rafmacalaba/datause-encoder-data huggingface dataset:
introduction of the flat tax rate. Revenue from income taxes has been undermined by many exemptions which remained in place when the flat tax was introduced.

# **Figure 8 Trends in the legal and implicit tax rates for personal income tax and social security contributions: 2001-10**

<!-- Start of picture text --> 59 6.5<br>Progressive taxation (from 18% to 40%) Flat tax of 16%<br>55.0 55.0<br>54 52.5<br>6.0<br>49.0 49.0<br>49<br>46.3<br>44.5<br>44 43.0 43.0<br>41.8 5.5<br>39<br>6.3<br>5.0<br>34 5.8 5.8<br>5.3<br>5.2<br>5.6<br>5.9<br>28.1<br>29 27.1 5.0 26.9 26.9<br>26.4 26.2<br>26.1 4.5<br>24.1<br>24 22.8<br>4.3<br>20.8<br>4.3<br>19 4.0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Implicit tax rate personal income tax (rhs) Legal tax rate social contributions<br>Implicit tax rate social contributions Share of employees in the informal economy<br><!-- End of picture text --> _Source: Eurostat, Romanian National Institute of Statistics , Romanian Ministry of Public Finance, Fiscal Council, and authors’ calculations_ Compliance with VAT is the lowest in the EU. This is illustrated by the VAT gap, which is calculated as the ratio of VAT not collected to total VAT liabilities. The VAT gap in Romania was about 37 percent, the highest in the EU by a large margin (see figure 9). The Romania Fiscal Council estimates that the losses arising from VAT fraud amounted to 3.8 percent of GDP in 2009.

24
around 4.7 percent. Finally, the bottom line of panel A shows the statistics for the banks’ ratio of exposure to the energy sector in the month prior to the energy price shock. On average, the exposure is around 8 percent, with banks in the bottom decile having zero exposure to the energy sector, whereas banks in the top decile have 19.6 percent exposure.

Table 1, panel B, reports the loan characteristics of firms operating in energy-related sectors. Although the average bank loan of an energy firm is around Mex$89 million, the median loan size is Mex$1.5 million. Interest rates average 11.4 percent, with loans in the bottom decile having rates as low as 4.4 percent and in the top decile 18 percent. The maturity of loans is on average around two years, with loans in the bottom decile having maturities of around two months, whereas the top decile has maturities of four years.<sup>10</sup> These short maturities are consistent with most loans being destined for working capital. Collateral rates average 14.6 percent of the value of the loan and, again, there is great variation in the amount of collateral required across loans. Although the median loan is uncollateralized, loans in the top decile require collateral of more than half their value. The loan characteristics of firms operating in non-energy sectors display similar patterns as those of firms in the energy sector (Table 1, panel C).

The last two panels in Table 1 present summary statistics for real outcomes at the firm-year level and aggregate production at the state-quarter level. As panel D shows, the median bank debt of firms according to the credit registry is around Mex$730,000. From the Orbis data set, we find that the median liabilities of firms are around Mex$350 million, with median assets and revenues of around Mex$1.1 billion and Mex$800 million, respectively. We construct _AvgExposureEnergym,Aug14_ , a measure of banks’ exposure to the energy price shock at the municipality level, as the average exposure to the energy sector in August 2014, weighted by loan value, of the banks serving municipality _m_ .<sup>11</sup> Given that lending tends to be local (Degryse and Ongena 2005), this measure allows us to capture variation in the exposure of firms to banks that were more affected by the shock.

Finally, panel E shows two statistics at the state-quarter level. The first one is the state GDP, which is measured as an index normalized for each state to its level in January 2014. The second variable, _AvgExposureEnergys,Aug14_ , corresponds to the average exposure to the energy

> 10 These maturities do not include revolving loans.

> 11 In Mexico, there are 2,448 municipalities, with an average population of around 400,000 people.

