rafmacalaba/data-use-mentions-extended huggingface dataset:
br>8<br>10<br>e<br>g<br><br><br>ual growth in employment<br>(%)|s|
|---|---|---|---|
|0<br>1987<br>Agr|1989<br>1992<br>1995<br>1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>2011<br>2013<br>2015<br>2017<br>iculture<br>Mining<br>Manufacturing|Agricultur<br>Minin<br>Manufacturing<br>Construction<br>Average ann|Service|
|Co<br>Bus|nstruction<br>Trade<br>Transport<br>iness<br>Other|1987-1997<br>1997-2007<br>2007-2018<br>1|987-2018|
|Source: Aut|hors’ calculations based on data from DOSM|Source: Authors’ calculations based on data from D|OSM|
|**Table 7: A**|**verage annual growth in employment**<br>**Average annual growth in employme**|**by sector, 1987-2018**<br>**nt (%)**<br>**Average annual growth in empl**<br>**(excluding crisisyears)**<sup>**♠**</sup> **(**|**oyment**<br>**%)**|
||**1987-1997**<br>**1997-2007**<br>**2007-2018**|1987-2018<br>**1987-1997**<br>**1997-2007**<br>**2007-2018**|1987-2018|
|Agriculture|<br
are similar, especially once we define the changes in import protection more broadly (to include additional policy instruments beyond tariffs), there are two subtle differences worth highlighting. The first difference is in how Argentina and Brazil arrived at this similarity – i.e., a comparison of columns (1), (2) and (3) in each table suggests that Argentina had potentially “more” of a building block effect (apparent in the tariff only data) for its TTBs to overcome than did Brazil. Second, in the raw data on TTB use, we have also observed a substantial difference in how each country utilized TTBs during this period (both overall and toward each other). We use the next section to explore the role of market power in potentially shaping these results. Before turning to the next section, a final point worth mentioning is that our results are robust to re-defining the measuring of TTBs in the dependent variable in the following way. We reestimated all the specifications from tables 4 and 5 but re-defining the dependent variable such that whenever there is a TTB imposed, it is considered as an increase in protection (i.e., in Table 2, the case in 23
migrant, and forced migrant students’ in terms of the aforementioned variables. The table reveals large gaps in college admission test registration and applications for government financial aid between native-born and migrant students. While 83 percent of native students registered for the test, only 49 percent of migrants and 31 percent of forced migrants did so despite having equal access to aid. These gaps generate differences in both access to benefits and enrollment in higher education: the number of forced migrants is almost half the percentage of natives who access these services. It is important to highlight that the largest difference in Table 5 relates to registering for the college selection test.

Table 5: Graduating High School Students: Access to Higher Education (2017)

||Ty|pe of Stude|nt|
|---|---|---|---|
|Variable|Chilean Students|Migrants|Forced Migrants|
|Financial Aid Application|65%|40%|27%|
|Benefts|32%|16%|8%|
|College Selection Test|83%|49%|31%|
|Enrollment Higher Education|49%|33%|19%|

**Note:** The table shows the percentage of high school graduates that are natives, migrants and forced migrants, and refugees that applied for financial aid, got financial aid, took the college selection test, and enrolled in higher education. The base category is the total number of students for each type of migrant status.

These patterns in our data are consistent with both national and international evidence indicating that migrant status is associated with fewer applications to admission processes or benefits programs (UNHCR, 2019). Survey evidence from Chile demonstrates that migrant students are not only less likely to enroll in tertiary education; they also tend to receive less government financial aid to fund their studies. In fact, 71 percent of surveyed migrants pay for their tuition themselves; 21 percent receive free tuition and 9 percent receive a loan or
district is a direct output of the SSBN/Fathom model [1], but this analysis is particularly interested in disaster impacts on the assets, consumption, and welfare of _households_ . One issue faced here is the difference between the Gross Domestic Income (GDI) derived from national accounts and the aggregate household expenditures (AHE) calculated from household surveys (in this case, HIES). As is well known, the latter tend to report lower expenditures than national accounts [3]. In this analysis, we work based on the AHE. Therefore, the expected losses in Table 1 have been reduced by a scale factor, equal to total household consumption over nominal regional productivity (cf. Tab. 5), and express average annual damage or destruction to assets used by Sri Lankan households to generate income, by district. Asset losses in Jaffna, Kilinochchi, and Matara are omitted due to their exclusion from available SSBN/Fathom flood maps.

