rafmacalaba/datause-encoder-data huggingface dataset:
|||(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_</sup><br>_Annual Import_|_Gains from_<br>_Variety_|
|---|---|---|---|
|_Year_|_Bias_|100<br>_(% of GDP)_|_(% of GDP)_|
|2018|1.024|-2.34<br>43%|-1.01|
|2019|1.000|-0.04<br>39%|-0.02|
|2020|0.995|0.52<br>39%|0.20|
|2021|1.002|-0.16<br>48%|-0.08|
|**Average per-annum**|**0.970**|**3.30**<br>**46%**|**1.49**|
|**Total (1995-2021)**||**89.08**|**40.25**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Table 5: _Import Price Bias and the Gains from Variety in Mongolia_

||||_Annual_|_Gains from_|
|---|---|---|---|---|
||||_Import_|_Variety_|
|_Year_|_Bias_|(<br>1<br>_bias _<sup>_−_1)</sup> <sup>_×_ 100</sup>|_(% of GDP)_|_(% of GDP)_|
||_(1)_|_(2)_|_(3)_|_(4)_|
|1995|1.000|0.00|29%|0.00|
|1996|0.679|47.34|34%|13.87|
|1997|0.997|0.32|40%|0.13|
|1998|1.004|-0.41|45%|-0.19|
|1999|0.996|0.43|49%|0.21|
|2000|0.979|2.18|54%|1.17|
|2001|0.998|0.17|50%|0.09|
|2002|1.028|-2.76|49%|-1.38|
|2003|0.961|4.01|50%|1.99|
|2004|0.992|0.81|51%|0.41|

21
US, Mexico and Australia and New Zealand, reflecting the existence of vast natural forests and pastures in those areas and the superior biomass productivity of tropical regions, whereas China and India, due to their immense food demand and relatively lower biomass land productivity, are not found to be regions supporting significant expansion of land for biofuel feedstocks. Hertel et al. (2010b) also show that largest net reductions in land cover tend to be in pasture land, although large percentage decreases in forest land are found in Brazil and the EU. Another important factor is that when biofuel producing countries convert cropland to biofuel production, the reduced food exports and higher commodity prices induce land to be cleared for crops in tropical countries such as Brazil, Argentina and Indonesia to satisfy the unmet food demand (Ogg, 2009).

Aside from land conversion for biofuel feedstock cultivation, Hertel et al. (2010b) use the Global Trade Analysis Project (GTAP) model show that the harvested area for various crops can also be expected to change as a result of expanded biofuel production from 2006 to 2015 to satisfy US and EU biofuel mandates. They find substantial increases in harvested area for oilseeds in the EU, Canada and Oceania (47.8%, 19.4% and 19.3%, respectively) and for sugarcane in Brazil (22.9%). Coarse grain acreage is seen to rise by 6.2% in the US but only increases moderately in most other regions (except for significant declines in Brazil and the EU). Oilseed acreage, however, exhibits significant gains in all regions, implying that the EU biofuels mandate will have immense repercussions on the global oilseeds market. Hertel et al. (2010a) incorporate market-mediated responses and by-product use into their analysis of the land requirements of increased maize ethanol production in the US to meet the mandated volume in 2015. They show that these factors reduce the gross feedstock land requirement of 15.2 Mha so that only 0.28 ha of land conversion occurs for every hectare of maize cultivation diverted to ethanol production, resulting in the global conversion of 3.8 Mha of forest and pasture land to cropland due to the US mandate.

One approach to counteracting the growing scarcity of arable land would be to bring abandoned agricultural land back into production. Field et al. (2008) estimate that significant amounts of abandoned agricultural land – between 475 and 580 Mha – could be allocated to biofuel production. Although abandoned cropland, pasture land, forests, or other natural areas could all be suitable for biofuel cultivation, De Vries et al. (2007) suggest that mainly grassland will be 35
# **TABLE 6.3: Household Level - Probit Regressions**

