rafmacalaba/data-use-mentions-extended huggingface dataset:
# Appendix I Data Sources

# Causal Variables:

1. Corporate income tax rate. Data on corporate income tax rates in 1991 are taken from Shome (1992). When the country has more than one rate, the highest rate is used. Bolivia is a special case. In 1991, there was no tax on corporate income _per se._ A tax of 3 % of net worth was levied instead. In order to estimate the implied corporate income tax rate, the net-worth tax rate is divided by the real interest rate. For the calculation of the real interest rate, the neoclassical-growth-model relation between growth, real interest and depreciation rates is used. Using the parameter values presented in Barro and Sala-iMartin (1995), the real interest rate in Bolivia is estimated at 8.1 %.

2. Labor market restrictions. The proxy for labor market restrictions is the ratio of the index of labor market regulations presented in Rama (1995) to real per capita GDP. The Rama index is divided by real per capita GDP in order to account for differences in labor productivity across countries. Rama considers eight types of labor-market regulations for 31 countries in Latin America and the Caribbean during the period 1980-92, when these regulations remained basically unchanged. The eight variables considered by Rama are number of ILO conventions ratified, number of days of annual leave with pay, number of days of maternity leave, social security contribution as a percentage of wages, government employment as a percentage of labor force, minimum wage as a percentage of average wage, severance pay and percentage of labor force in unions. After normalizing each variable, the Rama index is obtained by averaging over the eight variables. The real per capita GDP data correspond to the year 1990 and are taken from Summers-Heston Penn

-43-
|WheresMyTransport|
|Kigali|WorldPop|Rwanda Census|IGC|
|Bamako|WorldPop|Bamako Business<br>Registry|Bamako Mayor’s Office|
|Conakry|WorldPop|EOA Analysis|Plan de Déplacement<br>Urbain (EU, Min.<br>Transports)|
|Dakar|WorldPop|EOA Analysis|SAFEGE|
|Dar es Salaam|WorldPop|Dar Es Salaam Firm<br>Census|Collected by World Bank|
|Harare|WorldPop|EOA Analysis|WheresMyTransport|
|Doula|WorldPop|EOA Analysis|WheresMyTransport|

TABLE 2: DATA SOURCES

# **5 RESULTS**

The table below summarizes the results for each city. Average accessibility is the percentage of estimated employment opportunities throughout the city accessible by the average individual within 60 minutes. The best access level is the highest percentage of jobs accessible from the single best-positioned location.

||**Average Accessibility**<br>**Best access**<br>**level**|**Distribution of**<br>**Accessibility**<br>**‘Gini’**|
|---|---|---|
|**Nairobi**|28.5%<br>67.4%|0.36|
|**Conakry**|29.4%<br>65.1%|0.41|
|**Lusaka**|34.1%<br>86.0%|0.42|
|**Bamako**|34.3%<br>97.5%|0.37|
|**Kigali**|15.2%<br>23.0%|0.26|

10
Second, there are much larger overlaps in particular regions that are masked at the global level. We find that the overlaps are expected to be largest in Africa for both crops and for livestock, and particularly under SSP4 (Inequality Scenario). While Africa has smaller shares of global production, these regional dynamics have important implications for how we think about food trade, issues of perishability, and access to nutritious food for the most vulnerable. Moreover, the city-level examples in this paper highlight the importance of policy design and solutions at the local level, where the needs and risks would vary.

Third, for crops on aggregate and livestock on aggregate, urban expansion across all SSP scenarios will most likely occur under areas (i.e. grids) today that have higher levels of crop production and that have higher livestock density. This positive correlation between projected future urban expansion and current crop and livestock density is largest under SSP3 (Regional Rivalry Scenario) and smallest under SSP5 (Fossil-fueled Development Scenario). As such, while SSP5 impacts a lower food producing grid on average, it affects more grids in total so that the largest overlap of urban expansion with food production occurs under SSP5. In addition, while the estimated correlation is large and positive in both 2040 and 2100, it is smaller in 2100 than in 2040. That this correlation diminishes over time suggests that (in all SSP scenarios) suggests that urban expansion in the nearer-term (by 2040) will occur in higher food producing areas than the longer-term (by 2100).

