rafmacalaba/datause-encoder-data huggingface dataset:
|READ PNG<br>Binh Thanh Vu|
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|Colombia: Extractive Industries TransparencyInitiative ImplementatJavier Aguilar|
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|Yemen: Extractive Industries TransparencyInitiative Implementatio Javier Aguilar|
|Yemen: Extractive Industries TransparencyInitiative Implementatio Javier Aguilar|
|Yemen: Extractive Industries TransparencyInitiative Implementatio Javier Aguilar|
|Yemen: Extractive Industries TransparencyInitiative Implementatio Javier Aguilar|
|Yemen: Extractive Industries TransparencyInitiative Implementatio Javier Aguilar|
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|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
|BR Sao Paulo Trains and Signalling<br>Bianca Bianchi Alves|
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS interactive nature of the challenge of development in a given context. Over time, this composite picture helps us come to a more nuanced understanding of the relationship among the four dimensions. In the rest of this section we briefly discuss these four dimensions.

# **Monetary welfare**

Income and expenditure underpin a household’s economic life, and yet they are difficult to measure. Respondents may gain little from revealing their real income, or they may not remember and aggregate their numerous transactions. With these concerns in mind, the surveys in Dakar and Nairobi employed three approaches to estimate monetary welfare:<sup>3</sup>

- Assessment of household expenditures above or below the poverty line. The enumerator calculated a household-specific poverty line based on the age and number of household members, and the respondent answered whether expenditure was above or below that threshold.

- Total household expenditures adjusted for family size (measured in adult equivalents)

- Total household income adjusted for family size (also measured in adult equivalents)

The first of these measures was judged to be most reliable and is used as the primary measure of poverty throughout this report. In Nairobi, the analysis took the 1999 poverty line as defined by the Government of Kenya and adjusted it for inflation to calculate the poverty threshold for 2004. Using this expenditure-based poverty line—defined as an expenditure of Ksh 3,174 (US$42) per adult equivalent per month, excluding rent—about 72 percent of the slum households are poor and 28 percent are nonpoor.

In Dakar, the 2004 official poverty line was 27,705 F CFA (US$53) per adult equivalent, including rent. The discrete wealth indicator suggests that about 18 percent of the individuals have expenditures above the poverty line, while about 82 percent fall below the poverty line. For Johannesburg, the survey did not have information on income; so Figure 1 uses an estimated value of about 50 percent of households under poverty line.

# **Education**

Educational attainment varies markedly across the three cities (appendix tables 2 and 3). In Dakar, about a third of adults have a primary-level education, and fewer than 10 percent completed high school. Corresponding figures are much higher in Nairobi (79 percent have finished primary school; 31 percent high school) and Johannesburg (70 percent have completed primary; 44 percent high school).

It is worth noting the contrast between Dakar, which has low educational attainment and fairly good infrastructure, and Nairobi and Johannesburg, with their relatively high levels of educational attainment and poor infrastructure. This suggests that while human capital can certainly affect access to jobs and poverty levels, it appears to be neither necessary nor sufficient for slum households to attain decent living conditions or access to basic infrastructure services such as water and electricity.

> 3 Factor analysis based on household assets was also carried out, but slum households exhibited too little variation in assets for meaningful results.

