rafmacalaba/datause-encoder-data huggingface dataset:
range of additional training services offered by SHG hosts. One of, if not the, most technically sophisticated studies to date, Datta’s (2015) assessment of the JEEVikA program in Bihar, finds that economically and socially marginalized groups can benefit significantly from SHG membership through a reduction in reliance on high cost sources of borrowing, as well as increased participation of women in household decision-making.<sup>1</sup> In addition, there is also evidence of SHG impacts on other aspects of human development. Saha et al. (2013), for instance, use national data, from a district level household survey, to show that the uptake of maternal health services is greater in villages where an SHG is present. Furthermore, a study by Deininger and Liu (2013) that characterizes the State of Andra Pradesh _Indhira Kranthi Patham (IKP)_ as focusing on twin goals of financial improvement and empowerment, finds that social capital was enhanced, that program members had higher savings and were more able to move freely within their village and interact within their caste. Protein and energy intake, and consumption also increased though the authors importantly noted that this might well reflect agricultural aspects of the program as much as income or asset changes.<sup>2,3</sup> A more recent experimental paper study finds little impact on empowerment of a microborrowing scheme although as its authors accept, Banerjee et al (2015 p27), the

> 1 Datta’s study is perhaps the closest to ours though there are two significant differences. JEEVikA is funded by the World Bank and operates by saturation and so offers a best-case but difficult to replicate study of self-help. From a methodological perspective, his paper depends on recall: while he argues plausibly for the approach, no such requirements were made of subjects in this study.

> 2 For relevant background research on female empowerment, see Brody et al (2015), Doepke (2014), Ganle (2015), Prennushi and Gupta (2014) and Tromlmlerova (2015). Garikipati (2008) is interesting in the current context for the use of vulnerability and empowerment indicators, which are used in a study that concludes that women might be disempowered if loans are used for purposes by male household members. In our pilot work, this concern did not emerge as a significant issue – rather women tended if anything to have stories which showed how men came to support the program when they could see the potential benefits for the household that it brought.

3 Further related discussions of self-help groups can be found in Alemu et al (2018) Fafchamps and Ferrara (2012), Hasan (2017), Parida (2010), Seebohm (2013), Vinahagamoorthy (2017) and Weber (2014).

3
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder measuring the number of disorder events in the previous year are consistently and strongly associated with higher levels of urban social disorder.

The four models were also run on the reduced sample excluding Islamabad and cities in China and India,<sup>8</sup> and in separate runs without controls for armed conflict and prior social disorder. None of these alternative models produced discernable changes in the main results.

**<u>Table 1 Basic Models</u>**

|**Explanatory**<br>**Variables**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|
||**Lethal**<br>**events**|**Nonlethal**<br>**Events**|**Lethal**<br>**events**|**Nonlethal**<br>**events**|
|Urban male β<br>youthbulgesz|0.005<br>(0.28)|-0.023<br>(-1.58)|||
|Urban male<br>youthgrowth|||-0.381<br>(-1.58)|-0.255<br>(-1.15)|
|Urban male<br>youthbias|-0.057<br>(-0.12)|0.030<br>(0.08)|0.271<br>(0.51)|-0.125<br>(-0.28)|
|City pop|0.005**<br>(2.11)|0.003<br>(1.47)|0.003<br>(1.33)|0.002<br>(1.11)|
|Real gdp pc|-0.027<br>(-0.75)|0.027<br>(1.24)|-0.049<br>(-1.40)|0.029<br>(1.41)|
|Real gdp per<br>capita growth|-0.018***<br>(-2.49)|-0.010*<br>(-1.77)|-0.019***<br>(-3.31)|-0.012**<br>(-2.15)|
|Regime type|0.005<br>(0.49)|0.015*<br>(1.85)|0.009<br>(0.90)|0.019**<br>(2.44)|
|Regime squared|-0.011***<br>(-5.61)|-0.007***<br>(-4.65)|-0.010***<br>(-5.40)|-0.007***<br>(-4.09)|
|Missing regime<br>data|0.092<br>(0.39)|-0.033<br>(-0.12)|0.096<br>(0.41)|-0.030<br>(-0.11)|
|Ongoing armed<br>conflict|0.543***<br>(5.08)|0.221**<br>(2.31)|0.553***<br>(5.12)|0.204**<br>(3.41)|
|Lagged<br>dependent|0.101***<br>(6.39)|0.090***<br>(8.86)|0.096***<br>(6.02)|0.090***<br>(8.77)|
|Constant|-0.352<br>(-0.39)|0.504<br>(0.71)|0.055<br>(0.08)|0.206<br>(0.35)|
|N<br>No. of cities|2,042<br>50|2,089<br>52|1,883<br>50|1,930<br>52|
|LogLikelihood|-1540.00|-2006.96|-1458.98|-1884.63|