The remainder of this paper is as follows. Section 2 discusses data and methodology, including SSPs, urban expansion, and crop and livestock data. Section 3 presents the extent of urban expansion on food production (globally and regionally), and grid-level correlations of urban expansion and food production. Section 4 concludes.

# **2. Data and methodology**

To estimate the relationship of climate-induced urbanization with crop and livestock production, we combine three distinct global spatial datasets— projected urban expansion
# **I. Introduction**

Despite substantial gains in the participation of women in entrepreneurial activity globally (Kelley et al. 2016), a well-documented gender earnings gap persists. Women are found, on average, to earn considerably less than men (IWPR, 2018). Sectoral segregation<sup>2</sup> is often cited as a key determinant of this earnings gap. Women tend to start businesses in different industries than men, and the sectors dominated by women typically earn less. This pattern is often referred to as horizontal segregation. For example, in many countries, female-owned businesses are concentrated in retail trade and services sectors (mainly health, education, and social services), which are characterized by lower investments and growth compared to manufacturing, construction and mining sectors (Rosa and Sylla, 2016). The digital revolution may exacerbate the divide with the gender gap in participation being most pronounced in the information and communications technology (ICT) sector (Kelley et al. 2016).

Labor economists who study the earnings gap in wages also document a pattern of vertical segregation, meaning that within the same sector, women also tend to earn lower wages than men. This has been attributed to hours worked as well as employment flexibility (e.g. Rose and Hartmann, 2004; Goldin, 2014), as well as discrimination. “Glass ceilings,” for example, are a form of vertical segregation in which men hold positions in a company with the highest pay and more authority. When looking at firms, gender pay gaps in wages are harder to explain given the scope of data that are currently available. Hence, in this paper, we will focus on horizontal segregation, but will document some evidence of vertical segregation in firm profits as well.

This paper investigates the horizontal dimension of sectoral segregation by studying global data on female and male enterprise owners who operate in industries that are typically dominated by the same and opposite sex. We use a dataset spanning 97 countries that was
countries between 2000 and 2018. The countries were selected based on data availability and to maximize sectoral coverage. They include the following:

- East Asia and Pacific: Indonesia, Solomon Islands

- Europe and Central Asia: Albania, Bulgaria, Croatia, Georgia, Kosovo, Romania, Ukraine

- Latin America and Caribbean: Argentina, Brazil, Peru, Uruguay

- South Asia: Bhutan

- Sub-Saharan Africa: Burundi, Ethiopia, Ghana, Kenya, South Africa.

The database classifies an enterprise as an SOE if the state directly or indirectly owns more than 50 percent of its shares or is the ultimate controlling entity, through majority ownership of common stock or any other mechanisms of control. This definition is in line with the European Union’s definition of public undertakings in Commission Directive 2006/111/EC. It allows SOEs to be separated into two categories: fully owned SOEs (SOEs in which at least 99.5 percent of the shares are owned by the government or government entities) and partially privatized SOEs (all others).

The database provides panel data on the finances of SOEs in the power and transport sectors at the SOE/year level that are consistent over time and comparable across SOEs regardless of where they operate. To ensure consistency and reliability, researchers collected the data using a standardized accounting data template that was populated using the information on financial statements. To ensure that quantities like <mark>earnings before interest, taxes, depreciation, and amortization</mark> (EBITDA) and operations subsidies in the database are comparable across SOEs and years, they identified each item as defined by the template using the notes to the financial statements rather than relying on the way such items are presented in the SOEs’ main financial tables. Data reliability was further ensured through quality assurance checks by alternate analysts and accounting experts.

