rafmacalaba/datause-encoder-data huggingface dataset:
subsidizing education with taxpayer money, and then high-skilled individuals who emigrate not contributing back into the tax system. More recently, much of the concern has been with the supposed externalities health and education professionals have on the well-being of others in their communities. The potential importance of such externalities, complementarities, and fiscal costs in practice strike us as important economic questions.

Finally, it is also worth reiterating that in many developed countries, immigrants constitute important shares of the skilled labor force in many professions. Understanding the determinants of this migration should therefore be of interest even to those whose prime focus is the U.S. economy. To illustrate this, table 2 uses the ACS 2008 to show the share of all tertiary educated workers, and of all workers with Ph.D.s who are foreign-born, and who are born in developing countries for the professions identified in table 1 as being the main occupations for skilled migrants. We see developing country migrants constitute 47 percent of individuals with a Ph.D. working as computer software engineers in the U.S., 36 percent of medical scientists and 35 percent of engineers with Ph.Ds.

# **Question 3: Is Brain Drain Increasing?**

In absolute levels, skilled migration is increasing. However, skill levels in migrantsending countries are also rising, and for the world as a whole, skilled migration rose at about the same pace as overall education levels in the sending countries in recent decades, so that the brain drain rate remained quite stable for long periods of time and may have even fallen in the past decade.

Between 1960 and 2010, the global migrant stock increased from 74 to 188 million, only slightly faster than world population growth, so that the share of the world‘s population who are international migrants increased only from 2.7 to 2.8 percent (UNDP, 2009). However, while the global migration rate has been quite flat, the migrant flow has increasingly been from less developed to more developed countries, with a reduction in within-region flows. The number of individuals migrating from the ―South‖ to the ―North‖ increased from 14 million in 1960 to 60 million in 2000 (Özden et al, 2010).<sup>2</sup> At the same time, the percentage of migrants with tertiary education increased dramatically. Defoort (2008) uses data from six main OECD destination

> 2 Özden et al (2010) define the ―North‖ as Australia, New Zealand, Japan, Canada, the United States, the EU-15, and other nations of the European Free Trade Association, and the ―South‖ as all other countries. It therefore corresponds to countries which were developed or developing in 1960.

7
LIPS-R2°9 PoLicy RESEARCH WORKING PAPER 2309 \X } hat Drives Private S aving around the \X/ orld 2 Norman Loayza Klaus Schmidt-Hebbel . Luis Servén Saving rates vary considerably across countries and over time. Policies that spur development are an indirect but effective; way to raise . privaterisea with saving the levelrates and —ee growth which rate of real per capita income.

The World Bank Macroeconomics andDevelopment Research GrowthGroup March 2000 <!-- Start of picture text --> a)<br><!-- End of picture text -->
by previous employment, to more than 9 percent of GDP in Hungary, providing a universal pension coverage. Historically, the social benefits paid were not related to the social security contributions, and general revenues provided major source of financing the entire social security system.

Capital formation was also generally higher than in OECD countries, although often government expenditure data may have reflected fuzzy accounting procedures in the statistics of many RESEs. In Albania, for instance, all recorded investment was carried out by state enterprises, and took the form of central allocations to enterprises for investment purposes. Indeed, in keeping with the pure central planning model, enterprises were not allowed to retain any of their surplus, and could not, therefore invest on their own. A similar practice obtained in Romania and FSU. This had the effect of showing the entire domestic investment as a budgetary expenditure item, something that is not consistent with GFS practices with regard to general government reporting.

