rafmacalaba/data-use-mentions-extended huggingface dataset:
captures these attributes through stringency indices developed by the Oxford COVID-19 Government Response Tracker, which documents and compares worldwide government policy responses to the coronavirus using well-defined and consistent criteria across countries and over time. The stringency index is a composite index that combines different indicators of a series of policy responses. The policy responses considered include school closings, workplace closings, suspension of public transport, restrictions on the size of gatherings, cancellation of public events, stay-at-home requirements, and restrictions on domestic and international travel. Each country is evaluated at a daily level and given a score from 0 to 100 for each indicator. These scores are then averaged to get a composite index for the country at each point in time, which gives a comprehensive picture of the stringency level of its lockdown policy 7
societal norms, incorrect beliefs and a lack of awareness of women’s rights, and institutional and legal restrictions (Vilardo and Bittar, 2018).

Only a handful of studies currently examine the long-term impacts of war on school-age children. We review the most pertinent studies. Olga (2010) examines the impacts of war on school-age children and shows that girls at school age who were exposed to the 1992-1998 armed conflict had lower probability of completing their mandatory schooling than those who were not exposed to the conflict in Tajikistan, but there is no effect of the conflict on education of boys. Similarly, Ichino and Webmer (2004) show that children who were ten years old during World War II received less education and experienced a sizable earnings loss some 40 years later. Blattman and Annan (2010) find adverse impacts on children-abductees who were between 11 and 24 years old during the years they spent with the rebel forces on schooling, skilled employment and earnings in Uganda.

# **2.** **<mark>Data</mark>**

This study uses the Iraq Household Socio-Economic Surveys (IHSES) 2006-2007. IHSES 20062007 surveyed 18,144 households, including all three layers of the governorates: one layer for the urban area of the center of the governorate, one layer for the rest of the urban areas, and one layer for the rural areas. This survey served as the foundation for revising the Consumer Price Index (CPI) from an out-of-date 1990 one to a revised 2007 index. IHSES 2006-2007 is nationally representative and covers socio-economic data on migration, labor, education, health, anthropometrics, agriculture, nonfarm, expenditure and income. This survey was carried out by the Central Bureau of Statistics and the Kurdistan Regional Statistics Office, in the field for a year, between October 2006 and November 2007. It covered all 18 governorates in Iraq.

# **3. Methodology**

The empirical analysis relies on the variation in the timing of when children finished primary school and the war
Empirical results on the impact of foreign investor participation in developed country local currency bond markets are perhaps surprisingly few, though a recent noteworthy exception is Andritzky (2012) (and see also citations of previous work therein). Using a new dataset comprising 13 OECD economies, Andritzky finds a statistically significant relationship between bond yields and non-resident ownership, but of a smaller magnitude than Peiris (2010) does for EMs; a 1% increase in the share of securities held by nonresidents is associated with a 3.2 to4.3-basis point drop in yields (and a larger -6.6-basis point effect in the Eurozone). In contrast to Peiris (2010), this paper also finds a statistically significant relationship between foreign participation and volatility, although the effect is not large. Causality tests are somewhat ambiguous, but on the whole they suggest that lower yields may exert a “pull effect” on foreign investors (resulting in the negative relationship referenced above), rather than foreign investors themselves driving down yields.

In summary, it is fair to say that the literature examining the dynamics of foreign investor participation in EM domestic bond markets is nascent. Work so far suggests, tentatively, that increased foreign participation is associated with lower yields, while the impact on volatility is ambiguous. Foreign investment may also contribute to “virtuous cycles” in bond market development. Future research may uncover more robust evidence of impacts on yields and domestic market development, though data constraints and significant variation amongst markets will pose a continuing challenge for researchers.

**6**
>

> 10 The IHPS additionally collected detailed information on individuals’ ownership of mobile phones.

> 11 This was an upper limit that only applied to 1 percent of the sampled household population that had more than four adults. If a sampled household had more than four adult household members, following the preference given to the head of the household, and his/her spouse if applicable, the remaining interview targets (2 or 3 depending on the presence of a spouse) were selected at random from the remaining pool of adult household members.