9
<!-- Start of picture text --> Figure 2: Distribution of Ads by gender specification<br>Total ads 830,929<br><!-- End of picture text --> <!-- Start of picture text --> Gender-specified 301,929<br>(36%)<br><!-- End of picture text --> <!-- Start of picture text --> Male-targeted 180,058<br>(60%)<br>Female-targeted 121,324<br><!-- End of picture text --> <!-- Start of picture text --> Female-targeted 121,324<br>(40%)<br><!-- End of picture text --> <!-- Start of picture text --> Gender-unspecified 529,547<br>(63%)<br><!-- End of picture text --> <!-- Start of picture text --> Gender was not<br>mentioned in job<br>description (Text<br>analysis), 506,004<br>(95.6%)<br><!-- End of picture text --> <!-- Start of picture text --> Gender was not identified but<br>mentioned in job description<br>23,543 (Text analysis), (4.4 %)<br><!-- End of picture text --> <!-- Start of picture text --> Male was mentioned in<br>description 8,283 (35 %)<br>Female was mentioned in<br>description 15,260 (65%)<br><!-- End of picture text --> _Source:_ Babajob job portal data Our descriptive statistics show a higher share of job ads posted by households and small or medium enterprises (SME) explicitly specify gender in the ads. Other than households where women are hired mostly as maids, all other hiring agencies such as SME, human resource (HR) enterprise, and staffing companies that listed gender-targeted ads prefer men over women. BPO job ads are the most gender-neutral with only 14 percent specifying gender. Ads for elementary occupations, machinist, driver and garment workers are the ones with higher share of gender targeting.

<u>Table 1: Share of Job Ads by Gender Specification and Ad Characteristics</u>

|Characteristics|Gender<br>unspecified Ad|Gender specified<br>(men/women) Ad|Ad specifies<br>men|Ad specifies<br>women|
|---|---|---|---|---|
||1|2|2(a)|2(b)|
|Required experience|||||
|None or not specified|69|31|18|13|
|Less than 1 year,|71|29|17|11|
|1 to 2 years|55|45|26|19|
|2 to 3 years|47|53|32|20|
|3 to 4 years|47|53|34|18|
|4 to 5 years|44|56|40|16|
|5 years or more|43|57|42|15|
|Firm Ownership Type|||||
|HR enterprise|67|33|21|12|
|SME|59|41|25|16|
|Staffing company|74|26|17|9|
|Household|46|54|20|34|
|Unknown|68|32|20|11|
|Work Shift|||||
|Full-time|57|43|27|16|
|Part-time|55|45|20|25|

6
Appendix Table 13: Household relationships

||We are a<br>team<br>(planter)|We are a<br>team<br>(spouse)|Happy in<br>relationship<br>(planter)|<br>Happy in<br>relationship<br>(spouse)|<br>Stable<br>relationship<br>(planter)|<br>Stable<br>relationship<br>(spouse)|
|---|---|---|---|---|---|---|
|Seedling Subsidy|0.02|0.05|-0.01|0.01|0.00|0.04|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|
|Couples' Training|-0.02|-0.04|0.01|0.02|-0.02|0.01|
||(0.05)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|
|Seedling Subsidy + Couples'<br>Training (beta)|0.01|0.02|-0.01|0.03|-0.02|0.06|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.88|0.62|0.82|0.44|0.70|0.14|
|Observations|1376|1038|1377|1038|1377|1038|
|Mean for control group|3.13|3.11|3.18|3.17|3.17|3.14|
|Mean for individual training|3.13|3.15|3.15|3.18|3.15|3.18|

Robust and clustered standard errors in parentheses. * p<0.1 ** p<0.05 *** p<0.01 Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

4
**_-6-_**

services are the Zimbabwe National Family Planning Council's Community Based Distributors and Clinics, the Ministry of Health hospitals, clinics and rural health centers, Municipality clinics and private physicians and pharmacies. According to th, 1988 Demographic and Health Survey, 24 percent of current family planning users got their supplies from ZNFPC Community Based Distributors, 13 percent from ZNFPC static and mobile clinics, 59 percent from the Ministry of Health and Municipal clinics combined. Only 4 percent of users got their supplies from private physicians and pharmacies.

# **3.1. PUBLIC SECTOR SOURCES OF FAMILY PLANNING**

# **_3.1.1. Zimbabwe_** _Nadonal Family Planning Council_

The Zimbabwe National Family Planning Council is a parastatal organiza.ion under the Ministry of Health. ZNFPC is responsible for the coordination and implementation of family planning in Zimbabwe. The Council's mandate includes providing technical support and quality control for all public and private family planning services, training of family planning providers, conducting media campaigns and routine Information Education and Communication (IEC), advising youths and young adults on responsible parenthood, conducting relevant research and procuring and storing virtually all contraceptives used in the country.

