rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Girls' Enrollment - Grade 7**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding mosque<br>andprimaryschools|Excluding mosque and primary<br>schools + Multan + Khanewal|
|Number of Basic Facilities = 1|||0.161|0.160|0.0689|
||||(0.449)|(0.450)|(0.472)|
|Number of Basic Facilities = 2|||0.0411|0.0393|-0.0269|
||||(0.449)|(0.451)|(0.472)|
|Number of Basic Facilities = 3|||-0.151|-0.153|-0.252|
||||(0.456)|(0.457)|(0.479)|
|Constant|23.97***|18.50***|18.54***|19.05***|19.17***|
||(0.627)|(0.114)|(0.444)|(0.447)|(0.468)|
|4<br>Observations|71,607|71,607|71,607|70,065|65,469|
|3<br>Number of Schools||13,674|13,674|12,165|11,377|
|Adjusted R-squared|0.007|0.036|0.036|0.036|0.037|
|R-square within||0.0362|0.0362|0.0362|0.0371|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable is girls’ enrollment in grade 7. The average school in a treatment district school at baseline had 14 girls enrolled in grade 7. Column 1 reports estimates from an OLS specification. Column 2 reports estimates from a fixed effects specification at the school level. Column 3 adds to column 2 a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003. Column 4 excludes mosque schools and primary schools from the sample. Column 5 removes two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment).
nation's leading service firms in media, information technology, advertising, insurance, retail and whole trade also agglomerate in Tokyo. These industries intersect in unexpected ways, driving innovation and creating new markets (Fujita 2003). In stark contrast to global cities like New York and London, which specialize in finance and producer services at the expense of manufacturing (Sassen 1991), industrial diversity plays an extremely important role in Tokyo's innovation and wealth generating capabilities. (See Table 6)

# **_IX. The National System of Innovation_**

Tokyo's competitiveness is conditioned by the national innovation system in which it participates. A national innovation system, as Lambooy (2002) envisions it, forms a triangle made up of firms, scientific research organizations, and governments at the national and local levels. National innovation systems generate technical change. They span all industries. The system's core actors are private firms who both compete and cooperate with one another. Universities and public funds account for a significant fraction of R&D, but the percentage varies among countries.

The Japanese innovation system has several distinctive features. Industrial innovation centers in major cities, and especially in Tokyo, the nation's capital. The private sector is the leading actor but in a framework established by national development policy and state administrative guidance. A relatively homogeneous science and technology infrastructure spans the nation. Regional firms tend to use Tokyo as the gateway to national and world markets, while retaining their local production, R&D and cultural base. Innovation thus runs back and forth between Japan's capital city and outlying urban centers.

_Cross national differences in R&D._ Japan's national system of innovation contrasts markedly with North America and Western Europe. Japan invests more heavily in R&D than most developed nations in the West. The ratio of R&D spending to GDP in Japan was 3.29% in 2001, compared to 2.72% in the U.S., 2.18% in France, and 1.84% in the UK (OECD 2001c). Japan's R&D outlays on science and technology are overwhelmingly a private sector responsibility, however. Government's share of R&D spending is much lower in Japan than in most Western countries. Private companies accounted for 78.6% of Japan's total R&D spending in 2001, government contribution for 21% (Statistics Bureau 2003). (See Tables 7 & 8).

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# **4. DATA**

# **4.1. EXPORT DATA**

The principal trade data source is the BACI World trade database developed by CEPII, created using COMTRADE. BACI data from 1995 to 2009 are used for this exercise. In total, 20 MENA countries and 136 countries from other regions are covered (see Table 6 for a list of MENA countries). One advantage of the BACI to the COMTRADE is that exporter and importer declarations have been checked for gross inconsistencies. The version used is aggregated at the country-sector level, where sectors are defined by the ISIC 2-digit level. It is important to be clear that there is no information on exporter characteristics in this database such as age, legal status, ownership, share of exports over total sales, or employment levels. Moreover, this study cannot answer why some firms export and others do not, or what are the determinants of becoming an exporter.