7
# **2. The Epidemic**

The prevalence of HIV/AIDS within the entire population of Colombia is low. Data from 2009<sup>20</sup> show an adult HIV prevalence of 0.59%—which represents about 140,125 people between the ages of 15 and 49 living with HIV or AIDS in the country. Preliminary epidemic estimates for 2010 point to an HIV prevalence of 0.57%—and to 144,290 people between the ages of 15 and 49 living with HIV or AIDS.<sup>21</sup> Using different data sources, the graph below shows the evolution of HIV prevalence for the general population in Colombia from 1991-2015:

**Figure 2: Evolution of HIV Prevalence for the General Population in Colombia, 19912015**

<!-- Start of picture text --> 1,00%<br>0,50%<br>0,00%<br>Data:<br>1991 a 1994 a 1996 a 1999 a 2003 a 2009 b 2010 c 2011 d 2012 d 2013 d 2014 d 2015 d<br>0,10% 0,30% 0,40% 0,49% 0,65% 0,59% 0,57% 0,57% 0,52% 0,50% 0,50% 0,49%<br>1991 1994 1996 1999 2003 2009201020112012201320142015<br>0,65%<br>0,59%<br>0,57% 0,57%<br>0,52%<br>0,49% 0,50% 0,50% 0,49%<br>0,40%<br>HIV Prevalence<br>0,30%<br>0,10%<br><!-- End of picture text -->

**Sources:** Adapted by Técnicas de Salud using data from the following sources:
**6. Conclusions**

Even when they have the power to do so, it is not obvious that it will be in the interests of the non-poor to shift the burden of fiscal adjustmen **t** o the poor. And even a minority poor can have political influence. As this paper has demonstrated, the incidence of fiscal contraction, and hence the case for action to protect public spending on the poor at a time of overall fiscal austerity, is an empirical question. This paper has tried to address that question using various data sets for Argentina.

Aggregate budget cuts in the 1980s and '90s typically resulted in proportionately greater cuts in social spending; it was "non-social" spending that was protected. However, the proportionate cuts were about the same for types of social spending that matter more to the poor as for those that tend to favor the non-poor. The absolute cuts were in fact greater for "social insurance" that matters more to the non-poor.

However, spending on targeted social assistance and employment programs was more vulnerable to aggregate spending cuts than more universal social services. While social spending as a whole was clearly exposed to fiscal contraction, this was somewhat less true of pro-poor spending on things that benefited the non-poor too. Fine targeting may thus be a - mixed blessing for the poor; a higher mean may come with greater vulnerability to cuts and quite possibly the cuts will come at times when help is most needed. There is a strong case for action to protect pro-poor social spending at such times.

The paper studied one program that attempted to do so. The program was introduced (with support from the World Bank) to help compensate poor unemployed workers and their - families for the effects of a macroeconomic shock. The design features of the program - providing low wage work targeted to poor areas helped assure that the program was far better at reaching the poor than the pre-existing components of social spending in Argentina.

24
consequences for economic agents’ production incentives. Intuitively, revolutions and coups negatively affect growth by decreasing stability and infrastructure destruction.

The demographic characteristics of countries we use in our estimation are average age, religion, ethnolinguistic fractionalization, population growth, total labor force, ratio of workers in population, and language skills. Religion is found to be relevant to economic growth in Barro (1996). Population growth accounts for the neoclassical implication of a, ceteris paribus, decline in per capita growth with an increasing population. Language skills are approximated by the fraction of persons speaking English within a country and the fraction of persons speaking a foreign language. Hall and Jones (1999) demonstrate how better language skills are positively reflected in economic growth. They argue that this arises from facilitated internalization and the benefits of globalization. The full list of variable names and their abbreviations is presented below.

