rafmacalaba/datause-encoder-data huggingface dataset:
In both simulations exercises, however, we have neglected any possible incentive (or "brain gain") e¤ects of migration on human capital formation. Following an important theoretical literature, such incentive e¤ects have been identi...ed in a series of recent micro (e.g., Gibson and McKenzie, 2011) and macro (Beine et al., 2008, 2010) empirical papers.<sup>30</sup> The last question we want to ask, therefore, is the following: which countries would lose/gain from a marginal increase in skilled emigration if incentive e¤ects were accounted for? If we assume there are additional incentives to invest in human capital when there is a positive di¤erential emigration probabilities for highly educated (which is an empirical regularity), then we should write: with H 0 = <u>dH</u><sup>and</sup> <u>dH</u> = � dms<sup>> 0</sup> dms<sup>:</sup><sup><u>ms</u></sup> H<sup>�mu</sup> The derivative of institutional quality with respect to skilled emigration now becomes: After some manipulations, it is easy to see that the marginal impact of skilled emigration on institutional quality will be positive (i.e., dmdIs<sup>> 0);if:</sup> To be able to simulate the marginal impact of an increase in skilled migration in a context of endogenous human capital, we need to give a value to �, the e¤ect of emigration on human capital formation. We will use several values for �, in particular � = 0:05 (closer to the point estimate in Beine, Docquier and Rapoport (2010): � = 0:054)<sup>31</sup> , � = 0:01, � = 0:10 (which correspond to the values of the lower and upper bounds of the 90 percent con...dence interval of the estimated point), and � = 0:23 which corresponds to the long-run elasticity of human capital formation to high-skill migration prospects.<sup>32</sup>

> 30See Docquier and Rapoport (2009) for a survey of this recent brain drain literature.

> 31Note that since this value is for the short-run elasticity of human capital formation to the di¤erential emigration probability, it can be seen as a conservative estimate.

> 32In unreported simulations, we also use the lower and upper bound of the 90 percent con...dence

31
**TABLE 5: COVERAGE OF REPRODUCTIVE HEALTH SERVICES† AND PROVISION OF MODERN FAMILY PLANNING‡**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Skilled delivery|At least two ANC<br>visits|Tetanus vaccine<br>during pregnancy|Postnatal care|Modern<br>contraception|
|Post indicator|0.053***|0.022|0.001|0.105***|0.002|
||[0.019]|[0.014]|[0.019]|[0.031]|[0.044]|
|PBF/Post interact|-0.043|0.010|0.024|-0.029|-0.037|
||[0.028]|[0.020]|[0.023]|[0.041]|[0.054]|
|Control 1/Post|0.020|-0.024|0.003|-0.019|-0.054|
|interact<br>|[0.032]|[0.019]|[0.025]|[0.041]|[0.055]|
|Control 2/Post<br>|-0.050*|-0.044**|0.01|-0.070*|0.000|
|interact|[0.029]|[0.019]|[0.023]|[0.039]|[0.053]|
|p-value PBF vs. C1|0.055|0.111|0.369|0.798|0.731|
|p-value PBF vs. C2|0.828|0.010|0.520|0.277|0.429|
|p-value PBF vs. C3|0.117|0.617|0.306|0.484|0.486|
|Baseline mean C3|0.784|0.894|0.878|0.323|0.180|
|N|5858|5974|5975|5966|4498|

