rafmacalaba/datause-encoder-data huggingface dataset:
PTA of 2007, the Colombia-Northern Triangle PTA of 2009, and the Peru-Chile PTA of 2009. With the exception of the EFTA-Peru PTA of 2011, none of the other PTAs includes harmonization or mutual recognition of TBT.<sup>17</sup>

# **2.2 Firm Customs Data**

Our analysis relies on transaction-level customs data for Chile, Colombia, and Peru over the 1997-2015, 1996-2015, and 2000-2015 periods, respectively. The data obtained from the countries’ customs agencies was collected by the Trade and Integration Unit of the World Bank Research Department, as part of their efforts to expand the Exporter Dynamics Database described in Fernandes et al. (2016). Each country’s raw database covers the universe of exports and provides information at the exporting country-firm-destination-HS 6-digit product-year level on export values (in current USD) and weights (in kilograms but only for Colombia and Peru). Each country’s raw database was subjected to uniform reformatting and to a series of cleaning procedures detailed in Fernandes et al. (2016).<sup>18</sup> Firms are identified by their actual names, their tax identification number, or an artificial unique code randomly created by the customs agency, which allows us to have a panel of firms for each country. Regarding product nomenclatures, we convert the HS 1996, 2002, 2007, and 2012 versions (used in the raw data) to the HS 1992 version (as this is convenient to merge with the tariff variable described in Section 2.3).

While we have information for each firm on its exports in all sectors, we exclude from the analysis all observations pertaining to HS Chapters 25-27 to avoid potential biases related

> 17In the content of preferential trade agreements database, if two countries sign more than one PTA with each other, the numbers of provisions in each policy chapter are cumulated across all PTAs. For example if country A and country B signed an agreement in 2000 with provisions 1 and 2 in the SPS chapter and signed another agreement in 2005 with provisions 1 and 3, between 2000 and 2004, 2 SPS provisions would be in force while from 2005 onward 3 SPS provisions would be in force between countries A and B.

> 18Export values are Freight on Board (FOB) figures measured in USD converted from local currency to USD using exchange rates taken from the IMF’s International Financial Statistics. The quality of the customs data is assessed as in Fernandes et al. (2016) by a comparison of total exports obtained from aggregating the transaction-level customs data at the country level with the total exports obtained at the country level from COMTRADE/WITS (World Integrated Trade Solution). The ratios for Chile, Colombia and Peru average close to 100% over the sample period.

10
Table 11: Elasticities of trade flows with respect to distance

||_−t_<sup>_j_</sup>_θ_<sup>_j_</sup>|
|---|---|
|_j_ =_ICT_|-3.971**<br>(1.782)|
|_j_ = Manufacturing|-0.995***<br>(0.317)|
|_j_ = Retail|-1.354***<br>(0.354)|
|_j_ = Other activities|-1.189***<br>(0.178)|
|Observations|10,493|
|Firms|392|
|Survey weights|Yes|
|Origin FEs|Yes|
|Destination FEs|Yes|

Standard errors clustered at the firm level reported in parenthesis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Sources:_ Authors’ survey, Fratto (2019) for travel time.

in which travel times are included and one in which they are excluded. The increase in the pseudo- _R_<sup>2</sup> when including travel times suggests the variable has explanatory power. The resulting elasticity of trade flows to distance for manufacturing is about 1, consistent with estimates from other studies (for instance Monte et al. (2015)).

We estimate a high elasticity of trade flows to distance for the ICT sector. As a matter of fact, the answers from the survey indicate a strong geographic concentration of suppliers and buyers of ICT firms.

We use data on wages from the Labor Force Survey for the years 2000-2007. We regress the observed hourly wages on year fixed effects and individual characteristics. Finally, we include a fixed effect for each workplace location.

where _whit_ are the wages of person _h_ in workplace location _i_ in year _t_ , _Xht_ are a set of individual characteristics, and _ζi_ is a fixed effect for workplace location. More details are reported in the appendix.