Additionally, PWT is missing observations on Algeria, Haiti, and Nicaragua. Therefore, we have to drop them from the sample. Furthermore, the GFDD does not include data on Taiwan. Ultimately, we have 68 observations, encompassing both developed and developing countries. The list of countries is as follows: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Botswana, Canada, Chile, Cameroon, Congo (Brazzaville), Congo Dem. Rep (Kinshasa), Colombia, Costa Rica, Cyprus, Denmark, Dominican Republic, Ecuador, El Salvador, Ethiopia, Finland, France, United Kingdom, Germany, Ghana, Greece, Guatemala, Hong Kong, Honduras, India, Ireland, Israel, Italy, Jamaica, Jordan, Japan, Kenya, South Korea, Sri Lanka, Morocco, Madagascar, Mexico, Malawi, Malaysia, Nigeria, Netherlands, Norway, Pakistan, Panama, Peru, Philippines, Portugal, Paraguay, Senegal, Singapore, Spain, Sweden, Switzerland, Thailand, Tunisia, Turkey, Tanzania, Uganda, Uruguay, United States, Venezuela, Zambia, and Zimbabwe.

53
**Figure 12 - Labor Taxation is Higher for Low-Wage Earners in ECA Countries** Labor taxes, 2007

<!-- Start of picture text --> 45%<br>35%<br>25%<br>15%<br>5%<br>-5%<br>Percentage of Labor Taxes<br><!-- End of picture text --> Source: Koettl and Weber (2012) based on OECD Tax and Benefit model.

Note: Columns represent the tax wedge for low-income earners (singles with no children at 33 percent of average wage) in 2008 (for Bosnia, Macedonia, and Serbia, 2009).

The historical reliance of ECA countries on labor taxation cannot be easily disentangled from the generous social insurance systems. A redesign of tax systems aimed at reducing social contributions will need to be balanced with expenditure demands, fiscal constraints and the existing tax structure. Within this overall fiscal framework, countries may be able to facilitate the moving of workers to cleaner industries through the use of well designed tax incentive schemes.

It is important to recognize that in many countries high labor market flexibility often does not go hand in hand with generous social protection systems; however, this does not need to be the case. Overall, there is a negative correlation between flexibility and protection across countries. This correlation is even stronger when considering only high-income countries. As incomes increase, countries gravitate toward one of two work models: one that forgoes flexibility or another that forgoes protection. In that sense, transition countries might in the future embark on a pathway towards one of the two work models. Some already seem to have chosen—like Georgia seems to have chosen the high flexibility/low protection model, and Slovenia the low flexibility/high protection model (World Bank, forthcoming). Nevertheless, some countries, like Denmark, Austria, Switzerland, and Ireland to a lesser extent - have managed to find a balance between flexibility and protection. A
on village facilities. We find no correlation between distance to the fault-line and adult education, water supply or residence in a permanent structure before the earthquake. Neither do we find any correlation between distance to the fault-line and the distance between the household and the closest private school, public school, water pump, medical facility or market. We do find that households who lived farther from the fault-line were less likely to report that they had electricity before the earthquake; given that we assess 11 different variables, we would expect at least 1 to show up as significant by pure chance. Taken together, both village and household data appear to confirm that pre-existing characteristics were _not_ correlated with distance to the faultline.

# **Results: Destruction, Aid and Trust**

Four figures highlight the main results, following the discussion above. We present these and then turn to the regression results.

# <u>Destruction:</u>

Figures 1a and 1b plot the non-parametric relationship between village-level destruction and distance to the fault-line, showing the utter devastation as a result of the 2005 earthquake. Public reports on the earthquake document more than 75,000 deaths; our data bear out the heavy burden. Figure 1a uses information from the household census and shows that most of the deaths occurred in the immediate vicinity of the fault-line where the earthquake shock was the most intense. More than 15 percent of the households in villages those are within 5 kilometers of the fault-line report a death within the household due to the earthquake. The deaths gradually taper out as we move further away from the fault-line.

