rafmacalaba/datause-encoder-data huggingface dataset:
Saharan African countries urbanization without growth characterized the 1990s, creating a new type of housing demand in a much more urbanized if poorer world.<sup>5</sup> For shelter policy in developing countries, perhaps the most notable change in recent years is with respect to the spatial dimension of poverty. This pattern had changed in rather fundamental ways. Though the majority of the world’s poor continue to live in rural areas, poverty is rapidly becoming an urban phenomenon. Today, unlike the situation in 1985, in many of the Bank’s biggest borrowers – e.g. Russia, Brazil, Mexico - most of the poor already reside in urban areas. Moreover, while in India most poverty remains in the countryside, in most of its larger states the poverty rate in urban areas is for the first time higher than it is in rural areas.<sup>6</sup> By some estimates, over 50 percent of the world’s poor will be living in urban areas by 2035, see Ravallion (2002).

In such an environment, it is not surprising that urban slums are not being absorbed into formal urban economies. Surveys in Brazil and India, for example, indicate that in many places slum dwellers are no longer participants in a temporary demographic transition process as was earlier thought to be the case. It is now recognized that many slum dwellers are not new immigrants who recently arrived from rural areas in search of better livelihoods. Today, many of the 100,000 pavement dwellers in Mumbai, for instance, are secondgeneration residents, see SPARC (2002), as is the case in Rio’s Favelas, see Perlman (1976 and 2002).

> 5 There is some debate about the finding that sub-Saharan Africa has experienced rapid urbanization without accompanying economic growth.  Since there have been few censuses available for recent years, the data used in many such studies have relied on projections of urban population growth from the 70s and 80s.  By some accounts, these figures might exaggerate the urbanization rates in some sub-Saharan African countries.  For a more detailed discussion of these issues, see Satterthwaite (2003).  Nevertheless, even with this qualification, it is clear that in many large African countries, Ethiopia, Nigeria, Kenya to mention a few, urbanization accompanied impoverishment.

> 6 In 11 states which include Haryana, Kerala, Rajasthan, Andhra Pradesh, Maharashtra and Madhya Pradesh, the poverty rates in urban areas were larger than rural areas (see 10<sup>th</sup> Five Year Plan (2003).

3
to adapt quickly to the changing environment and, eventually, improve productivity in the longer term. However, South Asian firms lag behind the rest of the world, with only about a quarter of firms reporting an expansion in the use of digital technologies. This is significantly low compared to the state of digitalization in other non-high income countries (33%) and EAP (45%) (Figure 6, panel a).<sup>8</sup> It is interesting that the share in high-income countries is low at only 19%. This may be due to the fact that the question solicited response on the _increase_ in use of digital platform, rather than the level.

Figure 6: Adapting through digitization and adjustment of operations models <!-- Start of picture text --> Increased use of digital platforms Innovated in products and services<br>50 50<br>45<br>40 40<br>34<br>33<br>30 30 28 28<br>26<br>23 23<br>20 19 20<br>13<br>10 10<br>0 0<br>High income SAR SSA Non-high income EAP EAP SAR Non-high incomeHigh income SSA<br>(excl. SAR) (excl. SAR)<br>Probability of adjustment (%) Probability of adjustment (%)<br><!-- End of picture text --> Panel (a): Regional/by income group comparison <!-- Start of picture text --> Increased use of digital platforms<br>50<br>44<br>40 40<br>40<br>36<br>30 29<br>26<br>23<br>20<br>11<br>10<br>0<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SAR Rest of the world<br>Predicted probability of response (%)<br><!-- End of picture text --> Panel (b): Comparison by firm-size In order to qualify the findings for South Asia, we examine the change in the use of digital technologies by firm attributes. If, for instance, the low shares were driven mostly by larger firms, then this could be worrying as it will reflect a poor state of advanced digitalization in the region. Figure 6 8India could not be included in this chart because a comparable question was not asked in the survey.

