rafmacalaba/data-use-mentions-extended huggingface dataset:
heterogeneity across countries. Section 6 explores the role of exogeneous growth opportunities in the likelihood of firms to issue securities in capital markets. Section 7 concludes.

# **2. Data**

# _2.1 Data construction_

The data on firm capital raising activity cover the period 1991-2016 and come from the Refinitiv’s Security Data Corporation (SDC) Platinum database. This database provides transaction-level information on new capital raising issuances of common and preferred equity and publicly and privately placed bonds in capital markets around the world.<sup>5</sup> Given that the SDC Platinum database does not collect data on debt issuances with maturities shorter than one year, the dataset does not cover commercial paper.

To examine the comparative performance of issuing and non-issuing firms, we combine the data on equity and bond issuances with data on income and balance sheets for publicly listed companies from the Worldscope database. We match equity and corporate bond issuance data with firm income and balance sheet data using the CUSIP identifier. If the matching proves unsuccessful, we use the following identifiers: SEDOL, ISIN, and Thomson Reuters Permanent Identifier.<sup>6</sup> For the majority of countries in our matched sample, we have both capital market issuance and income and balance sheet data on at least 70 percent of the listed firms in each country, where data on the total number of listed firms per country come from the World Federation of Exchanges database.

Worldscope provides data at the consolidated level for listed companies with subsidiaries. For those parent companies, we aggregate the issuance activity of the parent and the related (listed and

> 5 SDC Platinum collects data on capital raising issuance mostly from filings with local regulatory agencies and stock exchanges, complementing this information with data from other sources such as offering circulars, prospectuses, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires.

> 6 This matching algorithm allows us to match 87 percent of the equity issuances and 70 percent of the
5</sup>

> 2 See Yang (2011) for a recent survey on the impact of international remittances.

> 3 See Beegle et al. (2011) and De Brauw et al. (2015).

> 4 For example, the Philippines recorded an almost equal number of domestic and international migrants in its 2010 census. But its labor policies are mainly focused on facilitating the employment of overseas Filipino workers (OFWs). In contrast, China is the second largest recipient of international remittances after India, but its government is more intent on regulating urban-rural migration, paying less attention to promoting overseas jobs for Chinese citizens.

> 5 Previous work includes Adams (1998), who looked at the impact of external and internal remittances on the accumulation of assets in rural Pakistan, finding that only external remittances increased agriculture land holdings; and Lokshin et al. (2010), who found that domestic migration did more to reduce poverty in Nepal than overseas migration.

2
3 There are three caveats associated with the figures for Dar es Salaam. First, the HBS report says “Note that there is some evidence that consumption expenditure was under-reported in Dar es Salaam in the 1991/92 HBS. This would mean that poverty levels may in fact have been slightly lower in 1991/92 and the decline smaller .... Although [an earlier report] attempted to adjust for this under-reporting in the 1991/92 data, this was not repeated in this analysis as it was difficult to assess its accuracy” (p. 80, footnote 21).

A second concern stems from the fact that both the 1991/92 and 2000/01 surveys used a sampling frame based on the 1988 national population census. Between 1988 and 2002, the population of Dar es Salaam grew extremely rapidly, at an annual growth rate of 4.4 percent, for a cumulative increase of 84 percent. Given such a high rate of population growth, it is possible that by the time of the second HBS survey, the true geographic distribution of the population differed substantially from that in the census-based sampling frame. In particular, it is likely that new settlements were created in areas of the city that were not populated or only sparsely populated in 1988. Consequently, households in such areas might not have been included in the survey sampling frame, or included with only a very low sampling probability. If households in new settlements are poorer than average households in the city then the drift of the population from the 1988 sampling frame biases the 2000/01 poverty figures downward from their true values. Both this phenomenon and the possible underreporting of consumption in the 1991/92 HBS imply that the drop in poverty in Dar es Salaam may not have been as steep as it appears in the survey data.<sup>3</sup> Finally, a related weighting issue has implications for the national poverty figures. Because the 2000/01 survey sampling frame was based on the 1988 census, the sampling
routine, physical tasks. Our owners say that the sex of the worker and physical strength of the worker are the two most important characteristics of workers they consider hiring, with education the least important of the characteristics listed. Search tends to be local, with a majority of workers hired being previously known to owners and living within one kilometer of the business.<sup>14</sup> We read these responses together with the response reported earlier just under a quarter of the owners eligible for the wage subsidy report not being able

> 12 These owners said in response to later questions that their business would not support or benefit from an additional employee. Only 11 percent of them indicated that the inability to find the right employee was a reason for not hiring.

