rafmacalaba/datause-encoder-data huggingface dataset:
Overall, the results coming out of the estimates from the RIF regression seem to indicate that the impacts of the covariates are highly nonmonotonic for both men and women. They also indicate that impacts are different for men and women, and these gender differences are statistically significant in some cases. As with most of the previous covariates, the estimates are highly nonmonotonic along the wage distribution, and very different for men and women. The RIF coefficient Changed occupation but not work of place shows up to be positive and significant at the 50th percentile, while it decreases along the wage distribution for women and is negative and significant at the 90th percentile.

The results are consistent with the fact that women are in jobs with fewer options for career development, either by choice or by lack of opportunity. Women tend to hold less productive occupations (that is, occupations that pay relatively less).

# **4.2. Decomposition results**

The results of the decomposition are presented in Figures 5–7 and Table 2.<sup>7</sup> The top part of table/figure shows the gender gap in earnings at each percentile (see Table A4 in the appendix, which shows the detailed decomposition results). As expected, the FFL tells a very different story than the one that has emerged from previous studies. The results that follow use as a base group (reflected in the coefficient of the constant) rural nonmarried workers with secondary education or less, in private domestic small firms that do not have any subordinates nor have switched jobs or occupations during the last year. When including occupation and industry, the base group is clerks and agriculture.

First, the decomposition results of the gender gap in pay into the composition and wage structure effect vary along the earnings distribution. Most of the existent studies for the Russian Federation find that differences in labor market characteristics of men and women explain about 30 percent of the gender gap in pay. Our results tell a very different story. The importance of characteristics (composition effect) decreases along the earnings distribution. At the 10th percentile, the composition effect explains almost half of the gender gap in pay (46 percent), while at the 90th percentile, the composition effect is negative. Having a negative composition effect indicates that women are overqualified compared to men at the same percentile. In other words, if women had the same characteristics as men, other factors held constant, the gender pay gap would be 37 percent larger. The composition effect is small at the median (5 percent of total gender pay gap) and crosses the zero value at the 80th percentile (see Figure 5).

> 7 Tables A2–A4 in the appendix show more details about the regressions.

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Table 2 Continued: Descriptive Statistics: Services sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|Change 2008-2013|||||||||
|**ICT variables**|||||||||
|∆Share of labor that uses computers|6.60|40.43|-40.00|-5.00|0.00|20.00|70.00|6,241|
|∆Share of labor that uses Internet|10.12|38.60|-30.00|0.00|0.00|23.00|70.00|6,241|
|∆ln(Stock of computer equipment/worker)|0.03|1.60|-1.96|-0.78|0.13|0.95|1.95|6,241|
|**Employment and wages**|||||||||
|∆ln(Number of white-collar workers)|0.00|1.01|-1.10|-0.56|0.00|0.51|1.19|6,241|
|∆ln(Number of blue-collar workers)|0.01|0.86|-0.83|-0.36|0.00|0.37|0.90|6,241|
|∆ln(Annual real wage white-collar workers)|-0.18|1.01|-1.49|-0.76|-0.13|0.43|1.01|6,241|
|∆ln(Annual real wage blue-collar workers)|-0.06|0.82|-1.00|-0.49|-0.06|0.35|0.90|6,241|
|∆White/Blue-collar workers|0.00|0.27|-0.28|-0.10|0.00|0.09|0.26|6,241|
|∆White/Blue-collar wages|-0.18|1.86|-2.40|-1.04|-0.09|0.71|1.82|6,241|
|**Firm characteristics**|||||||||
|∆ln(Capital/worker)|0.11|1.85|-2.00|-0.77|0.14|0.99|2.22|6,241|
|∆ln(Total employment)|0.03|0.65|-0.62|-0.27|0.00|0.32|0.73|6,241|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 17
is much more common, while “EAF (DRI)” refers to mills using direct-reduced-iron as an input instead of scrap.<sup>14</sup> The variety of methods used in individual steel plants as well as different sources of electricity, environmental regulations, and sources of intermediate inputs result in a range of carbon footprints. For example, Table 1, reproduced from a OECD (2002), depicts this variation across process and five important regions/countries of the world. The ratio of CO2 emissions for BOF to EAF (Scrap) ranges from 3.3 in North America to 4.3 for China to 8.3 for South America. The carbon footprint also varies importantly within processes across regions. Estimated Chinese BOF carbon emissions are almost twice as high per tonne of steel (3.9) as in North America (2.0) and the EU (2.1). There is less variation among EAF users where the main source of differences in carbon footprint depends on the type of electrical power. For example, South America has relatively important hydroelectric sources so that its EAF steel production emits the least amount of CO2.

