rafmacalaba/data-use-mentions-extended huggingface dataset:
is positively affected. Moreover, smaller firms are more adversely affected by TBT in their export participation and entry and exit decisions. Hendy and Zaki (2014) study the effect of red tape barriers on firms’ exports by merging the Doing Business data set with firm‐level exports data. They find that red tape barriers negatively affect Egyptian firms, with the effect being more robust for the extensive margin (the probability of exporting across different destinations) more than the intensive one (the value of exports).

# _3.3 Exports and Trade Agreements_

Two main methods have been used to examine the effect of trade agreements on trade flows: computable general equilibrium (CGE) models (ex‐ante) and gravity models (ex‐post). The CGE literature on the Africa region is relatively rich. Mureverwi (2016) use a multinational CGE model to assess the potential long‐term effects of the African Continental Free Trade Agreement on the African Union member states. The main findings show that African countries benefit from a higher labor demand, more capital accumulation and improved terms of trade, with Egypt being one of the major winners in absolute terms. In the same line, UNCTAD (2018) shows that GDP increases by 0.97 percent and total employment rises by 1.17 percent with a full liberalization in the African Continental Free Trade Agreement (AfCFTA). Mold and Mukwaya (2017) use the GTAP model and database and show a significant increase ‐ thanks to the AfCFTA ‐ in intra‐regional exports as a result of tariff elimination from 9.2% to 11.7%, especially in processed food, textile and apparel.

A gravity model is used by Abedini and Peridy (2008) to show that regional trade has increased by 20% over the period 1988‐2005, following the implementation of GAFTA. Yegizu et al (2103) also study the impact of GAFTA on the volume and pattern of bilateral agricultural trade during the period between 1995 and 2007 and find a heterogenous effect
points in the likelihood of being poor during 1992-2002 compared to their peers in the nonfarm sector. Moreover, household heads with spouse engaged in the off-farm sector experienced a lower probability of being poor than did household heads with spouse engaged in agriculture or not working at all. Moreover this finding became stronger throughout the 1992-2002 period and in 2002 household heads with spouse engaged in off-farm activities had a 13-percentage points lower likelihood of experiencing poverty. Also, Araujo (2003) addresses the impact of non-farm rural employment on poverty. Araujo uses municipality data and finds that in Mexican municipalities there is a negative effect of manufacturing and services employment on poverty in semi-urban municipalities and a negative effect of services employment on poverty in rural municipalities.

In the 1990s, heads of household that took a second job were neither more nor less likely to escape poverty than their peers that did not take on an extra job, but in 2002 the second job holders were 6 percentage points more likely to escape poverty than heads that did not take a second job. Hence, the effect of the second job became poverty reducing after 1996 and hence a way that some rural families managed to escape or reduce household poverty.

When comparing the likelihood of being poor for household heads with different labor status in the South region to Mexico as a whole, the findings show that only self-employed southerners in the agricultural sector are statistically significantly more likely to be poor (14 percentage points) than in the rest of the country in 2002. In 1992 there was no difference 27
program (if implemented ideally) is more than twenty times more effective in reducing poverty rates in Argentina than in Nicaragua under both modalities, and eight times more effective in reducing the poverty gap. Without even considering implementation issues, it is thus far easier to achieve a poverty impact in wealthier countries than in poorer ones. Two main reasons stand out: transfers remain higher in wealthier countries because of larger budgets available, making it easier to lift people above USD2.5 dollars a day: 0.5 percent of GDP corresponds to an average of 715 USD per capita (in PPP terms) in 2008 in Argentina, but only to 134 USD per capita in Nicaragua. In addition, wealthier countries also display lower poverty rates and a lower proportion of children in the population, and therefore, under the targeted modality, a higher budget is distributed among fewer beneficiaries.

**Figure 1: Changes in Extreme Poverty Rates - Children**

<!-- Start of picture text --> 80%<br>Categorical<br>60%<br>Targeted<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases

**Figure 2: Changes in Poverty Gap  - Children**

<!-- Start of picture text --> 80%<br>Categorical<br>60%<br>Targeted<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases 10
2 4. A summary of each renewable energy technology, including an assessment of the sensitivity of its economic viability to lower oil prices, is presented in Chapter 2. Conclusions and policy recommendations that can be drawn from this analysis are preserted in Chapter 3. Before proceeding to this discussion, however, it is useful to outline what has actually happened in international and domestic oil markets.

