rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. The impact of homicide rates enrollment rates using Census Data

|**Dependent variable: enr**|**ollment rate**|**s**|2000-2010|||||||2005|-2010|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Ages 6-11|Ages 12-14|Ages 15-17|Ages 18-20|Ages 21-24|Ages 6-11|Ages 12-14|Ages 15-17|Ages 18-20|Ages 21-24|Ages 6-11|Ages 12-14|Ages 15-17|Ages 18-20|Ages 21-24|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|Homicide Rate|-0.000229***|-4.12e-05|0.000231|-6.53e-05|-2.25e-05|-0.000133|0.000214|0.000687|-9.19e-05|-2.77e-05||||||
||(8.53e-05)|(0.000158)|(0.000319)|(0.000275)|(0.000147)|(0.000109)|(0.000204)|(0.000638)|(0.000435)|(0.000267)||||||
|Drug Related Homicide Rate|||||||||||-4.05e-05<br>(3.01e-05)|6.91e-05<br>(5.84e-05)|0.000338*<br>(0.000183)|-3.14e-05<br>(0.000115)|1.60e-05<br><br>(6.17e-05)|
|Constant|0.961***|0.901***|0.620***|0.284***|0.116***|0.961***|0.900***|0.619***|0.284***|0.116***|0.961***|0.900***|0.619***|0.284***|0.116***|
||(0.000530)|(0.00107)|(0.00185)|(0.00151)|(0.000946)|(0.000554)|(0.00112)|(0.00248)|(0.00192)|(0.00130)|(0.000495)|(0.00108)|(0.00205)|(0.00165)|(0.00111)|
|Observations|7,350|7,348|7,342|7,337|7,333|4,908|4,906|4,900|4,895|4,891|4,908|4,906|4,900|4,895|4,891|
|R-squared<br>**Notes.** Dependent variable is the th<br>Drug-related homicides are collected<br>from years 2000, 2005 and 2010 inc|0.665<br>e enrollment rate<br>by the National<br>luded in columns|0.740<br>by age group,<br>Council of Publi<br>(1) to (5) , and|0.798<br>calculated from<br>c Security (Con<br>for years 2005|0.797<br>census data. <br>sejo Nacional d<br>and 2010 in co|0.750<br>Homicide rates ar<br>e Seguridad Públi<br>lumns (6) to (15)|0.733<br>e normalized acc<br>ca) of the Federa<br>. Standard errors|0.784<br>ording to the nat<br>l Government an<br>clustered at the|0.826<br>ional homicide <br>d include all d<br>municipality le|0.833<br> rate per 100,0<br>eaths by "pres<br>vel in parenthes|0.804<br>00 inhabitants i<br>umed delinquen<br>es. *** p<0.01|0.733<br>n 2007 (genera<br>t rivalry".<br>All <br>, ** p<0.05, * p|0.784<br>l homicide rate<br>regressions inc<br><0.1|0.826<br>of 8.3 and drug<br>lude municipalit|0.833<br>related homic<br>y and year fixe|0.804<br>ide rate of 2.6).<br>d effects. Data|

22
**Table 6. Reduction (in %) of the fraction of children stunted and underweight**

|Per capita<br>income<br>growth<br>(in %) –|<br>Reduction in<br>income<br>poverty (%)|||Reduc|tion in malnut|rition (%)||||
|---|---|---|---|---|---|---|---|---|---|
|since<br>1993<br>||No additional<br>interventions<br>|<br> <br>Interventions<br>in additional<br>10% of<br>communities|Intervent<br>ions in<br>additional<br>50% of<br>communities<br>**Height for**|Intervent<br>ions in all<br>communities<br>**age **|Farm income<br>maximum of<br>75% of the<br>total<br>household<br>income|Motor<br>road in<br>every<br>village|Additional<br>year of<br>education<br>to the<br>father|<br>Ratio of<br>children<br>vaccinated<br>increased<br>to 95%|
|0|0|0.0|7.0|41.3|**68.7**|3.3|6.2|8.1|9.6|
|0.5|24.7|2.6|9.1|42.7|**69.2**|6.0|8.9|10.2|11.2|
|1|44.1|5.3|11.5|44.4|**70.3**|7.8|11.5|12.4|13.3|
|1.5|**55.7**|7.1|14.2|46.3|**71.9**|9.6|13.1|14.9|14.7|
|2|**66. 6**|8.7|16.3|48.1|**73.2**|11.6|14.6|16.4|17.2|
|2.5|**79.0**|11.8|17.7|49.5|**74.0**|13.9|17.7|18.5|18.6|
|3|**84.1**|13.9|19.2|**51.0**|**75.2**|16.6|19.9|20.5|20.5|
|||||**Underwei**|**ght**<br>|||||
|0|0|0|11.0|31.7|**53.4**|1.8|2.4|3.6|9.9|
|0.5|24.7|3.4|14.1|34.4|**55.5**|5.2|5.8|7.0|12.9|
|1|44.1|6.8|17.2|37.0|**57.6**|8.5|9.1|10.3|15.9|
|1.5|**55.7**|10.1|20.2|39.5|**59.6**|11.8|12.4|13.5|18.8|
|2|**66.6**|13.3|23.2|42.0|**61.5**|15.0|15.6|16.7|21.7|
|2.5|**79.0**|16.5|26.0|44.4|**63.4**|18.1|18.7|19.7|24.5|
|3.0|**84.1**|19.5|28.8|46.7|**65.1**|21.1|21.7|22.7|27.2|

