rafmacalaba/datause-encoder-data huggingface dataset:
improve its governance and economic management in order to continue improving its country risk ratings and bring about systematic reductions in its costs of debt and equity financing.

- **Changes in tax distortions:** The impact of a fiscal reform needs to be taken into account when assessing the EOCK. As a result of the fiscal reform recently approved in Mexico, some of the taxes and actual revenue collected will change. These changes need to be considered in any recalculation of the EOCK. More detail is required on the actual revenue measures and expected revenue impacts. However, rough simulations of a net increase in revenues of 3 percent of GDP over the medium term (as announced in 2013) that is widely spread across the various tax types is expected to raise the EOCK by about 0.2 to 0.3 percentage points. This will ultimately depend on the details of the economic reform package and tax measures, as well as its implementation.

- **Improvements in the financial sector:** From the analysis, we conclude that there is a huge potential for the financial sector to grow in Mexico. The size of the financial sector in relation to GDP is relatively small in the international context. Increased competitiveness, availability of more savings and investment instruments, and greater integration to world markets could lower the cost of capital and reduce the estimated EOCK. The countervailing forces (slightly higher tax externality effects and lower capital costs due to financial sector reforms and greater competitiveness in the sector) will need to be evaluated in the revisions of the EOCK.

- **The impact of reforms:** The impact of the multiple reforms approved in Mexico during the last months is still uncertain and could also affect the EOCK. It is recommended that a reassessment of the EOCK take place in about five years in order to capture the impact of the reforms and the evolution of economic trends and country risk rating.

- **Improvements required in data collection and availability:** In addition to national accounts information and data on labor markets, expanded data collection on country, sector risk, and costs of finance by different instruments and for different sectors and forms of funding will be required so as to improve future estimations of the EOCK. In addition, a clear roster data on tax collection by type of instruments and payers, as well as by type of transactions—especially domestic and foreign savings, as they affect different sectors of the economy—should be developed and maintained using different sources and checking for internal consistency with the national accounts and budgets in the public sector.

31
26

1.69 years later than Hutu women (all three figures are significantly different in means across Hutu and Tutsi). Other socio-economic characteristics differ significantly as well across Hutu and Tutsi, including education and wealth. However, it seems that these differences in fertility behavior and socio-economic status are driven by the place of residence: 14 percent of Tutsi women live in urban areas, compared to 5 percent of Hutu women. A regression analysis confirms this: When regressing the total number of children on multiple characteristics (including ethnicity, age, education, place of residence, and partnership characteristics), ethnicity is no longer statistically significant.

Fifth, we run all estimates based on the full sample of women in order to enhance the comparability and precision of the estimates. In both short-term and long-term analyses, the impact of conflict exposure is only slightly smaller in terms of the magnitude of effects and the level of significance compared to the original results derived from restricted samples.

# **7 Conclusions**

The paper analyzes the effects of mass violent conflict on fertility for conflict-survivors in the case of the genocide in Rwanda in 1994 using individual-level data. To enable an identification of the genocide at the micro-level, two types of proxies for conflict exposure are constructed using three waves of RDHS data from before and after the genocide. With this estimation strategy, we identify the ‘pure’ effects of genocide on fertility over and above conflict-related effects like urbanization, destroyed infrastructure, and weaker health infrastructure – which in turn also affect fertility. We study the short- and long-term effects of genocide and differentiate the analysis by cohort, kinship relation and gender of the deceased persons.

Our approach is unique in that we estimate the effects of genocide on fertility using multiple measures of conflict exposure and disaggregating in more dimensions than done previously. In Rwanda, this is important as by law collecting a key conflict proxy, ethnicity, is not possible. In other contexts using conflict proxies may be important if data-sets, which do not contain any direct identification of conflict at the micro-level, are to be analyzed retrospectively.