* Sign at 0.1 ** Sign at 0.05 ***Sign at 0.01.

In Table 2, the same four models are run separately for Sub-Sahara African cities. The models largely reproduce the results from Table 1, but there are some notable differences. In Model 5, urban male youth bulges are significantly and positively associated with higher levels of lethal events. However, the _growth_ in urban youth populations is associated with significantly lower

> 8 This was done to see if the results changed if we excluded the cities for which the city population only made up a very small share of the total urban population. The rationale for doing this was to reduce potential bias from using national-level age structure data rather than city-level.

13
Looking at the 204 individual authors ranked in the top 10% of all authors mapped to the field “development” in RePEc, we find that 27 are current Bank staff, and 48 are current or exBank staff. This too is a somewhat unusual feature of the Bank’s publication record: a relatively large share of its research output is produced by a rather small number of staff. A simple way to measure this is to ask: What proportion of the authors registered on the RePEc database has h- indices of 12 or more? For the Bank, the answer is 5%, and it is 11% for Economic Research, World Bank. These proportions are considerably lower than the US universities in Table 4 (though the figure for Economic Research, World Bank, is similar to the non-US universities and the figure for the Bank as a whole is almost the same as for the IMF as a whole). For the US universities in Table 10, the proportion of authors with h-indices over 12 is 24% or higher. The Bank’s relatively low density of active publishing researchers amongst authors registered in RePEc (already a small subset of Bank staff), probably reflects the fact that, for the vast majority of the Bank authors, publishing in scientific journals is not an essential part of their jobs, and (outside the Bank’s dedicated research department) may not even be rewarded by the Bank’s internal assessments of staff performance.

# **_Intra-Bank bibliometric comparisons_**

This takes us to the second aspect of authorship we want to explore—the bibliometric variation across authors _within_ the Bank. The Bank has no single unit dedicated to writing Bank publications; staff across the Bank can and do author journal articles, books, book chapters and working papers, typically in addition to their other duties. The Bank’s research department is unusual in that its raison d’être is _research_ , although its staff spend a significant part of their time providing advice and support to other Bank units. For the research unit, publications are a key vehicle for disseminating the unit’s findings to a broad audience, and the peer-review process is seen as a crucial part of the quality assurance process. The average (mean and median) h-index of the research department’s research staff is 18 and the highest is 81 (based on Google Scholar).

In this section, we examine the contribution of the research department to the Bank’s publication portfolio, and compare the citation data of research staff and other staff. Table 12 breaks down authorship of Bank publications by type into those authored by members of its research department and those authored by other Bank staff (and authors from other institutions). Two-thirds of Bank publications since 1995 were produced outside the Bank’s research 26
Expansions of government-funded social programs generate controversies regarding the perverse incentives that these programs may create. Opponents claim that despite their intended benefits, people become dependent on such programs and under-report or hide their income to ensure their eligibility. Informal employment is another possible unintended consequence of social programs. In this paper, we examine the impact of Colombia’s public health insurance, the Subsidized Regime (SR), on workers’ choice of formal or informal employment.

Informal employment, which is common in many developing countries, is particularly high in Latin America. Galiani and Weinschelbaum (2012) report an average formality rate of 50% between 2002-2004 across a group of 12 Latin American countries. In addition, a World Bank report indicates that during the 1990s, informality rates increased in several Latin American countries (Perry et al. 2007). In Colombia, the average informality rate from 1990–2005 was 51%.<sup>1</sup> From a social welfare perspective, informality is often seen as undesirable since the informal sector encompasses constrained and low productivity firms, where workers have unstable jobs, are unprotected by labor laws, and are unlikely to receive benefits such as health insurance or pensions.