**Note:** Simulations are based on the random effect regression model, which is our preferred estimation strategy. The simulations were done taking 1993 as the base year. Since the per capita income growth rate between 1993-2003 is known (0.7% per annum – URT 2004), the effective growth rates required to attain the 1993-2015 mean growth rates 0%, 1%, 2% and 3% for the 2003-2015 period are respectively: -0.5%, 1.3%, 3.1%, and 5.0%.  Figures in bold show the attainment of the MDG.

25
Although we can only focus on one accuracy statistic to estimate sample size, we also assess whether the sample also achieves the other proposed accuracy statistics in Section 4.

Table 4 summarizes the key parameters of the HIES 2016/17 sample design. The final sample includes all 64 districts in Bangladesh and 20 strata (eight divisions*urban/rural=16, plus four main CCs). Each district is assigned 36 PSUs, an equal number for each district that we derived by modifying Cochran’s formula to estimate the sample size for a target continuous variable for a stratified clustered sample design. The target variable used to estimate the sample size for the HIES 2016/17 is nominal total household expenditure, which follows the convention used in the three previous rounds of the HIES. The accuracy statistics used to assess the performance of the sample is an RSE within 10 percent for each district. The cluster is set at 20 households per PSU, which also follows the convention used in previous HIES rounds. The total number of PSUs is 2,304 and the targeted sample size is 46,080. In terms of field logistics, these numbers suggest that a total of 128 teams will be needed to collect field data, with each team consisting of two people (enumerators) that will visit 18 PSUs over 12 months.

**Table 4**

**HIES 2016/17 summary of sampling design**

|**Description**|**Number**|
|---|---|
|Number of districts|64|
|Number of strata|20|
|Number of PSUs in each district|36|
|Number of households in each PSU|20|
|Total number of PSUs in sample|2,304|
|Total sample size|46,080|
|Total number of teams|128|
|Total number of enumerators|256|

# 3.7. **Allocation of PSUs across sub-strata within districts**

In the second stage, we allocate the 36 PSUs assigned to each district across rural, urban, and CC using a modified version of the Neyman’s allocation rule (“optimal allocation rule”) that 16
time. Finally, there is also a concern that group liability loans are less useful for establishing credit records in credit bureaus than individualized loans, making graduation to larger loans more difficult (de Janvry et al., 2008).

Giné and Karlan (2008) carried out a randomized experiment with a microfinance bank in the Philippines to investigate the extent to which group lending really reduces the moral hazard problems. Half of the group-lending centers of the bank were randomly chosen to be converted to individual liability. They find no change in default rates after one and three years in the converted centers, and faster client growth in the converted branches. These results suggest that group liability is not that important for reducing moral hazard, but since the converted loans were all initially screened by groups, the paper can not say anything about the importance of groups for screening out bad risks. Ongoing work by the authors is examining this issue, comparing newly formed groups to new individual loan clients.

The third precept of microfinance that has been strongly challenged by recent impact evaluations is the belief that serving the poor requires low interest rates. Muhammad Yunus (2007) states “a true microcredit organization must keep its interest rate as close to the cost of funds as possible”, criticizing the high interest rates being charged by Banco Compartamos. This lies at the heart of the debate on commercialization of microfinance (see Cull et al., 2009 and Harford, 2008). However, the high returns to capital for many microenterprises in Sri Lanka and Mexico suggest the ability to repay loans at rates significantly higher than market interest rates. The problem, especially for urban business owners seeking individual loans, is often one of access rather than interest rate. In follow-up work in Sri Lanka, de Mel et al. (2009b) find that few of the high return microenterprises qualify for a loan from microfinance banks, which lend on a basis of physical collateral and not on whether the owner’s business shows high prospects for growth.