The paper has five major findings. First, we find a significant direct (or ‘pure’) effect of mass violent conflict in determining fertility. Of all conflict proxies studied, the loss of a child
# **Table 10. Teacher perceptions**

|_Dependent variable_|Sequence<br>vs.<br>Control<br>(1)|Coaches<br>vs.<br>Control<br>(2)|Wald test<br>(3)|
|---|---|---|---|
|A. I feel it the way I teach Science changes a lot|0.864***<br>(0.312)|0.953***<br>(0.247)|0.14<br>[0.708]|
|B. I like and/or enjoy more teaching Science than in<br>previous years|0.717**<br>(0.345)|0.974***<br>(0.307)|0.93<br>[0.334]|
|C. I feel that by implementing the ideas of this<br>training, my students learned more in comparison<br>with other groups and/or subjects|0.632<br>(0.333)|0.769**<br>(0.324)|0.30<br>[0.586]|
|D. I feel my students develop more skills than in<br>previous years|0.672**<br>(0.293)|0.876***<br>(0.306)|0.86<br>[0.354]|
|E. I taught more hours of Science classes|0.260<br>(0.247)|1.107***<br>(0.275)|18.33<br>[0.000]|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates). The dependent variables are four-point scale, where 1 means strongly disagree, 2 disagree, 3 agree, and 4 strongly agree.

7
|33-2011.02|Forest Firefighters|122<br>|17.39|23|4<br>Non-green|
|---|---|---|---|---|---|
|19-3092.00|Geographers|127<br>|16.67|12|2<br>Non-green|
|17-2151.00|Mining and Geological Engineers, Including Mining<br>SafetyEngineers|124<br>|16.67|18|3<br>Non-green|
|19-1012.00|Food Scientists and Technologists|129<br>|15.38|13|2<br>Non-green|
|49-3091.00|Bicycle Repairers|142<br>|12.50|16|2<br>Non-green|
|33-2011.01|Municipal Firefighters|150<br>|11.11|27|3<br>Non-green|
|19-1011.00|Animal Scientists|149<br>|11.11|9|1<br>Non-green|
|39-7011.00|Tour Guides and Escorts|152<br>|10.53|19|2<br>Non-green|
|31-9099.01|Speech-Language Pathology Assistants|160<br>|9.09|11|1<br>Non-green|
|43-5111.00|Weighers, Measurers, Checkers, and Samplers,<br>Recordkeeping|163<br>|9.09|22|2<br>Non-green|
|11-9161.00|Emergency Management Directors|164<br>|8.70|23|2<br>Non-green|
|53-5021.01|Ship and Boat Captains|172<br>|8.33|24|2<br>Non-green|
|19-3011.00|Economists|170<br>|8.33|12|1<br>Non-green|
|31-1015.00|Orderlies|171<br>|8.33|24|2<br>Non-green|
|49-9092.00|Commercial Divers|173<br>|8.00|25|2<br>Non-green|
|49-9097.00|Signal and Track Switch Repairers|179<br>|7.69|13|1<br>Non-green|
|25-4013.00|Museum Technicians and Conservators|178<br>|7.69|26|2<br>Non-green|
|23-1021.00|Administrative Law Judges, Adjudicators, and<br>HearingOfficers|177<br>|7.69|13|1<br>Non-green|
|19-1042.00|Medical Scientists, Except Epidemiologists|176<br>|7.69|13|1<br>Non-green|
|11-3011.00|Administrative Services Managers|180<br>|7.14|14|1<br>Non-green|
|53-7072.00|Pump Operators, Except Wellhead Pumpers|186<br>|7.14|14|1<br>Non-green|
|47-2151.00|Pipelayers|184<br>|7.14|14|1<br>Non-green|
|33-2021.02|Fire Investigators|182<br>|7.14|14|1<br>Non-green|
|33-3052.00|Transit and Railroad Police|183<br>|7.14|14|1<br>Non-green|
|19-3091.01|Anthropologists|181<br>|7.14|28|2<br>Non-green|
|47-5061.00|Roof Bolters, Mining|185<br>|7.14|14|1<br>Non-green|
|47-5021.00|Earth Drillers, Except Oil and Gas|192<br>|6.67|30|2<br>Non-green|
|27-1025.00|Interior Designers|190<br>|6.67|15|1<br>Non-green|
|25-4012.00|Curators|189<br>|6.67|15|1<br>Non-green|
|47-2161.00|Plasterers and Stucco Masons|191<br>|6.67|15|1<br>Non-green|
|17-3011.02|Civil Drafters|188<br>|6.67|15|1<br>Non-green|
|49-2095.00|Electrical and Electronics Repairers, Powerhouse,<br>Substation,and Relay|196<br>|6.25|16|1<br>Non-green|
|49-2098.00|Security and Fire Alarm Systems Installers|197<br>|6.25|16|1<br>Non-green|
|51-4052.00|Pourers and Casters, Metal|198<br>|6.25|16|1<br>Non-green|
|19-3091.02|Archeologists|195<br>|6.25|16|1<br>Non-green|
|53-5031.00|Ship Engineers|202<br>|5.88|17|1<br>Non-green|
|39-7012.00|Travel Guides|201<br>|5.88|17|1<br>Non-green|
|19-1031.03|Park Naturalists|203<br>|5.56|18|1<br>Non-green|
|29-1199.01|Acupuncturists|205<br>|5.56|18|1<br>Non-green|
|19-4021.00|Biological Technicians|204<br>|5.56|18|1<br>Non-green|
|17-2031.00|Biomedical Engineers|206<br>|5.26|19|1<br>Non-green|