> 13 The SME survey was conducted in urban areas throughout Sri Lanka, but a majority of respondents come from the same urban areas in which we conducted the experiment. The survey is described in Appendix 4.

> 14 The local nature of search holds both in these data and in data from a separate survey of wage workers. In the wage worker survey, among 171 workers in firms with 4 or fewer employees, 61 percent say they knew the owner before beginning work, and 45 percent say they live within one kilometer of the business. The wage worker surveys is described in Appendix 4.

26
2007|2,870|3,250|380|19.0|21.6|2.5|
|2008|2,775|3,057|282|18.1|19.9|1.8|
|2009|2,664|2,982|318|17.0|19.0|2.0|
|2010|2,597|2,846|249|16.3|17.9|1.6|

Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

**Figure 181 Entry and exit rates in Pakistan and in peer countries**

<!-- Start of picture text --> Exporter Entry Rate Exporter Exit Rate<br>0.7 0.6<br>0.6<br>0.5<br>0.5<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1 0.1<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

30
Ravallion, Martin, Shaohua Chen, and Prem Sangraula. 2007. "New Evidence on the Urbanization of Global Poverty," Policy Research Working Paper No. 4199, Washington, DC: World Bank.

Serven, Luis and Calderon, Cesar 2004. "The Effects of Infrastructure Development on Growth and income," Econometric Society 2004 Latin American Meetings 173, Econometric Society

- Székely Miguel and Marianne Hilgert (1999) "What‘s Behind the Inequality We Measure: An Investigation Using Latin American Data", Inter-American Development Bank Working Paper #409

- van der Mensbrugghe, D. 2003. ―Prototype Model for Real Computable General Equilibrium Model for the State Development Planning Commission, P. R. China.‖, mimeo, World Bank, Washington D.C.

26
1990 period. Source:  World Development Indicators, IMF database (for selected countries for 1960 data). Calculations based on change in country‘s GDP in constant US dollars as a share of the change in world GDP in constant US dollars, by decade.

The BRICs and other emerging economies also have increased their share of trade, capital flows and innovation. A growing trend within these dimensions is ―south-south‖ globalization as economic interactions between emerging markets have increased at an even faster pace than they have between high-income countries and developing countries. As growth poles emerge in the developing world, one way that the more rapid growth could be sustained is through increased ―south-south‖ globalization. Part of this phenomenon over the last decade may have been due to one-off effects, and the fact that the process started from a very low base. On the other hand, the growing integration of supply chains across emerging markets and the potential for intra-regional ―south-south‖ trade could be a driving force behind the ―great convergence‖ into the future.

To understand the patterns of south-south globalization, one can start by examining the evolution of the large fast-growing BRICs. In the trade dimension, after decades of stagnation in the developing country share of world exports, this share began to increase sharply at the turn of the century, and it now stands at about 30 percent. We see, however, that much of this recent evolution was driven by the increasing share from BRIC countries (Figure 9a). The BRIC share more than doubled from 2000 to 2009. A large part of the story has to do with increasing trade between developing countries – a trend that actually dates back a couple of decades (Figure 9b)—as south-south bilateral and regional trade agreements proliferated and as supply chains integrated across the developing world. The share of developing country imports that originate in other developing countries is now nearly double the share of the 1960s. The old global division of labor characterized by developing countries (the ―south‖) exporting primary products
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 13. Benchmarking ICT indicators**