|**Table 1: Tonn**|**es of CO2 emissions**<br>BOF|**per tonne of crude steel**<br>EAF (Scrap)|EAF (DRI)|
|---|---|---|---|
|EU (excluding Sweden and Finland|2.1|0.5|1.0|
|North America|2.0|0.6|1.0|
|Japan|2.5|0.4|-|
|China|3.9|0.9|-|
|South America|2.5|0.3|1.1|

Notes: BOF: Basic Oxygen Furnace; EAF: Electric Arc Furnace; EAF (Scrap): EAF furnace using scrap as input; EAF (DRI): EAF furnace using direct reduced iron Source: Reproduced from OECD (2002) The variation in steelmaking processes is similar to the scenario modeled in Figures 1 and 2. One can interpret the minimills’ production process as the clean technology in terms of carbon emissions while the integrated firms use the dirty technique. Note that there also can be important variation within production types: one BOF operation inside North America could be very carbon efficient while another BOF producer could use much older and more carbonintensive procedures. The same could be true for EAF production.

Table 2 depicts the world distribution of these production types as reported by the World Steel Association that makes clear that there is substantial variation of steel production process used across the world. EU-27 countries produce about 58 percent of its crude steel using BOF compared to 41.4 for EAF. The U.S. has the reverse composition (only 42 percent for BOF and 58 for EAF). Most relevant for the discussion here is the wide variation across developing countries: Brazil (74.8 for BOF and 23.5 for EAF); China (90.9 for BOF and 9.1 for EAF) and India (40 for BOF and 58.2 EAF). It is also important to note that these are not unchanging percentages. The U.S. for example used to look much more like the EU in decades past.

> 14 There is also a very much older process called an “open hearth furnace” (OHF), which represents a declining and very small part of world production. It is very carbon intensive and now considered obsolete.

11
drawbacks. Gill and Kharas (2007) estimate infrastructure investment requirements for East Asia and the Pacific using data up to 2005. Data for Latin America and the Caribbean were collected from primary sources for 1990–2005 and then extrapolated in posterior work as presented in Yepes (2008). The most recent figures are from South Asia, where Andres et al. (2013) have extrapolated previous estimations at the country level for South Asia.<sup>10</sup> The estimations from Sub-Saharan Africa come from Foster and Briceño-Garmendia (2010) that made a significant effort to collect data on infrastructure stock and present a bottom-up approach.

Some of the most recent sectoral estimates come from the International Energy Agency (IEA). IEA indicates that the annual investments in energy supply of $1.6 trillion need to rise steadily over the coming decades towards $2 trillion. Under IEA’s expectation, future electricity usage underpins changes in the installed capacity of power generation technologies and in transmission and distribution networks, which assume global electricity demand growth rate of 2.2% per year, projected to future demand. Annual spending on energy efficiency, measured against a 2012 baseline, needs to rise from $130 billion today to more than $550 billion by 2035. OECD presents estimates at sectoral level for transport, telecoms, and water and sanitation.

Comparing the different estimates presented some significant challenges (see Annex II). Recognizing the limitation of comparing results from different methodologies, the table below presents the most recent estimates on infrastructure investment requirements/demand in the literature.

> 10 Estimates by Chatterton and Puerto (2006) follow Fay (2000) methodology to estimate investment needs in South Asia but estimates are up to 2010.

