rafmacalaba/data-use-mentions-extended huggingface dataset:
Between 2000 and 2014, Mexico experienced income convergence across municipalities, but inequality increased within municipalities (López-Calva, Ortiz-Juárez, and Rodríguez-Castelán 2019). While the average real per capita income growth of municipalities in the lowest decile of the distribution was 25 percent in 2000– 14, municipalities in the highest decile grew at an average rate of 7 percent (Figure 1). Furthermore, the average income growth of the 10th percentile and the median was positive between 2000 and 2014, unlike the 90th percentile, the average income of which declined in real terms in 2005–10. Yet, contrary to previous trends, inequality within municipalities increased between 2010 and 2014. Before 2010, most of the inequality reduction occurred in municipalities with higher income per capita, while, in 2010–14, less than 20 percent of the municipalities in the highest quantiles experienced a reduction in the Gini coefficient (Figure 2).

# ii. Administrative data on public finances

The analysis uses administrative data on public expenditure among municipalities from the State and Municipal System Databases (SIMBAD) produced by the National Institute of Statistics and Geography (INEGI). SIMBAD provides access to public expenditure variables from 1988 until 2015, including investment spending. The analysis aggregated the fiscal accounts in the following groups: (1) own resources, including taxes; (2) unconditional transfers; (3) conditional transfers, excluding FAIS; and (4) the FAIS. (For a detailed breakdown of the fiscal accounts of municipalities, see Annex 1.) Reported data on the observed FAIS may often differ from the original allocation of resources because of revisions and reallocations between and within municipalities. To smooth the possible effect of measurement error, the anaysis therefore generated a variable that aggregates the monthly average of the FAIS per capita for the three consecutive years of the observation. For example, the variable FAIS in 2014 corresponds to the monthly average per capita of FAIS in 2011–13.

Municipalities with less ability to collect their own fiscal revenues typically receive more resources through
.4) and (C.5) also make it clear that we should use the poverty line 1 in the base survey (rather than the poverty line 2 in the target survey) together with the imputed consumption for the base survey 21 to obtain poverty estimates. Put differently, since all the estimates for zz 1, , and 1 in Equation (C.5) come from the base survey, the poverty zz yy� cc1 ββ υυ εε 74
--> Demand Shock: % Firms decreased monthly sales vs 1 yr ago<br>Demand Shock: Average % drop in monthly sales vs 1 yr ago<br>Demand Shock: % Firms increased monthly sales vs 1 yr ago<br>(Export) Demand Shock: % Firms decreased direct/indirect<br>exports vs 1 yr ago<br>Supply Shock: % Firms decrease in supply of inputs<br>Finance Shock: % Firms decreased liquidity or cash flow<br>availability<br>-25 -20 -15 -10 -5 0 5 10 15 20 25<br><!-- End of picture text -->

# _Source:_ World Bank Enterprise Follow-Up Surveys.

_Note_ : Chart reflects the average difference between SMEs and Large Firms within a country-sector with their standard errors. Country-sector fixed effects are included. Stratification weights adjusted for the follow-up enterprise surveys according to median eligibility were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market 9
restrictions and the autocorrelation tests confirm the validity of the instruments, as in the case of agriculture, the System GMM is considered more efficient than the difference GMM.

For regressions including all countries, as shown in columns 1-3 of Table 1, the two-step system panel GMM estimation results indicate that electricity consumption has a significant positive impact on the value added in agriculture, while the fixed-effects and two-step difference GMM estimations yield insignificant coefficients. The two-step system GMM estimation shows that a one-percent increase in electricity consumption raises the sectoral value added by 0.024 percent. Not surprisingly, the coefficients become insignificant when regressions include only the sample of non-high-income countries, as shown in columns 4-6 of Table 1 across all estimation methods. An increase in electricity consumption in developing countries does not necessarily lead to a growth in value added because of the dominance of labor-intensive technologies or manual work, which do not rely on electricity and also face other obstacles for agriculture growth, such as lack of irrigation, fertilizers, or poor roads to markets, that would continue constraining sectoral growth even with a higher supply of power.