Most of these deaths were due to the collapse of housing structures. Our census data also capture information on homes that were partially damaged and those completely destroyed. The 18
The paper’s empirical work adds to a large empirical literature on housing in developing countries, but there is little precedent for the actual empirical exercise that we carry out and thus little direct connection to any previous paper.<sup>4</sup> The empirical work is mainly devoted to testing the job-access prediction, and it relies on a number of data sets sourced from Cape Town’s city government. The first data set comes from digital aerial photography and highresolution satellite images, which give point locations of buildings along with an associated land-use classification per building, including informal uses. We overlaid these spatial data on a digital map of land-parcel contours from the City of Cape Town’s cadastre, yielding a count of the number of backyard dwellings per land parcel. After various exclusions, our data set 3
that the projected employment level for this sector is lower than the observed employment level in 2020. For the industry and service sectors, the estimation bias is positive in these four countries.

All in all, the sectoral employment projections based on elasticity estimations tend to be more biased for the agriculture sector, and when the direction of the bias differs between sectors, agriculture is the one behaving differently in most cases. This pattern could be related to the differential impact of non-pharmaceutical measures, such as mobility restrictions, across sectors. For instance, agriculture was considered an essential sector in some countries because of its role in food production. As such, the sector continued performing some of its production activities despite the confinement measures. In the next subsection we explore the association between the magnitude of the estimation biases and government non-pharmaceutical measures for the aggregate.

# **2.2 Explaining the bias in employment projections**

The large biases found for some of the countries and methods are related to the distinctive features of the COVID-19 crisis. The implementation of lockdown measures meant dramatic reductions in economic activity. Most governments implemented social protection measures which can protect employment in some cases and distort incentives to work in some others.

<mark>In order to explore the association between bias in the employment projections and the distinctive characteristics of the pandemic crisis, we use information on government non-pharmaceutical measures from the Oxford COVID Tracker, i.e., the severity of stay-at-home orders, changes in workplace mobility from Google mobility data, and a measure of government social protection</mark> policies from Gentilini et al. (2021).

<mark>The stay-at-home order from the Oxford COVID Tracker ranges from 0 to 4 with a higher value indicating stricter requirements. We calculate the average of the stay-at-home order for each country using daily information from March 15 to December 31 of 2020. The correlation between the stay-at-home order and the bias of our employment projections appears in Figure 2. For all the elasticity methods we find a positive association between the
income and wages, covering all areas of the country. In the next section, we will delve into looking at scale effects for cities in particular.

# **4.1 The data and the sample of countries and cities**

We use the Living Standards Measurement Study data of the World Bank, where we have detailed geocoding of where families live for six countries; allowing us to map data to our spatial units: rural, LD settlements and cities. The LSMS surveys have detailed and consistent data at the household and individual levels on income, education, labor allocation, asset ownership, and dwelling characteristics. The data sets are the Tanzania Panel Household Survey (2008 and 2010), the Nigeria National Household Survey (2010 and 2012), the Uganda National Panel Survey (2009, 2010, 2011, and 2012), the Ethiopia Socioeconomic Survey (2011, 2013, and 2015), the Malawi Integrated Household Survey (2010 and 2013), and the Ghana Socioeconomic Panel Survey (2010 and 2013). Note that the dates of surveys in countries are so close together that they do not provide the opportunity to look at dynamics nor to identify urbanization effects off of movers.<sup>7</sup> These sample countries account for approximately 35% of the subcontinent’s population.

Before proceeding we note how our African countries present in terms of aspects of their urban hierarchy and what the coverage of this hierarchy is by LSMS surveys. At the country level, the six countries collectively present a regular urban hierarchy. Figure 6a shows the expected (Eeckhout, 2004) log-normal distribution of all urbanized areas (cities and settlements), although there is a right tail skew. Figure 6b ranks cities from 1 to n by size with rank 1 being largest; and plots ln population against ln rank-size, so we see that rank rises (lower order) as population declines. We see that regularity holds over much of Figure 6b, governed by an approximate Pareto distribution to the right tail
Closeness to the EU explains the difference in the agglomeration dynamics observed between Central and Eastern European (CEE) countries and the Eurasian countries. After the overthrow of their socialist regimes in 1989–90, most CEE countries rapidly adopted market-based economic systems and redirected their political and economic relations toward the EU. One of the main benefits of the EU enlargement has been the boost in economic activity in both accession countries and member states. Eurasian countries, at the other end of the former communist empire, were exposed to weaker global trade flows. Russia remained the major trading partner for Central Asia, the South Caucasus, and other CIS countries, confining their exposure to the market forces of agglomeration, migration, and specialization to a Russiacentric bloc (Broadman 2005; World Bank 2009a).