|||Unit|Low-income<br>countries, nonfragile|**Mali**|Middle-income<br>countries|
|---|---|---|---|---|---|
||||2005|2005|2007/08<br>2005|
|GSM coverage|% populat|ion under signal|48|18|22<br>97|
|International bandwidth|Mbps/cap|ita|6|2.2|17<br>30|
|Internet|subscribe|rs/100 people|0|—|0.8<br>2|
|Landline|subscribe|rs/100 people|1|0.6|0.7<br>9|
|Mobile phone|subscribe|rs/100 people|15|7|35.4<br>87|
|_US dollars_||**Mali**<br>2007|Countries without<br>submarine cable|Countries with subm<br>cable|arine<br>Other developing regions|
|Price of monthly mobile ba|sket|11.0|11.1|11.12|9.9|
|Price of monthly fixed-line|basket|13.1|13.6|13.58|nav|
|Price of 20-hour Internet p|ackage|56.5|68.0|47.00|11.0|
|Price of a call to US per m|inute|0.34|0.86|0.48|0.66|
|Price of an intra-Africa cal|l per minute|0.29–0.33|0.7|0.57|non applicable|

Source: Mali 2005 data together with benchmarks are taken from the AICD Database. Mali 2007/8
# **1 Introduction**

Sub-Saharan Africa has experienced substantial economic growth in the past decade, after a quarter century of stagnation and contraction.<sup>1</sup> It is perhaps too early to answer the question of why this period of growth has arrived. However, it is important to know where it has happened within countries and why these places have been favored.

In this paper, I argue that changes in transport costs have played a critical role in determining the growth of cities. Sub-Saharan Africa has notoriously high transport costs compared to other major regions of the world. Population density is relatively low, with a substantial fraction of people residing far from the coast. Ocean-navigable rivers, which provide transport to the interior of most other regions, are virtually non-existent. And road networks are sparse and poorly maintained, on the whole. I ask whether periphery cities with lower transport costs to their country’s main port grew faster than those farther away or with poorer road connections, in the context of dramatically rising oil prices over the 2000s decade. A typical problem with testing this kind of question in poor countries is that relevant data on cities and transport costs do not exist. This paper provides novel measures of both. First, night time lights satellite data (Elvidge et al., 1997; Henderson, Storeygard and Weil, 2012) are used to construct a 17-year annual panel of city-level measures of economic activity for 287 cities in 15 countries. Second, a new set of roads data provides information about route length and surface material. Transport costs are thus identified by the interaction between world oil prices and distance along these routes. Because I have data on many cities per country over a substantial time period, I can control for annual shocks separately for each country, as well as initial characteristics and growth rates of individual cities.

Focusing on countries whose largest, or _primate_ , city is also a port, I find that as the price of oil increases from $25 to $97
|77|23|
|Labor productivity|1,000 traffic units per employee|40|481|84|26|37|
|Carriage productivity|1,000 passenger-km (pkm) per<br>carriage|900|862|416||737|
|Locomotive productivity|Million traffic units per locomotive|3|35|7|40|13|
|Wagon productivity|1,000 net tonne-km (ntkm) per wagon|74|1,020|458|804|59|
|Traffic unit—freight|Million ntkm|24|670|224|409|77|
|Traffic unit—passenger|Million pkm|18|25|64|113|174|
|Freight average yield|US cent/ntkm, average 2000–05|5.8|5.5|4.4|3.3||
|Passenger average yield|US cent/pkm, average 2000–05|2|3.3|2.4|2.2||

_Source_ : AICD railways database.

## **Challenges**

The establishment of OCBN increased the cost of surface transportation in Niger. Benin and Niger jointly operate the Cotonou-to-Parakou railway line and coordinate complementary road transport between Parakou (in Benin) and Niamey (in Niger). In Cotonou, as a rule, shipments are sent by train, but if OCBN cannot accept the shipments within a reasonable period for lack of capacity, a special waver is granted for trucks approved by OCBN. This situation often occurs because the train can only carry between 150,000 and 200,000 tonnes a year on its way from Cotonou, as the theoretical capacity of 600,000 tonnes per year is exceeded. The railroad goes as far as Parakou (see box 1), and then goods and containers are loaded on OCBN-approved trucks based on a rotation system.

