rafmacalaba/datause-encoder-data huggingface dataset:
**_Chart 5.5  Recruitment in Union Government in 2010 by Ministry_**

<!-- Start of picture text --> Others, 71<br>Interior<br>Interior, 107 Finance<br>Civil Service<br>Health<br>Health, 24<br>Others<br>Civil Service,<br>25<br>Finance, 38<br><!-- End of picture text -->

**_Chart 5.6  Recruitment in the Governments of Anjouan and Ngazidja in 2010 by Commission_**

<!-- Start of picture text --> Others, 167<br>Education<br>Health, 49 Education, Interior<br>384<br>Civil Service, Finance<br>73<br>Civil Service<br>Health<br>Finance, 154 Others<br>Interior, 231<br><!-- End of picture text --> Source: Author database, 2011.

Recruitment within the civil service, by Ministry or Commission, is somewhat different between the Union and Island Governments, as this analysis of 2010 suggests. In the Union Government, recruitment centered on the Ministry of Interior, which mainly employs police officers. Island governments primarily chose to recruit in the education sector in 2010, hiring 384 individuals, though significant hiring took place in the Commissions of Interior and Finance as well.

As mentioned previously, the recruitment information in this database does not quite cover all of the contractual workers who were selected as teachers. Interviews with officials in the Ngazidja Government suggest that the Education Commission chose to hire over 300 teachers as contractual workers. Meanwhile, the Government of Anjouan is reputed to have hired 500 teachers in the lead up to the election. As such, the data may underestimate the total amount of recruitment.<sup>9</sup>

> 9 We suspect that many contractual and volunteer workers who were hired during this election did not appear for the civil service census.

13
The selection of classrooms or sections was done by a random draw, typically in the presence of the principal and all grade teachers. The selection of students was done using the enrollment list to define the size of sampling intervals and randomly selecting the starting point. In third grade, ten students were selected randomly to take the oral test, which was administered individually outside class, but all students participated in the written test, as it required group administration inside the classroom. If the school had fewer than 10 students, the indication was to evaluate all third grade students present at the time.<sup>51</sup>

# **7.4. Population and sample description**

## **_a. Population description: Sample framework_**

**Table 16. Distribution of primary public schools for minors by UGEL, type and location**

||**Single-**<br>**Teacher**||**Multig**<br>|**rade**<br>|||**Fu**<br>|**ll grade**<br>|||**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||<br> <br>||**Shanty**|**Peri-**<br>|||**Shanty**|**Peri-**<br>||**Sub-**||
|**UGEL**|**Rural**<br>**Urban**<br>|**Rural**|<br>||**Urban**|**Rural**|<br>||**Urban**|||
||||**town **|**urban**|||**town **|**urban**||**urban **||
|Chanchamayo|217|139|3|3|4|5|3|4|6||**384**|
|Chupaca|9|55|||2|9|||6||**81**|
|Concepción|45|67||1|8|3|||5|<br>1|**130**|
|Huancayo|62|129|1|1|7|32|2|11|11|<br>10|**266**|
|Jauja|16|77||8|17|6||1|13||**138**|
|Junín|39|19||5|1|1||3|4||**72**|
|Satipo|210|177||3|1|4||3|4||**402**|
|Tarma|76|69||2|1|2||3|17||**170**|
|Yauli|12<br>1|12|||1|4||3|8||**41**|
|**Total **|**686**<br>**1**|**744**|**4**|**23**|**42**|**66**|**5**|**28**|**74**|<br>**11**|<br>**1.684**|

Source: Padrón Escolar 2006.

**Table 17. Distribution of third grade students of public primary schools for minors by UGEL and location in Junin, Peru**