These services are provided through six interdependent units. Although the services are directed at national level the actual imlementation is carried out at provincial right down to village level by ZNFPC's team of fieldworkers. The six units comprise of three service units- the Community Based Distribution, Medical/Clinical, and Youth Advisory Services Units, and three support units- the Information Education and Communication, Evaluation and Research and Training Units.

# a) The Community Based Distribution Unit

The Community Based Distribution (CBD) programme began in 1976. It is ZNFPC's principal means of outreach. The programme is community oriented and based. CBD workers are selected by the communities which they will serve under the guidance of the Ministry of Community Development and Women's Affairs. They are selected under criteria that require that they be mature (age between 25 and 40 years), literate (at least 7 years of primary education) and of high standing in their communities as well as that they successfully undergo a six week training in family planning, primary health care and interpersonal communication.

After successfully completing the six week training the CBD worker becomes a full time employee of ZNFPC and is deployed in his or her community. The CBD is equipped with the following:

- i) a bicycle;

ii) oral contraceptives; iii) condoms;
<!-- Start of picture text --> y=1 (n=6666)<br>Probabilities:<br>over 0.9<br>0.8 − 0.9<br>0.7 − 0.8<br>0.6 − 0.7<br>0.5 − 0.6<br>y=0 (n=8434) 0.4 − 0.5<br>0.3 − 0.4<br>0.2 − 0.3<br>0.1 − 0.2<br>under 0.1<br>(a) Model 1: Specification 1<br>y=1 (n=6666)<br>Probabilities:<br>over 0.9<br>0.8 − 0.9<br>0.7 − 0.8<br>0.6 − 0.7<br>0.5 − 0.6<br>y=0 (n=8434) 0.4 − 0.5<br>0.3 − 0.4<br>0.2 − 0.3<br>0.1 − 0.2<br>under 0.1<br>(b) Model 1: Specification 2<br><!-- End of picture text --> Figure 12: This plot visualizes the fit of the two specifications of Model 1. The width of the darker shades of red on the upper band of the second specification is wider than the width of the darker shades of red on the upper band of the first specification. The width of the lighter shades of red on the top band of the second specification is larger than the width of the lighter shades of red on the bottom band of the first specification. This suggest that compared to the first specification, the second one performs better in assigning high probabilities to actual events (trusting local government officials) and lower probabilities to actual cases of non-events (not trusting local government officials).

Figure 13: This plot visualizes the fit of Model 1 that was estimated on a subset of the data: Kono and Western Area Urban. These plots suggest that the models performs better with the former data than the latter. The model performs only moderately well in assigning very high probabilities to events and very low probabilities to non-events.

40
# Figure A.5.2 Inflation targeting in Chile

## A. Actual inflation and inflation expectations

<!-- Start of picture text --> Percent, year-on-year Target upper and lower limit<br>5-year-ahead expectations<br>3-year-ahead expectations<br>5-year breakeven inflation<br>10 Actual inflation<br>6<br>2<br>-2<br>C. General government structural balance and<br>debt<br>Percent of GDP Percent of GDP<br>Structural balance Gross debt (RHS)<br>8 25<br>6<br>20<br>4<br>2 15<br>0 10<br>-2<br>5<br>-4<br>-6 0<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2017<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018<br><!-- End of picture text -->

## C. General government structural balance and debt

## B. Central bank transparency

<!-- Start of picture text --> Index (15=best)<br>12 Chile Median of EMDEs<br>9<br>6<br>3<br>0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> D. Sensitivity of inflation expectations to shocks <!-- Start of picture text --> Percentage points<br>3<br>2<br>1<br>0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: Bloomberg, Consensus Economics, Dincer and Eichengreen (2014), Haver Analytics, International Monetary Fund, World Bank.

A.-D. The start of the inflation targeting regime is shaded in gray.

B. Transparency is based on information from the Central Bank of Chile’s website, statutes, annual reports, and other published documents, as calculated by Dincer and Eichengreen (2014).

C. Structural balance is the difference between government revenues and expenditures, adjusted for effects due to economic cycles.

D. Time-varying sensitivity is estimated by regressing long-term inflation forecast revisions on inflation shocks. Dotted lines denote 68 percent confidence interval. Appendix 3 provides details on the methodology.

51
were concluded in 1990 and it entered in force in 1991 as a free trade area. In 1994, Mercosur members decided to become a customs union, starting in 1995. Overall, Mercosur has been relatively successful in terms of achieving internal free trade and harmonizing external tariffs, although exceptions exist for each member (see the discussion in Olarreaga and Soloaga [1998]). The bloc also formed side agreements with Bolivia and Chile in 1997.