* = p < 0.10,  ** p < 0.05,  *** p< 0.01

<mark>† Results from difference-in-differences regression models examining the effect of PBF on reproductive health service use among female respondents included in the household survey who had been pregnant in the previous 24 months.</mark> ‡ <mark>Results from difference-in-differences regression models examining the effect of PBF on modern contraceptive use among female respondents of reproductive age (15 – 49) included in the household survey</mark> . <mark>Regression models adjusted for individual (age, marital status, education level, religion, ethnicity, working status and type of work) and household control variables (number of individuals in the household, housing type, house ownership, water source, and type of sanitation). Standard errors were clustered at the health facility level.</mark> 38
|**Table 1. Average Import Price Re**|**ductions, percen**|**t 1/**||||||
|---|---|---|---|---|---|---|---|
|<br>**COUNTRY/REGION**|<br>**WHEAT**|<br>**RICE**|**COARSE**<br>**GRAINS**|**SUGAR**|**MEAT**|**OILSEEDS**|**DAIRY**|
|**BUROPEANUNION**|-|-|-|-|-12|||
|**UNITED STATES**|**-15**|**-**|**_-_**|**_-_**|**-**|-||
|**JAPAN**|**-81**||-91|**-47**|-33|-|-14|
|**AUSTRAUA**|-|**-4**|**-**|**-**|-|-1|-19|
|**CANADA**|**_-_**|**-**|**-**|-|-|**-**||
|**EFrA**|**-**|**_-_**|**0**|-14|-|-18||
|**UPPER INCOME ASIA**|-106|-|**-76**|**-7**|**-44**|**-5**||
|**INDONESIA**|-|**_--_**|||**-**|**-33**||
|**INDIA**|-|-|-|**_-_**|-|-3||
|**LOW-INCOME ASIA**|**-**|-||||||
|**BRAZIL**|-31|-|-|||||
|**MEBXCO**|**-**|-|-|**_-_**|**_-_**|-1|-|
|**OTHERLATINAMBRICA**|-|-|-|**_-_**||||
|**NIGERIA**|-12|-|-16|||-||
|**MED1TERRANEAN**|-|-|**-**|||||
|**OTHBER**<br>**AFRICA**|**-**|**_-_**|-|-|-|**_-_**|**_-_**|
|**SOUTH AFRICA**|-|-|-|**_-_**|-|-|-|

# **MAGHREB-**

1/ Calculated as change intanffrate dividedby **the** power ofthe initial tariffrate. Wherethepost-UR rate is greaterthanthe counterfactuaI rate, a **zero** pricereduction is assumed, indicated by **_(-)._**

**2/** Japan delayed taiffication **in rice.**

Source: Ingco, 1995.

**Table 2. Impact of Uruguay Round liberalization on world agricultural prices** (percentage change from benchmark levels at the end of implementation period).

|Commodities|Brandao<br>&<br>Martin<br>(1993)<br>Dunkel<br>scenario|Goldin,<br>et.al.<br>(1993)|Goldin,<br>et.al.<br>(1995)<br>Scenario<br>I<br>79-93<br>base|Goldin,<br>et.al.<br>(1995)<br>Scenario<br>II<br>89-93 base|Goldin,<br>et.al<br>(1995)<br>Scenario<br>IV<br>(with no<br>slippage)|FAO<br>1995|
|---|---|---|---|---|---|---|
|Wheat|6.18|5.9|1.2|3.8|10.3|7.0|
|Rice|4.02|-1.9|-1.5|-0.9|3.6|7.0|
|Coarse<br>grains|3.30|3.6|0.1|2.3|5.4|4.0|
|Sugar|9.92|10.2|-1.0|1.8|11.4|na|
|Beef**&**veal|7.16|4.7|0.2|0.6|6.0|8.0|
|Otbier meats|4.02|1.0|-0.9|-0.6|2.3|na|
|Coffee|1.35|-6.1|-1.7|-1.5|-0.7|na|
|Cocoa|0.90|-4.0|-1.3|-0.7|0.3|na|
|Tea|2.66|3.0|-1.6|-1.4|0.9|na|
|Vegetable<br>Oils|3.77|4.1|-0,6|-0.3|5.4|4.0|
|Dairy|12.18|7.2|-1.3|1.2|12.1|7.0|
|Other Foods|1.33|-1.7|-1.3|-1.4|-0.7|na|
|Cotton|1.82|3.7|-1.1|-1.2|1.2|na|
|Other Agriculture|2.62|5.9|-0.5|0.8|2.9|na|

Sources: Brandao and Martin, 1993; Goldin and van der Mensbrugghe, 1995; FAO, 1995; Martin and Winters, 1995.