For some workers, informal employment is not always undesirable. Maloney (2004) indicates that informality status may be preferred by some workers because it grants them greater independence and work schedule freedom. Informality may also be favored if the cost of taxes or social security contributions exceeds workers’ valuation of the services they provide. This situation is exacerbated when the government provides informal workers with comparable free or discounted services, which is the case for the SR in Colombia (Levy 2008; Pag ́es and Madrigal 2008)<sup>2</sup> .

> The upward trend in informality rates in some Latin American countries coincides with the

> 1This rate is calculated using a benefit-based definition that refers to an employee who does not receive social security benefits through employment. In 2003, the International Conference of Labor Statisticians (ICLS) established the benefit-based definition of informality as the official one.

> 2See Loayza (1996) and De Soto (1989) for characterizations of the informal labor market and its determinants.

2
Ray, D., 2006. Aspirations, Poverty and Economic Change. In: _Understanding Poverty._ Oxford: Oxford University Press, pp. 409-22.

Ray, D., 2016. Aspirations and the Development Treadmill. _Journal of Human Development and Capabilities,_ p. 7(3): 309–323.

Riley, . E., 2019. Role models in movies: the impact of Queen of Katwe on students' educational attainment. _Revise and resubmit at the Review of Economics and Statistic._ Ross, P., 2019. Occupation Aspirations, Education Investment, and Cognitive Outcomes: Evidence from Indian Adolescents. _World Development,_ p. 123: 104613.

Ross, P. H., 2019. Occupation Aspirations, Education Investment, and Cognitive Outcomes: Evidence from Indian Adolescents. _World Development,_ p. 123: 104613.

Roy, S., Morton, M. & Bhattacharya, S., 2018. Hidden human capital: Self-efficacy, aspirations and achievements of adolescent and young women in India. _World Development,_ p. 111: 161–180.

Sadler, P. M., Sonnert, G., Hazari, Z. & Tai, R., 2012. Stability and volatility of STEM career interest in high school: A gender study. _Science Education,_ pp. 96(3), 411–427.

Siwatu, G. O. et al., 2020. _Impact of COVID-19 on Nigerian households : 5th round results (English).,_ Washington, D.C. : World Bank Group: s.n.

Sumberg, J. & Okali, C., 2013. Young People, Agriculture, and Transformation in Rural Africa: An “Opportunity Space” Approach. _Innovations, 8, ,_ Volume 8, p. 259–269.

Tabe-Ojong, M. P. J. & Nshakira-Rukundo, E., 2021. Religiosity and parental educational aspirations for children in Kenya. _World Development Perspectives,_ 23(100349).

The World Bank, 2022. _The World Bank in Africa._ [Online] Available at: <u>https://www.worldbank.org/en/region/afr/overview</u> [Accessed 15 August 2022].

Tjaden, J., Auer, D. & Laczko, F., 2018. Linking Migration Intentions with Flows: Evidence and Potential Use. _International Migration._ Van Hear, N., 2014. Reconsidering migration and class. _International Migration Review,_ 48(1), p. S100–S121.

Van Mol, C., 2016. Migration aspirations of European youth in times of crisis. _Journal of Youth Studies,_ pp. 1303-1320.

Wooldridge, J., 2002. _Econometric Analysis of Cross Section and Panel Data._ Cambridge: The MIT Press.

<mark>World Bank. Ethiopia - High Frequency Phone Survey 2020-2023. Ref: ETH_20202023_HFPS_v13_M. Dataset downloaded from h</mark> <u>ttps://microdata.worldbank.org/index.php/catalog/3716</u> <mark>on January 2, 2024;</mark> 30
# **Figure A2: Unconditional Multipliers**

<!-- Start of picture text --> A. Advanced Economies B. Developing Economies<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-1.0 -1.0<br>Impact 1 year 2 years Long run Impact 1 year 2 years Long run<br><!-- End of picture text --> _Note:_ The graph shows the unconditional fiscal multipliers for select horizons. Panel A uses a sample of advanced economies only while Panel B uses only developing economies. These are based on estimates from the SVAR model of Ilzetzki, Mendoza, and Vegh (2013) that features with no interaction terms. Bars represent the median, and error bands are the 16-84 percent confidence bands.