|**UGEL**|**Sin**<br>**Rural**|**gle-Teach**<br>**Urban**|**er**<br>**Peri-**<br>**urban**|**M**<br>**Rural**|**ultigrade**<br>**Urban**|<br>**Peri-**<br>**urban**|**F**<br>**Rural**|**ull grade**<br>**Urban**|<br>**Peri-**<br>**urban**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
|Chanchamayo|891|1||1,769|324|114|269|898|836|**5,102**|
|Chupaca|26|0||784|59||370|330||**1,569**|
|Concepcion|186|||878|193|14|80|310|100|**1,761**|
|Huancayo|244|28|10|1,890|547|198|2,096|2,815|3,559|**11,387**|
|Jauja|43|0||914|301|98|229|949|35|**2,569**|
|Junin|104|||171|40|85|19|323|98|**840**|
|Satipo|1,373|2|31|2,599|230|101|153|738|292|**5,519**|
|Tarma|185|0||869|168|41|79|1,289|138|**2,769**|
|Yauli|32|1||152|43||190|610|156|**1,184**|
|**Total**|**3,084**|**32**|**41**|**10,026**|**1,905**|**651**|**3,485**|**8,262**|**5,214**|**32,700**|

> 51 Schools were given a quota of 10 students because we are interested in understanding the variance across schools and not within schools.

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rural towns, and peri-urban areas, excluding the primary production of crops, fish, trees, and livestock. The more organized part of the RNF sector consists of micro, small, and medium enterprises (simply termed microenterprises).<sup>2</sup> There were some 4 million rural microenterprises in Bangladesh in 2003 (a number that has certainly increased) accounting for 30 percent of overall manufacturing value-added and 70 percent of the nonagricultural labor force (World Bank, 2007). Given the scope of this sector both in terms of employment and income, growth in the nonfarm sector, especially in microenterprises, can play a significant role in furthering Bangladesh’s overall growth and poverty reduction. Growth in microenterprises seems to suffer from a variety of factors, of which lack of access to finance, infrastructure, and markets, and poor quality technology and regulatory barriers appear to be most common (World Bank, 2004; World Bank, 2007). Income from microenterprise sources, however, seems to have a greater effect on poverty reduction. For instance, using cross-sectional data from Sri Lanka, Deininger et al. (2007) find that self-employment in the RNF sector has a significant impact on poverty reduction and is not simply used by those unable to find gainful employment elsewhere. They also find that perceived constraints related to infrastructure and access to finance hinder micro-enterprise adoption.

This paper addresses the role of finance as a barrier to microfinance growth in Bangladesh. Microenterprise investment is financed largely by informal sources such as individual savings and informal loans from friends and relatives. Institutional credit can play a role, but it has until recently been marginal, as found in our analysis. With the advent of microfinance institutions, microcredit is expected to play a bigger role in supporting microenterprise development in rural areas. However, scaling-up microcredit to support progressive microenterprises with diversified loan and competitive

> 2 By microenterprise, we mean manufacturing and processing industries, transport, trade, services and other miscellaneous activities.  This does not include farm-related activities such as poultry raising, cow fattening, and other small activities.

3
around 6 GW (almost 25 percent of peak demand) in 2012 which had been scarcely reduced by 2016 (the reserve margin was around -20 percent). However, the large amount of new investment coming on stream, partly as a result of the China-Pakistan Economic Corridor (CPEC) initiative, indicates that the deficit is expected to disappear in 2018 and a surplus will emerge thereafter.

**Figure 5. Capability and peak demand between 2006 and 2021 (MW)**

<!-- Start of picture text --> 45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Historic capability Forecast capability Historic peak demand Forecast peak demand<br><!-- End of picture text --> Source: NEPRA, State of Industry Report (various issues) Two factors help explain this persistent deficit—the inefficiency of the public generation companies (GENCOs), and the lack of investment despite the shortage of supply. Table 5 compares the ratios of dependable to installed capacity for the four GENCOs created from the unbundling of WAPDA to those of four of the IPPs. According to NEPRA in the 2016 State of Industry Report, the loss of dependable capacity of the GENCOs since their commissioning is due to lack of timely overhauling, missing annual scheduled maintenance routines, and poor operating schedules. The IPPs, by contrast, were able to maintain capacity through timely O&M. Had the GENCOs been better run, then the available capacity would have been higher and the deficit smaller.