5
by differences between schools and villages is 21 in MP, 17 in UP and 13 in KA. The third point is that in all of the states observed teacher characteristics explain less than 6 percent of the variation in teacher effort within schools. These observations are again consistent with other studies, although mainly from developed countries, that find substantial variation in teacher quality within schools and observed teacher characteristics explain very little of this variation in teacher quality (Rockoff, 2004; Rivkin, Hanushek and Kain, 2005; Aaroson, Barrow and Sander; 2007).

# **4.4 Are Local Communities Aware of Their Roles and Active in Managing Schools?**

Last we discuss an important strand of the survey that measures local participation in school management. In line with the variation in teacher effort, there is a parallel variation in community participation across the three states.

# **4.4.1 The Committees with Oversight Responsibilities**

Tables 19-24 present the awareness and participation of VEC, PTA and SDMC members in oversight of the school. A large proportion of committee members in all the three states stated not having received any training regarding their roles and responsibilities as members. Parent members of these committees had the lowest yes responses. About 20 percent of parent members reported receiving any training in Karnataka, compared to 8 percent in MP and 2 percent in UP.

In UP, most members seem to be aware that they are members of the VEC. However headmasters seem to be most informed about the roles and responsibilities of the VEC. Parent members of the VEC seem to be the least informed and participating the least (Tables 19-20). When asked to list the roles and responsibilities of VEC, 52 percent of the parent members could not list a single one. These members could name on average only one out of five VEC members.

# **Figure 10 UP    Village Education committee members’ responses**

<!-- Start of picture text --> 100<br>80<br>Headmaster<br>60<br>Pradhan<br>40<br>Parents<br>20<br>0<br>Whether Percent of other Whether VEC<br>received training VEC members meeting held in<br>named this school year<br>Percent<br><!-- End of picture text --> Like in UP, headmasters in MP seem to be most informed of the roles and responsibilities of the PTA (Tables 21-22). Parents seem to be the least informed of their 17
WPS4199

# New Evidence on the Urbanization of Global Poverty

## Martin Ravallion, Shaohua Chen and Prem Sangraula<sup>*</sup>

### _Development Research Group, World Bank_

_We find that one-quarter of the world’s consumption poor live in urban areas and that the proportion has been rising over time. By fostering economic growth, urbanization helped reduce absolute poverty in the aggregate but did little for urban poverty. Over 1993-2002, the count o the “$1 a day” poor fell by 150 million in rural areas but rose by 50 million in urban areas. The poor have been urbanizing even more rapidly than the population as a whole. There are marked regional differences: Latin America has the most urbanized poverty problem, East Asia has the least; there has been a “ruralization” of poverty in Eastern Europe and Central Asia; in marked contrast to other regions, Africa’s urbanization process has not been associated with falling overall poverty. Looking forward, the recent pace of urbanization and current forecasts for urban population growth imply that a majority of the world’s poor will still live in rural areas for many decades to come._

**Key words:** Urban poverty, rural poverty, migration, urban population growth.

**JEL:** I32, O15, O18

World Bank Policy Research Working Paper 4199, April 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful to the many colleagues in the Bank and staff of governmental statistics offices throughout the world who have helped us in assembling the data set used here, and answering our many questions. Helpful comments were received from Stephan Klasen, Dominique van de Walle, seminar participants at the World Bank, the University of California, Berkeley, and the International Food Policy Research Institute. This paper was supported in part by the Bank’s 2008 _World Development Report_ . These are the views of the authors, and should not be attributed to the World Bank or any affiliated organization.
We do not find statistically significant impacts on the number of animals from all of the projects in the sample, though the coefficient for cattle is large. However, we do find statistically significant and very large effects on the total number of assets in the household. For livestockonly projects, we find statistically significant effects for both cattle and total household assets. The effects on cattle are large at 0.31 additional animal per household, an increase of 15% relative to the control group.<sup>18</sup> As there were 10 households per community that received cattle as part of livestock projects, this represents an increase of approximately 3 cattle per community, on average.