The database provides a standardized representation of the income statement, the balance sheet, and the cash flow statement of each SOE; it includes a set of selected items from the statements while maintaining
# **Figure 5 Confidence Intervals for Net Transfers Received by Rural Elderly By Migration Status of Adult Children**

<!-- Start of picture text --> No Migrant Children Has Migrant Children<br>1995-1998 1995-1998<br>1/4 1/2 1 2 4 1/4 1/2 1 2 4<br>0 2000 4000 6000 0 2000 4000 6000<br>Pre-Transfer Income Pre-Transfer Income<br>Estimated Transfer 95 % Confidence Interval Estimated Transfer 95 % Confidence Interval<br>Poverty Threshold Poverty Threshold<br>No Migrant Children Has Migrant Children<br>2000-2003 2000-2003<br>1/4 1/2 1 2 4 1/4 1/2 1 2 4<br>0 2000 4000 6000 0 2000 4000 6000<br>Pre-Transfer Income Pre-Transfer Income<br>Estimated Transfer 95 % Confidence Interval Estimated Transfer 95 % Confidence Interval<br>Poverty Threshold Poverty Threshold<br>2000 2000<br>1500 1500<br>1000 1000<br>500 500<br>Net Transfer Net Transfer<br>0 0<br>-500 -500<br>2000 2000<br>1500 1500<br>1000 1000<br>500 500<br>Net Transfer Net Transfer<br>0 0<br>-500 -500<br><!-- End of picture text --> Notes: Vertical lines indicate multiples of a nutrition based poverty line, which is equal to 875 RMB Yuan Per Capita in 2000 RMB. All values are in 2000 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

32
shown in Figure 4, while the national poverty rate in 2019-20 was lower than the 55 percent estimated in 2016-17,<sup>14</sup> poverty trends were showing diverging patterns in urban and rural areas. In urban areas, the observed increase in poverty is the result of the compounding effect of the progressive decline in aid as well as the onset of the COVID-19 crisis, while in rural areas, poverty was on a declining path spurred by strong agricultural production in the 2019 and 2020 seasons.

> 14 World Bank (2018) _<u>Poverty in Afghanistan : Results based on ALCS 2016-17.</u>_ Washington, D.C. : World Bank Group.

15
# **Data Appendix**

## **Appendix 1. Description of Outcomes and Other Variables**

### **Outcomes from individual-level administrative data**

_Learning outcomes._ We use individual-level standardized test scores on the reading and quantitative components of the SABER PRO exam taken by the cohort of students who that took the test in 20112 as our learning outcomes. As in Domingue et al. (2017), we focus on reading and quantitative reasoning scores because these general skills may be more directly related to SCP value added.

_Educational attainment._ Using data from SPADIES, we create an indicator for graduation as our measure of educational attainment. Every semester, we track cohorts of students who entered higher education between 2007-1 and 2009-2 and define graduation as an indicator that takes the value of 1 if the student graduated within six years after entering the program, and 0 if the student dropped out or has not graduated within six years.

_Labor market outcomes:_

- <u>Formal employment: Since we only observe the status of individuals who make social security</u> contributions (paid employees or self-employed entrepreneurs), we define formal employment as a binary variable that equals one if, having graduated between 2010 and 2012, the individual was employed or self-employed during any period between 2010 and 2013. Note that individuals who do not make social security contributions could either be unemployed or employed in the informal sector.

- <u>Annual wage: To construct this outcome, we use only the labor income reported during the</u> first year of the individual’s employment between 2010 and 2013 to avoid salary changes associated with experience. Moreover, we use only the sample of paid employees because the salaries of self-employed entrepreneurs are recorded as zero in OLE. In sum, the wage measure is the total annual labor income during the first year of formal employment after graduation. Annual wages are expressed in dollars; we conducted a purchasing power parity (PPP) adjustment of wages using
22 the simple lack of the external constraint imposed by military service could have induced some of the less motivated individuals to stay on and complete the degree.