**Proposition.** Given the dispersion of productivity, _θ_<sup>_j_</sup> , estimates of wages, employment, marginal cost for intermediate goods _{wi, L_<sup>_j_</sup> _i_<sup>_, xj_</sup> _i_<sup>_}_,andaparameterizationoftradecosts</sup> ( _τni_<sup>_j_)</sup><sup>_θj_,thereexistuniquevaluesoftheunobservedproductivities</sup><sup>_Aj_</sup> _i_<sup>foreachlocation</sup><sup>_i_and</sup> each sector _j_ that are consistent with the data being an equilibrium of the model.

The proof is available in the appendix.

The resulting estimates of the internal trade shares are the solution to the following set 27
**Table 3.** Household distance from nearest forest defined as A) 30% tree cover threshold and B) 50% tree cover threshold

# A) 30% tree cover threshold

|**Country**|**Extent of tree cover**<br>|**Percent tree cover**<br>|**Households in our**<br>|**Share (%) of house**<br>|**holds with trees on**<br>|**farms within**<br>|
|---|---|---|---|---|---|---|
||**(ha) by country**<br>**(2000)**|**relative to country**<br>**land area (2000)**|**sample (#)**|**10km of forest**|**20km of forest**|**50km of forest**|
|Ethiopia|12,040,763|10.72|3,388|55.81|73.91|93.3|
|Malawi|1,521,741|16.17|2,272|85.87|100|100|
|Nigeria|10,033,216|11.13|2,692|36.33|46.51|59.7|
|Tanzania|26,42,2567|29.85|2,621|79.82|88.1|94.2|
|Uganda|7,768,069|37.83|1,815|91.85|98.02|100|
|_Overall_|6,272,758|17.95|14,839|58.47|68.91|77.05|

B) 50% tree cover threshold

|**Country**|**Extent of tree cover**<br>|**Percent tree cover**<br>|**Households in our**<br>|**Share (%) of house**|**holds with trees on**|**farms within**|
|---|---|---|---|---|---|---|
||**(ha) by country**<br>**(2000)**|**relative to country**<br>**land area (2000)**|**sample (#)**|**10km of forest**|**20km of forest**|**50km of forest**|
|Ethiopia|5,426,282|4.83|3,388|32.05|44.19|74.62|
|Malawi|313,115|3.23|2,272|53.57|87.81|100|
|Nigeria|4,716,199|5.23|2,692|20.17|29.27|42.53|
|Tanzania|9,702,599|10.96|2,621|66.84|77.68|86.65|
|Uganda|3,271,840|15.94|1,815|55.59|81.76|98.95|
|_Overall_|3,905,006|17.95|14,839|38.45|53.22|68.13|

_Note_ : To protect confidentiality household location coordinates in LSMS-ISA data are not exact, but rather based on a random distortion of 0-5km. Data on extent of tree cover by country and percent tree cover relative to country land area derive from Hansen et al. (2013). Note that “tree cover” is not the same as “forest cover” in these data. “Tree cover” refers to the biophysical presence of trees, which may be a part of natural forests or tree plantations. Information on household distance to forest are based on the authors' calculations from LSMS-ISA data sets (World Bank, 2015) and “MOD44B MODIS Vegetation Continuous Field Coll. 5–2000 through to 2010:�Percent Tree Cover” (DiMiceli et al., 2011).

26
Timor Leste never accepted this imposition and tried to preserve their own culture and identity. The Santa Cruz massacre in November 1991, in which 200 protesters were killed by Indonesian forces, was broadcasted by international media. This raised considerable international attention to the brutalities and human rights violations during the Indonesian occupation. Timor Leste‘s independence movements started then to receive support from the Portuguese government and international organizations, including the UN. These events, together with the 1997 financial crisis which badly affected the Indonesian economy and its political situation, resulted in the decision by the Indonesian government, in agreement with the Portuguese government, to hold a referendum on the independence of Timor Leste.