Source of Data: Households' Survey Dependent variable: DIS1=Household discouraged in at least one institution (0,1) DIS2=Household discouraged in at least two institutions (0,1) Marginal Effects evaluated at mean values are reported

||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|---|---|---|---|---|---|---|---|---|
|**Dependent variable:**|**DIS1**|**DIS2**|**DIS1**|**DIS2**|**DIS1**|**DIS2**|**DIS1**|**DIS2**|
|Middle Income|0.036|0.036|0.035|0.036|0.036|0.036|0.041|0.038|
||[1.17]|[1.35]|[1.15]|[1.34]|[1.19]|[1.34]|[1.34]|[1.39]|
|High Income|-0.097|0.004|-0.079|0.02|-0.095|0.005|-0.068|0.023|
||[1.42]|[0.08]|[1.17]|[0.35]|[1.39]|[0.10]|[1.01]|[0.40]|
|Secondary Education|0.124|0.101|0.121|0.098|0.134|0.105|0.116|0.097|
||[2.74]***|[2.27]**|[2.69]***|[2.21]**|[2.99]***|[2.37]**|[2.57]**|[2.18]**|
|University Education|0.147|0.141|0.151|0.146|0.163|0.15|0.143|0.144|
||[3.14]***|[2.86]***|[3.24]***|[2.96]***|[3.51]***|[3.03]***|[3.04]***|[2.92]***|
|female|-0.049|-0.008|-0.049|-0.008|-0.053|-0.01|-0.047|-0.007|
||[1.90]*|[0.34]|[1.92]*|[0.34]|[2.09]**|[0.44]|[1.86]*|[0.33]|
|age|0.014|-0.002|0.015|-0.001|0.015|-0.002|0.015|-0.001|
||[2.18]**|[0.30]|[2.24]**|[0.18]|[2.29]**|[0.26]|[2.24]**|[0.17]|
|age2|0|0|0|0|0|0|0|0|
||[2.16]**|[0.25]|[2.20]**|[0.16]|[2.26]**|[0.21]|[2.22]**|[0.15]|
|Dishonesty institutions - Number|0.004<br>[2.71]***|0.002<br> [1.64]|0.004<br>[2.90]***|0.002<br> [1.87]*|||0.004<br>[3.00]***|0.002<br> [1.91]*|
|Dishonesty institutions - Average|||||0.001<br>[0.04]|-0.004<br>[0.33]|||
|Trust in State Institutions|-0.01|-0.017|-0.01|-0.017|-0.021|-0.022|-0.008|-0.017|
||[0.84]|[1.63]|[0.83]|[1.63]|[1.77]*|[2.03]**|[0.72]|[1.60]|
|Knowledge report corruption|0.037|0.058|0.041|0.063|0.036|0.058|0.04|0.063|
||[1.45]|[2.58]***|[1.61]|[2.81]***|[1.40]|[2.57]**|[1.57]|[2.78]***|
|Member of association|0.049<br>[4.54]***|0.043<br> [4.97]***|||0.05<br>[4.70]***|0.044<br> [5.08]***|||
|Member of at least one association|||0.102<br>[4.07]***|0.092<br> [4.14]***|||0.109<br>[4.29]***|0.095<br> [4.23]***|
|region==Lima<br>region==Costa|||||||-0.046<br>[1.30]<br>0.044<br>[1.06]|-0.02<br>[0.63]<br>0.01<br>[0.27]|
|region==Sierra|||||||-0.019|-0.018|
||||||||[0.47]|[0.52]|
|Observations|1558|1558|1558|1558|1558|1558|1558|1558|
|Pseudo-Rsq|0.04|0.04|0.04|0.03|0.03|0.04|0.04|0.04|

Absolute value of z statistics in brackets * significant at 10%; ** significant at 5%; *** significant at 1% 33
- Morón, E., E. Salgado, and C. Seminario. 2012. “Financial Dependence, Formal Credit and Firm Informality: Evidence from Peruvian Household Data.” Working Paper 288, Inter-American Development Bank, Washington, DC.