6
# **Introduction**

The empirical testing of the median voter hypothesis broadly defined has undergone a revival. In addition to the papers that do the usual cross-country testing of the median voter hypothesis and often find a rather weak support for it (Dalgaard, Hansen and Larsen 2005, Kenworthy and McCall 2008, Lind 2005, Moene and Wallerstein 2006, Nel 2007, Creedy and Moslehi 2009), a number of other papers redefine, perhaps precisely because of the weak empirical support for the straightforward application of the hypothesis, either the "identity" of the median voter or the domain of the voters. Dhami and el-Nowaihi (2007) redefine the voter so that he or she is concerned with "fairness" of the distribution and not only with individual well-being. Shayo (2009) introduces "individual national identification", which he argues, based on World Values Survey data, to be stronger among the poor voters, and which reduces their propensity to vote for redistribution. To paraphrase Marx, "national identification" here acts as "the opiate of the people." In Hodler (2008), voters, in addition to income, care about leisure; societies with greater preference for leisure also select more redistributive policies. Berenboim and Karabarbounis (2008) propose a "one dollar, one vote" hypothesis whereby the economically stronger the group (poor, middle class, or rich), the more able it is to impose its redistribution preferences. Here, income matters but, up to a certain point, in a different way from the original hypothesis. While in the medium-voter hypothesis it is the numbers of the poor vs. rich that matter, here is it their aggregate economic power. Tanninen and Tuomala (2001) and Scervini (2009) focus on a more exact definition of who the median voter is, arguing that he or she needs to be placed within the "inherent" or market income distribution (not as is more commonly done within the disposable income distribution). Mahler (2006), Chong and Olivera (2008), and Arawatari (2009) redefine the population of voters by endogenizing electoral turnout, that is by showing that inequality itself may influence who votes (e.g., electoral turnout of the poor), which would then, in turn, influence the extent of redistribution that a society selects. Campante and Do (2008) find that greater population density, through the implicit threat that many poor people in close proximity pose to rulers, is conducive to greater redistribution. Finally, there are papers that look at the theory alone, and review the reasons why the 2
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reaching low-income and rural households.<sup>4</sup> Using private school census data from 2000, Andrabi et al. (2008b) find that there was an exponential increase in the number of private schools over the 1990s, with over 50% of existing private schools established in or after 1996. Furthermore, they find that while existing schools set up before 1990 were predominantly in urban areas, the distribution since then has become increasingly rural.

In line with this increase in institutions, the share of enrollment in private school has also increased. As Panel B in Table 1 shows, in 2004/05, 15.8% and 18.5% of children ages 6–15 years were enrolled in private schools in all-Pakistan and Punjab, respectively; these shares represent significant increases from 1998/99. The increases were particularly dramatic in rural areas and for households in the poorest expenditure quintile. Similar evidence is provided by Andrabi et al. (2008b) who find that the growth rate in private school enrollment over the 1990s was highest among low-income households nationally, and among middle-income households in rural areas, whereas the growth rate in public school enrollment over the same period was negative in both urban and rural areas and across the household income distribution. They also find that fees in private schools are generally low: median annual fees per student in 2000 in Pakistan were 960 (US$23.4<sup>5</sup> ) and 751 rupees (US$18.3) in urban and rural areas, respectively, and account for a small percentage of mean annual household expenditure. The corresponding statistics for Punjab were lower, at 828 rupees (US$20.2) and 600 rupees (US$14.6).

> 4 It is conceivable that much of the growth and metamorphosis in the private school system is in direct response to the rigidities and shortcomings in the public school system.

> 5 The exchange rate in 2000 was 41 rupees per US dollar.

8
intergenerational transmission of economic status, where the focus is on the role of family background in generating and sustaining educational inequality. Because it implicitly assumes that parents’ education is effectively a sufficient statistic for family background relevant for children’s education, and thus ignores the role of parents’ occupation, regarded as the most important indicator of socio-economic status in a large literature on mobility in sociology (see, for example, Grusky and Cumberworth (2010)).<sup>3</sup> This omission seems especially surprising in the context of villages in developing countries, given substantial evidence that non-farm occupations yield higher income, and non-farm income may be an important source of increasing inequality in rural areas (for recent surveys, see Lanjouw and Feder (2001), Haggblade et. al. (2007), and on rural China see Benjamin et. al. (2005), Rozelle (1996, 1994), Knight et, al. (2010), Yang and An (2002), Mcguire et al. (2007)).<sup>4</sup> The rural economy in China has experienced fundamental structural transformation over the reform period, with impressive growth in non-farm employment and output in the recent decades. The share of non-farm sector in household income in rural China increased from 22 percent in 1980 to 51 percent in 2001.<sup>5</sup> There are potential interactions between education and non-farm occupations which may exacerbate inequality. Consistent with Schultz-Nelson view that education equips people to deal with disequilibria, the evidence indicates that educated families in rural China were the first responders to the incentives created by household responsibility system and related reforms, and were able to take advantage of the growing non-farm sector to reap higher income (Yang (2004), Yang and An (2002)). Positive feedbacks among education, nonfarm occupation, and income may lead

> 3Grusky and Cumberworth (2010) note: “...sociologists typically carry out analyses of intergenerational mobility in terms of occupations....., because occupations are so deeply institutionalized in the labor market, they serve as a powerful omnibus indicator of life conditions and chances.”