The other multi-country RTA in South America is the Andean Community. Officially formed in 1969, the agreement was effectively ignored during the 1980s. At the end of that decade, however, its members (Bolivia, Colombia, Ecuador, Peru, and Venezuela) decided to revamp the process of internal liberalization. Just as in Mercosur, internal liberalization started in 1991 and the group became a customs union in 1995. Peru left the group in 1992, but it maintained bilateral agreements with all other CAN members.<sup>20</sup> While relatively successful in terms of eliminating internal trade barriers, the group itself recognizes the difficulties in the implementation of their CU.<sup>21</sup> Colombia and Venezuela also have a free trade agreement with Mexico since 1995 under the Group of Three, and each of them has an FTA with Chile (Venezuela since 1993, Colombia since 1994, Mexico since 1999). Chile and Mexico started agreements with other countries during the 1990s as well—Chile with Canada (1997) and Peru (1998), Mexico with Costa Rica (1995) and its North America partners under NAFTA (1994).

In sum, the 1990s marks the period where South America countries, as well as Mexico, seriously started to engage themselves in initiatives of regional trade liberalization.

> 20 President Hugo Chávez has recently decided to take Venezuela out of CAN, while signing an FTA with Mercosur. This change took effect in 2005, however, well after the last year in our dataset (2001). 21 See http://www.comunidadandina.org/endex.htm.

21
- 28 -

# Table 3.2. Proportion of Electric Power Supply from Own Generators by Total Electric Consumption Category For Firms with Own Generators

|Total<br>Electricity<br> <br>|Nfg|eria|Indo|nesla|Tha|iland|
|---|---|---|---|---|---|---|
|Consumpt<br>Ia<br>(1000kWh)|Nuder of<br>Firm|Percent|Number of<br>Firm|Percent|_kumber_of<br>Firms|Percent|
|1- 4-|||5|28.6|||
|5S-9|||3|30.4|_||
|10- 19||-|13|20.8||-|
|20-49|9|36.5|21|38.7||.|
|50- 9|21|37.8|19|22.9|1|90.0|
|100- 19|17|38.5|32|28.7|1|0.0|
|200 - 499|41|43.5|44|12.0|-||
|500 - 999|34|36.1|23|45.3|4|0.0|
|1000- 1999|20|43.4|15|19.1|3|21.1|
|2000Amore|23|36.6|15|79.8|9|35.9|
|AIl|165|37.2|190|66.7|18|35.2|

MOTE: Froo Appendix Tables 31, Cl, and Di.

_jV_ Total *Iectricity cornumption consists of electric power uppiLy from own generators and pubtic source.

Source: IDS/18RD Project Estabilshment Survey for Nigeria 1988. World 8ank Research Project (RPO 676-71) EstabLishment Survey for **Irdknesla** 1992.
# **Religious Terrorism, Forced Migration, and Women’s Empowerment: Evidence from the Boko Haram Insurgency**<sup>∗</sup>

Paola Elice<sup>1</sup> , Fernanda Martínez Flores<sup>2</sup> , and Arndt R. Reichert<sup>3</sup>

> 3 _Leibniz University of Hannover, The World Bank, RWI_

_JEL Codes:_ D74, J22, O12, O15, R23 _Keywords:_ Boko Haram, Fulani, conflict, forced displacement, employment, fertility, social norms, gender norms ∗This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership.” The program is funded by the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO) and is managed by the World Bank Group. The program was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The aim of the program is to expand global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. The work was initiated by the authors while Arndt Reichert was an economist at the Development Impact Evaluation Department of the World Bank. This work does not necessarily reflect the views of FCDO, the World Bank Group, or UNHCR. We thank Julia Bredtmann, Georgia Christina Kosmidou Bradley, Catherine Marie Rose Defontaine, Tilman Brück, and Barbora Sedova for their comments. We also thank Henry Kwenin and David Musombi for guidance on the use of International Organization for Migration data. We also thank the participants of the 2022 German Development Economics Conference, internal seminars of the RWI - Leibniz Institute for Economic Research, the University of Duisburg-Essen research seminar, the Leibniz University Hannover Brown Bag seminar, and the International Security and Development Center’s New Methods for Impact Evaluations in Conflict and Humanitarian Emergency Settings workshop for helpful comments. Lesman Ghazaryan and Duaa Kattana provided excellent research assistance. Computational reproducibility was verified by Development Impact Evaluation Analytics. All remaining errors are our own. All correspondence may be directed to Fernanda Martínez Flores, RWI - Leibniz Institute for Economic Research, Hohenzollernstr. 1-3, 45128 Essen, Germany, email: fernanda.martinez@rwi-essen.de.
# **Figure 13. Human development indicators**