The World Bank’s Export Dynamics Database (EDD) is also used as a second source of customs data. The EDD includes exporting data from 1997-2011, but analysis is limited to 2005-2010 since data are available for the majority of countries during this period only. All agricultural and extractive industries are excluded. The EDD’s country coverage is narrower than BACI’s, however EDD comprises additional information in terms of mean and median export value per exporter, which allows consideration of the intensive margin of trade. The full EDD data set comprises 43 countries, 7 of which are in the MENA region. Kuwait is the only high-income MENA country in the EDD. Egypt, Morocco, and Yemen are lower-middle-income, and Iran, Jordan, and Lebanon are upper-middle-income. Depending on our level of analysis and availability of control or business climate variables, MENA coverage ranges from three countries to seven countries.

# **4.2. DEPENDENT VARIABLES**

The share of total exports in a given sector is calculated per industry for every country and every sector using the BACI. Trade specialization at the sector-level in a given country (���� ) is calculated as the value of total exports in sector _s_ divided by total export value in country _c_ (Table 7 in the Appendix). A sector’s export value is calculated as the number of exporters multiplied by the mean exporter size.

In addition to the share of exports, the mean and median value of exports per exporter is also used from the EDD to examine the intensive margin.

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Table 2: New dyadic PTA relations by country in period 2007-2015 (top 15 countries of 122).

|Country|New dyadic relations|Total depth|Core depth|WTO-X depth|
|---|---|---|---|---|
|Romania|95|0.577|0.905|0.403|
|Bulgaria|95|0.577|0.905|0.403|
|Rest of EU (each country)|87|0.602|0.907|0.441|
|Moldova|46|0.641|0.804|0.555|
|Croatia|44|0.629|0.933|0.469|
|Korea, Rep.|43|0.440|0.903|0.195|
|Peru|43|0.620|0.926|0.458|
|Montenegro|41|0.274|0.775|0.009|
|Yugoslavia|40|0.259|0.704|0.024|
|Bosnia and Herzegovina|39|0.253|0.702|0.015|
|Colombia|39|0.642|0.922|0.494|
|Costa Rica|38|0.753|0.939|0.654|
|Honduras|37|0.758|0.913|0.676|
|El Salvador|34|0.788|0.920|0.719|
|Guatemala|34|0.784|0.923|0.710|

# **2.3 Global Value Chains Data**

The WIOD data set is a widely used Global input-output tables source. We use the the information contained in the 2016 release which covers 43 countries and 56 ISIC Rev. 4 two-digit (primary production, manufacturing, and services). In order to match the WIOT data with the information contained in ORBIS, we aggregate the 56 sectors up so as to obtain 38 sectors. Moreover, we group the countries in 22 major world regions<sup>1</sup> according to the detailed United Nations geoscheme and substitute coefficients for those countries in ORBIS which are not specifically contained in WIOT by the respective annual group average.

For a more formal account of the WIOT-data construction for our purpose, let us closely follow the notation in Antr`as and Chor (2018) and define a world economy with _J_ countries (indexed by _i_ or _j_ ) and _S_ sectors (indexed by _r_ or _s_ ). Also let us use _Zij,t_<sup>_rs_as total value of inputs used by country</sup><sup>_j_’s sector</sup><sup>_s_originating from country</sup> _i_ ’s sector _r_ in year _t_ ; _Fi,t_<sup>_r_and</sup><sup>_Y_</sup> _i,t_<sup>_r_asthetotalvalueofthefinalgoodssoldandthe</sup> gross output by industry _r_ in country _j_ , respectively.

These basic definitions serve to define three measures which are informed by and reflective of a country-sector pair’s positioning in the global value chain. These

> 1Northern America, Central America, Caribbean, South America, Northern Africa, Western Africa, Middle Africa, Eastern Africa, Southern Africa, Southern Europe, Western Europe, Northern Europe, Eastern Europe, Western Asia, Central Asia, Southern Asia, Eastern Asia, Southeastern Asia, Australia and New Zealand, Micronesia, Polynesia, Melanesia

7
escalated, there was a 21 percentage point increase in the share of the population that was experiencing acute food insecurity or worse relative to 2014 (IPC 2015). Furthermore, the Emergency Food Security and Nutrition Assessment estimated that 65 percent of Yemenis had poor or borderline food consumption in 2016, a 57 percentage point increase relative to 2014 (FAO 2017; World Bank 2017). Since 2015, the severity of the food emergency has varied along with variation in the shocks to income and prices described above. And food insecurity worsened each year after 2015, teetering on the edge of famine at the end of 2018 (e.g., IPC 2017; IPC 2018; OCHA 2019a).