> 2 SSBN is a global model subject to limited availability and variable quality data. Therefore, its precision and accuracy can be low in some places, and we would seek ideally to replace these inputs with highresolution local models.
them. The grid contains 7 GBFs: Business administration, production planning, sourcing and procurement, marketing, sales, payment methods, and quality control. Each function considers between 4 and 7 technologies. For example, an establishment can gather and analyze customer information for marketing purposes using face-to-face conversations, online chats via WhatsApp or the internet, structured customer surveys, customer relationship management (CRM) software to store contact information, interaction history, and communication preferences, or big data analytics and/or artificial intelligence to uncover trends and make informed marketing decisions. Figure 2 presents the grid for one sector-specific module, agriculture. The grid considers six SSBFs for agriculture which include land preparation, irrigation, weeding and pest management, harvesting, storage and packaging. For example, to prepare the land for cultivation, a farm can use manual labor with simple tools such as hand-held hoes, or rakes, animal-aided instruments such as ploughs, equipment manually operated such as tractors, motor tillers, or rotators, or equipment supported by digital technologies such as GPS, software or precision agriculture tools. Section A.1.1 of the appendix reports the grids for all other SSBFs and the implementation manual precisely defines each of the technologies in the grid.

# **2.3 Ranking of Technology Sophistication**

In addition to identifying key business functions and relevant technologies, industry experts ranked the technologies in each function based on their sophistication. More sophisticated technologies can perform a wider variety of tasks, more complex tasks, or perform tasks with greater accuracy and speed. The experts’ deliberations and resulting sophistication rankings, shown on the grid, were produced before the survey administration. This approach to ranking technologies resembles the World Management Survey (Bloom and Van Reenen, 2007), which relies on experts to rank management practices according to their quality.

Given the importance of the ranking for our analysis, we evaluated the coherence of the expert rankings through a three-stage validation process implemented in 14 of the 63 business functions on the grid including most of
In terms of the distortions that generate misallocation in the model, these take the form of idiosyncratic taxes and subsidies to the firm’s revenue. We adopt the assumption that there is heterogeneity in the degree of taxation across firms but we assume this rate to be the same across products within a firm. This is the assumption that we consider suits best the nature of the data from which we calibrate the functional form of the distribution of distortions. Since firm-level data are typically reported at the firm or establishment level, reported statistics about distributions of _TFPR_ and _TFPQ_ can only be informative about firm-wide or establishment-wide, rather than product specific, distortions. Thus, the combination of misallocation frictions with the multi-product feature of the model creates another layer of misallocation across firms, namely the distribution of products across producers. However it obliges us to abstract from a channel of misallocation within firms.

While we specify a particular functional form governing the degree of relationship between idiosyncratic distortions and physical productivity once we get to the quantitative analysis, at this stage we require that whatever functional form we impose, it does not create reversals in the positive relationship that exists between productivity and profitability in the undistorted equilibrium. That is, while it is certainly the case that distortions that correlate positively with productivity flatten the positive relationship between productivity and profitability, we rule out cases where a relatively unproductive firm is turned more profitable than a relatively more productive one. We find empirical support for this assumption in that all of the existing estimates in the literature of the regression coefficient between the log of _TFPR_ and the log of _TFPQ_ are far below one.

Letting _τω_ denote the idiosyncratic distortion corresponding to a firm with firmwide productivity _e_<sup>_ω_</sup> , the price and the labor demand associated with the provision of variety _e_<sup>_λ_</sup> of a product _k_ solves the following static profit maximization problem: which yields the following expressions for labor demand, output, revenues, and profits 8
2011|40.1|41.1|41.5|41.8|71.6|72.1|72.1|72.3|
|2012|39.5|39.9|40.1|40.3|71.2|71.3|71.3|71.3|
|2013|38.9|38.9|39.0|38.9|70.6|70.2|70.0|69.8|
|2014|38.3|37.7|37.5|37.4|69.8|69.1|68.6|68.3|
|2015|38.0|37.4|37.1|36.8|69.8|68.9|68.5|68.0|
|2016|38.6|38.2|38.1|38.0|70.4|70.1|69.9|69.7|
|2017|38.9|38.7|38.7|38.5|70.7|70.6|70.5|70.4|
|2018|39.0|39.0|39.0|39.0|70.9|70.9|70.8|70.8|
|2019|39.1|39.1|39.1|39.1|71.0|71.0|71.0|71.0|

Note: the table shows different backcasted series for different values of the pass-through rates and different values of the poverty line. Estimates use sectoral GDP growth rates mapped to household’s head sector of employment to backcast household consumption from the 2018/19 NLSS over the previous decade.