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34
who had to implement the survey. In the PSF sample, more than a third of households contain at least three cells (see Table 1).<sup>6</sup> Households have on average 2.5 cells, but that uncovers a wide diversity of situation. In fact, nearly 18% of the households have only one cell, while 38% have 3 cells or more. Nuclear households (composed of husband, wife and their unmarried children) account for about 40% of the households, while nearly 49% include some type of horizontal (siblings, but also uncles/aunts or nephews/nieces) or vertical extensions (parents, grand-parents, grand-children). The cohabitation of 3 generations or more is particularly frequent in rural areas, where it is the case for 44% of the households (vs 22% in Dakar). The remaining extended households include cousins or non-family members. Despite these frequent extensions, only in 15.8% of the households do we observe more than one married man with at least one wife present in the household. About one fifth of the households are headed by a woman and two-thirds by someone without any formal schooling (either no education at all or only koranic educations).

When zooming to the cell level, 40% are headed by the household head (a man in nearly 80% of the cases), 35% by his spouse, and 11% by one of his children or a son or daughter-inlaw. Cells other than that of the household head are in vast majority (81%) headed by a woman. Nearly a third of the cells include at least two adults above the age of 21 (47% if considering individuals over 15), while the number of children below 15 varies from 0 (for nearly 44% of the cells) to 8, with an average of 1.36. Nearly half of the cells are those of a mother and her children. Women also tend to have under their responsibility grand-children, younger unmarried siblings or nephews and nieces.

# Consumption

Contrary to traditional consumption surveys that only aim at estimating the level of household consumption, PSF is designed to approximate, as much as possible, individual access 8
borders creates new forms of cross-border policy spillovers beyond the traditional terms-of-trade externality. Second, governments may face credibility problems with respect to behind the border measures in the context of GVCs. Third, the costs created by coordination externalities (e.g. the costs of heterogeneous regulations) may be higher in the presence of cross border production. These spillovers and credibility concerns generate a demand for deeper forms of integration.

Despite the rich set of arguments that are present in the literature, many aspects of the relationship between deep PTAs and GVCs have not been incorporated in formal models. First and foremost, the fundamental question of the role that deep agreements play in the presence of GVCs has not been rigorously investigated. For instance, Antras and Staiger (2012) show that behind the border policies create cross-border spillovers when production is internationally fragmented. While they indicate that deep provisions in PTAs may allow governments to internalize these externalities, the model does not provide a formal treatment of this point. Similarly, the commitment rationale for deep agreements has only been formalized for specific provisions (e.g. domestic subsidies in Brou and Ruta, 2013), and this has not been done in a GVC context. Finally, papers that study the harmonization of standards and other forms of regulatory cooperation (e.g. Costinot, 2008) generally rely on traditional trade models that assume that production is purely national.

Several other interesting questions are open and would constitute important avenues for future research. A first set of questions relates to the content of deep agreements. A large trade literature has recently investigated the role of institutions in shaping the international organization of production (Antras, 2015). Osnago et al. (2015) find evidence that the content of deep PTAs affects FDI decisions, suggesting that the role of specific provisions in shaping GVCs may be relevant. But more work is needed to understand the specific channels. A second area relates to the role of preferential as opposed to multilateral deep integration. Why is deep integration generally taking place in PTAs? How are PTA partners selected in a GVC context? I will elaborate on these questions in Section 7.

# **4. Do Deep Agreements Promote GVCs?**<sup>**8**</sup>

The relationship between GVCs and PTAs runs in both directions. An important question from a policy perspective is to which extent trade agreements, and particularly deep PTAs, can boost GVC integration. Osnago, Rocha and Ruta (2016) use a gravity model to empirically investigate this question. The analysis exploits the new World Bank data on the content of PTAs described in Section 2. Bilateral GVC integration is measured in two ways: trade in parts and components and value-added trade. Trade in parts and components has the advantage of being available for a larger set of countries and years covered by the new data set on PTAs. Value-added trade is a

> 8 This section is based on Osnago, Rocha and Ruta (2016).