Estimation results for the manufacturing sector using all countries’ data indicate that the system GMM provides efficient and consistent coefficients, with a magnitude of 0.036 for the elasticity of the sectoral value added with respect to the electricity consumption, as shown in Table 2, column 3. Similar to the agriculture sector, the system GMM provides an efficient and consistent estimator, as opposed to fixedeffects and two-step difference GMM approaches in the manufacturing sector. Electricity consumption proves to be positive and significant across all approaches, with the coefficients varying from 0.036 to 0.096. As expected, the electricity-value added elasticity is higher in the manufacturing sector with the two-step system GMM as opposed to agriculture, given the nature of production technologies in manufacturing that somewhat rely on automation and, hence, electricity.

Table 2, columns 4-6 show the regression results for the manufacturing sector using the sample of nonhigh-income countries, with column 6 reporting the coefficients for
each have advantages and disadvantages. We chose to use the GPW 3, in part out of anticipation of a forthcoming GPW refinement which should give an improved measure of population distributions.

Using GPW 3, we tabulate the cumulative distribution of population and land area by population density. We perform these tabulations for LAC as a whole and for the larger individual countries. We partition the population distributions by remoteness, cropping suitability, forest cover, and biome. We do not nominate a threshold population density cutoff for determining rurality, leaving that to the user of this information.

# **Criteria for rurality**

‘Rurality’ and ‘urbanity’ are comfortable but imprecise terms. On the surface, they seem to express a clear and meaningful dichotomy. We anchor the concepts with contrasting images of an isolated, bucolic farm on the one hand, a conglomeration of skyscrapers and slums on the other. The opportunities, constraints, and living conditions in these two locales are
included in the Appendix.

## **3.1. Empirical Methodology**

This method predicts the conditional distribution of per capita expenditure, ych, for household, h, within cluster, c, of the target data set that is missing actual consumption data (in our case the SCS Health 2017/18). The model is estimated in two steps. The first step is to develop an empirical model that predicts the log of per capita household consumption, ln(ych) from the source (or training) data set, the CES 2011/12 in this case. We adopt a log linear specification relating per capita consumption expenditure to household and district level variables as follows:

> 9 The underlying causes of this evolution are still subject to study. Regarding changes in inequality, Chodrow-Reich et al. (2020) and Chanda and Cook (2019), find a negative short-term impact of the demonetization introduced in November 2016 among the poorest groups, which then dissipates after several months.

> 10 The urban to rural population in CP’s sample is distributed by a ratio of 7 to 3; in contrast, India’s aggregate urban to rural population is distributed by a ratio of 3 to 7. The estimates of consumption reported in this paper are weighted to correct for the oversampling. Moreover, we exclude expenditures on monthly installments, premiums and pocket monies from CP’s consumption aggregate in order to make it as close as possible to CES’ basket of items.

6
great promise for improving education but also presents unique challenges for engaging with families that have been historically marginalized.

Given the gradual roll out of AGE, some schools in these states were already participating in AGE while others were not. Thus, the government designed two randomized controlled trials to tease out the effect of the financial component from the information intervention of the parental involvement program. The design of the experiments is outlined in Figure 2.

The first experiment consists of 250 public schools that were already participating in AGE.

> 3Cost calculations are based on administrative data from CONAFE during the expansion of information interventions in 2006.

> 4To encourage the proper dissemination of funds and information to parents, the Mexican Secretariat of Public Education conducts an audit for a random sample of schools each year.

6
Protection (LEAP) tool developed in 2008 by the GoE and the World Food Programme (WFP), the Livelihood Impact Analysis Sheet (LIAS) developed in 2008 by the GoE and USAID, hotspots assessments conducted by the Early Warning and Response Directorate (EWRD) as well as other ad hoc assessments to determine the need for assistance in specific areas within Ethiopia.