23
_5_ to OECD Europe.<sup>3</sup> Second, the analysis is based on empirical information which is tabulated in terms of the Standard International Trade Classification (SITC - Revision 1) and no account could be taken of any differences in demand or competitive factors that may have occurred within these product groups. Third, in order to provide a more accurate representation of the broad influence of the demand, competition and diversification factors on African exports, the analysis focuses on non-oil goods. Inclusion of crude petroleum (which accounted for 49 percent of SSA exports in 1994, and is largely produced by only four countries (namely, Angola, Congo, Gabon and Nigeria) would have obscured the true picture of what was occurring for all other export products and countries.

Several considerations resulted in the 1962-64 to 1991-93 period being selected for this analysis. First, it was felt desirable to analyze Africa's export performance over as long an interval as possible in order to identify secular trends. The earlier period was chosen since 1962 was the first year COMTRADE records became available. Similarly, at the time this study was initiated several OECD countries had not yet reported their full 1994 trade statistics. Second, a three year average was used for compiling trade data in both the base and end periods in order to reduce the potential influence of any atypical factors which might influence the statistics -- like abnormal commodity prices in a given (single) year. This could be an important consideration for African countries whose exports are heavily concentrated in primary products. Finally, some maintain that Africa's competitive position in OECD markets has been adversely affected by the erosion of their tariff preferences due to MFN tariff cuts or the spread of industrial countries' regional arrangements. In 1962-64 neither the GSP nor the Lome Convention had been implemented (both began in the early 1970s). As such, this study's comparisons are between the early 1960s when Africa did not benefit from tariff concessions and the early 1990s when 3UNCTAD (1993, Table Al) provides estimates of the geographic destination of African exports. This information is updated in each annual issue of the UNCTAD report so time series on the direction of this trade can be compiled back to the early 1950s. Table 3.4 in the document provides detailed information on the destinations of individual African country's exports while Table 3.3 provides similar information on the origin of imports.
Because of the US-Jordan FTA in the year 2000, the largest share of exports to the United States are textiles and clothing, but other sectors such as chemicals have surged in the past few years. There are interesting patterns when zooming in on the share of products exported from Jordan to the United States since 2015. While the textiles and clothing sector remains the highest exporting sector, accounting for 66.8 percent of total exports in 2021 (86.6 percent in 2015), the chemicals sector has gained relative importance, growing from 1.9 percent in 2015 to 16.4 percent in 2021. In terms of US$, this means an increase from US$28 million to US$425 million in 2021 (textiles increased from US$1,251 million to US$1,544 million).

# **4. The Impact of the JC**

The JC had a clear objective of boosting Jordan’s exports to the EU. Figure 17 shows the evolution of Jordan’s export value by main commodity sector. As it can be noted, only for manufacturing the country experienced a sharp change in export values flowing to the EU zone after 2016. The observed change in trend was not immediately after the JC was implemented but it rather took off in 2020-2021. Later in this study, we suggest that a skill mismatch of Syrian workers may explain the weak integration of Syrian refugees in the labor market early after the implementation of the JC, therefore affecting the effectiveness of the agreement in boosting targeted exports.

**Figure 17: Jordan’s total export value to EU in Agriculture, Mining and Quarrying, and Manufacturing**

<!-- Start of picture text --> Jordan's Exports to EU<br>by Main Sector<br>Agriculture Mining and Quarrying<br>800 JC JC<br>600<br>400<br>200<br>0<br>2000 2005 2010 2015 2020<br>Manufacturing<br>800 JC<br>600<br>400<br>200<br>0<br>2000 2005 2010 2015 2020<br>Year<br>Export Value in M US$<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Integrated Trade Solutions (WITS). The vertical red dotted line splits the periods before and after the implementation of the JC.

16 | P a g e
These figures indicate that the potential financial incentives of tropical timber certification from the perspective of producing countries are limited, at least for the short to medium term. Thus, if returns to TC are this low, producers may not have sufficient incentives to adopt it. However, individual producers that first become certified could establish themselves in a market niche and potentially profit from early entry in this market. In addition, producing countries may have other non-economic long-term benefits accruing from the adoption of better forest management practices and the maintenance of biodiversity.

# **Costs Associated with Timber Certification** 25

The analysis developed above has not addressed the costs associated with TC. In practice, a TC scheme entails two type of costs at the company level. The first is the cost for the company to operate in the sustainable manner, according to a set of the agreed principles and criteria. The second is the cost of the certification process.