Looking at the impacts for the scorecard sample in columns 5 and 6, we test the full model presented in Equation (1), which includes a dummy for social accountability training treatment, scorecard treatment, and the interaction of the two treatments. None of the coefficients in column 5 is statistically significant. However, a joint test of the social accountability training plus scorecard plus interaction terms is significant at the 2% level for cattle. We cannot reject equality of the social accountability training and scorecard treatments. We find similar results for the total assets indicator.

The results from the household survey analysis show that the interaction of the two treatments drives impacts, not training or the scorecard alone. Households that received both the social accountability training and project quality information scorecard interventions obtain an increase in their number of animals of 0.61, or approximately 27% relative to the control group.<sup>19</sup> This is a highly statistically significant effect of large economic magnitude.

# **5.3 Potential mechanisms: Impacts on community monitoring and reporting**

During the project assessment survey, we measured the actions taken by communities to monitor their projects. Table 10, column 1 presents the results for an index of how much the broader community monitored the program, while column 2 presents the monitoring activities of the local accountability group that is present in all communities. The broader community does not

> 18 While NUSAF2 delivered only one animal to households, a goal of the project was to lead to new animal births, thus theoretically multiplying the number of animals beneficiaries have over time. Thus, the comparison of treatment effects should not be made to the one animal delivered, but to the number of animals in the control group. 19 We also test for whether impacts are concentrated in communities that had the lowest scores (not shown) and do not find a relationship between the absolute score and the number of animals in households. The impact of the training and scorecard information appears to exist across the distribution of scores.

23
100 RENT CONTROL IN KUMASI, GHANA maintenance and taxes. At the end of the 10 year period the unit (structure and land) have some salvage value.,1 This model is quite simple, 5 yet it allows us to compare two different rent regimes (labeled the baseline regime and the revised regime in Table 6.1, sometimes referred to controlled and uncontrolled). We can examine the interaction between controls, taxes, maintenance, depreciation, profitability and affordability in a simple but consistent framework.

Of course the model has limitations. It focuses on a "representative" investment, and the exact numbers presented aren't exact for all or even most units. But we can analyze more than one "representative" investment (including different structure types, service levels, locations, and rents). We don't want to focus on point estimates but rather on robust qualitative conclusions. Also, as it happens in Kumasi at least physical structures and current controlled rents are fairly standardized.

The cash flow model ts only as good as its inputs; "garbage in, garbage out." But we can and have tested the model with a range of inputs not all reported here, and while the exact numbers change, the qualitative conclusions drawrn from the simulations reported below remain robust.61 We note once again that the real valued inputs to the simulations in this chapter are all in 1986 prices. We have assumed a constant 20 percent annual inflation rate for the ten year horizon of the model; while official data on recent price changes are not available yet, it appears that inflation has run well above 20 percent recently.

- i/ The salvage period is often discussed as if the owner sells the unit. Actually it makes no difference to the analysis if the owner retains it; the salvage value is the opportunity cost of doing so.

- ./ For example, we assume the landlords pay cash for the unit. In Ghana, few rental units are financed through the formal financial system. While it remains true that all durable assets must be financed in some way (even if self financed) ignoring finance is an appropriate simplification for the present purpose. The analysis could be readily extended to evaluate proposals for rental finance.

- i/ We encourage interested readers to undertake their own analysis of other representative investments and using other parameter values (especially representing other changes in controls). The model itself is written in Lotus 1-2-3, and is available upon request from the authors. Using the computer model requires the Lotus spreadsheet system, version 2.0 or higher, which is not available from the authors.
After the initial question mentioned above, the survey included the following: a) _“Are there any children helping in the family’s income? How many? (under 18 years old)”_ , b) _“How many hours do they work daily?”_ and c) _“How many days per week do minors work?”_ On top of that, from those households that have children, 25.5% responded that they have minors helping the family’s income (sample 35.4% and control group 64.8%).