**Table 5: Ratio of dependable to installed capacity for public and private generation companies**

|2014<br>2015<br>2016|
|---|

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# **A Global Urban Risk Index**

Henrike Brecht, Uwe Deichmann and Hyoung Gun Wang<sup>1</sup>

## **1 Introduction**

The potential for losses from natural hazards is particularly high in urban areas. 1.5% of the world’s land is estimated to produce 50% of worldwide Gross Domestic Product (GDP). The same area accommodates about one-sixth of the world’s population (World Bank 2009). Concentrations of population, industry, infrastructure, and economic activities in cities contribute to increased exposure and susceptibility to natural hazards. In fact, the ongoing process of urbanization is one of the main reasons for the staggering increase in disaster death tolls and economic losses over the past decades (e.g., Quarantelli 1996, Wisner 2003, Pelling 2003, Lall and Deichmann 2012).

The impacts of disasters are on the rise. Statistics show that, even when adjusted for inflation, the losses caused by natural catastrophes have been rising at an increasing pace since 1950, even when considering improvements in record keeping over time that could bias such comparisons. In the period between 1990 and 1999 the costs of disasters in constant dollars were more than 15 times higher than during the period 1950-59 (World Bank 2006). The number of people affected by natural hazards each year nearly quadrupled from 1975-84 to 1996-2005 (EM-DAT 2007). Several factors contribute to this increase, for example land use changes, social inequalities, subsidence, and environmental degradation (e.g., Smith 2012, Mileti 1999, Blaikie et al. 1994). Studies suggest that climate change has not significantly 1 The authors are respectively in the East Asia and Pacific Region, the Development Research Group, and the Urban and Resilience Management Unit of the World Bank, 1818 H Street, NW, Washington, DC, 20433, USA (Email: hbrecht@worldbank.org, udeichmann@worldbank.org, hwang4@worldbank.org). The research presented in this paper received support from the Global Facility for Disaster Reduction and Recovery (GFDRR). The authors thank Saroj Kumar Jha, Apurva Sanghi, Francis Ghesquiere, and Brian Blankespoor for helpful discussions and support. 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.

2
excluded Yemen because of an abnormal association between mean income and GDP per capita suggesting that data quality may be an issue. We are left with 343 observations over 110 countries. By taking five-year averages, the proportion of observations from Latin America also falls to 24 percent. We use the dataset to capture, in each country, the proportion of people living in poverty (below 2.5 US dollars a day in per capita PPP terms), the percentage of the population that lives with more than 10 US dollars a day (our “middle class threshold”), and overall income inequality as measured by the Gini coefficient. In using 10 dollars a day as our middle class threshold, we follow analyses that have looked at vulnerability to poverty as a prerequisite to middle class status. These analyses find that if the middle class threshold is set excessively close to the poverty threshold, people remain excessively vulnerable to shocks that would bring them back into poverty, and may not behave differently than the poor (see Goldthorpe and McKnight, 2004; and Lopez-Calva and Ortiz-Juarez, 2011). Using a 10 dollars a day threshold also brings us close to papers investigating middle class composition and trends at a global level (Milanovic and Yitzhaki, 2002; Kharas, 2010). Observe, also, that we refrain from using an income ceiling to limit the size of the middle class from above. As we pool together countries from all income levels, an income ceiling may lead to strange measurements by which the middle class in rich countries may be artificially too small. Our results should, correspondingly, be interpreted as the impacts of a growing proportion of people with sufficient income to undertake activities beyond constantly fighting poverty, instead of the impacts of a class “in the middle.” Table 2 shows the correlation between income distribution variables: while poverty and the middle class are negatively correlated, they do so imperfectly, suggesting that each measure carries some information. Other variables that we use in the analysis are GDP per capita in 2005 PPP terms, public expenditures in education and health, and mean applied tariffs, all from the World Development Indicators; credit market liberalization, from the _Economic Freedom of the World_ project; the polity score for democracy, from _Polity IV_ ; and the corruption index from the _International Country Risk Guide_ .

# **3. Econometric Methodology**

We estimate a set of regression equations of the form, 5
||_All child_<br>|_ren who were_<br>_attending_<br>|_currently_<br>|
|---|---|---|---|
||_IKP_<br>_particip_|_Non-partic_|_Difference_<br>_partic. vs._<br>_non-partic._|
|2003|69|72|-3|
|2008|70|64|6|
|_Diff. over time_|1|-8|9*|

Note: Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent. The data refer to households who had children in school in both periods, all poverty categories and lengths of exposure combined, with regression adjustments; detailed results are in Table A.5.