All capitals and leading cities of Eurasia attracted labor-intensive economic activities, but countries closer to the EU experienced the emergence of specialized urban centers along the border region. Cieślik (2004), looking at the difference in the attractiveness for foreign direct investment (FDI) of various Polish regions, found that regions bordering Belarus, Russia, and Ukraine were less attractive than regions along the European Union-15 (EU15) border of Poland. And Traistaru, Nijkamp, and Longhi (2003) found similar results for the geographic concentration of manufacturing in Bulgaria, Estonia, Hungary, Romania, and Slovenia. They found that other things equal, industries with large economies of scale tended to locate close to large urban centers while research-oriented industries were attracted by regions closer to the EU15. Using regional data for 1996–2000, Brülhart and Koenig-Soubeyran (2006) also found similar results in their analysis of the structures of wage and employment within the Czech Republic, Hungary, Poland, the Slovak Republic, and Slovenia.

Entrepreneurs prefer to concentrate production when their activities generate increasing returns to scale. By contrast, public policy in Eurasia is still concerned with spatial equity, even after the breakup of the Soviet Union (World Bank 2009b). The same concern is expressed in
)|(0.032)|
|_N_|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|
|log employment|No|Yes|No|Yes|
|occupational structure|Yes|Yes|Yes|Yes|
|imported inputs|No|No|Yes|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales). The instruments are the weighted average of the real exchange rate of a firm export partners, _z_<sup>0</sup> , and its interaction with initial sales (in 2001). Columns (1) and (3): controls for occupational structure measured with a vector of employment shares by task; columns (2) and (4): add log total employment (firm size); column (3) and (4): add the share of imported inputs. All regressions include firm fixed-effects and year fixed-effects; industry-specific trends (i.e., interactions between year dummies and industry dummies); firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

20
# **4 Data Sources and Descriptive Statistics**

## **4.1 Data**

**Trade data** : Data on exports and imports by municipality, year, destination and product come from the tax authority of Mexico (Servicio de Administracion Tributaria), and covers each municipality over the 2004-2014 period. Data are reported using the HS (Harmonized System) 2002 classification at the 4-digit level, and converted to the ISIC Rev. 4 classification using correspondence tables. These data are also used to construct the RCA weights _ωm,i,_ 2004 from equation 1.

**Automation data** : Data on the stock of robots by country, year and sector of economic activity comes from the International Federation of Robotics (IFR). The data are based on a survey of robot suppliers, and cover 50 countries which represent 90 percent of the world market of robots. The sector classification follows the ISIC classification closely (but not exactly), at roughly the two-digit level for the manufacturing sector, and two-digit level for non-manufacturing sectors. The stock of robots is estimated using the perpetual inventory method, where the initial stock is adjusted by the yearly delivery of robots and depreciation rate.

To express the number of robots in per worker terms for the group of European countries, the US, Mexico and Brazil, we use data from several sources. For Europe, we use data on employment from EUROSTAT for circa 2000; for the US, from the 2000 Current Population Survey (CPS); for Brazil, we estimate the number of employees by sector using microdata from the IPUMS sample of the 2000 Census of Population; for Mexico, we use data from the 1999 Economic Census.

**Labor market indicators** : We use tabulations from the 2000 and 2010 Census of Population and Housing and the 2015 Population Count to obtain labor market indicators, as well as demographic characteristics of the population at the municipal level.<sup>2</sup> Given that

> 2Even