to the firm characteristics reported in each column , column (1) controls for 3-digit industry fixed effects (total 27 categories) and for country fixed effects separately, column (2) controls for country-industry fixed effects and column (3) through (5) control for country-industry-size fixed effects. All the variables are defined in table A1 in the appendix. The specification in column (5) will be the baseline specification throughout the paper. The control variables in the baseline specification, in addition to the country-industry-size dummies, include exporter, foreign ownership, technology adoption, age of the firm, public ownership, managerial education and (log) sales per employee.