The cost of sustainable forest management, which is also referred to as compliance cost to TC scheme, varies widely across types of forests (heterogenous vs homogenous, tropical vs temperate and boreal). As pointed out by Haji Gazali and Simula (1994), the lack of reliable estimates on the cost of sustainable forest management is mainly caused by the lack of commonly agreed operational definitions of sustainability. In Sarawak, an ITO study reported that adopting nearly zero impact logging would increase the cost of logging by adopting 100% or about US$60 per cubic meter log. In the Philippines, Paris and Ruzicka (1991) reported that sustainable forest management would put an extra US$38 per cubic meter log. As estimate by Dianasari (1993) for the externality costs of forest destruction due to logging in Indonesia, leads to additional cost of about US$70 per cubic meter log.<sup>26</sup> Jaakko Poyry (1993) estimated that the cost of compliance ranges between US$0-13 per cubic meter log. Most of these estimates suggest that the cost of sustainable forest management per cubic meter log likely lies between 10-20% of the current average international tropical log price of about US$350. Application of common principles and criteria for sustainable forest management is expected to eventually lower the compliance cost over time.

25This section draws mainly on Ahmad (1994).

26According to Ahmad (1994), another rough estimate of the cost of sustainable forest management in Indonesia is to take the official forest plantation planting cost of US$1,000 per hectare divided by about 30 cubic meter log harvested per hectare concession. It generates the estimated additional costs of about US$35 per cubic meter log harvested.

28
6 literature with analysis at the household level.

We investigate and compare the two methods for estimating nutrient adequate diets using the case of rural Malawi and discuss the policy implications of methodological choice and empirical results. The case of Malawi is of particular interest. Although Malawi has made substantial progress reducing stunting in the last decade, diets remain of poor quality largely dominated by maize and other staples, and with insufficient variety and nutrient-dense foods, micronutrient deficiencies persist (Pauw, Verduzco-Gallo and Ecker 2018; National Statistical Office (NSO) Community Health Sciences Unit (CHSU) [Malawi], Centers for Disease Control and Prevention (CDC) and Emory University 2017; Gilbert, Benson and Ecker 2019; Schneider 2021a). Malawi further presents an interesting example because of the social norm of shared plate eating, a pronounced form of meal sharing. But a methodology for establishing a range of least-cost diets at the household level is relevant throughout the world, given how many food and anti-poverty programs target households and the widespread practice of eating common meals (i.e., “family style”) across cultures (Hjertholm et al. 2019; Gelli et al. 2020; Hjertholm et al. 2018).

The remainder of this paper proceeds as follows. Section 2 presents the conceptual framework describing the two approaches to estimating household diet costs. Section 3 presents the data sources as well as key background features of Malawi’s food system. Section 4 explains the methods used to define the nutrient requirements and calculate diet costs; it reviews how the extent of seasonality in diet costs and food group prices will be evaluated and introduces the criteria to assess affordability. Section 5 discusses the findings, followed by section 6 which concludes.
recovery. While growth returns after one year in most products, iron and steel and commodities tend to exhibit more protracted slowdowns.

Already some similar patterns are emerging in current data. Data at the industry level for the United States and Japan through March show foodstuffs among the least affected, with metals and mineral products the most affected.

# **VI.  What Regions Are Most Affected?**

Figure 15 shows export value growth by region. In previous crises, the Middle East and North Africa has been most affected and Europe and Central Asia has been least affected; while other regions show remarkably similar patterns. Europe may be less affected because in-built social safety nets ensure against a sharp drop in spending and most exports are within the region. By income group, the least affected are the high income OECD and the low income countries (Figure 16).

The results from Section III suggest that exports from Asia are likely to fall relatively more rapidly during downturns because of the high elasticity of trade to income, however, we do not find this to be the case. This may be because downturns are different. First, they tend to affect petroleum and commodities exporters most heavily, which Asia does not export. Second, while expanding world income has facilitated greater fragmentation of production and rapid trade growth in Asia, it is unclear that a _temporary_ decline in income would reverse this trend, as there are large sunk investment costs to building production chains.