Finally, in a number of countries (particularly the FSU) large military expenditures contributed to overall high spending levels. For instance, it was estimated that at the end of the 1980s, military expenditures in the Soviet Union budget amounted to over 7 percent of GDP.<sup>5</sup>

# **Transition and Government Spending**

Spending ratios have changed drastically in a number of countries following the onset of economic liberalization (Figure 1). Between 1988/89 and 1993/94, the average general government expenditure declined from 53 to 39 percent of GDP. Out of our sample of 26 transition countries, 18 countries decreased their total expenditure ratios. The reduction has been limited in some (for example, in Russia or Uzbekistan it was about 3 percent of GDP), but very drastic in others, such as Kazakhstan and Turkmenistan, where the size of government has shrunk by more than 30 percent of GDP (and where, as we will discuss later, the decline in expenditure was accompanied by swelling government arrears in pensions and other assigned benefits). On the other hand, 8 countries expanded their governments. In 1993-94, for instance, government expenditure in Macedonia and Slovenia surpassed the 1988 average of the former Yugoslav government by more than 7 and 9 percent of GDP, respectively.

_5_ These figures are disputed, as alternative estimates put military expenditures to as high as 20 percent of GDP. See IMF, World Bank and OECD, 1991.

5
**<u>Table 7: Traffic overview for the Caribbean countries</u>**

|**Group**|**Country**|**Estimated**<br>**Seats 2013**|**Share**<br>**2013**<br>**(%)**|**Annual**<br>**Growth**<br>**2001-13**<br>**(%)**|**Annual**<br>**Growth**<br>**2008-13**<br>**(%)**|
|---|---|---|---|---|---|
||Antigua|784,940|2.5%|-0.2%|-5.3%|
||Dominica|142,740|0.5%|-2.2%|-5.4%|
||Grenada|283,322|0.9%|-3.2%|-4.9%|
|Small Island States|St. Kitts & Nevis|286,806|0.9%|-2.8%|-8.9%|
||St. Lucia|829,738|2.6%|-0.1%|-0.6%|
||St. Vincent & the Grenadines|122,642|0.4%|-6.2%|-14.0%|
||**Subtotal**|**2,450,188**|**7.8%**|**-1.5%**|**-4.9%**|
||Bahamas,The|5,038,319|16.1%|-1.0%|-0.2%|
|Ril Hb|Barbados|1,689,623|5.4%|-1.4%|-5.2%|
|egona us<br>(either maritime air or|Dominican Republic|10,528,375|33.6%|6.4%|6.6%|
|, ,<br>both)|Jamaica|5,188,586|16.5%|1.8%|1.2%|
||Trinidad & Tobago|3,085,316|9.8%|3.1%|1.5%|
||**Subtotal**|**25,530,219**|**81.4%**|**2.5%**|**2.4%**|
||Belize|1,089,010|3.5%|2.5%|0.6%|
|Continental Members of the|Guyana|435,123|1.4%|0.0%|2.8%|
|CGF|Suriname|527,865|1.7%|4.7%|5.1%|
||**Subtotal**|**2,051,998**|**6.5%**|**2.4%**|**2.1%**|
|il|Haiti|1,346,202|4.3%|3.4%|2.5%|
|Frage State|**Subtotal**|**1,346,202**|**4.3%**|**3.4%**|**2.5%**|
|**Total**||**31,378,607**|**100.0%**|**2.1%**|**1.7%**|

_Source: Authors’ calculation based on data by Diio (2014)._ Continental Caribbean countries (Belize, Guyana and Suriname) and the Caribbean Fragile State (Haiti) account for the remaining 11 percent of the Caribbean traffic with 2 and 1.3 million of seat capacity per year, respectively. The traffic of each of the Continental and Fragile countries is growing solidly, with Suriname’s traffic growing twice as fast as the rest of the countries in this group, likely due to the country’s high annual GDP growth rate.

What drives the traffic in each category? In general it is clear that it is the extra-regional international traffic that drives the bulk of the air traffic (Figure 9). There are a few interesting issues, though. First, small island states have no scheduled domestic flights for.<sup>13</sup> Second, traffic between Fragile (Haiti) and other Caribbean Countries is almost inexistent even with the hubs (in practice Haiti does not belong to the hub-and-spoke air system of the Caribbean). Third, continental Caribbean countries rely importantly on the Caribbean hubs for their connectivity. Over 20 percent of the traffic of this group is with the hubs.

> 13 In practice, this means that domestic connectivity within each country is provided by charters or very small airlines.