However, given the importance of violence to displacement, it is important to note that those who live in close proximity to violence do not have worse well-being on average. There is little correlation between the location of violence and access to food and basic services, and there is little change in well-being in response to a number of sudden increases in violence and violent offensives (e.g., Almoayad et al. 2020; Tandon and Vishwanath 2020; Favari et al. 2022; etc.). One of the reasons for the lack of a relationship is that particular instances of violence have impacts far beyond the location where they take place (e.g, Tandon and Vishwanath 20202), and many of the other shocks described above have much stronger and more robust impacts on the humanitarian situation (e.g., Favari et al. 2021; etc.). Combined, these results suggest that the violence that might be causing households to become displaced does not necessarily result in a worsening humanitarian situation experienced in those locations from which displaced households might be fleeing.

# **Section 3a. Data**

Given the tumultuous environments in which large-scale displacements occur, it is very challenging to find data that assess how well-being responds to shocks in real time. The Republic of Yemen is no exception, where the poor security situation (e.g., Sundberg and Melander 2013), the budgetary problems of the government and the Central Statistical Organization (e.g., World Bank 2017b), and the significant population movement since the last census (conducted in 2004) all make it very difficult to conduct traditional 8
Grindle, 2007, 2004). For example, from an analysis of eight controlled case comparison in Indonesia, which includes 1,000 business surveys and 120 interviews, von Luebke (2009) finds that the quality of government leadership plays a central role in driving the quality of policy outcomes.<sup>8</sup> Specifically, he finds that district heads with solid managerial skills and long-term career aspirations are more likely initiate broad-based reform and supervise bureaucratic performance. Similarly, we expect the quality of local government heads to affect whether they adopt procurement reforms and try to improve local governments’ procurement performance.

There are a number of different arguments as to how leaders can positively impact these outcomes. Research points to the role that leaders play in overcoming collective action and coordination problems within organizations by reducing information asymmetries and building trust between agents (Ahlquist and Levi, 2011; Calvert, 1992). Other research shows how leaders can effect change in an organization through the institutionalization of a system of rewards and punishment that shifts how individuals work together (Andreoni et al. 2003). Within an organization, when leaders treat subordinates with respect, subordinates respond with loyalty and greater productivity towards producing public goods (Cook, Hardin and Levi, 2005; MIller, 1992).

The social choice tradition stipulates that leaders who know how to manipulate the institutional situation can shape outcomes to their own benefit or can modify institutional design to suit their goals (Arrow, 1970). A leader who can control the agenda can also shift the equilibrium from any status quo to almost any desired outcome (Ahlquist and Levi, 2011). In other words, in the presence of multiple equilibria, leaders can help to shift the group decision to their preferred outcome (Ahlquist and Levi 2011). Consequently, leaders often serve a narrow constituency at the expense of others. Yet, institutional arrangements, specifically agenda control and the presence of multiple veto players including opposition parties and oversight institutions, can constrain leaders. The extent to which leaders are able to influence policies depends on institutional context — whether the country is, for example, a democracy or autocracy. Even within democracies, however, leaders do not necessarily serve the interests of the country. A larger literature documents how lead8 von Luebke (2009, 210) measures the quality of government leadership using n index consisting of perception based indicators of the district heads’ integrity, political will, communication skills, power to change and popularity levels.

19
the worst scenarios (sc6) in which the GDP loss is cumulated over 40 years without discounting. Note, however, that O’Neill’s figure is only for losses due to the reduction of labor supply.

## **_Impacts of AMR on Poverty_**

The impact of AMR on economic growth will result in a pronounced increase in extreme poverty. The main reason is the disproportionate impact of AMR on low-income countries, which experience substantial and protracted shortfalls in economic output and other negative economywide repercussions from the combined effects of the shocks in labor supply, labor productivity, trade, livestock production, and health care cost. The economic repercussions and effects on factor prices (e.g. wages) of each scenario will alter the income distribution of countries in each income region, from which we calculate the poverty indicators through the GIDD framework.