50
among them to reach two-thirds for that year. They tend to be more concentrated in rural areas (more than 80 percent in 2006), are in great majority children of uneducated parents (more than 90 percent in any year), and children of agricultural (or elementary) workers (more than 60 percent in 2006). At the other end, college graduates vastly increased from about 220,000 individuals aged 2124 in 1988 to more than 1,100,000 in 2006; girls became the majority of them (57 percent in 2006). While the share of college graduates in rural Lower Egypt increased, no increase was observed in rural Upper Egypt. The shares of individuals from more disadvantaged backgrounds diminished among college graduates. For instance the share of children from agricultural or elementary workers diminished from 46 to 26 percent, while the share of children of higher occupation fathers increased from 35 to 55 percent.

**Table 3: Characteristics of those with low and high educational attainment**

|||**No schooling**||**Co**|**llege educat**|**ion**|
|---|---|---|---|---|---|---|
||<br>**1988**|<br>**1998**|<br>**2006**|<br>**1988**|<br>**1998**|<br>**2006**|
|female|0.538|0.547|0.642|0.440|0.534|0.570|
|Great Cairo|0.132|0.0717|0.0484|0.485|0.346|0.210|
|Alex, Suez, P. Said|0.0751|0.0315|0.0344|0.0912|0.119|0.108|
|Urban lower|0.0733|0.0560|0.0516|0.146|0.127|0.167|
|Urban upper|0.0343|0.0462|0.0588|0.0890|0.0784|0.146|
|Rural lower|0.365|0.360|0.387|0.0982|0.255|0.272|
|Rural upper|0.320|0.434|0.420|0.0904|0.0753|0.0973|
|Father w/o schooling (or missing)|0.982|0.922|0.928|0.481|0.343|0.311|
|<br>Father w/ basic education|0.0135|0.0699|0.0379|0.159|0.137|0.133|
|Father w/ secondary education|0.00449|0.00611|0.00863|0.188|0.189|0.300|
|Father w/ college education|0|0.00215|0.0251|0.173|0.331|0.256|
|Mother w/o schooling (or missing)|0.994|0.984|0.955|0.665|0.505|0.446|
|Mother w/ basic education|0.00556|0.0156|0.0173|0.120|0.187|0.148|
|Mother w/ secondary education|0|0|0.0158|0.155|0.208|0.290|
|Mother w/ college education|0|0|0.0116|0.0595|0.101|0.116|
|Profes., manag., techn., clerks|0.0270|0.102|0.0936|0.352|0.497|0.554|
|Service, craft, and industry workers|0.113|0.267|0.286|0.185|0.181|0.184|
|Agriculture, elem. occup., missing|0.860|0.631|0.621|0.463|0.322|0.262|
|Population|1,492,612|953,225|1,345,590|220,180|496,557|1,089,725|

_Source:_ 1988 LFSS; 1998 ELMS; and 2006 ELMPS. Sample: 21-24 year-olds Another way is to group students into least and most advantaged based on their individual circumstances. Here we define least advantaged youth as those with unfavorable circumstances, such as being born in rural areas, from uneducated parents, and having a father with elementary or 8
Fitzroy, 2002, Basu et al. 1997, Haltiwanger and Vodopivec, 2003) and in the context of trade liberalization (Revenga, 1997). However, the analyses are not disaggregated by gender and do not encompass a crisis period.