On the 30<sup>th</sup> of August 1999, 79 per cent of the Timor Leste population voted in favor of independence. The output of the referendum generated a wave of destruction, violence and human rights violations by Indonesian forces and pro-autonomy militia (Alonso and Brugha 2006). Large displacement movements (both forced and spontaneous) took place before and after the popular consultation in 1999. More than half of the population was displaced. Around 40 percent of these were forcibly displaced to refugee camps in West Timor, and about 60 percent escaped towards the mountains or somewhere else within Timor Leste (UNDP 2002). The majority of displaced and refugee populations returned back to Timor Leste shortly after the violence ended. The number of killings during this wave of violence has been estimated to be around 1,000 to 2,000 people (UNDP 2002). In addition to these deaths, the wave of violence in 1999 resulted also in the massive destruction of infrastructure, health facilities, schools and public and private buildings that the Indonesian forces found on their way towards West Timor. In October 1999, a United Nations Transitional Administration was settled in Timor Leste (UNTAET).

# **3.2. Conflict variation across time and space**

The secession conflict in Timor Leste has evolved over time and across space in different ways. The Timor Leste Commission for Reception, Truth and Reconciliation (CAVR), established in 2001 and mandated by the UNTAET to ―undertake truth seeking‖ for the 1975-1999 conflict, has identified three distinct phases of the conflict during the entire period between December 1975 and September 1999. The first phase, from 1975 to 1984, is related to the initial Indonesian invasion and occupation of Timor-Leste. The first few years, from 1975 to 1979, were the most intense in terms of killings and destruction. The second phase, from 1985 to 1998, was characterized by the consolidation and normalization of the occupation. Although people were killed in this phase (for instance, during the Santa Cruz massacre), violence during this period was of relatively low 8
presented by equation 3 where _Rit_ is instrumented by the instrument defined in equation 2. Since we use all provinces in the main IV estimations, _i_ is defined as _i_ = (1 _, ...,_ 81) while _t_ is defined as _t_ = (2011 _,_ 2014). Following Del Carpio and Wagner (2015), we use the year-specific natural logarithm of the distance to the closest border crossing, _LDit_ as a control variable. The year-specific distance control is defined as _LDi_ 2011 = 0 and _LDi_ 2014 = _LDi_ .

Our second strategy is to estimate a linear difference-in-differences model with province level fixed effects for all outcomes, which is the method used by Ceritoglu et al. (2017). Their approach defines the years 2012 and 2013 as treatment years and the previous years as pre-treatment. We do exclude 2014 in the DD model since Syrian refugees have been spreading across Turkey from 2014 onwards, while they were more concentrated near the border areas that we define as the treatment region in 2012 and 2013.<sup>4</sup> In effect, the DD estimates use treatment years that are completely excluded from the IV model: 2012 and 2013. A second issue in the DD specification is the definition of the control area. We follow Ceritoglu et al. (2017) and define the control region as the northeastern part of Turkey, which neighbors the treatment region. Ceritoglu et al. (2017) argue that the common trend assumption is more likely to hold in this region which is more similar to the treatment region than western Turkey.

The basic DD specification is presented in equation 4. _Yit_ is the outcome variable, ( _Treatment_ ) indicates regions hosting refugees and ( _post_ 2012) indicates the period after refugees arrive. The main parameter of interest is _ρ_ , which gives the DD estimate for the effect of hosting refugees. The controls for ( _Treatment_ ) and ( _post_ 2012) are dropped once we include year ( _Tt_ ) and province fixed effects ( _Pi_ ). The analysis is performed on data from 29 provinces since we limit the control group provinces to eastern Turkey following Ceritoglu et al. (2017). Subscript _i_ still stands for individual provinces but is now defined as _i_ = (1 _, ...,_ 29). Since refugees spread out across Turkey in 2014, we only exploit the refugee shock in 2012 and 2013 when most of the refugees were located 9
hurdles to surmount before letting them go, or because severance payments would be cheaper for employers. The fourth model assumes that the first workers to be cut would be the least educated. Finally, we assumed that worker layoffs could be random  that observable characteristics did not predict layoff patterns.

We assumed that all workers who lose their jobs receive an income equal to about 28.3 percent of their pre-crisis wage, which is the average share of unemployment benefits. This is because households that lose a significant source of income typically cope by tapping other sources of income, such as unemployment benefits, remittances, or a part-time job, for example.<sup>8</sup> Since household coping strategies for income loss could not be predicted ex-ante, we assumed that postlayoff income would equal total unemployment benefits.