The results suggest the following: Of the additional 28.3 million people in extreme poverty in 2050 under the high-AMR scenario, the vast majority (26.2 million) lives in low-income countries (Figure 5). In the baseline scenario, the world is broadly on track to eliminate extreme poverty (at $1.90/day) by 2030, reaching close to the target of less than 3% of people in extreme poverty. Because of AMR, however, the target would be harder to reach: there could be an additional 24.1 million of extremely poor by 2030 in the high-AMR scenario, of whom 18.7 would be in low-income countries. Tables 6 and 7 have additional poverty estimates.

<!-- Start of picture text --> Figure 5. AMR Impact on extreme poverty Figure 6. AMR Impact on extreme poverty<br>(PPP$1.90/day) by 2050 in low-income countries (PPP$1.90/day) by 2030 in low-income countries<br>and the world, under low-, mid-, and high-case and the world, under low-, mid-, and high-case<br>scenarios scenarios<br>Low income World Low income World<br>28.3<br>26.2<br>24.1<br>18.4 18.7<br>17.2<br>15.2<br>12.0<br>7.7<br>6.3 6.9 6.2<br>2050 2050 2030 2030<br>Low Middle High Low Middle High<br>Source: Simulation results and authors’ calculations<br>30 30<br>20 20<br>millions millions<br>increase in poverty 10 increase in poverty 10<br><!-- End of picture text -->

# **VI. Conclusions**

In this paper, we revisit the AMR pandemic, assessing its potential impact. Compared to earlier work, our analysis presents several improvements. We use a recursive dynamic global computable 16
<!-- Start of picture text --> Correlation between sectoral productivity and change in employment share<br>Po Egypt<br>Ss<br>7 1970-1980 1980-1990<br>=) 9.69 P mi : 503<br>ral ° : oO<br>Ts] tat tat}<br>So - LQwre - Dre<br>ug cs imzer a <————¢ hp<br>9 -15 =1 =.05 fe) .05 | AS -1 -.05 it] 05 Al<br><!-- End of picture text -->

<!-- Start of picture text --> a2)<br>2<br>5 1990-2000 2000-2010<br>DSea.o +oO | CERF:ow me<br>$aa<br>=ooo7 AMONCON -s - >it. Cla eyo<br>iSf ‘ ( =) C3)~ ‘ |Lf)a JX<br>5 150-1) SUS ON 45— 415-1 0-0 0<br>w .<br>Zz Change in employment share<br><!-- End of picture text -->
<!-- Start of picture text --> −.4 −.2 0 .2 .4 −.4 −.2 0 .2 .4<br>Inverse Normal Inverse Normal<br>Source: Authors’ calculations. Source: Authors’ calculations.<br>(a) Q-Q residual plot, ( P2 ) (b) Q-Q residual plot, ( P5 )<br>1.5 1.5<br>1 1<br>Residuals 0.5 Residuals 0.5<br>−.5 −.5<br>−1 −1<br><!-- End of picture text --> Figure A.3: Quantile plot of residuals for specifications ( _P2_ ) and ( _P5_ ). The clustering of most residuals around the 45-degree line corresponding to the theoretical normal distribution suggests a reasonable fit (and hence normality of residuals), although some outliers are clearly apparent.

# **A.3 Additional tables**

Table A.1 reports robustness results for the proximate correlates of the aggregate growth polarity measure. Odd-numbered specifications are coefficient estimates using group effects models, while even-numbered specifications are estimates using dynamic panel models. The top panel introduces additional variables for: ( _R1_ )–( _R2_ ) technology coverage; ( _R3_ )–( _R4_ ) infrastructure quality; and ( _R5_ )–( _R6_ ) regional dummies. The bottom panel substitutes measures for: ( _R7_ )– ( _R8_ ) primary with secondary enrollment; ( _R9_ )–( _R10_ ) life expectancy with the under-5 mortality rate; and ( _R11_ )–( _R12_ ) the total dependency ratio with the child and aged dependency ratios.

By and large, the coefficients on the main variables that were found to be significant in benchmark Table 4 retain their expected signs and statistical significance. The additional variables introduced in the top panel are generally not significant (which partly justifies their exclusion from the benchmark), with perhaps the exception the Latin America dummy. The marginally statistical significant coefficient on the under-5 mortality variable in specificaiton ( _R10_ ) is consistent with the fact that a higher child mortality rate would also mean a lower child dependency ratio, which enters with a negative coefficient in specification ( _R11_ ).