Here too, though, we need to disentangle the effects of IKP from those of the midday meal program, which continued to provide an important incentive to attend school during the period under study. To do so, we look at current attendance in households that report _not_ benefiting from the midday meal in 2008 (almost half the households with children in school) and we find that the decline was significantly smaller for children of IKP participants. We also find a very significant increase in children currently attending school among households that report benefiting from the midday meal, IKP participants and non-participants, confirming the importance of the program (Table 15).

**Table 15: Percentage of school-age children who were currently in school, by reported benefits** **<u>from the midday meal program</u>**

||_All child_<br>_attendin_<br>_mi_<br>|_ren who were_<br>_g but not benef_<br>_dday meal in 2_<br>|_currently_<br>_iting from_<br>_008_<br>|_All chil_<br>_attending_<br>|_dren who were_<br>_and benefiting f_<br>_meal in 2008_<br>|_currently_<br>_rom midday_<br> <br>|
|---|---|---|---|---|---|---|
||_IKP_<br>_particip_|_Non-partic_|_Difference_<br>_partic. vs._<br>_non-partic._|_IKP_<br>_particip_|_Non-partic_|_Difference_<br>_partic. vs._<br>_non-partic._|
|2003|70|75|-5*|74|75|-1|
|2008|63|56|7|96|94|2|
|_Diff. over time_|-7|-19|12**|22|19|3|

Note: Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent. The data refer to households who had children in school in both periods, all poverty categories and lengths of exposure combined.

In sum, the additional investment in education by IKP members observed earlier appears to have translated into more children staying in school, particularly for the poorest, where the midday meal program was not functioning well.

On health, we find some signs of improvement but many more areas of concern. On the positive side, the percentage of births over the two years prior to the survey that took place at a facility or were assisted by a trained birth assistant increased, in both participant and non-participant households. On the other hand, immunization cards declined for participants and exclusive breastfeeding declined for all, but particularly among IKP participants (Table 16).

**Table 16: Incidence of assisted deliveries, exclusive breastfeeding,  presence of immunization cards**

|||_Assisted deliver_|_y_|
|---|---|---|---|
||_IKP_|_Non-partic_|_Difference_|
||_particip_||_partic. vs._|
||||_non-partic._|
|_2003_|76|75|1|

16
across the whole income distribution, while in others like Mexico or Brazil, universal access is now almost the rule.

While this has in some cases led to large progress in access to energy consuming devices such as refrigerators or washing machines, different patterns emerge. In Brazil, ownership of refrigerators did increase in lower income deciles following connection to the electric grid, but the process was much slower in Mexico. Similarly, comparing El Salvador and Peru, two countries with similar profiles of electrification, reveals quite different evolutions. Access to refrigerators stagnated and if anything progress was greater at the top of the income distribution in the latter, while the former witnessed large increase in ownership.

Next, we use the complete panel, based on the global quantiles distribution, to analyze how the income elasticity of infrastructure access varies across services and countries, and within these, at different levels of the income distribution over the 1992-2012 period.

Table 5 presents two sets of results using the household survey data. First, in Panel A, we report an average Latin American elasticity given by the following estimation: where _D_<sup>_A_</sup> _dct_ is the average access / ownership rate for infrastructure asset A, in country c, by households in income decile d, at time t, _log(income)dct_ is the average decile income, and _θct_ are country-time fixed effects.<sup>10</sup> This simple specification assumes that infrastructure demand indicators at the decile level are a function of average income in that decile, and of the price of infrastructure as proxied by country-time fixed effects. The underlying assumption is thus that this price is uniform within countries in a given period, but varies over time. This is much milder than proxying prices through country dummies in the country-level panel, in effect assuming that prices are invariant across time period. In addition, the country-time fixed effects absorb any other relevant countrylevel determinants, such as the ones discussed above.

Second, in Panel B, we differentiate elasticities by country, through the following specification:

> 10 Using the percentiles instead yields very similar results.