8
Table 6: Marginal effects of distance to roads in good condition on seasonal employment and cash earnings

|||Seasonal employ|ment||Cash earning|s|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||All|Women|Men|All|Women|Men|
|Logdist. good road|-0.055**|0.010|-0.107***|-0.056**|-0.093***|-0.016|
||(0.024)|(0.038)|(0.025)|(0.024)|(0.029)|(0.033)|
|Age|-0.035***|-0.032**|-0.034**|0.030***|-0.011|0.065***|
||(0.010)|(0.015)|(0.013)|(0.010)|(0.013)|(0.014)|
|Gender (woman=1)|-0.063***|||-0.245***|||
||(0.020)|||(0.020)|||
|Education in years|-0.034***|-0.038***|-0.024***|0.066***|0.073***|0.035***|
||(0.005)|(0.007)|(0.006)|(0.005)|(0.008)|(0.007)|
|Number of children under 5|-0.032*|-0.019|-0.033|0.017|-0.042*|0.046**|
||(0.017)|(0.025)|(0.022)|(0.017)|(0.022)|(0.024)|
|Owns agricultural land|0.015|-0.090|0.039|-0.171***|-0.239***|-0.083|
||(0.040)|(0.063)|(0.050)|(0.044)|(0.059)|(0.056)|
|Other individual controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Geographical controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,869|966|890|1,884|981|903|

Geographical controls include: Distance to Marz center, Distance to Yerevan, Altitude, Slope, Population density, Nightlights composite, Aridity. Additional individual and HH controls include: Age squared, individual weights, HH wealth categories, family size. Robust standard errors in parentheses * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01
Syria to Diyala in Iraq (Wilson Center 2019). According to the Wilson Center, ISIS held about a third of Syria and 40 percent of Iraq’s territory at their peak of control. By December 2017, the terrorist organization had lost 95 percent of their territory, including their two biggest cities (Mosul in Iraq and Raqqa in Syria) Mosul (Iraq’s second largest city) and Raqqa in northern Syria, following attacks from the Iraqi military, Popular Mobilization Forces formed by an Iraqi sponsored coalition of <mark>Shia Muslim</mark> and other minority groups, and an international military coalition. Despite reclaiming territories from ISIS, the war brought long-term and deep-rooted consequences for Iraq.

We use a cross-section of individual-level nationally representative survey data to study relationships in how adults in Iraq report confidence in institutions, the quality of service delivery, and expectations of future economic performance. To understand the influence of ISIS on overall perceptions, the paper looks at three time periods: pre-ISIS (2010-2012), ISIS-control (2014-2016), and post-ISIS (2018-2019). It measures vertical inequality as the relative perceptions of women and poorer adults (defined as adults in the bottom 40 percent of the income distribution), and adults living in rural areas, and measure horizontal inequality by ethno-religious categories including Shias, Sunnis, Kurdish Muslims, and Baghdad residents (see more detail in Appendix A).<sup>1</sup> The analysis finds that positive perceptions across these ethno-religious categories declined during ISIS-control and has slowly picked up in the recent years. The poor are more likely to be food insecure and have a poorer outlook on standard of living over the last decade throughout the three time periods. Men are less likely than women to report higher leadership approval ratings, a better standard of living, and improved job prospects in the post-ISIS period. The results find that Shias, Kurds, and adults in living in Baghdad are significantly more likely to have a poorer quality of life, over time, and in contrast to Sunnis, in the post-ISIS period. Nearly all ethno-religious groups, during all periods, perceive the government to be corrupt.

# **2. Data**

Our empirical analysis uses individual-level microdata from the Gallup World Poll (Gallup Inc., 2019). Importantly, the data are randomized at the individual level, which allows us to summarize the data by individual characteristics, including age (identified as young for adults ages 15-24 and older for adults age

> 1 Other religious groups, including Christians, constitute only 1% of the resident Iraqi population.