# Actual Oil Price Changes

_5._ From November 1985 to July 1986 the average FOB price of crude oil on world markets slid from above US$ 28/bbl to below US$ 11/bbl. Since mid 1986, crude oil FOB prices remained relatively stable between US$ 15/bbl and US$ 18/bbl. As crude oil prices are projected to continue within this range in the medium term, the general economic conclusions reached in this paper are expected to be valid for some time to come.

<!-- Start of picture text --> Diesel & Fuel Oil Retail Price<br>Changes in Current US$ by Region<br>30% 9 Month Current US$ Price Change '85-'86<br>20% Diesel . Fu ... .<br>-1 .,. .. . 0% , .. .. ... ... ,.. .. .. ..,. .. .. .. .,,, ..... .. .<br>0 sia Africa Latin merica Asia Africa Ltin Anmerica<br>I 90 Least Drop _ Region Average C Most Drop<br>Source: see Annex<br><!-- End of picture text --> Figure 1 Retail Price Changes in Current US Dollars 6. Data collected from almost 20 developing countries indicate that most countries made only slight retail price adjustments in response to the -60% drop in international oil prices between November, 1985 and July, 1986.3' In fact, a few countries
Firm-Level Adoption of Technologies in Senegal<sup>_∗_</sup> Xavier Cirera Diego Comin Marcio Cruz Kyung Min Lee World Bank Dartmouth College World Bank World Bank

> _∗_ The authors would like to thank Mark Dutz, Carlos Rodr ́ıguez-Castel ́an, Laurent Corthay, Meriem Ait Ali Slimane, Laurent Gonnet, Nathan M. Belete, Consolate Rusagara, Denis Medvedev, and Idrissa Diabira (Director General, ADEPME) for their useful comments. We are very thankful for the inputs provided by Antonio Soares Martins-Neto. We also thank Insa Sadio (Head of the Bureau of Business Statistics at ANSD) and the ANSD team for their collaboration on providing the sampling frame for conducting the Firm-level Adoption of Technology survey in Senegal. Financial support from the Competitive Industries and Innovation Program (CIIP), the Korea World Bank Group Partnership Facility (KWPF), and the Facility for Investment Climate Advisory Services (FIAS) is gratefully acknowledged.
<!-- Start of picture text --> Figure 4.9. Increases in final demands in 2030 by<br>scenario (% of base GDP in 2030).<br>50<br>45<br>40<br>35<br>Gov't inv.<br>30<br>25 Gov't cons.<br>20 Private inv.<br>15 Private cons.<br>10<br>5<br>0<br>limbo rec-t rec-ta rec+t rec+ta rec+ta+<br><!-- End of picture text -->

# 5. Conclusions

This paper explores potential scenarios for Yemen’s economy up to 2030, drawing on a new database, including a SAM, put together from scattered data, and SDGSIM, a CGE model that imposes rigor based on first economic principles. The results suggest that, if the conflict subsides, the donor community is supportive, and the economy responds to increased spending on human development and infrastructure in ways that are typical of low-income countries, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great. However, high payoffs will require strong governance in general, including efficient government spending and the ability to raise additional taxes without a negative impact on income distribution or private sector incentives. Nevertheless, foreign aid may play a crucial role by reducing the need to raise domestic taxes, making it easier to realize badly needed improvements in living standards during years of expanded public investments in Yemen’s development.

32
The results show that Honduras has the highest vulnerability to poverty rate (41%), and the Dominican Republic has the lowest (7%). The vulnerability incidence in Peru is 24%, indicating that almost a quarter of the households have a predicted probability over 0.50 of falling into poverty. In Mexico, the vulnerability rate is 20%, El Salvador 17%, Brazil 16%, and Ecuador 14%.

**Figure 4.** Vulnerability incidence (% of vulnerable households with a predicted probability greater than 0.50 including all hazards).

<!-- Start of picture text --> Honduras 41%<br>Peru 24%<br>Mexico 20%<br>El Salvador 17%<br>Brazil 16%<br>Ecuador 14%<br>Dom. Rep. 7%<br>0% 10% 20% 30% 40% 50%<br><!-- End of picture text --> Source: authors' calculations.