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Policy Research Working Paper 6180

# **Abstract**

This paper analyzes the evolution in socio-economic and ethnic disparities in tertiary education attainment, participation, and completion and labor market outcomes in the six countries of Central America. There is evidence of differential progress, with Costa Rica, a middle-income country, and Nicaragua, a low-income country, having improved participation of low-income students in tertiary education, while this continues to be negligible in Guatemala, El Salvador, and Honduras. Wide differentials in salaries linked to socio-economic background can signal differences in the quality of tertiary education or prior educational experiences. The analysis distinguishes between long-term and short-term constraints and the key transitions in the education cycle that impede access to tertiary education. The main obstacle to accessing tertiary education for poor students is the failure to either start or complete secondary education, suggesting different priorities for different countries in addressing long-term constraints. However, problems also arise within tertiary education, as in all countries the average tertiary education completion rate is below 50 percent, with even lower rates for students from low-income families and indigenous backgrounds. The paper uses an OECD framework for public policies for promoting equity in tertiary education to assess policies in Central American countries and concludes that many of them currently lack the policies, instruments, and institutional mechanisms to promote greater equity in tertiary education. The paper highlights how valuable insights can be obtained from analysis of household survey data in the absence of comprehensive data on tertiary education which is typical of many developing countries.

This paper is a product of the Human Development Department, Latin America and the Carribean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sbashir@worldbank.org.

_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 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._ Produced by the Research Support Team
**Table 8: NTMs and energy subsidies account for the higher profitability of connected firms**

|_Dependent Variable:_|||||**ln(Profits**|**/Assets**|**)**||||
|---|---|---|---|---|---|---|---|---|---|---|
|PC broad firms|1.38***|1.36**|-2.32|1.56**|.267||||||
||(2.30)|(2.30)|(-1.19)|(2.37)|(0.33)||||||
|PC owner firms||||||.428|.423|-4.96**|.491|-.677|
|||||||(0.58)|(0.57)|(-2.04)|(0.62)|(-0.65)|
|NTMs||-.144|-.218||||-.156|-.019|||
|||(-0.56)|(-0.78)||||(-0.62)|(-0.88)|||
|**PC broad * NTMs**|||1.03**||||||||
||||(2.40)||||||||
|**PC own * NTMs**||||||||1.50***<br>(2.85)|||
|High energy||||-.634|-2.60**||||-.282|-1.34|
|||||(-1.04)|(-2.73)||||(-0.44)|(-1.09)|
|**PC broad * High energy**|||||4.55***<br>(3.77)||||||
|**PC own * High energy**||||||||||3.57**<br>(2.58)|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|No. of firms|253|253|253|253|253|253|253|253|253|253|
|R2|0.061|0.061|0.063|0.063|0.094|0.044|0.049|0.062|0.050|0.066|

Source: Orbis database, yearly panel 2003-2011 and list of politically connected firms. Note: All regressions control for firm age and time dummies. PC broad refers to firms that meet any definition of connectedness. PC owner refers to firms whose owners are politically connected. The sample includes all firms from the Orbis data with available information for the corresponding variables. The standard errors are clustered at the year level accounting for the fact that the standard errors might be correlated for all firms in a given year due to year-specific shocks. *,** denote significance at the 10%, 5% significance level, respectively, t-statistics are reported in parentheses.

Table 8 shows that politically connected firms of any type are much more profitable than unconnected firms, even after accounting for differences in firm age. The second column reveals that the profitability differential remains after we control for the distribution of NTMs across sectors. However, column three indicates that the interaction of NTMs and politically connected firms across 4-digit industries accounts for the entire profitability differential. That is, politically connected firms of any type are significantly more profitable than unconnected firms if their products are protected from import competition but not otherwise. This result holds for both the broad definition of political connectedness and when connectedness is defined by the political connections of the owner.<sup>38</sup> In the latter case, we also find that the few firms owned by connected businessmen that did operate in manufacturing sectors not (less) protected by NTMs have lower profits than unconnected firms in these sectors.

Results are similar when we account for the joint distribution of political connections and energy subsidies in high energy intensive industries. Connected manufacturing firms in high energy-

> 38 Note that all of the firms managed by a politically connected CEO with available profitability data operate in high energy intensive sectors and sell products that are protected by NTMs. Thus, we cannot repeat the exercise for this most restrictive sub-sample of connected firms.