36
a multisectoral study, Feenstra et al. (1997) test the hypothesis of semiendogenous growth using data on bilateral trade between the United States and South Korea and between the United States and Taiwan. Their study focuses on sixteen industrial sectors, for which they test whether changes in the relative varieties of inputs affect the growth rate of relative total factor productivity between South Korea and Taiwan. They classify seven of these sectors as primary and nine as secondary, defining firms that use raw materials and natural resources as inputs as belonging to the primary sector. Their results show that variety of inputs affects the growth rate of total factor productivity in seven secondary sectors but only one primary sector. The mining sector displays a positive relationship, although this effect disappears after controlling for imperfect competition. The remaining sectors in the primary sector 15
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These data are merged at the commune level to construct current and long‐term weather variables for each month. For each commune, the household interview dates are specified and the current weather variables are defined for the 12 months before that interview date (e.g. if the interview date was in May 2012, the current weather variables cover June 2011‐ May 2012). The long‐term means and standard deviation of monthly rainfall, minimum, mean and maximum temperature are calculated for 30 years before these 12 months (e.g. in the above example June 1981– May 2011). Based on these data several variables measuring current weather conditions are constructed as explained in section 4.

Although gridded data set, such as CRU are commonly used in economic studies as they provide a balanced panel that adjusts for missing data and spatial factors (e.g. elevation), they suffer from some limitations for assessing weather variation at subnational level. Optimally such data would be measured based on ground station data, which, however, is not readily available for all of Vietnam. The precision of the CRU data at the subnational level depends on the interpolation method and the availability of station data for the areas of interest (Dell et al., 2014). Although this is an important concern that deserves further investigation, this is beyond the scope of this paper. Moreover, only monthly average rainfall and temperature data are available from CRU. Given high intra‐annual variations, the distribution of

> 4 http://www.cru.uea.ac.uk/cru/data/hrg/

5
might swing highly, and second, multicollinearity reduces the precision of the estimated coefficients, weakening statistical power. These problems are evident from the regression results once the incomplete roads dataset is integrated in the analysis: while coefficients keep the same sign, the coefficient value and statistical power fluctuate highly.

# **Location of mines**

Location of historical roads might be influenced by other factors than geography and military strategies. For instance, roads could have been built in areas with greater economic potential. During the period, which the historical map is depicting, the main non-agricultural sector with economic potential in Armenia was mining. Armenia has a long history of metal mining and is accounting a large part of the economy. According to the UN Comtrade, metals and ore concentrates accounted for more than half of Armenia’s exports in 2017.<sup>18</sup> Copper mining first started in the Alaverdi area of Lori Marz in 1770s. Later, it started in Kapan in Syunik Marz in 1840s and by the mid-20th century the Kajaran copper-molybdenum mine in Syunik Marz started production. Kajaran mine is the largest operating mine in Armenia, accounting for 60% of the total mining turnover in the country (World Bank, 2016). In spite of the historical importance of the mining sector in Armenia, it is unlikely that by the end of the 19th century primary road placement would have been influenced by mine locations. There are two main reasons to argue this. First, most of the historical mines have been operated in Lori (in north Armenia) and Syunik (in south Armenia) regions of the country. In both regions, as 6 shows, large mines are located very far from the historical routes. However, they are connected by railway lines. The only mine which seems to be on the primary historical road route is the Ararat mine. However, the operation of the Ararat mine had been relatively small until 1975. In addition, it is not an active mine but rather a processing plant.
calculate the propensity of every country in the destination subregion to accept migrants from elsewhere in the world. All of the propensities sum to one. These shares are multiplied by the total migrant stock figures provided in the Trends in International Migrant Stock database to calculate the bilateral numbers.

# _III. RELIABILITY OF THE ESTIMATES_

The previous section described the challenges in constructing the matrices and the range of measures used to generate the missing observations. This section highlights the extent to which the estimates are based on the underlying raw data and their reliability.

# _Categorizing the Methods Used_

Nine main methods were used to generate the cells:

1. _Pure raw:_ Derived directly from the raw bilateral census data.

2. _Raw scaled:_ Based on the underlying raw bilateral data scaled to the UN numbers.

3. _Pure remainder:_ Assigned directly from the disaggregation of aggregate categories applying one of the propensity measures.

4. _Remainder scaled:_ Based on disaggregations using one of the propensity measures and then scaled to the UN numbers.