In Figure 5, the maps show the percentage of vulnerable households in each state in the case of the regressions with only one natural hazard at a time. In the case of Brazil, droughts are the hazards included in the model. The results show higher vulnerability in the north in line with the fact that poverty, exposure, and natural hazards concentrate more in that region and that households in this area have fewer coping mechanisms than those in the south. The Dominican Republic shows that some administrative units present a vulnerability rate for landslides notably higher than the national average. The vulnerability rate of the provinces Independencia, Baoruco, and Pedernales is 36%, 25%, and 24%, respectively. Also, the map shows that Barahona and Puerto del Plata have districts with a higher vulnerability rate than the rest. In Ecuador, the vulnerable households are concentrated in the east, the region with more effective poverty rates. But where the TH index of landslides suggests more
Figure 3 displays broadly the same information in a different way. It shows the distribution of per capita $PPP incomes received by the lowest and highest ventiles in all countries in 2005. Two points stand out. First, the distributions are quite wide indicating that people who are nationally poor (or nationally rich) receive vastly different incomes, depending on what country they live in. Second, the two distributions do intersect, implying that there are countries such that people who are poorest there are yet better-off than the richest people in other countries. The link between being nationally and being globally poor is severed.

Figure 3. Income of people belonging to the poorest and richest country ventiles (year 2005, 114 countries) <!-- Start of picture text --> poorest ventiles richest ventiles<br>2 4 6 8 10 12<br>log of per capita income in 2005 international dollars<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Note: Based on national household surveys; adjusted for price differences between the countries using most recent PPPs.

The income gap between countries’ average incomes is much greater today than around 1850. Again, using Maddison’s data, in order to keep comparability with the 1850 results, we find that the top-to-bottom ratio in 2007 was in excess of 100 to 1 (as opposed to 4 to 1 that it was in 1850). The increase in the gap is easy to understand: while the poorest countries today are 11
the coefficients from regressions of a binary indicator (coded to 1 if the school is a private school, 0 otherwise) on the standardized index of three intermediate school outcomes: teacher shortage, teacher motivation and teacher effort. The second row reports coefficients from regressions of the standardized people management index on each of the intermediate school outcomes. The people management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include PISA _school final weights_ and country fixed effects. School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources, and average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added to the specifications.

31
**Appendix E. Variable Descriptions and Definitions of Data from the Brazil Electoral Study**

|**Variable**|**Question**|**Scale**|
|---|---|---|
|Bolsonaro vote round 2|Voted for Jair Bolsonaro in the second round of the<br>presidential elections|0=No<br>1=Yes|
|Bolsonaro-PT switch|Switched from voting for Dilma in 2014 to Bolsonaro<br>in 2018||
|Age|Four age categories: <30 years, 30-44 years, 45-59<br>years,60years or older|Age in years|
|Gender|Gender of the respondent<br>|Male/female|
|Has a partner|Dummy taking the value of 1 if the respondent is<br>married or has a domesticpartner,and 0 otherwise.|1=yes; 0=no|
|Other people in<br>household|Dummy taking the value of 1 if the respondent has<br>other people than their partner living in the same<br>household|1=yes; 0=no|
|Evangelical|Religious denomination: evangelical<br>|1=yes;0=no|
|White|Ethnicity: Preto, Pardo, Branco, Amarelo, Índio|1=White (Branco)<br>2=Other|
|Immigrant|Wereyou born in Brazil,or not?|1=no;0=yes|
|Lives in a rural area|The respondent’s self-reported type of settlement: (1)<br>Rural; (2)urban|1=rural; 0=urban|
|Region|Region in which the respondent is living|1= North<br>2= Northeast<br>3= Central-West<br>4= Southeast<br>5= South|
|Education level|Highest level of attained education|1=Elementary<br
a household survey for the period after the shock is not yet available. However, and this is even more important, even if survey data were available, they may not provide an adequate counterfactual given that, simultaneously to the oil price drop, the Russian economy has been influenced by other shocks, for example, economic sanctions. For these reasons, the analysis is performed using a macro‐micro simulation model which allows to generate a counterfactual scenario in which the oil price change is the only shock affecting the economy.

> 2 The economic effects of a natural resource boom were modeled by Corden and Neary (1984) who distinguished between a _resource movement effect_ – related to the reallocation of labor and capital from the tradable sectors to the booming natural resource sector – and a _spending effect_ – which brings a real exchange rate appreciation and further de‐industrialization. The real exchange rate appreciation is caused by the fact that higher incomes from natural resources raise demand for both tradable and non‐tradable goods, but while domestic prices of tradables are linked to international prices and thus remain constant, domestic prices of non‐tradables go up.

2
variable (IV) approach using a popular instrument in migration literature, that is, historical migration networks. Specifically, using the 2001 Nepal Census, I compute the percentage of international migrants from a village as an instrument for migration in 2010/11. A decade lagged migration shares are unlikely to aect local economic conditions and hence, labor supply decisions. Furthermore, I use GDP growth between 2001 and 2011 of the most popular destination countries interacted with the local level decade lagged migration shares as an additional instrument and the results are robust.