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# **III. Data**

Our primary data consist of quarterly banks’ balance sheets and unaudited information on the number of branches, deposit accounts, and loan accounts, from the Comisión Nacional Bancaria y de Valores (CNBV), the banking regulatory and supervisory authority in Mexico. While balance sheets are available at the bank level, data on branches, deposit, and loan accounts were obtained per bank, per municipality, per quarter. In other words, we have information on the number of branches, deposits, and loans for each bank in each of 1,192 municipalities for each quarter from 1997 through 2005.<sup>9</sup> We also have information on the ownership type of all banks and the mergers and acquisitions that took place over the period 1997 through 2005 (Aguilar and Cabal (2004)).

From the data discussed above, we create different indicators of outreach. In particular, following previous work (Beck, Demirguc-Kunt, and Martinez Peria (2007)), we develop both indicators of access to (i.e., the possibility to use) and the actual use of financial services. In terms of access, we focus on the presence of bank branches across municipalities both at the aggregate, i.e. Mexico-wide level (share of municipalities served), the bank level (number of municipalities served) and the bank-municipality-level (probability of a bank being present or operating a branch in a specific municipality). We also consider the number of branches per capita at the country-level and the log of the number of branches at the bank and bankmunicipality levels. These indicators are proxies for the extent to which the Mexican population as a whole and across different municipalities has geographic access to bank services, as well as, the geographic outreach effort of individual banks.

> 9 Mexico has close to 2,500 municipalities, however, detailed information is available for 1,163 municipalities. For 29 of the 32 Mexican states (“entidades federativas”), a category labeled “others” aggregates information for the smallest municipalities in each state. Hence, in total there are 1,192 observations in the municipality-level estimations.

10
- 3 -

On the supply side, limited connectivity complicates other factors as well. For instance, doctors and nurses are generally less available in rural health facilities. In the United States, only 9 percent of physicians practice in rural areas where about 20 percent of the total population lives (Rosenblatt and Hart 2000). As a result, rural physicians often have to see more patients than urban doctors do, therefore, potentially compromising the level of services they provide. In rural areas, fewer medical ancillary services are provided (Bronstein et al., 1997). Medical supplies may also be insufficient in rural and remote areas because of unreliable transport accessibility (Iimi and Rajoela, 2018). These supply-side constraints further reduce the rural residents’ demand for health services.

The current paper attempts to estimate the net impact of transport connectivity on people’s health care service access, by taking advantage of spatially disaggregated rich micro data from Mozambique. It will be focused on the people’s decision-making process on whether or not they visit a health facility when they have a fever. This is a critical decision in particular in many African countries. To prevent illness and death, it is essential to have good access to primary care services (e.g., Starfield et al., 2005). According to Laditka et al. (2009), rural residents are more likely to be hospitalized for ambulatory care sensitive conditions. This could have been prevented if people had better access to primary care, but because they do not, their condition tends to deteriorate.

Despite recent marked progress, malaria remains an important risk in many developing countries, including Mozambique. Children aged under 5 years are the most vulnerable group affected by malaria. Globally, it is estimated that 228 million cases of malaria occurred worldwide in 2018 and 405,000 people died from malaria (WHO 2019). Africa accounts for about 94 percent of global malaria deaths. Nigeria accounts for 24 percent, followed by 11 percent in the Democratic Republic of Congo, 5 percent in Tanzania and 4 percent in Mozambique.

From the methodological point of view, the paper also contributes to exploring the way of combining rich micro data in the health sector with emerging spatial data, which can be used
Figure 4. Shares of high-educated in high- and low-exporting industries. 2011-2015 <!-- Start of picture text --> 45.8<br>44.1<br>41.9<br>39.5<br>35.1<br>16.1<br>11.6 12.7 13.6 14.3<br>2011 2012 2013 2014 2015<br>High-exporting industries Low-exporting industries<br><!-- End of picture text --> Source: Authors’ calculations based on Sakernas 2011-2015.

Notes: High-educated workers are workers having at least complete secondary education. High- and low-exporting industries defined as industries having exports above the median value each year.