5. _R&R_ [ _Raw and Remainder combined not scaled:_ Based primarily on bilateral raw data and to which disaggregations of certain aggregate categories were added.

6. _R&R_ [ _Raw and Remainder combined_ ] _scaled:_ Similar to R&R not scaled except that the resulting value was scaled to the UN numbers.

7. _Pure interpolation:_ Calculated solely by interpolating missing end and middle censuses, but not scaled to the UN data.

8. _Interpolation and scaled:_ Both interpolated and scaled, for countries with poor data or for cells calculated by interpolating missing and end decades which then had to be scaled.

9. _Missing:_ For countries for which bilateral data were missing for every census round, such as Somalia.

The data used in the first six methods are from the raw census data. The data for the last three methods are missing because of omissions in the underlying data and need to be filled. Therefore, varying percentages of observations in each decade
for a semiparametric test of the impacts of deviations from the long-run mean. It also allows for impacts to be both nonlinear and nonsymmetric around zero. These properties are important for distinguishing the possible heterogeneous impacts of deluges versus droughts.

Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of global total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).

To give an indication of how wealth and economic composition impact the relationship between droughts and economic growth, we use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries. Classifications are based on mean per-capita GNI in 2015 where low-income countries have GNI per capita below $1,025, middle-income countries are between $4,036 and $12,475, and high-income countries are above $12,475. We also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity. Additionally, local and upstream shares of forest cover are measured using satellite data from the European Space Agency.

# 3. Empirical Strategy

Our econometric specification uses a panel fixed-effects model to link data on droughts to data on economic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the equator) between 1991 and 2014, the period for which economic data is available at a granular scale.

In order to estimate the impact of droughts on economic growth, we follow much of the empirical climate change literature and estimate a reduced-form production function-style equation (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015; Deryugina and Hsiang, 2017; Newell
under-5 mortality.

> 12 When there are multiple estimates of adult mortality for the same country and same period (based on different rounds of the DHS) the estimates are averaged, weighting each estimate by the inverse of the variance of the estimate so that more precise estimates are given more weight.

> 13 These regional averages are population weighted, where the weights are the total population at the beginning of the period in question.

> 14 Under-5 mortality for each period is the average of the rate reported for the beginning and end of the period.  So, for example, under-5 mortality for 2000-04 is estimated as the average of the WDI reported mortality rates for 2000 and 2005.

9
|701|1,018.02|446.88|1,608.47|66.00|2,471.17|6148|
|Share of white-collar workers|0.26|0.19|0.23|0.06|0.59|701|0.37|0.24|0.33|0.06|1.00|6210|
|Exports/total sales|0.39|0.15|0.43|0.00|1.00|701|0.16|0.00|0.33|0.00|0.93|6132|
|Share of FDI|44.17|0.00|48.90|0.00|100.00|701|17.16|0.00|36.74|0.00|100.00|6210|

_Source:_ Authors’ calculations with data from ENTIC 2009 and 2013, INEGI
country using the constructed measure of doing business and the business freedom index (Table B.2 in the Online Appendix).

One interesting observation from the Table 2 is that higher GDP per capita (PPP) is associated with a higher number of visits by tax officials and a more common perception of tax administration being an obstacle to business operations and growth. This signals that richer developing countries also have a more cumbersome tax administration bureaucracy. We also find that more productive and larger firms and those that export face a higher tax compliance burden.

# **_4.2. Competitiveness of public procurement and E-procurement_**

We next analyze the impact of e-procurement implementation on competitiveness of public procurement and the probability that firms had to pay bribes to secure a government contract. Table 2, Columns V and VI present the results. Although we expect the adoption of e-procurement to increase the likelihood that firms participate in public tenders and to limit corruption, our estimates demonstrate this is not the case. We obtain insignificant coefficients on both informational and transactional e-procurement systems. When we control for a country’s overall business environment the results remain insignificant (and we therefore we do not report them). The results also show that firms with higher productivity, state ownership, larger size, publicly traded, or web-presence tend to participate more often in government tenders. Firms with lower productivity, smaller size, higher export shares, or which are not publicly traded are more likely to pay bribes to secure a government contract.

Our results suggest that automation may be less effective in public procurement. By its nature, public procurement requires more discretion from government officials than tax administration, particularly for complex activities where evaluating bids for technical compliance or value for money requires considerable judgment. Public procurement also involves decisions over potentially much larger sums of money than tax returns, thereby increasing the opportunities and incentives for corruption. Given that far fewer firms bid for government contracts than file taxes, it is more susceptible to collusive relationships between bureaucrats and firms. In