rafmacalaba/datause-encoder-data huggingface dataset:
# **Previous research**

Three studies in the literature on the impact of conflict on economic activity are especially relevant for the design of the methodology in this paper. These studies deal with the impact on aggregate output, on the formal sector and on illicit activities respectively. Differences in the approaches used in each case provide a useful backdrop to guide the empirical work for Afghanistan.

At the aggregate level, Abadie and Gardeazabal (2003) use a quasi-difference-in-differences approach to assess the impact of terrorism on the GDP per capita of the Basque Country from 1955 to 1977. To do so they construct a “synthetic control region”, as a weighted combination of other Spanish regions. The economic evolution of this synthetic control region without terrorism is compared to the actual experience of the Basque Country using an autoregressive distributed lag model.

experience of the Basque Country using an autoregressive distributed lag model. The gap in per capita GDP between treatment and control is the dependent variable in the analysis, and the number of fatalities from terrorism (almost 800 over time) is the independent variable. The ∆YY study estimates the impulse-response function to conflict assuming that the output gap in year is CC determined by the number of deaths from terrorism both in the same year and in the two previous ∆YY tt years, as well as by the output gap in the two previous years: CC The error term in this equation is assumed to follow a first-order auto-regressive model. Therefore, the parameters in equation (1) are estimated using data on conflict and the output gap lagged for up to three tt uu years. No other control variables are considered in the proposed specification. The results reveal a negative impact of deaths from terrorism in the previous year, but not in the same year or two years earlier ( is statistically significant but and are not). Based on the estimated coefficients, after the outbreak of terrorism, per capita GDP in the Basque Country declined about 10 μμ<sup>1</sup> μμ<sup>0</sup> μμ<sup>2</sup> percentage points relative to the synthetic control region.

Impacts on the formal sector are assessed by Blumenstock et al. (2018), who investigate how private sector firms in Afghanistan respond to changes in the local security situation. Whether the firms are active is assessed based on call detail records from one of the country’s largest mobile network operators between April 2013 and December 2016. Call detail records are from cell phone towers spanning two7
Table 9: Placebo Test: West Manggarai (Restaurants)

**Control vs. POS recipient restaurants**

|**Dependent Variable:**|**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**Log (restaurant tax receipt)**|**RPOS**|**TMD**|**WS**|
|ATET (given RPOS vs. not)|-0.00657<br>(0.00424)|||
|ATET (given TMD vs. not)||-0.00656<br>(0.00423)||
|ATET (given WS vs. not)|||-0.00508<br>(0.00328)|
|Constant|0.00891**<br>(0.00257)|*0.00902**<br> (0.00260)|*0.00909***<br> (0.00262)|
|Observations|1,920|1,895|1,880|
|Adj. R<sup>2</sup>|0.004|0.055|0.072|
|Robust standard erro<br>*** _p <_0_._01, ** _p <_|rs in parenthese<br> 0_._05, * _p <_0_._1|s||

Table 10: Placebo Test: West Manggarai (Hotels)

|**Control vs. POS recipie**|**nt hotels**|
|---|---|
|**Dependent Variable:**|**(1)**|
|**Log (hotel tax receipt)**|**WS**|
|ATET (given WS vs. not)|3.086***<br>(0.358)|
|Constant|0.002<br>(0.048)|
|Observations|10,350|
|Adj. R<sup>2</sup>|0.063|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 23
who were re-surveyed in late 2007 as a baseline sample ( **_original sample_** ). This sample is used to analyze factors affecting initial job decisions in 2007.

In 2007, the survey added 151 new fishing families to our original sample; these families had never owned a boat before the tsunami but donors gave them a chance to be the first time owners. Besides, 288 previous owners were additionally added from Aceh Jaya (kabupaten which is heavy devoted to fishing) which is located in the south of Aceh and was not originally covered in 2005. Of the 439 households, only 244 households could be re-surveyed in 2009.<sup>vii</sup> I added these to 313 households (which were surveyed in all three rounds among 348 original sample), and formed 557 families ( **_supplementary sample_** ). This is the two-year panel between 2007 and 2009 to examine the job switching behaviors (see Figure 3).

Summary statistics for the original sample and the supplementary sample are reported in Table 1. In supplementary sample, new boat owners were oversampled in Aceh Jaya. This sampling heterogeneity needs to be controlled in the later sections.

# **3.      Theoretical predictions**

The model is built in two stages where fishermen make career decisions in face of aggregate and idiosyncratic quality shocks (see Appendix 1 for details).