32
performance and growth, whereas my findings point to these obligations being an important obstacle for the development of female-owned firms in Latin America.

The rest of this paper is organized as follows. Section 2 examines the distribution of female business ownership across firm size and industries. Section 3 describes differences in characteristics and performance of female- and male-owned firms. Section 4 investigates possible reasons for the differences across female- and male-owned firms. Section 5 concludes.

# **2. Female Businesses Ownership by Firm Size and Industry**

This section examines how female business ownership varies across firm size, industry, and countries within Latin America. The statistics in this section are based on national household surveys that, in most cases, are representative for the whole country. For Brazil and Peru, these surveys are only representative for the largest cities.

I chose to use household surveys rather than firm surveys in this section since most surveys that cover a large number of firms in different industries and across different firm sizes do not include information on firm owner’s gender. Based on the household surveys, I construct a sample of individuals that when asked about their economic activity say that they are either selfemployed or business owners. If they are business owners, the surveys also ask them how many employees the firm has. For all individuals, the industry in which they work is also available.

Figure 1 displays the percentage of micro, small, and medium-size firm-owners that are women, averaged over all industries. Micro firms are defined as having less than five employees (including the owner). This category thus also includes the self-employed. The percentage of female business owners in micro firms ranges from 33 percent in Argentina to 50 percent in Honduras. As firm size increases, the percentage of female business owners drops in all countries. Only between 18 and 31 percent of small firms owners are women, where small firms are defined as having 5 to 10 employees.

A drawback of using national household surveys is that they capture few large firm owners. The largest category (medium size firms) is thus defined as firms that have more than 11 employees. For Bolivia and Peru, there are only a handful of firms in this category, leading to imprecise estimates of the percentage of female ownership. Other than for these two countries, the data show consistently that female ownership declines as the firm size category increases. Medium size firms have the lowest percentage of female owners, going down to 12 percent in Mexico.

4
6

# **Table 2: Export Concentration, Growth and Stability of Economies of Ghana and** Malaysia **(1965-1989)**

||**Ghana**|**Mala -ia**|
|---|---|---|
|Siare of primary commodities|99 (1965)|94 (1965)|
|exports in total exports (%)'|92 (1989)|56 (1989)|
|Average annual growth rate|1.4|6.9|
|Standard deviation of growth rate<sup>2</sup>|4.1|0.49|
|Average annual inflation (CPI: %)|45.3|4.6|
|Memo items:|||
|Per capita income (US$)<br>1965|260|330|
|1989|390|2,160|

Share of fuels, minerals, metals and other primary commodities in total merchandise exports.

2 Mean-adjusted standard deviation.

Source: World Development Report, 1991 Intemational Financial Statistics, IMF We also compared the growth performance of developing countries during the period 1965-89 for two groups of countries, based on the degree of diversification of their production and exports. The countries which have significantly diversified their production and exports between 1965 and 1989 experienced more stable and higher growth rates than those countries which did not improve their production and export diversification significantly during the period Tables 3 and 4. Again, this does not imply the causality between the two variables but suggests a correlation between them.
**Table 13: Airport Pairs Served at Regional Caribbean Airport Hubs, with the Hub being the**

|**T**|**ransfer Point**<br>||
|---|---|---|
|**Hub Airport**|**Theoretic Airport Pairs**<br>**Served (2013)**|**Realistic Airport Pairs**<br>**served (2013)**|
|Antigua and Barbuda|33|14|
|Barbados|24|11|
|Trinidad and Tobago|19|7|
|Total(without duplication between hubs)|64|30|

_Source: Authors’ calculation based on data by Diio (2014)._ Estimating the volume of seats that are involved in the actual hub activity is difficult, since there might be several “feeder” flights bringing in passengers from different locations, with each flight having a small number of passengers going to the same final destination. However, one can state confidently that the any capacity limits will occur on the outgoing flight, and one can count how many incoming flights may be feeding the outgoing flight.