The most striking evidence that high interest rate loans can improve welfare comes from a study of consumer loans in South Africa. Karlan and Zinman (2008) conducted a randomized experiment with a microlender, in which applicants which were marginally rejected for consumer loans were randomly selected into two groups, one of which received a second look and higher probability of getting a loan. The loans were 4

- 9 -
**Table 3.2. Net Debt and Equity IIP, Percent of GDP**

||||Net Debt||N|et Equity||
|---|---|---|---|---|---|---|---|
||Country|2002|2007|2009|2002|2007|2009|
|New Member States|Bulgaria<br>|-8<br>|0<br>|-12<br>|-23<br>|-95<br>|-105<br>|
||Czech Republic|33|14|9|-51|-60|-56|
||Estonia|-4|-33|-37|-57|-47|-46|
||Hungary|-19|-44|-60|-56|-51|-68|
||Latvia|-14|-46|-46|-29|-34|-40|
||Lithuania<br>|-8|-27<br>|-33<br>|-28<br>|-34|-29|
||Poland|-12|-16|-26|-26|-42|-40|
||Romania|-6|-11|-21|-18|-39|-45|
||Slovak Republic|9|-3|-15|-36|-58|-58|
||Slovenia|11|-15|-28|-13|-10|-10|
|Candidates and Potential Candidates|Albania|9|5|-7|-22|-33|-42|
||Bosnia and Herzegovina|-17|-1|-18|-12|-35|-39|
||Croatia|-12|-29|-39|-18|-73|-47|
||Macedonia, FYR|-8|2|-9|-32|-49|-57|
||Serbia|||||||
||Turkey|-30|-17|-19|-7|-31|-26|
|Eastern Partnership Countries|Armenia|-36|4|-6|-29|-27|-42|
||Georgia|-38|-19|-34|-26|-65|-87|
||Moldova|-55|-20|-27|-38|-42|-49|
||Ukraine|-14|5|5|-15|-27|-45|

_Source_ : Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: This table reports net debt which is international debt assets plus foreign exchange reserves minus international debt liabilities, and net equity which is international equity assets minus international equity liabilities.

.

Table 3.2a compares the evolution of net debt and net equity for Emerging Europe to the Euro Periphery, Emerging Latin America and Emerging Asia. There are some striking differences across these regions. While emerging economies in Latin America and Asia accumulated positive net debt positions between 2002 and 2009, only the Eastern Partnership bloc in Europe saw a reduction in net debt liabilities. In contrast, the growth in net debt liabilities was most rapid for the Euro Periphery and the new member states.

**Table 3.2a. Net Debt and Equity IIP, Percent of GDP**

|Country||Net Deb|t|N|et Equit|y|
|---|---|---|---|---|---|---|
||2002|2007|2009|2002|2007|2009|
|Euro Periphery|29|-3|-20|-78|-73|-81|
|Emerging Latin America|-23|3|7|-24|-27|-30|
|Emerging Asia|0|27|35|-14|-27|-17|
|New Member States|-2|-18|-27|-34|-47|-50|
|Candidates and Potential Candidates|-12|-8|-18|-18|-44|-42|
|Eastern PartnershipCountries|-36|-8|-16|-27|-40|-55|

Source: Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: These tables report net debt which is international debt assets plus foreign exchange reserves minus international debt liabilities, and net equity which is international equity assets minus international equity liabilities.

In relation to net equity positions, these were relatively stable for the Euro Periphery, Emerging Latin America and Emerging Asia, whereas net equity liabilities grew significantly for each region in Emerging Europe.

We dig deeper into the net debt positions in Table 3.3. In particular, we recognize that gross debt liabilities matter, in addition to the net position. This is especially the case during periods of market turmoil, in which gross debt liability positions may have to be rolled over or redeemed, while not all types of gross debt assets might be fully liquid or available to net off 10
processing of accommodation services. This last sector exhibited an important decrease in the wage gap between 2008 and 2013. Similarly, water transportation showed a decrease in this ratio. On the other hand, for the commerce sector, increases in the wage gap are observed in most of the subsectors.