# **VII.  Conclusion and Looking Forward**

12
see impacts on outcomes and focusing on them could therefore be misleading (King and Behrman 2009). Our focus on knowledge and behavior is also, however, based on the independent interest in how to best affect those.

# **_2.3 The evaluation design_**

The interventions we evaluate were implemented in 3 districts of Indonesia: Tulungagung, Malang and Sumbawa.<sup>10</sup> These districts were selected based on having enough schools in the program monitoring survey to carry out the experiment with sufficient sample size, as well as on their willingness to participate in a randomized pilot. Implementation constraints associated with the program made it impossible to simply randomize the interventions across schools in the various districts. With district administrations in charge of implementing the program it was deemed infeasible to have multiple approaches plus a control group within the same district. The evaluation design, therefore, consists of a collection of three district-level experiments based on within-district randomization of schools to one type of treatment (with two variants in the case of Tulungagung) or a control group. While not threatening the internal validity of the evaluation results for each intervention, the fact that we are comparing different interventions implemented in different districts means that the comparison across interventions is less clean. The approach is arguably better, however, than the oft-made comparisons of different interventions implemented in different countries (see for example Kremer, Brannen and Glennerster 2013 or McEwan forthcoming). This is because the institutional setting is consistent across districts, and the social context is broadly similar.

A total of 148 Primary schools in 3 districts were part of the experiment.<sup>11</sup> Figure 1 illustrates the implementation of the evaluation. In Tulungagung, a total of 81 primary schools were available for the pilot. They were randomly divided into 3 groups: in the first group of 27 schools, a letter signed by the principal was given to students to pass on to their parents; in the second group of 27 schools students were given a colorful pocket book for the same purpose; the 27 schools in the third group did not receive any materials. Ten parents in each of these schools were surveyed at endline. In Malang, where there were only 26 primary schools at baseline, SMSs were

> 10 Tulungagung and Malang are in the East Java Province, Sumbawa is in the West Nusa Tenggara Province. 11 Note that the intervention was also implemented in some Junior Secondary schools.  However, because the number of those schools is small, and because Junior Secondary schools are different in a number of ways from Primary Schools, we focus only on the intervention impacts in the latter.

9
# 1. **Introduction**

Policy makers often promote trade liberalization and openness as a way to increase living standards and welfare in developing countries. For example, before the 1999 WTO ministerial meeting in Seattle the U.N. secretary general Kofi Annan and the WTO director general Michael Moore urged developed countries to "extend the benefits of free trade fully to the developing world" since free trade enhances economic development and standards of living (Wall Street Journal (1999)). Brazil, like many other Latin American economies, followed these policy recommendations and underwent drastic trade liberalization episode from 1988 to 1994 in pursuit of higher living standards and productivity growth. The reforms not only reduced the average tariff level from about 60 percent in 1987 to 15 percent in 1998, but also changed the structure of protection across the industries. These drastic tariff reductions were mirrored in increased import penetration in most manufacturing sectors.

While empirical studies have documented that the Brazilian trade reforms have increased efficiency and growth (see Hay (2001), Muendler (2002)), trade liberalization might have also contributed to the growing wage inequality. Several studies have documented growing returns to educated workers in Brazil that coincide with the timing of trade liberalization (see Blom, HolmNielsen, and Verner (2001), Green, Arbache, and Dickcerson (2001), Behrman, Elirdsall, Szekely (2000)). Although the causal link between growing skill premium and trade liberalization has not been established, this second development would be alarming for Brazil because it might translate into growing wage inequality in a country with extremely high levels of inequality prior

> ' Although the theoretical relationship between free trade and welfare is ambiguous, careful empirical work based on cross-country data by Frankel and Romer (1999), confirms that countries that countries with higher exposure to trade have higher living standards as measured by GNP per capita.

> **2** Growing skill premium has been documented in Mexico and many other liberalizing Latin American economies (see Robbins (1996), Cragg and Epelbaum (1996), Hanson and Harrison (1999), Robertson (2000), Behrman, Birdsall, and Szekely (2000), Attanasio, Goldberg, and Pavcnik (2002)).

2