# **2.2 Context: The Indonesian Crisis**

In the early 1990s Indonesia was at the height of an extended period of industrialization with manufacturing being an important engine of growth. (Dwor-Frecault et al. 1999). In the wake of the devaluation of the Thai Baht in July 1997, flows of foreign capital reversed drastically and the rupiah depreciated dramatically. Interest rates were raised to defend the currency, which exacerbated the decline in demand. Inflation rates, which had been 12%, shot up to close to 100% (Hill, 1999). GDP contracted severely in 1997, and fell by over 13% in 1998. Manufacturing output followed a similar pattern, though manufacturing growth had already suffered a mild dip in 1996. The drop in manufacturing output was large relative to the corresponding dip in GDP. Manufacturing, a sector with 46 percent women employees at the inception of the crisis, was one of the hardest hit sectors, making it an interesting case study to examine how employer responses to shocks affected men and women differently.

Estimates based on SAKERNAS indicate the spike in inflation facilitated real wage adjustment by reducing the relevance of nominal wage rigidities and minimum wages. Across the earnings distribution, both men and women experienced real wage cuts on the order of 40%. Yet the changes in total employment rates were relatively small: between 1997 and 1998, female employment in urban areas declined by 0.7% and male employment declined by 2.2% (Thomas et al. 2002). Labor force participation rates increased slightly more for women (1.5%) than men (1.4%) (see also Cameron, 2000).<sup>8</sup> Thus, the impact of the crisis on aggregate employment was muted (Fallon and Lucas, 2002).<sup>9</sup> Thomas et al. (2002) conclude that ―the evidence suggests that short-run labor supply functions are fairly inelastic in Indonesia‖ (p. 177). If this is true, then a reduced form

> 8 By contrast, their estimates based on the Indonesian Family Life Survey, a rotating panel of households, suggest that women‘s employment rates went up slightly, primarily because many women started working as unpaid family workers.

> 9 The aggregate figures masks reallocation of employment across sectors, including a reversal of rural-urban migration patterns and an increase in informality. Both the industrial distribution of jobs shifted and patterns of job creation altered substantially: while manufacturing, construction and retail sectors had been growing rapidly in the lead-up to the crisis, that trend was reversed once it had begun.

7
**Table 4.3. Quality survey monitoring scores. Retail value for money areas at selected BAA airports. 1995/96**

|**Othe-r**<br>**Th**<br>- **1g**<br>4<br>**tering**|_3.4_|3.8|_3.8_|
|---|---|---|---|
|<br>:|~~~3.61|3.7|3X71|
|1, P|3.4|3.5|3.5|
|_ft~uxc<br>Z3.3||3.5|3.4|
|Longtermpe|rking<br>3.3|3.6|3.4|
|$xei|npjrkh<br>g|2.7|3.1|3.5|
|ArnWSl:,<br>**_leW_**<br>w|........<br>**_3.4_**|**_3.61_**|igagI<br>**_3.63_**|

Note: A score I is "extremely poor', 2 is "poor", 3 is "average", 4 is "good" and 5 is "excellent". Source: Monopolies and Mergers Commission (1996).

**Table 4.4. BAA check-in queue targets**

<!-- Start of picture text --> Heathrow Gatwick Stansted Gatwick<br>Scheduled Charter<br>20 20 15 Short-haul Long-haul 18<br>10 18<br><!-- End of picture text --> Services provided to passengers may sometimes be perceived as inadequate. Airports usually devise a mechanism for tending to passenger complaints. However, the sensitivity of airport authorities to such complaints is dependent on the degree of monopoly power and regulatory provisions.<sup>2 9</sup> Complaints and suggestions from passengers may arrive in a variety of forms: comment cards, letters, telephone calls, e-mail or in person. The processing and treatment of those may be subject to regulation. Usually a customer services department handles complaints, but the regulator may be the ultimate arbitrator. In addition, there may be fixed targets regarding prompt responses.