> 4The role of non-farm occupations in the recent rise in inequality in has also attracted increasing attention in India (Himanshu et al. (2013), Rama et al. (2014)).

> 5See National Bureau of Statistics of China, Statistical Year Book, 2003.

2
Figure 3 shows the evolution of employment by foreign firms by gender for the period 20002007. The number of workers employed by foreign firms more than quadrupled, increasing from 0.41 million in 2000 to 1.04 million in 2004 and to 1.69 million in 2007. The acceleration of growth in employment in 2001 shown in Figure 3 may reflect the impacts of the U.S.-Vietnam BTA, which came into effect in December 2001, with the United States granting MFN status to Vietnam (Fukase and Martin, 2000). <mark>As a result, Vietnam’s exports to the United States, in particular, those of labor-intensive manufactured goods (e.g., c</mark> lothing, footwear and furniture), <mark>expanded dramatically (Fukase, 2013b). Vietnam's FDI inflow in the aftermath of the BTA experienced a disproportionately large increase in those sectors in which exports to the U.S. expanded (Parker, Riedel, and Quang, 2007)</mark> .

Figure 3 demonstrates that the expansion of employment by foreign firms was faster for female workers relative to male workers, as the share of females employed by foreign firms increased from 60.4 percent in 2000 to 66.5 percent in 2004 and further increased slightly to 67.5 percent in 2007 (the _Enterprise Survey_ data, GSO). As Vietnam became increasingly integrated into the world economy, the expansion of export-oriented and female-intensive manufacturing production in which Vietnam had a comparative advantage (e.g., clothing, footwear and electronics) appears to have created employment disproportionately for females (Fukase, 2013a). Overall, high export-orientation and high female intensity in foreign firms in Vietnam are consistent with the view that expansion of exports has boosted the demand for female labor in manufacturing in developing countries (e.g., Wood, 1991).<sup>7</sup> In addition to the employment effect described above, the relatively high earning opportunities in the foreign sector may be another reason why foreign firms attract migrants. Previous studies

> 7 Measuring the impact of North-South trade on the female intensity of manufacturing, Wood (1991) finds that developing countries which exported a rising proportion of their manufactured output tended to experience rising female intensity in their manufacturing sectors.
Table A8: Heterogeneous effects of payment delays on transfer and fee spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|1-day delay|-33.475|-369.376|-575.246|-193.834|77.228|
||(392.927)|(647.934)|(496.903)|(559.991)|(407.588)|
|8-day delay|-729.221**|-1119.101*|-752.333|-1057.882|**<br>-335.109|
||(322.901)|(587.006)|(461.228)|(492.529)|(421.113)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|1608.13|1552.29|1815.74|1957.17|1316.35|
|R-squared|0.206|0.293|0.191|0.155|0.227|
|P-value: 1-day=8-day|0.042|0.183|0.643|0.060|0.282|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|1-day delay|-178.377|-160.676|575.481|46.441|-238.399|
||(582.873)|(375.611)|(452.081)|(339.262)|(489.439)|
|8-day delay|-597.176|-812.426**|-673.182*|-367.269|-1026.894**|
||(539.522)|(349.030)|(348.521)|(333.372)|(421.200)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|1317.84|1498.06|1222.05|1084.20|1694.44|
|R-squared|0.070|0.082|0.125|0.136|0.138|
|P-value: 1-day=8-day|0.275|0.024|0.001|0.129|0.013|
|P-value: equal efect of 1-day vs 0-day|0.828|0.768|0.076|0.702|0.606|
|P-value: equal efect of 8-day vs 0-day|0.825|0.635|0.887|0.226|0.225|
|P-value: equal efect of 1-day vs 8-day|0.571|0.869|0.040|0.378|0.427|