EWS are used to feed into Ethiopia’s well-established drought response mechanisms, notably the Productive Safety Net Programme (PSNP) and humanitarian appeals, by means of which assistance is provided to the affected populations. The PSNP, which was developed by the GoE and its DPs and launched in 2005, has provided assistance to almost 8 million chronically food insecure households in 2015, of which 6.6 million beneficiaries were participating in public works activities, and 1.4 million labor-poor beneficiaries received direct support<sup>8</sup> . Since 2008, the PSNP has a drought response mechanism<sup>9</sup> , which permits the rapid scale-up<sup>10</sup> of the PSNP to include additional beneficiaries that are pushed into transitory food insecurity due to drought, and to extend the duration of payments made to 1 World Bank (2014b).

2 UN OCHA (2011).

> 3 OECD (2014).

> 4 World Bank, World Development Indicators.

> 5 CIA World Fact Book, https://www.cia.gov/library/publications/the-world-factbook/geos/et.html.

> 6 Based on Guha-Sapir, Below and Hoyois, EM-DAT database. Average number of people affected by drought based on EM-DAT data, taking into account droughts in 2003, 2005, 2008, 2009, 2011 and 2015.

7 Based on HRD Appeals for the years 2005 to 2014 (average beneficiaries across the assessments for the main harvesting seasons _belg_ and _meher_ ).

8 FDRE Ministry of Agriculture and Natural Resources, Rural Job Opportunity Creation and Food Security Sector, Food Security Coordination Directorate (2016)
social network. That discount rates vary with wealth and income has been confirmed experimentally for landless workers and farmers in South India (Pender 1992).

The educational and informational status of different interest groups affects their ability to evaluate the impact of any given policy or program on their welfare. With low levels of education, it is difficult to detect misinformation (in the form of ideology or "obfuscation") that may be spread by other interest groups. High levels of education, informational advantages, and the ability to appeal to popular ideologies, by contrast, can greatly enhance a group's ability to appropriate resources for itself. Clearly observable and distinguishable group characteristics, in addition to furthering collective action, can facilitate discrimination-using characteristics such as cultural identity, class, gender, race, or skin-color as proxies for economically relevant traits such as skills. Historically, discrimination has been used to create and maintain privileges for certain races and cultures, and to hide the real economic costs of existing privileges.21 The continuing importance of discrimination is confirmed by studies that demonstrate the existence of systematic wage differentials that are not related to relevant performancecharacteristics, such as schooling or individual ability. Psacharopoulos and Patrinos (1995), for example, document such differentials for Brazilians of African origin (who make up much of the poor rural population of the northeast) and for Indian groups in several Latin American countries (which are over-represented in poor peasant populations). Observable wage differentials may, of course, be compounded by the fact that certain groups are denied equal access to economic opportunities, such as education, markets, or credit, a factor that could-through unequal access to education and its returns-lead to persistence of such differences across generations. Rural panel data from South India, for example, show that higher caste status and male gender are associated with better access to education, even

> 21 Roback (1990) illustrates that in many cases racial differences that had fallen into oblivion were resurrected to legitimize privileges that had no objective justification. Akerlof (1985
.9|4.7|7%|
|Peru,2004–10|7.6|5.4|-5%|4.2|10.4|16%|3.9|8.5|14%|
|Romania,2001–09|8.1|8.7|1%|29.7|||19.4|33.8|7%|
|Thailand,2000–09|9.9|10.7|1%|18.6|19.1|0%|11.7|14.3|2%|

_<mark>Source</mark>_ <mark>: Authors' calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA, and National Household Surveys.</mark> 29
# **1. Introduction**

A large body of research finds a positive connection between capital market development and national growth rates (Levine and Zervos, 1996, 1998; Demirguc-Kunt and Maksimovic, 1998; Rajan and Zingales, 1998; Henry, 2000; Levine, 2002; Beck and Levine, 2004; Bekaert et al., 2005). This macrolevel work, however, does not necessarily imply that firms use the funds raised in these markets to increase their productive capabilities—human capital, physical capital, and intangible capital—and grow. Firms have access to multiple sources to finance their growth, including bank credit. Moreover, capital markets can foster aggregate growth indirectly, by facilitating risk diversification, enhancing information dissemination, and boosting savings, without necessarily being the conduits of capitalaugmenting funds for corporations (Levine, 1991, 2005; Holmstrom and Tirole, 1993; Obstfeld 1994; Acemoglu and Zilibotti, 1997; Aghion et al., 2018).