53
|R&D|0.07|99|0|429|
|---|---|---|---|---|
|Production|0.07|99|0|409|
|Quality test|0.06|99|0|399|
|Environment|0.06|99|0|396|
|Safety and health|0.06|99|0|397|
|Computers|0.06|99|0|396|
|Others|0.06|99|0|398|
|Total|0.12|99|0|720|
|Calculated total(excl Total)|0.75|1089|0|4680|

Source: Data from the 2nd study in technological development and in innovation in Colombia 2005 To get a broader view of the content of the data please read the “Innovación y Desarrollo Tecnológico en la Industria Manufacturera Colombia 2003 – 2004” report from DANE (2005), which provides an extensive view of the data that is the foundation for this paper.

38
significant relative to the five percent predicted probability of investment. The results indicate that the marginal effects of several investor and host attributes are of similar scale. Strong commitment to international environmental treaties and policies resulted in a two to three percent increase in predicted investment among potential partners. Surprisingly, the ability to attract direct investment generally had a quantitatively small effect on investment, even though the underlying parameter was statistically significant. More important was the related measure of political stability, which generated the largest marginal effect.

# **7. Alternative Estimates**

# 7.1. <u>Count data</u>

In this section we take up the question of whether results reported earlier depend significantly on our decision to view investment events as dichotomously measured partnerships. As an alternative, we consider the number of projects observed or project counts with the consequence that our applied models are expected to relate to different probability distributions.<sup>28</sup> In a way that is analogous to our earlier analysis, it is possible to test whether the observed pattern of project counts is an outcome that is statistically unlikely and related to bilateral aid in the context of these alternative distributions.

As a first step, we consider a mixed regime model. In this model, the probability of an event characterized by the set of investment determinants alone is compared to a mixed regime model in which the probability of project investment is decreased by an additional process. In particular, we estimate two zero-inflated models in order to test whether the mixed-regime model given in 4) can be set aside in favor of a standard Poisson or negative binomial regression consistent with 3). In contrast to selection models, it is possible, in the context of the mixed regime models, to test directly whether the probability of an investment is impinged upon by an additional process related to bilateral aid. Following Greene (1994), we used Vuong’s (1989) nonnested test to make this determination. The test results, given in table 5, suggest that the mixed regime models better explain the project count data than do either the Poisson or the negative

> 28 As discussed, investment relationships are rare in our data. Moreover, when relationships are observed, the number of investment projects is generally low.  Both characteristics are suggestive of the Poisson and negative binomial distributions, which are associated with many forms of count data.

17
foundations behind the new index and defines a set of verifiable, comparable, and transparent indicators for index construction.

The following section summarizes the recent literature on Green IT measurement. Section 3 describes the specific features of IT systems in government institutions that justify the need for the government-specific Green IT index. Section 4 provides a theoretical framework for the new index. Section 5 discusses the index dimensions and aggregation approaches. Section 6 presents the approach for the practical implementation of the GGIT Index and develops the survey instrument, and Section 7 concludes.

# **2. Measuring Green IT: Literature review**

Over the last two decades, studies related to Green IT have been consolidating into a wellestablished research stream (Ribeiro et al. 2021). But, despite the importance of ICT for the green transition agenda, the limited quantitative and longitudinal research represents a significant gap in this area (Singh and Sahu 2020). The literature on the integrated indexes of Green IT is sparse, and most of the Green IT metrics have focused on the energy efficiency of the hardware side of IT. Only a few papers developed frameworks for measuring the environmental impact of software products (e.g., Bozzelli et al. 2014).

Several indicators and frameworks were recently proposed to measure the “greenness” of IT organizations and departments. <u>Molla et al. (2009)</u> introduced the Green IT Readiness (Greadiness) model that conceptualizes Green IT from the infrastructure and capability perspective. The analysis in the paper is based on a survey of CIOs of more than 2,100 private companies from the US, Australia, and New Zealand. The paper identifies four components of the Green Readiness assessment: attitudes, policies, practice, and governance. These main components comprise eight subcomponents and 32 indicators. The averaging of the components produces the summary G- readiness index to identify areas needing improvement. While the G-readiness index was one of the first to measure the environmental sustainability of IT infrastructure, it is mainly based on subjective assessments of CIOs that might be biased toward presenting their organizations in a favorable way. The G-readiness index is also designed without a clear methodological framework, both in terms of selecting individual indicators and in the choice of the aggregation methodology.

5
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