> 7 Organisation Commune Bénin Niger.

16
these districts and were likely to have been mistaken for interface meetings. Nearly 95 percent of staff in NFA clinics have heard of the NFA intervention; conditional on knowing about the program, 84 percent report participating compared to 48 percent among control villages. Staff in CM and control clinics also appear to have heard about the NFA program, albeit at lower rates. Conditional on having heard about NFA, 13 of 84 control clinics report participating. Again, this likely reflects misperception and not implementation failures or contamination: control clinics cannot opt into the NFA competition, which requires the research team to rank each participating clinic. Moreover, the research staff did not detect any deviations from treatment assignment when monitoring implementation of the NFA treatment.<sup>19</sup> If clinic staff in control clinics are motivated by the mistaken belief that they are eligible for an award, this would attenuate our treatment effects.

# **3.5 Specifications**

## **3.5.1 Survey Outcomes**

The analysis plan specifies outcome variables, the construction of indices, and the specifications we estimate; and we flag and explain any subsequent deviations in Appendix Section B.1.

Our baseline survey included a smaller sample of households. Moreover, some outcomes are only defined for individuals who recently experienced health episodes. Some households that experienced health episodes at endline may not have also experienced episodes during baseline. For both reasons, controlling for a household’s baseline outcome would reduce the size and representativeness of our sample. We instead employ an ANCOVA-type model that controls for the village-level average at baseline, estimating: where _yivc,EL_ is the outcome of household (or individual) _i_ in village _v_ in clinic catchment _c_ at endline ( _EL_ ). (Models for community-level outcomes omit the _i_ subscript.) _αb_ represents the matched-triplet fixed effects. Treatment status, which is randomized across clinics, is denoted by the indicator variables 1( _CM_ ) _c_ and 1( _NFA_ ) _c_ . _Y vc,BL_
(comprised of the poorest 10 percent of households) received a net cash benefit equivalent to an increase of 53.4 percent of their Market Income; these households in the poorest decile benefitted more from direct transfers and indirect subsidies than total direct and indirect taxes that they had to pay. In contrast, the rest of the deciles were net cash payers into the fiscal system, with the cash loss after taxes and cashable transfers (reduction in Market Income) ranging from –1.4 percent in decile 2 to –19.7 percent in decile 10 (the richest 10 percent of households). The fact that decile 2 (which is below the official poverty line) experienced a cash loss is consistent with the national poverty increase estimated after the combination of taxes and cashable transfers modeled. While there was a slight poverty gap increase at the national level, the effect was very small and likely because of the net cash gain in decile 1. Also, the fact that the cash loss was higher for richer deciles relative to poorer deciles is consistent with the estimated inequality reduction after the combination of taxes and transfers modeled.

**Figure 5-4 Households in decile 1 are net cash beneficiaries of fiscal policy in the West Bank and Gaza**

Net cash position of households after taxes and transfers, by deciles <!-- Start of picture text --> Net cash beneficiary<br>100<br>Contributions<br>Direct taxes<br>50<br>Indirect taxes<br>Education<br>Health<br>Indirect subsidies<br>Direct transfers<br>0<br>Net total benefit<br>Net cash benefit<br>-50<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Decile of Market income<br>Percent of Market income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes:

[1
The survey questionnaires of LSMS-ISA supported surveys typically consist of:

- An **agricultural questionnaire** , which is most often divided between post-planting and postharvest questionnaires. These questionnaires usually elicit information on land ownership and use; land use and agriculture income tax; farm labor; use of inputs; GPS land area measurement and coordinates of household fields; agricultural capital; irrigation; and crop harvest and utilization. An additional livestock questionnaire usually collects information on animal holdings and costs, production, and costs and sales of livestock by products. While base units of observation may change between modules and surveys, these data are typically observed at the parcel, plot, or plot-crop levels – or livestock type level in the case of the livestock modules.