_Health and educational expenditure is highest among EMDE region and close to the OECD average. The region made significant advances in the area of human development. Nevertheless, important shortcomings remain. Among the basic education indicators, the region scores below the OECD average in math and science outcomes. The region also lags behind both the OECD and the EMDE average in attracting and retaining talent_ .

# **A. Selected health care indicators**

<!-- Start of picture text --> Per capita Percent of population<br>5000 120<br>4000 100<br>80<br>3000<br>60<br>2000<br>40<br>1000 Range AE 20<br>EMDE ECA<br>0 0<br>Health Improved Improved<br>expenditure sanitation water<br>(LHS) (RHS) source<br>(RHS)<br>C. Math and science outcomes<br>Score<br>6 2016-2017 OECD average<br>5<br>4<br>3<br>2<br>1<br>0<br>Ukraine Albania Croatia Armenia Serbia Russia Poland Montenegro Kazakhstan Macedonia, FYR Moldova Bulgaria Tajikistan Azerbaijan Hungary Bosnia & Herz. Georgia Turkey Kyrgyz Republic<br><!-- End of picture text -->

# **B. Selected education indicators**

<!-- Start of picture text --> Percent of per capita<br>income, ratio<br>45<br>Range AE<br>40<br>EMDEs ECA<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Government Pupil-teacher ratio<br>expenditure<br><!-- End of picture text -->

# **D. Attracting and retaining talent**

<!-- Start of picture text --> Score<br>5 2016-2017 OECD average<br>4<br>3<br>2<br>1<br>0<br>Azerbaijan Tajikistan Kazakhstan Russia Ukraine Armenia Georgia Turkey Bulgaria Montenegro Poland Albania Kyrgyz Republic Hungary Macedonia, FYR Croatia Bosnia & Herz. Moldova Serbia<br><!-- End of picture text --> Sources: Haver Analytics, World Bank, World Economic Forum.

A. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 19 ECA economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 22 ECA economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 22 ECA economies. Latest data available during 2011-15.

B. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 10 ECA economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 20 ECA economies. Latest data available during 2011-15.

C.D. The score is from 1 to 7 (best). The OECD and EMDE average are the simple averages of all the countries in the respective subgroupings.

E. Regional aggregated number. Data are for 2014 or latest available. GDP data are in constant 2011 “international dollars.” F. Share of renewable energy consumption as percent of total final energy consumption in 2012. EMDE averages are the simple average of all the countries in the respective subgroupings.

26
question. The findings suggest that the incidence of poverty is key, but not the only factor affecting the relative effectiveness of targeted vs. categorical transfers. The rural-urban analysis strengthens the view that the relative effectiveness depends on a variety of factors, in particular the depth of the poverty gap: if it is relatively low, the difference in poverty impact of categorical vs. targeted transfers may not be that large.

The comparison of programs for children and the elderly also supports the view that choosing carefully the beneficiaries is almost as important as targeting to achieve poverty reduction. The simulations show that because, overall, there are less elderly than children who are poor, for given resources social assistance programs to the elderly achieve a lower poverty impact – even if they are poverty targeted. This does not necessarily call for phasing out social pensions, but for a rebalancing of the budget of social assistance programs towards the largest vulnerable groups.

Overall, the findings support the view that in the Latin American context targeting assistance to the poor tends to deliver higher poverty impacts. There are nonetheless circumstances under which simpler schemes, such as categorical cash transfers for vulnerable groups that are geographically confined to regions with strong pockets of poverty, may be a valid option to consider as an alternative to means tested or proxy means tested programs. This is particularly the case in low income countries with widespread pockets of poverty.

We would like to conclude with a note of caution. To achieve comparability across countries, the simulations abstained from looking at country specific factors that ought to be considered in the design of effective social assistance programs. Among these are more precise identifications of vulnerable groups and of geographic differences. The results should thus be taken as they are – a suggestive cross country comparison with no ambition to provide guidance for specific countries, which ought to include more country specific factors.

21