30
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Appendix **2%** Lorenz Curve Parameterizations The decomposition of poverty alleviation gains into "growth and equity' components in Section 4 uses the following parameterization of the 1984 Lorenz curve (following Kakwani, 1989b): L(p) = p- apa (lp)PeV (Al) where L(p) is the proportion of total expenditure by the poorest p proportion of the population and a, a and fl are positive parameters to be estimated (with convexity of the Lorenz curve requiring that neither a nor p exceed unity) and v is a random error process. The P17* poverty measures in equation (2) are then calculated from the parameterizea 1984 Lorenz curve and the 1987 mean, using the formulae given in Datt and Ravallion (1989). Similarly, P&7** is estimated using the parameterized 1987 Lorenz curve and the 1984 mean. The density at any point can also be retrieved from the parameterized Lorenz curve and the mean of the distribution, noting that f(x) = l/QjL"(p)) where p = F(x) is the cumulative distribution function and i is the mean. This allows estimation of the growth elasticity 'o (equation 3). The parameters of the Lorenz curve are estimated from the following linear regression implied by (Al): log[p-L(p)] = log a + a log p + _8_ log(l-p) + v (A2) The accuracy of these methods depends on the precision in estimating the underlying Lorenz curve. Table 5 gives the estimated parameters of Indonesia's Lorenz curves in Table 4 for each sector in 1984. Table 6 gives the corresponding estimates of the national Lorenz curve. The overall fit is excellent; the standard deviation of the errors in estimation is 0.082
<!-- Start of picture text --> —— «<br>4<br>' ged .<br>“aeZe \ «ge 23<br>& f= 7 : .<br>10 20 30 Cross-dimensional40 50 eutott (8)60(in %) 70 80 90 10 20 30 Cross-dimensional40 30 cutoff (&)60 (in %) 70 80 90 a10 20 30 Cross-dimensionalot40 a50 cutoffa(&)60 (in %) 70 80 90<br>95% CI (7} 95% cr CO 95% cr<br><!-- End of picture text --> <!-- Start of picture text --> ge : a* | c— 5 .<br>«a? . é 2 —<br>aNS/\<br>-s4 5 *<br>10 20 30 Cross-dimensional40 50 cutoff (2)60Ga %) 70 80 90 10 20 30 Cross-dimensional40 50 euroff(&)60(in %) 70 80 90 10 20 30 Cross-dimensional40 50 cutoff (&)60(in %) 0 80 90<br>TIT] 95% cr TIT] 95% cr CITT] 95%1<br><!-- End of picture text -->
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changes in the absolute price level. Real household expenditure per capita meets these criteria, and was constructed by deflating total household expenditure by household size, by a regional price index and by the consumer price index (see Grootaert, 1993, for details).

Real household expenditure per capita can then serve as dependent variable in a model with exogenous household endowments and characteristics as explanatory variables.

- where E, = real expenditure of household i

- N, = number of members in household i

- Ai = assets of household i

- R; = a set of characteristics which summarize the economic environment in which i operates.

Equation (1) is a reduced form of the various structural equations which express the income earning and consumption behavior of the household. Glewwe (1991) has estimated such a model on the CILSS data.

In the case of panel data, equation (1) needs to receive a time subscript and first differences can be taken (where A is the difference operator) This is a fixed effects model, and the Ri variables disappear since for each household they are
# **Location**

In most countries, where a family lives has a significant correlation with poverty. In the FSU and particularly Russia, there is a strong regional component to poverty, relating to the legacy of the planned economy. There are many one-company towns in the FSU which produced military-industrial goods for which demand has either disappeared or sharply declined. Russia has its "rust belt" where textile production has been displaced by competition from cheaper imports and service or other industry has not developed to fill in this gap. Additionally, the quality, irrigation, and altitude of agricultural land varies, which means that rural poverty is not homogenous.