- A **household questionnaire** , which contains information about the household (household modules) and the individual household members (individual modules). The household questionnaire typically starts with a roster of all members of the household. Information about the household members, collected at individual level includes demographics; education; health (including anthropometric measurement for children); financial inclusion; labor. Information about the household, collected at the household level includes food and non-food expenditure; consumption; household nonfarm activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; credit, tax, and income; and other sources of household income.

- A **community questionnaire,** which contains information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail prices. While this information is not included in the HP, data users can, in most cases, merge in community-level data using EA (enumeration area) identifiers.

In addition to the survey questionnaires, a set of **geospatial variables** are derived by the data producers at the household and plot levels and provided to data users as part of the raw data files. These typically include measures of
--|---|---|---|---|---|
||Mean|SD|Mean|SD|Public|
|Registration fee|15.47|(38.58)|6.19|(13.53)|9.27<sup>***</sup>|
|Other scheduled fees|12.92|(33.79)|4.80|(8.86)|8.12<sup>***</sup>|
|Exam fees|0.23|(1.66)|0.01|(0.05)|0.22<sup>**</sup>|
|Books/writing supplies|5.57|(11.16)|2.61|(5.24)|2.95<sup>***</sup>|
|Uniform and sports supplies|5.54|(10.35)|5.16|(8.35)|0.38<br>|
|Transportation costs|3.87|(18.50)|0.25|(1.57)|3.63<sup>***</sup>|
|Food/housing costs|13.96|(29.43)|9.40|(15.98)|4.57|
|Special courses|0.93|(13.70)|0.00|(0.00)|0.93|
|Other school expenses|1.22|(7.14)|0.00|(0.00)|1.22<sup>***</sup>|
|Observations|4|30|7|6|506|

Notes: Sample is obtained from IFLS 3 (2000) conditional on enrollment
escaping poverty and changes during 1992-2002. This section also addresses the extent to which the rural South region is different from rural Mexico as a whole. Finally, Section 6 presents a three-pronged rural poverty reduction strategy guided by the findings from the poverty profile and determinants of poverty analysis. The strategic principles for reducing poverty involve seeking to strengthen the key assets of the poor, taking into account geographic differences in the poverty situation and priorities.

# **2. Populations and Land**

The degree of poverty a society might experience depends on the volume and distribution of resources and on the size and distribution of the population among households. These two basic determinants of poverty, however, are not independently determined. On one hand, the size and age structure of a population are consequences of fertility decisions taken over past decades that were influenced by economic conditions. On the other hand, the volume of resources available today is influenced by the size and age composition of the labor force. This section analyzes recent changes in demographics in Mexico’s rural areas. The following section very briefly addresses agriculture, land, and rural living in Mexico.

# **2.1 Population**

## **The importance of demographic factors**

Demographic factors have direct and indirect impacts on prices and poverty. As the size and age composition of the population changes, the relative size of the labor force and the number of dependents also change, modifying the dependency ratio of families, and therefore their level of poverty. This is the direct effect of demographic changes. It captures the effect that demographic changes have on quantities: number of children, size of the labor force, and the number of elderly people. These changes in quantities, however, will, in general, influence prices in the economy. In particular, changes in the rate of growth of the population and in the age structure may have important impacts on labor supplies, savings, and household production decisions and migration. As a consequence, demographic changes may
knowledge, we are the first in this literature to quantify the entrepreneurship effects in a panel setting that uses state-level variation in implementation timings for identification, with Beaman et al. (2010) providing related evidence using micro-data from West Bengal.<sup>1</sup> Second, our work builds upon prior studies of gender differences in entrepreneurship<sup>2</sup>

> 1 Beaman et al. (2010) show an increase in the number of self-help groups with their own bank accounts after the implementation of reservations, and that these groups had on average larger bank accounts. To the extent to which these self-help groups are associated with more entrepreneurship, this increase would be consistent with our findings. We further discuss this study below.

4