Through the analysis of these outcomes at the geographical level, for the manufacturing sector we observe in Figure 6 that the states where there is a higher proportion of white-collar workers relative to blue-collar workers, are the ones that exhibit higher results in terms of economic development and where the most important cities are located: Mexico City, Nuevo Le ́on and Jalisco. Accordingly, in the South-East of Mexico, which is the more disadvantaged region of the country, this ratio remains low. It is important to note that between 2008 and 2013 some states in the Central-North region increased this ratio significantly (San Luis Potos ́ı and Quer ́etaro) which is consistent with the emergence of an industrial corridor in what is regarded as the Baj ́ıo zone, where the automotive industry has exhibited a high growth rate. Similar figures for the Services and Commerce sectors are shown in Appendix B. For these other two sectors, the geographical patterns are not as clear as in the Manufacturing sector. For the Services sector, the highest white/blue-collar ratios are observed in the South and North East of the country while for commerce in the Northern region.

In terms of the wage gap (Figure 7), for 25 states out of the 32, the differences in terms of wages between white and blue-collar workers in the manufacturing sector reduced between 2008 and 2013. The gap increased only in the case of some states of the Central-North region which are part of the Baj ́ıo corridor, in Jalisco and in two states which are among the five states with the highest levels of poverty in the country according to Coneval figures for 2013: Oaxaca and Michoac ́an. The highest wage gaps are observed in the North of the country while the lowest tend to be in the South. For the services and commerce sectors, the graphs shown in Appendix B indicate that, once again, different states exhibit the highest white/blue-collar wage ratios in comparison to manufacturing. For services, the states with the highest wage gaps in 2013 are distributed across the country, while for commerce most of them are neighboring states in the Pacific Coast (Nayarit, Jalisco, Colima, Michoac ́an and Guerrero).

When we analyze ICT use at the firm level proxied by the mean of the share of labor that uses computer (Figure 8), we observe that firms located in the Central region of the country, mainly Mexico City and its Metropolitan Area tend to use ICT more intensively. Something similar occurs with some states in the North of the country such as Nuevo Le ́on. In this case it is clear that the states in the South-East of the country remain lagged even though in all of the states the use of ICT increased. The services and commerce sectors show similar results for this variable (Appendix B).

Figure 9 shows the average of a TFP index calculated at the firm level for the manufacturing 12
19 E Should equity (or poverty alleviation) be a key condition of policy-based lending and a determinant of tranche disbursement? Should enough weight be given to equity objectives so that if a government's policies are grossly unjust (however defined), its access to policy-based loans would suffer?

The authors of the 1990 _Worla Development Report_ on poverty answer in the affirmative. They argue that "External assistance should be more tightly linked to an assessment of the efforts that would-be recipients are making to reduce poverty, ,"<sup>3</sup> urging that countries where policies are inconsistent with efforts to reduce poverty should receive only "moderate" quantities of aid targeted at highly vulnerable groups. In the past, the World Bank has tended to measure poverty alleviation in a project-based fashion, attempting to assess what proportion of project benefits accrued directly to the poor. The authors of the World Development Report suggest an economy-wide approach for the future, wherein government policies as a whole would be evaluated in terms of their impact upon the poor. Indeed, the most recent replenishment of IDA stressed the importance of poverty criteria in allocating the organization's resources.14 Poverty allev ition in Sub-Saharan Africa during the 1990s will not be an easy task. As the aoove-mentioned 'World Bank study asserts, even to hold the number of poor at the 1985 level will require a massive effort.'5 This effort, the Bank staff concludes, must involve not only more effective foreign assistance, but also comprehensive domestic reforms in industry, agriculture, infrastructure, and social services -- in sum, virtually the entire gambit of 13. World Bank, 1990 World Develo2ment Regort: Poverty. Oxford: Oxford University Press for the World Bank, 1990, p.<sup>4</sup> .

14 Ibid, p. 134.

15. Ibid, p.5.
# **Business Closures**

Tanzanian entrepreneurs perceive lack of access to formal credit as their main reason for closure. A surprising finding is that only a minority of rural entrepreneurs attribute “traditional” business failure, such as the lack of market demand, as an important reason for closures. Lack of market demand is often cited amongst the most important causes of business failure in Sub-Saharan Africa (Liedholm and Mead, 1999). Another surprising finding is that electricity access ranks second even though a large majority of rural entrepreneurs are traders without immediate need for electricity. One reason might be a difficulty in separating household and enterprise needs. It is also remarkable that the reasons for closure and preventing start-up are almost identical (figure 7), which could suggest that those who have closed their enterprises were able to immediately set-up a new business, and were as such not able to separate the constraints.