Additionally, the diagram below summarizes the household’s answers (2007 and 2008) about weekly hours of child labor. The main issue that arises is related to the fact that for the sample, children are working lower hours (and even households that have children working in 2007 changed path in the survey’s second round) and for the control group the scenario gets worst over time.

Figure 4: Child Labor Force Hours Worked Weekly x Number of Households <!-- Start of picture text --> 10<br>9<br>9<br>8<br>8<br>7<br>6<br>5<br>4 2007<br>4<br>3 3 2008<br>3<br>2<br>1 1<br>1<br>0<br>0<br>1 - 4 h 5 - 9 h 10 - 19 h 20 - 29 h 30 - 39 h 40 or more<br>Hours worked weekly<br>N° of households<br><!-- End of picture text --> Source: Research from the Osasco Land Title Survey – 2008

# **6. Econometric Model: Difference-in-Difference Estimates**

# **6.1 Difference-in-Difference Estimates: Estimator – General Framework**

The econometric method used was Difference-in-Difference Estimatation, known as DIFF-in-DIFF (DD), given the data characteristics described above. As Bertrand _et al._ (2004) define, Differences-in-Differences consists of identifying a specific intervention or treatment (often a passage of a law). One then compares the difference in outcome after and before the intervention for groups affected by the intervention to the same for unaffected groups.

Such approach involves basically two regimes: "0" and "1" given an observed outcome _Y_ , which means _Y_ 1 _=dY_ 1 _+(1-d)Y_ 0. Given _d=1_ , we observe _Y_ 1 and with _d=0_ , _Y_ 0 is observed.

As Heckman & Hotz (1989) state, the parameter most commonly invoked in the program evaluation literature, although not the one actually estimated in social experiments, is the effect of randomly picking a person with characteristics _X_ and moving from "0" to "1":

# _E(Y_ 1 _-Y_ 0 _/X)=E(Δ/X)_

In practice, most non-experimental and experimental studies do not estimate _E(Δ/X)_ . Instead, studies usually estimate the effect of treatment on the treated.

12
**Graph 2.5: Reinsurance supply by market**

<!-- Start of picture text --> 45<br>35 30 |C119 098<br>30<br>25<br>-35<br>flo,mud; Condneabd Eumpe Lnndon US<br>Market<br><!-- End of picture text -->

"Cat" bonds (Box 2.8) and the creation of a public-private partnership (Box 2.9) are mechanisms that aim at restoring a stressed capital base. Their relevance for the Caribbean region is thus limited.

Box 2.8: Catastrophe Bonds: An Option for the Caribbean Region?

The damages from Hurrcane Andrew (Florida, 1992) and the Northridge Earthquake (Los Angeles, 1994) alone exceeded US$45 billion, with the insured component running into US$30 billion. The losses are comparable to cumulative insured losses from natural catastrophes in the previous decade of about US$25 billion. These two events led to the collapse of several property and casualty insurers.

The capitalization of the US insurance industry (estimates of total capital and surplus were about US$350 billion at end-2000) may not be sufficient to cover a series of catastrophic events of the same magnitude as Hurricane Andrew, the Northridge Earthquake or a remake of the 1906 San Francisco Earthquake. Essentially, the "big one" could wipe out the entire capital base of the insurance industry.

In response, the insurance industry has issued Catastrophe ("cat") bonds designed to spread the industry's catastrophic risk to the capital markets rather than keeping it on insurers' own balance sheets, thereby reducing exposure to major catastrophic events. This also reassures policyholders that they would be protected against even the largest of losses. The first "cat" bonds came to market in 1994, and many more have since been issued. Aside from spreading huge losses more widely, the bonds may bring more stability to a reinsurance market known for fluctuating rates and for developing sudden aversions for certain types of risks.

However, "caf' bonds have little relevance for the Caribbean region. Indeed, intemational reinsurance exposures to the region is extremely limited (estimated at 2 percent of total premiums), which little justifies the transfer of such risk to the capital market.

23