In our setting, youths from the quake region born before 1966 were simultaneously exempt from compulsory military service. Because they were at different points of their life cycle, however, exemption status modified in different ways their incentives leading to different choices. Those born in 1963-65 were exempt before becoming formally eligible, when still of high school age and able to change their human capital accumulation strategy. On the contrary, older individuals were exempt after high school age and when already formally eligible for service. Keane and Wolpin (1997) show that drop out decisions are fairly irreversible so that youths who had not completed high school were unlikely to go back once exempt.

# 5.1 Instrument validity

The randomly assigned exemption from military service can serve as instrument for schooling in estimating the effects of the latter on subsequent mortality provided it does not affect directly or through channels other than schooling the outcome of interest.

As concerns the possibility that exemption enters subsequent mortality through other channels, for example because the exemptions correlated with compensatory interventions, the previous discussion has shown that exemption status is only associated with significantly higher schooling achievements and lower mortality rates among young exempt cohorts, while leaving unaffected all other observable circumstances. However, the following empirical analysis will address this possibility in more detail.

As concerns the direct effects of military service on health, the conventional presumption is that army service increases unhealthy behavior and exposure to harmful environments. Yet, several recent studies that use the Vietnam draft lottery do not find increased mortality, worse health conditions or more unhealthy behavior among Vietnam veterans years after discharge (among others, Angrist and
with higher ability managers are more likely to adapt to pollution by protecting their workers through the provision of equipment and flexibility in work schedule, that is via _within sector adaptation_ mechanisms rather than avoiding locating in well-connected polluted areas ( _spatial adjustments_ ) (Bassi et al., 2021).

# _Firm ownership structure_

‘Footloose’ multinationals or foreign-owned plants are more likely to exit the market

> 1The authors studied 12,300 geocoded establishments in Mississippi from the Census Bureau’s Longitudinal Business Database (LBD), including over 1,500 businesses in four counties that experienced significant storm damage as determined by the Federal Emergency Management Administration (FEMA).

5
(compared to plots measured during “partly cloudy” or clearer weather). In all other countries and specifications the cloudy or rainy weather does not have a statistically significant effect on the probability of area being measured with high bias.

# _2.Comparison of SR and Objective Measurements_

While Table 7 illustrates the degree to which farmer self-reported estimates differ from compass and rope measurements, it does not offer any explanation as to why the two systematically diverge. For this we turn to regression analysis. Table 10 presents the results of four OLS regression models: the first on the measurement bias (farmer self-reported estimate minus CR measured area), the second on the absolute value of this bias, the third on the relative bias, and the fourth on the absolute value of the relative bias (in percentage terms).

The claim of plot area affecting the direction and degree of error associated with self-reported area estimates is supported by the regression results. In the first specification (on bias) the coefficient on plot area is negative quadratic and positive in the cubic term in Ethiopia, Nigeria and the pooled data. In the second specification (on absolute value of bias), the coefficients on plot area are positive suggesting that as plot size increases the degree of farmer over-reporting shrinks while at the same time the absolute value of the bias increases. In this second specification, the Tanzania data exhibit a negative quadratic term and positive cubic term. When looking at the relative bias and absolute value of relative bias, the linear term is negative and the quadratic term positive in each country but at very different magnitudes, potentially driven by the difference in average plot size observed across the countries.

The distance from the plot to the dwelling holds significant explanatory power in the Ethiopia data, but not in Tanzania. The results from Ethiopia suggest that self-reported estimates of area diverge more from compass and rope measurements on those plots that are further from the household. This could be theoretically explained by assuming the farmer spends less time on plots more distant from the
inspections, and price control measures. The large incidence of SPS measures and TBTs raise concerns for developing countries’ exports. The costs of compliance of SPS measures and TBTs are higher for lowincome countries, and can erode the competitive advantage that these nations have in terms of lower labor costs and preferential access (Cadot et al., 2012). Only a small proportion of these NTMs are still in the form of quotas and export restrictions, since most quantitative restrictions are illegal under WTO rules. In the cases where quantitative measures are allowed, they materialize in the form of nonautomatic licensing, often necessary to administer the importation of goods where SPS- and TBT-related issues are of particular importance. Pre-shipment inspections are, in many cases, necessary to assure the quality and quantity of a shipment, thus facilitating trade, but they do add to the costs of trading. Price control measures are one of the least used forms of NTMs. These barriers affect only a small share of goods and are largely related to anti-dumping and countervailing duties, as well as some forms of administrative pricing for staple food and energy or other sensitive products.