First, donors provide production assets (i.e., fishing boats) with heterogeneous quality across villages. In the first stage, some villages encounter serious quality problem. Households in such communities face a decrease in labor demand (aggregate shock; _AGGSHOCK_ ) as it reduces the number of boat owners in the community, suppressing labor demand for crew as well. Besides, it could also have a price effect by reducing compensations for crew. In the first stage, actual boat quality is imperfectly observed by individual fishermen.<sup>viii</sup> After they use the fishing boats, they may discover that their boat is not durable (idiosyncratic shock; _IDIOSHOCK_ ). If the aggregate quality shock continues to constrain job opportunities, those who initially resumed fishing may have common motives to switch jobs in the second stage.

6
-67-

------------------------------------------------------------------------

__---__----

**TABLE** 4. 10

# Quantities (Value Added, Former National Accounts Concept) by Major Branch of Manufacturing, Mexico/Brazil (1975)

|--------------------------------------------------------|at Mex|ican "price|_<br>s"|_-------------------__----<br>at Br|azilian<br>"|prices"|
|---|---|---|---|---|---|---|
||Mexico<br>1975<br>(1975 Ps.|Brazil<br>1975<br>million|Mexico/<br>Brazil<br>) (%)|Mexico<br>1975<br>(1975 Cr.|Brazil<br>1975<br>million)|Mexico/<br>Brazil<br>(%)|
|Food Products|20,446|44,984<br>|45.45<br>|12,323|27,759|44.43|
|Beverages|8,170a|10,418|78.42|3,806|4,565|83.37|
|Tobacco Products|1,177|4,928<br>|23.89<br>|717|2,987<br>|23.99<br>|
|Textiles<br>and Wearing Apparel<br>|15,334<br>|37,943<br>|40.41<br>|10,229<br>|2 2<sup>, </sup>9 40 <sup>c</sup>|<sup>44.59</sup><br>|
|Footwear<br>and Leather<br>Product|s2,472|9,060|27.29|1,104|3<sup>, </sup>9 77 <sup>c</sup>|27.76|
|Wood and Paper Products|13,121wa<br>|52,566<br>|24.96<br>|5,142<br>|27,696<br>|18.56<br>|
|Chemical Products|26,226|46,885|55.94|28,068|42,511|66.03|
|Rubber and Plastic<br>Productss|6,264|30,245|20.71|2,133|10,260|20.79|
|Stone, Clay and Glass Produc|ts8,857|29,754|29.77|5,980|15,365|38.92|
|Metal products|23,949|48,913|48.96<br>|17,706<br>|31,176|56.79|
|Electrical<br>Machinery|9,557|21,552|44.34|5,873|15,437|38.04|
|Machinery and Transport|||||||
|Equipment|19,423|95,749|20.29|9,046|44,231|20.45|
|Other|2.494|14,107|17.68|1,643|8,109|20.27|
|Total|157,488|447,104|35.22|103,779<br>|257,012|40.38|

- a) indirect taxes and subsidies are deducted (see table 2.3).

- b) includes 3,831.7 million pesos (excl. indirect taxes and subsidies) for petroleum refining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

- **c)** the footwear industry (2,675.9 million cruzeiros) was reallocated from wearing

- apparel to footwear and leather.

Source: Mexican gross value added in national currencies calculated from Resumen General (see table 2.3 which does not exclude bank costs). Brazilian gross value added in national currencies calculated from Censo Industrial (see table 2.1 which does not exclude bank costs). Bank costs derived from the same sources (for calculation see chapter II). PPPs from table 4.9.
Page 6 1986; Dwyer 1983; World Bank WDR 1988). (See figure 1 and annex on women's labor force participation rates and the female share of the labor force.)

# Figure I

Women's Labor Force Participation <!-- Start of picture text --> ' e b.ca tMant tmyd Iueg u<br>f~~~~~0 - - -_ __<br>1n 21 n<br>hUTE: Averaps are based on the mast recent countr data since 1970. The n umber<br>of countries was S in Aic. 12 in N. Africa and the Middle East, 13 in Asia<br>and 21 in Latin Amw'c.<br>10URCE See Table 2.3 and Table '-6<br>FIGURE 2.2 Women's average share of non.agrkicltural occupations in major<br>JSfo0rphicwA regions, including self-employed and unpaid family workers<br>Source: Department of Commerce, 1985.<br><!-- End of picture text -->
_____ (1999), “Exceptional Exporter Performance: cause, effect or both?”, Journal of International Economics, 47, 1-25 Bernard, A.B, J. Eaton, J.B. Jensen and S. Kortum, (2003),”Plants and Productivity in International Trade”, American Economic Review, 93, 1268-1290 Bernard, A.B, J. Branford Jensen, S.J Redding, and Schott, P.K. (2007), “Firms in International Trade”, Journal of Economic Perspectives, 21(3), pp.105-30.

Clerides, Sofronis K., Saul Lach, and James R. Tybout (1998), “Is Learning by Exporting Important? Microdynamic Evidence from Colombia, Mexico, and Morocco,” Quarterly Journal of Economics 113, no. 3:903-947.