3
Table C.2: Coverage of Final Database Relative to Eurostat (SBS) - Manufacturing

|**Country**|||**E**|**mployme**|**nt**||||||**Turnove**|**r**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|Austria|14%|21%|34%|41%|44%|45%|47%|12%|17%|35%|51%|54%|58%|58%|
|Belgium|63%|64%|64%|65%|67%|67%|66%|78%|76%|76%|72%|74%|79%|76%|
|Bosnia and Herzegovina||64%|44%|67%|60%|62%|59%||62%|49%|68%|62%|66%|62%|
|Bulgaria|54%|65%|73%|71%|70%|74%|70%|45%|50%|41%|40%|42%|45%|45%|
|Croatia|61%|65%|65%|62%|65%|67%|65%|68%|76%|79%|75%|73%|71%|65%|
|Czechia|74%|77%|76%|77%|75%|79%|72%|50%|52%|53%|55%|53%|62%|60%|
|Estonia|39%|41%|41%|43%|44%|43%|44%|33%|33%|33%|35%|35%|36%|38%|
|Finland|47%|52%|53%|54%|49%|48%|48%|45%|50%|52%|49%|46%|46%|49%|
|France|30%|28%|23%|31%|37%|38%|40%|27%|25%|19%|30%|35%|37%|37%|
|Germany|32%|34%|34%|34%|35%|34%|33%|46%|49%|48%|48%|50%|50%|49%|
|Hungary|57%|61%|62%|62%|64%|63%|60%|75%|78%|79%|83%|89%|89%|90%|
|Italy|47%|59%|59%|62%|63%|65%|64%|54%|61%|59%|62%|61%|61%|62%|
|Luxembourg|60%|67%|64%|62%|62%|55%|53%|80%|100%|96%|77%|77%|67%|62%|
|North Macedonia|||61%|65%|66%|60%|61%|||56%|63%|47%|43%||
|Norway|32%|36%|40%|28%|25%|68%|75%|31%|35%|31%|27%|26%|64%|73%|
|Poland|26%|22%|16%|10%|7%|7%|16%|31%|25%|20%|12%|10%|10%|23%|
|Portugal|61%|65%|66%|68%|68%|69%|68%|62%|64%|65%|67%|56%|59%|56%|
|Romania|50%|53%|55%|57%|59%|60%|59%|45%|48%|57%|57%|60%|59%|58%|
|Serbia|||||||65%|||||||87%|
|Slovak Republic|61%|59%|63%|63%|61%|57%|52%|44%|48%|57%|59%|53%|43%|42%|
|Slovenia|71%|74%|75%|71%|72%|74%|66%|71%|74%|75%|72%|70%|73%|65%|
|Spain|52%|54%|54%|57%|59%|59%|56%|46%|46%|43%|49%|47%|49%|47%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
# **Box 4 - G20 Principles for Innovative Financial Inclusion**

Innovative financial inclusion means improving access to financial services for poor people through the safe and sound spread of new approaches. The following principles aim to help create an enabling policy and regulatory environment for innovative financial inclusion. The enabling environment will critically determine the speed at which the financial services access gap will close for the more than two billion people currently excluded. These principles for innovative financial inclusion derive from the experiences and lessons learned from policymakers throughout the world, especially leaders from developing countries.

1. **Leadership** : Cultivate a broad-based government commitment to financial inclusion to help alleviate poverty.

2. **Diversity** : Implement policy approaches that promote competition and provide market-based incentives for delivery of sustainable financial access and usage of a broad range of affordable services (savings, credit, payments and transfers, insurance) as well as a diversity of service providers.

3. **Innovation** : Promote technological and institutional innovation as a means to expand financial system access and usage, including by addressing infrastructure weaknesses.

4. **Protection** : Encourage a comprehensive approach to consumer protection that recognizes the roles of government, providers and consumers.

5. **Empowerment** : Develop financial literacy and financial capability.

6. **Cooperation** : Create an institutional environment with clear lines of accountability and coordination within government; and also encourage partnerships and direct consultation across government, business and other stakeholders.

7. **Knowledge** : Utilize improved data to make evidence based policy, measure progress, and consider an incremental “test and learn” approach acceptable to both regulator and service provider.

8. **Proportionality** : Build a policy and regulatory framework that is proportionate with the risks and benefits involved in such innovative products and services and is based on an understanding of the gaps and barriers in existing regulation.

9. **Framework** : Consider the following in the regulatory framework, reflecting international standards, national circumstances and support for a competitive landscape: an appropriate, flexible, risk-based Anti-Money Laundering and Combating the Financing of Terrorism (AML/CFT) regime; conditions for the use of agents as a customer interface; a clear regulatory regime for electronically stored value; and market-based incentives to achieve the long-term goal of broad interoperability and interconnection.

Source: Pearce (2010) 41