# <u>Continental Caribbean Countries</u>

The connectivity between Belize and the rest of the Caribbean is non-existent, and the only connectivity between Suriname and the rest of the Caribbean is via Trinidad (Table 14). Belize briefly had connections with Jamaica (data shows only one year – 2003), and Suriname had a similar brief connection in 2007.

||**Table 14: Intr**<br>**Oh Ci**|**a-Regional Tr**<br>**E S**|**affic for C**|**ontinental C**<br>**Gh**|**aribbean C**<br>**Gh**|**ountries**|
|---|---|---|---|---|---|---|
|**Country**|**ter arbbean**<br>**Country**|**st. eats**<br>**2013**|**Share**|**rowt**<br>**01-13**|**rowt**<br>**08-13**|**Notes**|
|Belize|No co|nnections – bri|eflyin 2003|with Jamai|ca,27,976 es|timated seats|
|Si|Trinidad|77,168|100.0%|22.8%|-2.27%|Briefly with Jamaica in|
|urname|**Total**|**77,168**|**100.0%**|**22.8%**|**-2.27%**|2006,20,592 estimated seats|
||Trinidad and Tobago|280,007|82.2%|0.8%|3.6%||
||Barbados|35,945|10.6%|-6.3%|-11.6%|Antigua and Jamaica became<br>|
|G|Jamaica|13,455|4.0%|||new connections in 2013,<br>Si h b|
|uyana|Suriname|9,828|2.9%|0.0%||urname as een<br>ititttl td|
||Antigua and Barbuda|1,326|0.4%|||nermeny connece over<br>the ears|
||**Total**|**340,561**|**100.0%**|**-0.1%**|**2.5%**|y.|

_Source: Authors’ calculation based on data by Diio (2014)._ Within the Caribbean, Suriname’s sole connection is to Trinidad. There was a very brief connection, in 2006, with Sangster International Airport in Montego Bay, Jamaica. The route to Port of Spain, Trinidad is currently being serviced both by Caribbean Airways and Surinam Airways, with Trinidad’s Caribbean Airways currently having by the far majority of the capacity. Volumes grew until 2010, and have been in steep decline since (Figure 16).

45
The macroeconomic closure for this simulation is consistent with the medium-term timeframe and assumes a constant level of employment, with perfect mobility of skilled and unskilled labor between sectors and none between countries or regions. Since the model does not keep track of differences between foreign and domestic assets, we assume that Levant countries’ trade balances are fixed as a share of the size of the economy.

# _3.2 Simulation scenario of war and trade disintegration in the Levant_

We consider trade disintegration an indirect but essential effect of the Levant conflict. Therefore, the database obtained from the pre-simulation scenario of deep trade liberalization is the initial point for the war and trade disintegration scenarios. We implement shocks that completely reverse the deep trade reforms discussed in 3.1 as well as shocks reflecting: (i) the change in population and labor force size due to loss of life in Syria; (ii) the change in population and labor force size due to refugee movements across countries; (iii) infrastructure destruction in Syria; (iv) increases in trade costs in the Levant; (v) embargo on trade with Syria; and (vi) deterioration in productivity in Iraq. The CGE framework then helps us assess the implications of these shocks on other economic variables in the model. Next, the paper presents details on the elements of the war scenario and the changes to the macroeconomic closure in order to accommodate labor mobility across countries in response to refugee movements.

The war in Syria triggered massive displacement of people and outflows of Syrian refugees into neighboring countries, especially Jordan and Lebanon. In order to implement cross-border movements of people in the model, we relax the assumption of no international labor mobility and adjust the population and labor force of both refugee-receiving and refugee-sending countries, using information from UNHCR Population STATICS and ILOSTAT Database.

16
- Blackman, A. (2020). _Latin American and Caribbean forests in the 2020s: Trends, challenges, and opportunities._ Washington, DC: Interamerican Development Bank.

- Cattaneo, A. 2005. Inter-regional innovation in Brazilian agriculture and deforestation in the Amazon: income and  environment in balance. Environment and Development Economics 10:485-511. Cambrideg University Press. DOI:10.1017/S1355770X05002305 .