Further, we assumed that households with a main source of income from the informal sector would experience a welfare decline in line with the decline in per capita GDP. However, the welfare decline of all other households, i.e., those without a worker in either the formal or informal sector, is determined endogenously such that the cumulative welfare decline is equal to overall GDP contraction for the economy.

The objective of micro-simulations was to estimate Latvian income distribution under different scenarios. The work follows the spirit of Oxaca (1973), Juhn, Murphy and Pierce (1993) and, more recently, Bourguignon, Ferreira and Lustig (eds.) (2004). Formally, defining the Latvian income distribution at year _t_ as Dt: where yht stands for the household per-capita income of household _h._ The total income of the household comes from the sum of the labor income (Y<sup>L</sup> ) and non-labor income (Y<sup>NL</sup> ) of all the members of the household: On the one hand, labor income equals the hourly wage earned by the individual ( ), times the number of hours worked ( ):

> 8  Dasgupta and Ajwad (2011), using crisis response survey data from five Eastern European countries, showed that households impacted by income shocks adopt a host of coping strategies.

7
Table 1: Eect of Imported Malaria Incidence

||(1)<br>Fixed<br>Eects|(2)<br>Fixed<br>Eects|(3)<br>Random<br>Eects|(4)<br>Random<br>Eects|(5)<br>Arellano<br>Bond|
|---|---|---|---|---|---|
|Imported Incidence|1.230**|1.175**|0.793**|0.864**|1.094***|
||(0.452)|(0.476)|(0.398)|(0.381)|(0.357)|
|Lag Incidence|0.330***||0.434***|0.418***|0.563***|
||(0.0509)||(0.0579)|(0.0552)|(0.129)|
|Lag Imported Incidence||0.449*<br>(0.231)||||
|Lag Non-imported Incidence||0.340***<br>(0.0571)||||
|Rain in cm|0.00639|0.00592|0.00348|0.00449|-0.000654|
||(0.00934)|(0.0102)|(0.00904)|(0.00853)|(0.00790)|
|Lag Rain in cm|0.0292|0.0294|0.0294*|0.0300*|0.0269|
||(0.0189)|(0.0188)|(0.0177)|(0.0177)|(0.0181)|
|Lag 2 Rain in cm|0.0381**|0.0372**|0.0376***|0.0382***|0.0319**|
||(0.0158)|(0.0155)|(0.0138)|(0.0141)|(0.0156)|
|Constant|-0.0381|-0.0396|-0.0487**|-0.0621**|-0.0688**|
||(0.0294)|(0.0317)|(0.0237)|(0.0264)|(0.0290)|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Health Post Area Controls|No|No|No|Yes|No|
|Health Post x Month Obs|432|396|432|432|432|
|R-squared|0.509|0.512||||
|Hausman Test Comparing Co|lumn 1 and|Column 4|p-|value=0.008|3|

Testing Predictions

|Test Lag Imported =Lag Non-Imported|p-value=0.650|
|---|---|
|Test Imported=1|p-value=0.792|
|Test Lag Incidence=0.273|p-value=0.0247|
|Test Imported + Lag Incidence=1|p-value=0.0428|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Notes: Column 4 includes controls for health post area population density, a dummy for urban health post areas and a dummy for non-border health post areas.

19
# **2 Data and measurement**

This paper uses data on online job postings from Lightcast to capture jobs related to emerging technologies, and matches this at the firm level with data on firm-to-firm linkages from Factset. In this section, we first describe the online job postings dataset, the classification of new technologies, and the matching process with FactSet firms. We then discuss additional data cleaning and complementary sources. Finally, we provide different measures of technology concentration including the Gini coefficient and the coefficient of variation.