To contribute to the study of capital markets and growth, we use firm-level analyses to assess the relation between firms issuing securities and changes in their productive capabilities and overall growth rates. Using firm-level data, we examine how firms that issue equity and bonds grow before, during, and after they raise new capital compared to firms that do not issue securities. We examine both overall firm growth, as measured by sales and assets, and growth in productive capabilities, as measured by human capital, physical capital, and intangible capital. Furthermore, we complement an extensive literature that suggests that the relation between securities issuances and firm growth might depend on (a) whether firms are issuing equity or bonds, (b) the severity of financing constraints on firms, and (c) the extent to which firms are issuing securities in more market-based or more bankbased financial systems. We do so by studying how the relation between issuing securities and growth varies across each of these dimensions.

To conduct the study, we assemble a comprehensive dataset of 150,165 firm-level issuances
_17_ _Settlement failure and policies to contain systemic risk and daylight overdrafts._ The risks associated with a settlement failure on a wire transfer network differ depending on how payments are settled. On a net settlement network, payments are sent and received during the business day and only the net position of each participant is settled at the end of that day (or the next day). On a real time gross settlement (RTGS) network, each payment sent is settled when it is made. Of the 9 major wire transfer networks outlined in Table 2, 5 have employed net settlement (SAGITTAIRE, EAF, ME, CHAPS, CHIPS) while 3 are RTGS (TBF, SIC, Fedwire). One network handles both net settlement and RTGS payments (BOJ-NET).

Until recently, the failure of a participant on a net settlement network would typically have led to the unwinding or reversal of all of that day's payments and receipts by the failed participant. The remaining participants would then be faced with trying to settle a recalculated net debit or credit position-recalculated after excluding the payments and receipts of the failed participant. This would not be a problem if receivers of funds did not use them-by releasing them to customers-until after the day's transactions were settled. However, because of the float cost of waiting until the next morning to use the funds received, receivers typically use funds before the transactions are finally settled.

If there is an unexpected settlement failure, then it may not be possible for other participants on the network to obtain sufficient funds to settle their recalculated net debit or credit positions. Indeed, in simulations performed for the CHIPS net settlement network using actual payment data (Humphrey, 1986), the increases (decreases) in other participants' net debit (net credit) positions in many cases exceeded their capital levels. Faced with either (a) settling their recalculated payment position by effectively selling or pledging the entire value of their capital or (b) not settling and having their own payments and receipts that
The starting point is to take a pair of subsequent port _d_ (departure) and _a_ (arrival) in a shipping sequence. The time span between departure at _d_ and the next departure T( _da_ ) (or d ) captures most possible disruptions affecting port _a_ (Figure 2), whether at _a_ or before. These include disruptions _en route_ from _a_ to _d_ , or disruptions at the _a_ (arrival) port. Therefore, looking at the lead time statistics of possible inbound TT connections reveals potential disruptions affecting _a_ .

Figure 2. Lead Time Between a Pair of Subsequent Ports Source: Authors.

For each pair of ports ( _d,a_ ), a dataframe with the following variables was constructed from vessel tracking data:

- Period _t_ (in practice monthly data from February 2016 until most recent).

- Port arrival _a_ .

- • Previous port _d_ . • Median shipping time for period _t:_ d _(t)._ Using median as opposed to mean avoids problems with outliers (vessels staying in port for noncommercial reasons, such as repairs or crew changes).

- • A reference transit time for the edge TT _(d,a):_ d is representative of normal shipping and port conditions on lane _d_ to _a_ above which vessels are considered delayed (see below).