> **29** Here monopoly power refers to the existence of competing airports.

40
**Figure A.1. Informal Businesses Started Because of Job Shortages in Large Firms, by Owner Educational Attainment and Gender, 2012** **_(%)_**

<!-- Start of picture text --> a. By Owner educational attainment<br>b. By gender<br>70 70 64.76<br>62.63<br>60 60<br>50 50<br>40 40 35.24<br>30 30<br>20 15.32 20<br>12.66<br>10 5.02 4.37 10<br>0 0<br> Primary Some Post other post Men Women<br>No diploma completed secondary secondary primary<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data

# **Figure A.2. Size of Informal Firms** **_(%)_**

<!-- Start of picture text --> Self-employed 2 3 - 5 6 and up<br>0.99<br>6.6<br>10.59<br>81.82<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data.

28
Policy Research Working Paper 8869

# **Abstract**

The goals of ending extreme poverty by 2030 and working toward a more equal distribution of income are prominent in international development and agreed upon in the United Nations’ Sustainable Development Goals 1 and 10. Using data from 164 countries comprising 97 percent of the world’s population, this paper simulates a set of scenarios for global poverty from 2018 to 2030 under different assumptions about growth and inequality. This allows for quantifying the interdependence of the poverty and inequality goals. The paper uses different assumptions about growth incidence curves to model changes in inequality and relies on the Model-based Recursive Partitioning machine-learning algorithm to model how growth in GDP is passed through to growth as observed in household surveys.

When holding within-country inequality unchanged and letting GDP per capita grow according to International Monetary Fund forecasts, the simulations suggest that the number of extreme poor (living below $1.90/day) will remain above 550 million in 2030, resulting in a global extreme poverty rate of 6.5 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 5.4 percent in 2030, equivalent to 100 million fewer people living in extreme poverty. Reducing each country’s Gini index by 1 percent per year has a larger impact on global poverty than increasing each country’s annual growth 1 percentage point above the forecasts, suggesting an important role for inequality on the path to eliminating extreme poverty.

This paper is a product of the Development Data Group, the Development Research Group, and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at clakner@worldbank.org, dmahler@worldbank.org, mnegre@worldbank.org and eprydz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
for a monetary reward, redeemable upon return to temporary Voluntary Consulting and Testing (VCT) sites where results where provided. The VCT sites were set up such that all respondents' homes are within ve kilometers distance from at least one site. Approximately 70 percent of those tested chose to pick up their results. In 2006 and 2008, rapid blood tests were adopted, eliminating the time delay between testing and provision of results.

For my analysis, I am using a subsample of married women in their rst marriage, who did not become pregnant from a relationship with men other than their future husbands prior to marriage. I restrict the sample to women in unbroken rst marriages because I abstract from modeling any decisions related to marriage or partnership. I also restrict the sample to include only women who were interviewed in at least one of the 2006 and 2008 rounds. Data collected in these rounds include family rosters, containing information on all children of respondents, and the elicitation of subjective expectations. I exclude women who were born before 1960, because the last age of fertility in the model is assumed to be 44 and 2004 is the earliest year included for fertility outcomes. After excluding additional women for missing information, the estimation sample consists of 1006 women.

Tables 1 to 4 and Figures 1 and 2 provide descriptive statistics for the variables used in the analysis. The average age of women in the sample is 26.6 in 2004. 29 percent of the sample resides in Balaka (south), 35 percent in Mchinji (center), and 36 percent in Rumphi (north). The median number of years of schooling is 5. The women in Balaka have the lowest schooling levels with 31 percent of women never having attended school and only three percent having attended some secondary school. The women in Rumphi have the highest schooling levels, with 99 percent of women ever having attended school and 26 percent some secondary school. The average age of marriage in the overall sample is 17.5 and is similar across regions. Polygamy is most prevalent in Rumphi, with 33 percent of the women in the sample from that region married to a polygamous husband. The share of polygamous women is 27 percent in Mchinji and 20 percent in Balaka. As shown in Table 2, HIV testing take-up rates were high in all the rounds: 87.9 percent in 2004, 93.3 percent 18