## **2.1 Online job postings**

Our primary dataset includes 573 million scraped online job postings from Lightcast (formerly Burning Glass Technologies). Our sample covers 17 advanced economies from 2014 to 2022.<sup>2</sup> We use the job postings data to identify jobs relating to emerging technologies for several reasons. First, it allows us to identify jobs related to specific and granular technologies over time. In the absence of detailed data on the deployment of multiple technologies across firms, regions, and countries, real-time rich text data sources have been increasingly used as a barometer for the diffusion of new technologies, as reflected through their skills demand footprint or extent of discussion in company boardrooms (see for example Acemoglu et al. (2022) and Bloom et al. (2021)). These datasets are particularly useful for studying rapid advances in new technologies that have not yet been clearly defined or captured by statistical agencies using traditional data sources. Moreover, Lightcast provides not only the raw text of each job description, but also additional information about the industry, occupation, and location of each job posting, allowing us to trace diffusion through each of these channels.

The main potential shortcoming of the Lightcast data on online job postings concerns the representativeness of actual vacancies. First and most notably, it only includes jobs posted online and this subset of vacancies may not be fully representative of all vacancies, while vacancies are also a flow measure of labor demand, rather than a measure of the stock of employment. A growing literature has now shown that online job postings tend to be over-representative of higher-skilled occupations and industries, although recent research has generally found that in advanced economies a very high share of jobs are now posted online. For the US, Hershbein and Kahn (2018) show that the share of jobs online as captured by Lightcast is roughly 90% of the jobs in JOLTS. Bloom et al. (2021) also show that the trend in Lightcast postings after 2010 parallels that of JOLTS openings in the US.

> 2The sample includes: Austria, Belgium, Denmark, France, Germany, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, United Kingdom, Singapore, Australia, New Zealand, Canada and the United States.

4
This section documents stylized facts on public investment in Sub-Saharan Africa: (a) Its trends and interplay with the private sector, regarding crowding-in or crowding-out investments, and the current spending levels. The second section (b), reviews the interplay with the private sector in providing economic infrastructure for the region: what have been the mayor roles, concentration of projects, if any, and the financing structure from both public and private counterparties?

# **D.1. Public Investment in SSA**

The unprecedented economic performance in Sub-Saharan Africa (SSA) over the past two decades, coined as _Africa Rising_ , was characterized by many SSA countries growing at a rate that exceeded 5 percent per annum. The _Africa Rising_ ’s earlier narrative attributes the region’s faster growth to external tailwinds, progress in macroeconomic management and robust public investment. As economies in the region decelerated in the post-crisis period, there was a significant countercyclical effort to support aggregate demand among countries in the region; partly reflected in a large increase of public investment. For instance, the cumulative increase of public investment exceeded 5 percentage points of GDP in Togo, Guinea and Ethiopia during the period 20082015.

The evolution of public investment as a percentage of GDP in the region exhibits three marked periods: (a) a rising trend in the 1970s that reaches a peak of about 7.8 percent in 1977-78, (b) a steady decline throughout the 1980s and stagnation in the 1990s that reached a trough of 3 percent of GDP in 2002, and (c) an increase in public investment that reaches a peak of 5.8 percent of GDP in 2014. When comparing the public investment for the region, East Asia and the Pacific outperform all other regions with levels of public capital spending that exceed 10 percent of GDP (figure 12).

**Figure 12. Public investment in Sub-Saharan Africa, 1960-2015** _(% GDP)_

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>SSA 1IND EAP SA<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> _Source: International Monetary Fund (2017)_ Focusing on averages for Sub-Saharan Africa masks the heterogeneity across country groups. Splicing the data across resource abundant countries in the regions shows different trends and phases in public investment across resource and non-resource abundant countries (figure 13). Public investment, on average, among on oil-abundant nations followed a rising path since early 1990s. After reaching a peak of about 12 percent of GDP in 2012-14, public investment among oil-abundant countries declined sharply to 6.6 percent of GDP in 2015 –partly due to lower commodity-based public revenues. For non-oil abundant and nonresource rich countries, public investment has experienced a steady increase since 2003 (with a cumulative variation up to 2015 of about 2 and 1.7 percentage points of GDP respectively).

_Infrastructure in Sub-Saharan Africa - 27_