NTMs can have different effects on trade. “On the one hand, requirements on marking, labeling, and packaging, although adding to costs of production, generally are not discriminatory. On the other hand, quotas, voluntary export restraints, and non-automatic import authorizations often have much more significant effects” (Gourdon and Nicita, 2012). To analyze the extent to which NTMs constitute an important barrier to trade, we use the indicators constructed by Gourdon and Nicita (2012), which employ a newly collected dataset on NTMs constructed by the World Bank, the International Trade Center, and UNCTAD. The analysis relies on two indicators: the frequency and coverage ratio of NTMs. The frequency ratio accounts only for the presence or absence of an NTM, and summarizes the percentage of products to which one or more NTMs are applied. In more formal terms, the index for country _j_ is calculated
entry regulation,<br>infrastructure indicators, regulatory and<br>administrative delays (including<br>customs clearance)|
|World Economic Forum<br>: Global<br>Competitiveness Report<br>(GCR)|117|annual|Growth Competitiveness Index and<br>other measures of economic<br>performance; Index of perception of<br>roads, railway and air services; Index of|
||||<br>Port efficiency|
|||**Governanc**|**e**|
|Governance database|160|1996-2002|Voice and Accountability, Political<br>Stability, Government Effectiveness,<br>Regulatory Framework, Rule of Law,<br>CorruptionControl|
|Business Environment<br>and Enterprise<br>Performance Survey<br>(BEEPS)|20,000<br>firms in<br>ECA<br>countries|1999, 2002<br>and 2005.|Doing Business, Corruption, Crime,<br>Regulations, Customs, Infrastructure|
|Transparency<br>International|Between<br>90-100<br>countries|1995-2002|Corruption Perception Index|
|||**Trade**||
|Global Monitoring||2003|trade effects ofpolicies onan importing|

62 __________________________________________________________________________________________________________
> Amid these positive trends are a few key areas of concern, some of which were pointed out previously. Adolescent fertility and childbearing prevalence are on the rise. Preterm births are estimated to have more than doubled since 2000<sup>37</sup> and DHS surveys conducted between 1993 and 2015 found that LBW prevalence (among all live births in the 5 years preceding the survey) increased from 14.7 to 21.4 percent. However, the most recent DHS estimate from 2017 found LBW prevalence to have fallen significantly to 14.5 percent. Estimates from the NNS of the share of children under the age of 4 whose mother reported (with or without a written record) a low birth weight was 15.9 percent in 2011, 11.1 percent in 2013, and 14.5 percent in 2015.<sup>38</sup> While not deteriorating, a measure of maternal nutrition used by the Department of Science & Technology’s Food and Nutrition Research Institute (DOSTFNRI) in its last three NNS reports - the percentage of pregnant women who are ‘nutritionally-at-risk’ - has not improved. In 2011, 2013 and 2015, around 1 in 4 pregnant women were estimated to be nutritionally-at-risk. Further, in 2015, pregnant women under the age of 20 were twice as likely to be nutritionally-at-risk than women above the age of 20 (39.6 vs. 21.9 percent). The relative risk of having deficient nutrition during pregnancy associated with adolescent age is greater than the risk associated with wealth.

Coverage of all 8 basic vaccinations according to the government’s vaccination schedule slipped from 79 to 71 percent between 2000 and 2003 and has remained around that level since.

The overall child nutrition picture in the Philippines presents a puzzle. Why did the country’s stunting rates stagnate between 2000 and 2015 amid improving living standards, improved child survival, expanding coverage of key health interventions, the implementation of a targeted cash transfer scheme and sustained progress against stunting elsewhere in