The analysis has four major ndings. First, solely on the extensive margin, having international migrants in the family discourages the left-behind members from participating in wage-employment. This is true for both male and female members. However, female members signicantly increase participation in self-employment, almost entirely through subsistence farming. Whereas, having 2
# _3.3. Key descriptives of identified metro areas_

Table 1 summarizes key statistics for metro areas delineated by each of the four approaches. For Duranton’s algorithm, we present statistics for commuting flow thresholds between 27 percent – which is when the first metro area appears using this algorithm – and 7 percent. Although we generated results for all commuting flow thresholds between these two values at 0.5 percent intervals, we only show results for selected thresholds. Meanwhile, for the cluster algorithm, we show results based on both the “Urban Cluster” (UC) set of thresholds (i.e. T<sup>�</sup> � = 300 people per km<sup>2</sup> , T<sup>�</sup> � = 5,000) and the “High Density Cluster” (HDC) set of thresholds (T<sup>�</sup> � =1,500 people per km<sup>2</sup> and T<sup>�</sup> � = 50,000). Finally, for the NTL approach, we present selected results based on the thresholding of the lights data at different points in the distribution of luminosity values.<sup>19</sup> In presenting results, we include information not only on the total number of metro areas, but also on the number of metro areas that belong to the official island-region of Java-Bali, which is where the majority of Indonesia’s urban population – approximately 70 percent in 2016 – resides and which, overall, is significantly more urbanized and densely populated than Indonesia’s other island-regions.<sup>20</sup>

**<u>Table 1: Comparison of key statistics</u>**

||<br>**No.**|<br>**No. me**|<br>**tro district**|**s*
authorities in improving the system or enlarging its mandate:

- Will the demand from retail investors be sufficient to achieve the program’s objectives? There has not been a formal survey of the demand for such a product. It is assumed that time deposit account holders will be the main users of the Expanded Small Investors Program. This assumption relies on another, stronger assumption: that small investors are rational and will switch to better investment options when they become available. But that may not be true. An investor survey or focus group could be used to measure demand, and a marketing campaign may be required to draw attention to the system and educate investors. The Treasury will be responsible for such efforts.

- With the infrastructure in place, could the system be used to distribute a wider range of instruments—starting with other government securities and eventually moving to private securities? Although the program offers only Treasury bills, there are plans to include all retail government securities and longer-term bonds.

- There are no technological limits on such a system. All limits imposed are related to policy or legal concerns, such as the ceiling on the amount that can be purchased in each transaction and the system’s buy-and-hold nature. Are these limits needed, or could they be removed to make the program more attractive?

- Is there are a need to distinguish between institutional and retail investors for government securities? Although the system of securities dealers helps create markets for these securities and facilitates their distribution, do the dealers’ functions justify their privileges?

- Could the Expanded Small Investors Program be offered to offshore Filipino investors? Doing so would require exploring laws in foreign countries that could block such investments, as well as registration procedures, money laundering concerns, and many other issues. But such an exploration might be worthwhile if it increases the demand for the program.

- Could telecommunications technology be used to expand the program’s outreach? In the Philippines mobile phone use is increasing much faster than Internet use. The program currently uses mobile
.013)|-0.156***<br>(0.013)|11,247|1|135|
|Oppose taking Syrian refugees|-0.213***<br>(0.027)|-0.213***<br>(0.027)|-0.195***<br>(0.019)|-0.195***<br>(0.019)|11,152|1|223|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. Each cell is a separate regression at the individual level with the binary dependent variable listed in the left-most column and the independent variable is our main outcome: 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?” and 0 otherwise. _Years_ refers to the number of survey-years the outcome variable is included in GWP. _Regions_ refers to the number of sub-national regions observed in GWP. Results use the Gallup sampling weights and standard errors are clustered at the region (sub-national) level.

immigrants to live, correlates strongly with perceptions toward immigrants. Regionyears where more respondents report their area is a good place for immigrants have a smaller percentage of the population reporting that they would not want immigrants as neighbors, believe immigrants take jobs, oppose further immigration, etc. This holds across regions and within-region changes over time.

Based on the strong individual- and regional-level relationships, we argue that our main outcome serves as a useful proxy measure for a general perception toward immigrants and refugees. We believe it is likely that many respondents approximated the question as a version of “are immigrants good or bad”. Where possible, however, we support our main findings with the additional related outcomes.

# **4.1.3 Policies**

For data on