13
# **Table 4. Percent Underemployed by Gender and Primary Sector of Employment**

|**Sector**|**Male**|**Male**<br>**Population**|**Female**|**Female**<br>**Population**|**Total**|**Total**<br>**Population**|
|---|---|---|---|---|---|---|
|Farming|12.8|46.3|14.1|45.1|13.3|45.8|
|Plantations|12.3|10.9|12.0|8.5|12.3|9.9|
|Livestock &|||||||
|Fisheries|20.0|9.3|26.7|6.2|22.1|8.0|
|Forestry & Hunting|21.1|1.1|25.3|0.8|22.5|1.0|
|Mining & Oil|13.4|0.8|17.9|0.4|14.6|0.6|
|Food & Tobacco|9.7|1.1|13.9|3.1|12.5|1.9|
|Textiles & Leather|8.5|0.4|12.7|1.7|11.7|0.9|
|Wood Products|10.5|1.3|15.2|1.9|12.8|1.6|
|Other Industries|8.9|1.6|14.5|3.0|12.2|2.2|
|Electric, Gas &|||||||
|Water|13.0|0.1|43.2|0.0|15.7|0.0|
|Construction|10.3|1.7|20.7|0.1|10.7|1.1|
|Trade&Retail|5.7|15.9|6.2|25.8|6.0|20.0|
|Transportation|5.4|5.3|9.3|0.2|5.4|3.1|
|Finance, Insurance|7.9|0.1|0.9|0.1|5.7|0.1|
|Civil Service|14.6|0.1|0.0|0.0|11.8|0.0|
|Private Social|||||||
|Work|9.8|1.7|19.3|1.3|13.2|1.5|
|Others|12.6|2.4|19.9|1.8|15.2|2.1|
|Unclear Activities|23.4|0.1|23.5|0.3|23.5|0.2|
|**Total**|11.7|100|13.0|100|12.2|100|

_The Seasonality of Underemployment in Indonesia_ Underemployment is generally believed to fluctuate with the agricultural seasons for two reasons. First, because most of the underemployed are located in rural areas and are engaged in agriculture-related tasks, the swings in local labor demand may have an immediate and direct effect on underemployment. Second, seasonal agricultural employment may not only affect overall employment levels directly, but also indirectly, as fluctuating rural incomes would affect the demand for other rural products and services. Table 5 presents a breakdown of underemployment by quarter and by gender. Surprisingly, there is no discernible pattern of seasonality in the national underemployment statistics.

**8**
# **Figure 8. Growth in GDP per capita vs Initial Poverty, 1960–2010**

A higher poverty rate is associated with lower growth in subsequent decades.

<!-- Start of picture text --> 0.10<br>0.05<br>0.00<br>-0.05<br>-0.4 -0.2 0.0 0.2 0.4<br>Initial poverty rate, deviation from the median<br>capita, percent<br>Annual growth in GDP per<br><!-- End of picture text --> _Source_ : Marrero and Servén 2018.

_Note_ : Data cover 156 countries and are controlled for initial income. The year of the initial poverty is 1960 and the average growth in GDP per capital is over the period 1960-2010. Initial poverty is expressed as deviation from the median.

# **4.1.2. From Inequality to Growth**

As an illustration of the relationship from inequality to growth, Bénabou (1996) compares the growth outcomes of East Asian and Latin American economies conditional on the initial levels of income inequality. According to Bénabou (1996), the conventional wisdom among development economists is that the relatively equal distribution of income and land in East Asian economies contributed to their observed high economic growth rates. By the same token, the lack of a similar economic dynamism in Latin America has been attributed to the consequences of high concentration of wealth and income in that region.<sup>13</sup> The panel a of Figure 9 reports the correlation between income inequality in 1980 and the average GDP per capita growth in the subsequent 30 years for selected Latin American and Asian economies. Consistent with Bénabou (1996), on average countries that exhibited lower levels of initial inequality also experienced higher rates of economic growth. While there are many other factors that might explain the economic dynamism of these Asian economies, such as the quality of institutions and high rates of saving and investment (Collins and Bosworth 1996), this figure illustrates that income distribution might be one key element for understanding differences in economic performance. An extended sample of advanced and developing countries

> 13 “Poverty trap” is a common narrative of economic development whereby some countries are stuck in poverty and would need external support (or a “big push”) for them to escape it. Easterly (2006) rejects, however, the claim that “well-governed poor nations” are stuck in a trap just because they are poor. The author cannot statistically discern any effect of initial poverty on subsequent growth once bad governance is controlled for.

24