The top panel includes respondents from female-headed households and those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values in the bottom three rows are from tests of equality of the 1-day delay coefficients, the 8-day delays, and the differences between 1-day and 8-day delay coefficients, respectively, in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 50
In this atmosphere, new firm formation and investment are challenging for entrepreneurs that are not members of existing networks. Connections matter for access to financial resources, quality inputs, skilled labor, and information on technology and markets. Network “insiders” have better access to these productive resources and therefore have larger size at entry, higher productivity and faster growth rates, as we have shown. Relationships also matter for entering certain business activities. New entrants have to deal with established market participants and create new business relationships. Some way has to be found to establish trust-based relationships and to enforce contracts. New investors with contacts in these business activities, because of referrals from other network “insiders” or relatives, have a distinct advantage. As a consequence, entrepreneurs in SSA are inclined to enter businesses where they are known and connected.<sup>9</sup> Ultimately, patterns of network specialization are established in specific activities, and information sharing, referrals, and existing relationships cause these patterns to persist over time. Distinct patterns of ethnic concentration in particular businesses are quite evident in the RPED enterprise data (Biggs and Srivastava 1996; Bigsten et al 2000). This zoning of economic activity by business networks has distributional consequences. Networks controlling highly profitable activities do better, and the resulting differentials in income that arise between groups can persist. Non-members of the network are in effect excluded from many opportunities. Exclusion and the persistence of income differences can lead to conflict, reduced investment, and capital flight.

Partition of economic activity by networks also influences the allocative efficiency of financial and human capital. Where business activities are controlled by

> 9 This has some similarities with what studies find in more developed countries. Entrepreneurs tend to start up businesses in industries where they have “experience” (Audretsch 1995).  Experience implies connections, as they are similarly useful in advanced countries for learning and getting access to resources and markets.  But in developed countries, where there are strong market institutions and more generalized trust, experience relates more to industry-specific technical knowledge and customer connections. Technical experience and customer knowledge are important in Africa too, as indicated in our regressions on determinants of size at start up and firm performance. But the argument here is that, in the presence of weak or missing market institutions and high transaction costs, connections are essential.  Without them entry and survival are highly problematical, as firms will have difficulties getting access to credit and inputs, as well as enforcing contracts.

25
Financial Times, Measuring what matters: the scramble to set standards for sustainable business, 14 May 2021 <u>Measuring what matters: the scramble to set standards for sustainable business | Financial Times (ft.com)</u>

Gibson, R., P. Krueger, N. Riand, and P. S. Schmidt, ESG Rating Disagreement and Stock Returns. SSRN Scholarly Paper ID 3433728, Social Science Research Network, Rochester, NY, 2019.

Haralampieva, V., Ramping Up Climate Action by Enhancing Companies' Governance Framework, - Law in Transition Journal, 2019 Articles | EBRD LiT 2019 (lit <u>ebrd.com)</u>

ILO, Small matters, Global evidence on the contribution to employment by the self-employed, micro-enterprises and SMEs, International Labour Organization, 2019 ILO, The power of Small: Unlocking the potential of SMEs, International Labour Organization, October 2019 OECD-JRC, Handbook on Constructing Composite Indicators, 2008, <u>https://www.oecd.org/els/soc/handbookonconstructingcompositeindicatorsmethodologyanduser guide.htm</u> Moody’s, ESG Score Predictor: Applying a Quantitative Approach for Expanding Company Coverage, July 2021

- <u>60ec66d0af57878032604495_ESG Score Predictor_090721.pdf (website files.com)</u>

SASB, SASB Conceptual Framework and Rules of Procedure <u>Academic ESG data review | PRI Web Page | PRI (unpri.org)</u>

World Economic Forum, Statement of Intent to Work Together Towards Comprehensive Corporate Reporting (Summary of alignment discussions among leading sustainability and integrated reporting organisations CDP, CDSB, GRI, IIRC and SASB), September 2020 Statement-of-Intent-to-Work- <u>Together-Towards-Comprehensive-Corporate-Reporting.pdf (netdna-ssl.com)</u> Oh, S., D. Noh, Are Corporations feeling the heat?, World Bank Sovereign ESG Data Framework, September 2021 <u>Sovereign ESG Data Framework | Sovereign Environment Social Governance Data | World Bank</u>

# **7 Annex**

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