In all the countries compared in this paper, urban poverty is markedly lower than rural poverty, and the poverty rate in the capital city is the lowest (except in Hungary, Table 1). For the East European countries, this is a conventional finding. In many countries of the world, the rural poverty rate is higher than the urban poverty rate, and the higher living standards of urban regions were portrayed as the major explanation for ruralurban migration many years ago (Harris and Todaro, 1970). However conventional this finding may be for Eastern Europe, it is not standard for the FSU. In almost every World Bank poverty assessment completed to date for FSU countries, rural poverty has been found to be somewhat less or markedly less severe than urban poverty both in terms of the headcount and in terms of various measures of severity. 12

> **12 World Bank,** 1995 **a-c,** 1996 **a-d.**

45
# **_3.4 Within-country spatial price adjustments_**

At any given time, prices vary not only between countries, but also within countries. To compare real standards of living within a country accurately, national poverty analysts often make adjustments for spatial (geographic) differences in prices.<sup>38</sup> Without spatial-price adjustments, a national poverty line could over-estimate poverty in areas with low prices (typically rural areas) and under-estimate poverty in areas with high prices (typically urban areas). Even if the total count of poor in the nation were correct, the geographic profile of where the poor live might be incorrect. To reduce this sort of mis-classification errors, many countries have separate poverty lines for urban and rural areas (or by other sub-national geographies), or adjust consumption and income to account for differences in prices.

For the purposes of the global poverty count, the focus is on obtaining the correct count of poor people in each country; the global count reported by the World Bank typically does not include sub-national estimates of poverty. By design, the data underlying the PPP conversion factors are usually collected from rural and urban outlets and should reflect average national price levels. Similarly, household survey data is sampled from rural and urban areas and by design is nationally representative.<sup>39</sup> Therefore, for the global poverty count, national PPP conversion factors are generally applied to spatially unadjusted income and consumption distributions, under the assumption that this results in the correct national poverty headcount (and without concern for whether the sub-national profile of poverty is correct). However, there are several important exceptions, for which spatial price adjustments are made to the PPPs or to the income/consumption distributions themselves.

# _Rural and Urban PPPs for China, India and Indonesia._

For China, India and Indonesia, our poverty estimates use spatial-price adjustments to produce separate rural and urban international poverty estimates. These countries are home to a large share of the world’s poor, and their vast geographies may allow for particularly large rural-

> 38 See chapter 5 in Jolliffe et al. (2014) for an extensive discussion of the importance of spatial price difference in national poverty assessment. Gimenez and Jolliffe (2014) discuss this issue in the context of Bangladesh, and Jolliffe (2006) illustrates how the failure to account for spatial difference in prices in the US results in a re-ranking of relative urban-rural poverty rates.

> 39 One distinction though is that household survey weights expand to the population of individuals, PPP price data is weighted by the volume of total expenditures in the country.

22
# Table 9: Program Impact on School Absenteeism

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||6|-15 years ol|d|6|-12 years ol|d|13-|15 years|old|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|0.659|0.26|1.012|0.706|0.345|0.982|0.098|-0.65|-0.701|
||[0.280]**|[0.399]|[0.353]***|[0.302]**|[0.442]|[0.366]***|[0.699]|[1.243]|[0.880]|
|Dry Rations|0.369|n.a.|1.014|0.459|n.a.|1.042|-0.377|n.a.|-0.603|
||[0.287]|n.a.|[0.389]***|[0.310]|n.a.|[0.406]**|[0.870]|n.a.|[1.456]|
|Follow-up|-0.987|-0.787|-1.107|-1.073|-0.892|-1.123|-0.298|0.151|0.823|
||[0.225]***|[0.285]***|[0.338]***|[0.249]***|[0.334]***|[0.355]***|[0.354]|[0.559]|[0.748]|
|Girl|-0.057|||-0.098|||0.756|||
||[0.097]|||[0.104]|||[0.381]**|||
|Constant|1.168|1.536|0.541|1.218|1.547|0.576|-0.112|-0.011|0.336|
||[0.332]***|[0.505]***|[0.225]**|[0.335]***|[0.514]***|[0.233]**|[0.497]|[0.763]|[0.477]|
|Observations|1935|1116|819|1738|973|765|197|143|54|
|R-squared|0.11|0.15|0.18|0.11|0.15|0.19|0.22|0.27|0.94|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable number of days missed in May. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

32