- Cattaneo, A. 2008. Regional comparative advantage, location of agriculture, and deforestation in Brazil. Journal of Sustainable Forestry. 27:1-2, 25-42, DOI: 10.1080/10549810802225200.

- Dutz, M. _Jobs and Growth: Brazil’s Productivity Agenda._ (2018). Washington DC: World Bank.

- Golding, N.; Betts, R. (2008). Fire risk in Amazonia due to climate change in the HadCM3 climate model: Potential interactions with deforestation. _Global Biogeochemical Cycles_ 22, GB4007, doi:10.1029/2007GB003166.

- Ferreira filho, J.B.S; Horridge, M. (2012). Endogenous Land Use and Supply, and Food Security in Brazil. In: 15th Annual Conference on Global Economic Analysis, 2012, Geneva, Switzerland. Conference Papers.

- Ferreira Filho, J.B.S; Horridge, M. (2014).Ethanol expansion and indirect land use change in Brazil. Land Use Policy, v. 36, p. 595-604.

- Ferreira Filho, J.B.S; Pinto, L.F.G; Farias, V.G; Sparovek, G. 2018. Environmental and welfare impacts of deforestation reduction in Brazil. 21<sup>st</sup> Conference on Global Economic Analysis. Proceedings. Available at

https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=5497

- Franklin, S.; Pindyck, R. (2018). Tropical forests, tipping points, and the social cost of deforestation. _Ecological Economics_ , 153.

- Helfand, S.; Taylor, M. (2018). The inverse relationship between farm size and productivity: Refocusing the debate, Working Paper 201811, University of California at Riverside, Riverside, CA.

- Hertel, T. W. (2012). Implications of agricultural productivity for global cropland use and GHG emissions: Borlaug vs. Jevons. GTAP Working Paper No. 69.

- Horridge, M. (2011). The TERM model and its database. Centre of Policy Studies. General Paper no. G-219.

- Hsieh, C.; Klenow, P. (2009). “Misallocation and Manufacturing TFP in China and India”. _Quarterly Journal of Economics_ 124: 1403–1448.

- International Food Policy Research Institute – IFPRI. (2016) Agriculture R&D Factsheet: Brazil.

- Instituto Escolhas. (2017). Qual o impacto do desmatamento zero no Brasil? Available at <u>http://escolhas.org/biblioteca/estudos-instituto-escolhas/</u> .

- Intergovernmental Panel on Climate Change – IPCC. (2015). Climate Change 2014. Synthesis -

- Report. Available at https://archive.ipcc.ch/pdf/assessment <u>report/ar5/syr/SYR_AR5_FINAL_full_wcover.pdf</u> .

Jevons, W. S. "VII". (1866). The Coal Question (2nd ed.). London: Macmillan and Company.

13
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 5 Productivity compactness – as measured through different urban form metrics - and its subsequent economic growth, where a city’s rate of economic growth is estimated on the basis of nighttime lights data.

Another recent development on the calculation of shape metrics in a GIS context is the Metropolitan Form Analysis Toolbox developed by the City Form Lab (Amindarbari and Sevtsuk, 2013), an open source toolbox for ArcGIS that includes eight urban form and landuse metrics that can be used to track growth and change in cities: Size and Density, Coverage, Polycentricity, Compactness, Discontinuity, Expandability, and Land-Use Mix. Libraries such as the Metropolitan Form Analysis Toolbox and The Shape Metrics Tool have been used to analyze large samples of cities: Angel, Sheppard et al (2005) analyzed the urban extents of 3,493 cities in 1990 and 2000. Amindarbari and Sevtsuk (2013) calculate urban form and land-use metrics for Los Angeles, Singapore, Jakarta, Guangzhou and Chengdu. Other studies on urban form, that do not necessarily use the libraries mentioned above, are: Bertaud and Malpezzi (2003) calculate a range of measures of urban form for 48 major metropolitan areas around the world, and analyzed the impact of income, population, and the nature of the regulatory regime as potential determinants of urban form; Huang et al (2007) calculate seven spatial metrics of urban form for 77 metropolitan areas in Asia, US, Europe, Latin America and Australia; they found that urban agglomerations in the developing world are more compact and dense than their those in Europe or North America.