- • Sum of vessel capacity moving from _a_ to _b_ for the period TT d _(t)._ • The same concept with vessel count d _(t)._ • Delay for period or transit time above the reference. CC NN

<!-- Start of picture text --> i d d<br>d 0 i d d<br>(tt) −TTd<br> TT (tt) ≥TT<br>ΔΔ (tt) = � TTd<br> TT (tt) < TT<br><!-- End of picture text -->

# **Estimating Reference Transit Time**

The
by Chakravorty, Pelli, and Marchand (2014) in several ways. First, we analyze the effects of rural electrification based on a more recent two‐round panel survey conducted in 2005 and 2012. In addition to providing updated evidence, the survey data for this period are particularly interesting for understanding rural electrification in India. An important reason is that 2005 was the year in which the Indian government launched its national rural electrification program committed to connecting all 100,000 unelectrified villages. By 2010 the program had connected 283 million people to the grid. But despite the improvement in the connection rate, fewer homes benefited from uninterrupted power supply in 2012 than in 2005, suggesting that outages became more common during this period. The relatively large variation in both connection rates and reliability over time provides a unique opportunity to identify how lack of reliability interferes with achieving the benefits of electrification. In addition to analyzing the effect of electrification on households’ income, as done in Chakravorty, Pelli, and Marchand (2014), we also analyze its effect on a broad range of households’ social and economic outcomes, including expenditure, education, employment, and poverty status.

3
countries. A cross-country approach enables us to juxtapose contextual factors, such as sociocultural aspects, with gender disparities in access to finance. For example, gender gaps in bank account ownership, savings, and borrowing opportunities are larger in countries with gender-restrictive laws and regulations (Demirguc-Kunt et al., 2013) and women’s access to credit is particularly difficult in countries with persistent cultural belief in traditional gender roles (Ongena and Popov, 2016). Legal barriers, such as inheritance laws, can also hinder women’s ability to access credit, given the salience of land as collateral for formal finance.<sup>3</sup> Our research systematically examines gender disparities in access to finance across a range of countries, focusing on both extensive and intensive margins. While our study is comparable to Ongena and Popov (2016), it differs in the measures of extensive and intensive margins and covers a larger number of countries from WBES. Furthermore, we build on this work by examining the underlying factors that contribute to observed disparities and illustrating that gender gaps in credit access can have broader implications for capital misallocation. We show that these gender-driven disparities in credit access and misallocation are more prominent in countries with traditional social and cultural norms, defined as "a society’s informal rules about appropriate or acceptable behavior" for women (Jayachandran, 2021). Specifically, we focus on norms that potentially constrain women’s economic choices and outcomes. Using data from the World Values Survey, we proxy these norms with measures of social perceptions about women’s roles at home and in the labor market. By classifying countries based on the restrictiveness of their social and cultural norms for women and merging this country classification with rich micro-data from the World Bank Enterprise Surveys (WBES) for 61 countries from 2008-2023, we find that: _First_ , female-managed formal firms sampled and surveyed by WBES do not face credit constraints on the extensive margin, measured by the likelihood of applying for credit or their credit application being rejected, conditional
# **Table A5 continued.**

|**Variable**|**Short Name**|**Long Name**|**Description**|
|---|---|---|---|
|**ODB.2013.D13**|Crime|Crime statistics data|Annual returns on levels of crime and/or detailed crime reports.Crime<br>statistics can be provided at a variety of levels of granularity, from annual<br>returns on levels of crime, to detailed real-time crime-by-crime reports<br>published online andgeolocated,allowingthe creation of crime maps.|
|**ODB.2013.D14**|Environment|National environmental<br>statistics data|Data on one or more of: carbon emissions, emission of pollutants (e.g.<br>carbon monoxides, nitrogen oxides, particulate matter etc.), and<br>deforestation. Pleaseprovide links to sources for each if available.|
|**ODB.2013.D15**|Elections|National election results<br>data|Results by constituency / district for the most all national electoral contests<br>over the last ten years.|
|**ODB.2013.D16**|Contracting|Public contracting data|Details of the contracts issued by the national government.|

45