Several additional variables do enter as statistically significant in some specifications, and when they do, they generally do so with the _ex ante_ expected signs. For example, a positive and significant coefficient on investments appears in several specifications, as does population growth (although the sign on this latter variable is somewhat less stable).

32
levels, further improvements in terms of impact on the FGT2 with a given budget are attainable, and are non-negligible.<sup>17</sup> Third, while the general patterns we observe are similar across our different budget and poverty line scenarios, they are not identical. We see for example, that the gains from optimal targeting at the district level in rural areas are most striking when an extreme poverty line of 75% of the official poverty line is assumed to hold. In this case a given budget can yield a FGT2 measure that is just over one third (34%) of the FGT2 that would obtain if the budget had been allocated uniformly across the population. Similarly, in urban areas, district level targeting and an extreme poverty line that is 75% of the official poverty line yield an FGT2 measure that is less than one-fifth the value of the FGT2 that would have obtained with a uniform transfer. As is intuitively clear, the lower the poverty line the greater the scope for errors of inclusion with a uniform transfer. Finally, fourth, even though our base-case low budget represents a considerable resource envelope (as evidenced by the sizable impact on poverty of even a uniform lump-sum transfer) it is clear that optimal targeting at the lowest possible level of disaggregation is far from sufficient to eliminate poverty altogether (see further below).<sup>18</sup> Table 2 repeats the simulations presented in Table 1 but focuses now on the headcount, or FGT0, measure of poverty. As mentioned above the optimization procedure outlined in Kanbur (1987) applies to the squared poverty gap or FGT2 measure. There is no analogous optimization algorithm for the FGT0 measure. We report in Table 2, however, the resulting FGT0 estimates from having applied the procedure to allocate our budget in such a way as to minimize the resulting FGT2 measure. Table 2 reveals that the gains in terms of the FGT0 of

> 17   In urban areas, the lowest level of government administration is at the commune level.  Urban communes are often very large agglomerations.  We apply the census concept of a district in urban areas to explore the potential gains from targeting within communes, but note that such districts do not have an obvious analogue in terms of local administration or government.

> 18   Elbers et al (2004) assess the statistical precision of comparisons of poverty across scenarios.  The statistical significance of poverty differences is ascertained by returning to the optimal transfer simulations and estimating not FGT2 values, but rather the

13
# **Table 14 Implications of Tunisian unilateral and bilateral services trade reform initiatives for sectoral export volumes from Tunisia (percentage deviation from baseline)**

||_Exports from Tun_|_isian firms own_|_ed by_|
|---|---|---|---|
|_Sector_|_Tunisia_|_EU_|_Rest of world_|
|Live animals, products|-5.6|0.0|0.0|
|Dairy products|-1.3|0.0|0.0|
|Coffee, etc, sugar, cut flowers|-1.5|-1.5|-1.5|
|Fruit and veg|-3.9|-3.8|-3.8|
|Cereals|-2.6|-2.5|-2.5|
|Oil seeds and fats|-0.9|0.0|0.0|
|Beverages and tobacco|-4.4|-4.3|-4.3|
|Other agricultural products|-1.7|-1.6|-1.6|
|Fish and products|-2.3|-2.2|-2.2|
|Mineral products|-0.1|0.0|0.0|
|Metals, products|-1.8|-1.7|-1.7|
|Chemical etc|-1.2|-1.1|-1.1|
|Leather products|-1.0|-0.9|-0.9|
|Wood products, pulp, paper|-1.2|-1.1|-1.1|
|Textiles and apparel|-1.0|-0.9|-0.9|
|Transport equipment|-1.1|-1.0|-1.0|
|Other machinery and equipment|1.3|1.4|1.4|
|Electrical machinery|-1.2|-1.2|-1.2|
|Other manufacturing|-1.1|-1.0|-1.0|
|Electricity|-3.9|0.0|0.0|
|Gas and water|-3.4|0.0|0.0|
|Construction|-3.3|-3.2|-3.2|
|Trade|2.3|2.4|2.4|
|Other transport|-2.7|-2.6|0.0|
|Air transport|28.3|28.8|28.8|
|Communications|58.6|73.1|73.2|
|Other financial services|11.7|18.9|18.9|
|Insurance|-0.9|-0.8|-0.8|
|Other business services|29.1|49.4|40.1|
|Other services|-8.1|-8.0|-8.0|

_Source_ : FTAP model projections.

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