**Figure 7: Perceived Reasons for Closure of Business – and Reasons Preventing Start-up, 2005**

<!-- Start of picture text --> Closure Start-up<br>29%<br>21%<br>15% 15%<br>13%<br>12%<br>8%<br>7%<br>6%<br>2%<br><!-- End of picture text --> <!-- Start of picture text --> Closure rank 1st 2nd 3rd 4th 5th<br>Start-up rank 1st 3rd 2nd 4th 11th<br> Source: 2005 Tanzania RICS<br><!-- End of picture text -->

# **Enterprise growth**

Growth of rural nonfarm enterprises can be measured in several ways, including sales growth, profits, and number of working days. If measurement error were not a problem, defining growth in terms of sales or profits might be preferable to a labor-based measure. However, the Tanzania RICS 14
<!-- Start of picture text --> Figure 3.2: Marriage and Children in Argentina, 2005 (percent)<br><!-- End of picture text --> <!-- Start of picture text --> 100.0<br>90.0<br>80.0 Have child<br>70.0 No child<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Females Males Females Males<br>Married Not married<br><!-- End of picture text --> _Source_ : Own calculations on ESCVJA (four urban areas) Social interaction is important for youth to thrive. A strong relationship to the family is crucial, but it is also important for youth to be engaged through cultural societies or sports clubs. Furthermore, it is important for youth to exercise citizenship by being active in political debates or participate in youth societies regarding, for example, the community, studies, religion, etc. (World Bank 2007).<sup>5</sup> Argentine youth in the four cities covered are generally well integrated since more than 50 percent participate in sports and 17 percent take part in cultural activities. Also, in respect to voting, most youth (above 18 years) are active: Almost 3 out of 4 answered they voted at the last election. Being well connected and active are protective factors that are likely to lead to desirable outcomes such as exercising citizenship by voting.

# **3.3 Education and Income**

Education is an important protective factor for youth, because it increases productivity and facilitates social integration. Until a certain age, youth should be in school to build their skills, but later in life a smooth transition to the labor market is optimal, in order to obtain a financially independent and stable life. Early dropout from school and idleness or early work are risk factors for youth, because these issues are connected with low income, substance abuse, etc. (see Section 3.4).

The education system in Argentina is fairly advanced compared to those in most Latin American countries. In fact, primary education enrollment is at par with European countries such as France, Germany and Italy.<sup>6</sup> Furthermore, it is noteworthy that females tend to be better educated than males (Table 3.5).

> 5 The World Development Report 2007 defines a citizen as to be a member of a political community and to enjoy the privileges and protections, as well as the incumbent obligations associated with community membership. Active citizenship emphasizes how individuals should hold public officials accountable for their actions, demand justice for themselves and others, tolerate people who are ethnically or religiously different, and feel solidarity with their fellow human beings.

> 6 WDI (2006).

9
12

**Table 8: Pro-poor growth indices (0) in selected African and Asian countries**

||**_Growthepisod_**|**_es:_**||
|---|---|---|---|
|Ghana,1992-1998|1.07|Thailand,1992-1996|0.61|
|Mauritania,1987-1995|1.10|Lao PDR,1993-1998|0.21|
|Uganda,1992-1997|1.14|Korea, 1990-1996|1.03|
||**_Recession/stag_**|**_nadenepisodes:_**||
|Madagascar, 1993-1997|2.85|Thaland,1996-98|0.73|
|Zinbabwe,1991-1996|1.81|Korea,1997-1998|0.84|

For details of method see text.

Asian country estimates are simnple **means** across years within the sub-period shown. Sources: Table 7; **Kakwani** and Pemia (2000).

There is evidence from international cross section data that initial income inequality can be harmful for subsequent growth and poverty reduction (Alesina and Rodrik, 1994; Temple, 1999; Ravallion, 2001). Initial (income or asset) inequality tends to affect growth itself, with countries with lower initial inequality typically growing more rapidly in subsequent years. In addition, initial inequality reduces the poverty impact of subsequent growth. If initial inequality is large, the poor find themselves often further away from the poverty line and income increases (even when equi-proportionate) are less likely to lift them out of poverty.

The experience of this (albeit small) sample of African countries is consistent with this view (Figure 1). The countries that had lower levels of initial inequality (as evidenced by the Gini ratios), were more likely to experience declines in poverty in subsequent years. That said, it is worth noting that the three countries with identical initial year Gini ratios (of 0.43)-Ethiopia, Mauritania, and Madagascar -experienced subsequent annual poverty changes of (respectively) -8.7, -5.0, and +0.3 percent. While the broad pattern across countries suggests that higher levels of inequality are associated with lower subsequent growth and poverty reduction, there is also a lot of variation around this empirical regularity to counsel caution.