Urban planners, in the search of an integrated theory of city planning, have explored multidisciplinary approaches that integrate areas such as economics, network science, and geometry, to understand the impact of size, scale, and shape on city sustainability (Batty, 2008). This process led them to have a more elaborated concept of urban form. As in Batty and Longley (1994), urban form, from an urban planning perspective, “represent the spatial pattern of elements composing the city in terms of its networks, buildings, spaces, defined through its geometry mainly, but not exclusively, in two rather than three dimensions” [pp:42]. Thus, a proper characterization of city form should include not only information on the shape of its border, but also characteristics of its urban texture and land use planning (a similar warning is made by Prosperi et al., 2009). In fact, the characterization of urban form as a combination of external shape and internal structure was suggested for the very first time
**Table 5: Observed (2001 sentinel), predicted (2001 sentinel) and predicted (2000 DHS) HIV prevalence among** **<u>women age 15-49, by region/district, %</u>**

||Observ<br>sentinel|ed (2001<br>survey)<br>Predict<br>sentine|ed (2001<br>l survey)|_Predicted, SAE methodo_|_logy (2000 DHS)_||
|---|---|---|---|---|---|---|
|Region/district|N|Mean|Mean|N|Mean|Std. Err.|
|**North**|||||||
|Chitipa||||190|8.9|_1.5_|
|Karonga|188|12.2|14.1|941|9.6|_1.1_|
|Mzimba|759|16.6|17.4|781|8.6|_1.4_|
|Nkhata Bay|485|18.4|19.1|186|9.5|_1.1_|
|Rumphi|528|13.8|14.4|89|14.2|_1.5_|
|_Total (region)_|_1,960_|_15.9_|_16.7_|_2,187_|**_9.8_**|_1.3_|
|**Central**|||||||
|Dedza|155|3.9|5.5|497|5.4|_1.6_|
|Dowa|149|4.7|4.8|447|2.1|_0.5_|
|Kasungu|153|5.2|8.8|728|8.5|_1.7_|
|Lilongwe|596|20.0|21.6|871|11.1|_1.3_|
|Mchinji|506|23.5|25.1|340|8.5|_1.4_|
|Nkhotakota|525|18.9|22.6|221|10.4|_1.3_|
|Ntcheu|510|18.6|18.9|435|8.9|_1.9_|
|Ntchisi||||185|4.1|_1.0_|
|Salima||||784|7.2|_0.9_|
|_Total (region)_|_2,594_|_17.5_|_19.3_|_4,508_|**_8.2_**|_1.3_|
|**South**|||||||
|Balaka||||226|11.7|_1.7_|
|Blantyre|623|28.6|30.5|1,023|20.4|_1.4_|
|Chikwawa||||337|12.9|_1.2_|
|Chiradzulu|201|15.9|26.3|253|22.1|_1.9_|
|Machinga|131|13.0|13.7|798|15.7|_2.7_|
|Mangochi|526|16.0|23.2|654|17.9|_2.9_|
|Mulanje|459|24.6|25.5|905|25.6|_2.5_|
|Mwanza||||121|11.0|_1.5_|
|Nsanje|475|35.8|39.4|188|19.3|_1.9_|
|Phalombe||||239|22.9|_2.9_|
|Thyolo|185|17.3|18.4|882|16.5|_2.4_|
|Zomba||||899|13.4|_1.6_|
|_Total (region)_|_2,600_|_24.1_|27.7|_6,525_|_18.0_|_1.1_|
|**Total(country)**|7,154|19.4|_21.4_|_13,220_|**13.2**|_1.0_|

Note: all descriptive statistics involving DHS uses DHS sample weights DHS-based predictions for each group of women are based on estimating the model which uses groupspecifc weights making the distribution by age/gender in the sentinel survey reflect that in the DHS 26