Cohen, Wesley and Steven Klepper (1996), “Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D,” Review of Economics and Statistics, 78(2), pp. 232-243.

Cohen, Wesley M. and Daniel A. Levinthal (1989), “Innovation and Learning: The Two Faces of R&D,” Economic Journal, 99(397), pp. 569-596.

Constantini, James A. and Marc J. Melitz (2008), “The Dynamics of Firm-Level Adjustments to Trade Liberalization,” in _The Organization of Firms in a Global Economy_ , E. Helpman, D. Marin, and T. Verdier (eds.), Cambridge: Harvard University Press Criscuolo, Chiara, Jonathan Haskel, and Mathew Slaughter (2005), “Global Engagement and the Innovation Activities of Firms,” NBER Working Paper 11479.

Dunne, Timothy, Mark J. Roberts, Larry Samuelson (1989), "The Growth and Failure of U.S Manufacturing Plants” The Quarterly Journal of Economics, 104(4):671-698.

Eaton, Jonathan and Samuel Kortum (1999), “International Patenting and Technology Diffusion: Theory and Measurement,” International Economic Review, 40, pp537-70 Eaton, Jonathan and Samuel Kortum (2002), “Technology, Geography, and Trade,” Econometrica, 70:5, pp. 1741-79 Goldberg, Pinelopi K., Amit Khandelwal, Nina Pavcnik, and Petia Topalova (2008), “Imported Intermdiate Inputs and Domestic Product Growth: Evidence from India”, NBER Working Paper 14416.

Grossman, G. M. and Helpman, E., 1991, _Innovation and growth in the global economy_ . Cambridge, Massachusetts, MIT Press.

Halpern, Laszlo, Miklos Koren, and Adam Szeidl (2006), “Imports and Productivity,” Mimeo, Department of Economics, University of Berkeley Keller (2004), “International Technology Diffusion,” Journal of Economic Literature, 752-782.

Kasahara, Hiroyuki and Beverly Lapham (2007), “Productivity and the Decision to Import and Export: Theory and Evidence,” _Mimeo_ , University of Western Ontario.

32
countries within each region that sent responses, one can still draw some interesting conclusions.

The other categories of respondents were based on the income category of the country in which the firm is located (Table 2), the sector in which the firm operates (Table 3), the size of the firm in terms of number of employees (Table 4), and the number of MBAs the firm hires yearly (Table 5). As the tables below show, the sample of responding firms is distributed quite evenly, with the only exception being the large proportion of firms that fall into the Finance and Insurance sector.

**Table 2:  Breakdown by Income Category of Country**<sup>**10**</sup>

|Income Category<br>of Country|Number of<br>Respondents|Percentage|
|---|---|---|
|I|82|29 %|
|II|36|13 %|
|III|85|30 %|
|IV|43|15 %|
|V<br>|25|9 %|
|Not Available<sup>11</sup>|12|4%|
|Total|283|100%|

**Table 3: Breakdown by Sector of Firm**

|Sector|Number of<br>Respondents|Percentage|
|---|---|---|
|Finance &Insurance|121|43 %|
|Food &Agribusiness|27|10 %|
|Industrial/Consumer<br>Products,<br>Health/Education,<br>Utilities &Technology|63|22 %|
|Metallic and<br>Nonmetallic Mineral<br>Products|24|8 %|
|Oil, Gas & Chemicals|24|8 %|
|Transportation,<br>Warehousing & Retail<br>Trade|24|8 %|
|Total|283|100%|

10 World Bank income categories are based on the per capita incomes of countries. The range of the income categories is I-V, with I being the lowest and V being the highest: Countries in Category I have a per capita income of $746 or less; those in Category II, between $746 and $1435; Category III, between $1436 and $2975; Category IV, between $2976 and $5185; Category V, over $5185. 11 Information is unavailable because the firm is part of a regional conglomerate and did not specify country.

8
Therefore, increases in _MAXf,j_ capture technology improvements as those in quality ladder (e.g., Aghion and Howitt, 1992) or horizontal variety (e.g., Romer, 1990) conceptualizations of technology in production.

Increases in _MOSTf,j_ occur when a new establishment’s most widely used technology in the business function is more sophisticated than the previous one. This new technology may be entirely new to the business function or an existing technology whose use has been expanded. Therefore, _MOSTf,j_ is more related to Mansfield (1963)’s concept of technology diffusion within the firm, specifically within the business function, rather than to innovation.

Relevant outcomes and observable characteristics are often reported at the establishment level. We construct establishment-level technology measures as simple averages of _NUMf,j_ , _MAXf,j_ and _MOSTf,j_ across the business functions of an establishment. Specifically, we define _NUMj_ , _MOSTj_ and _MAXj_ as: where _S_ = _{NUM, MOST, MAX}_ , and _Nj_ is the number of business functions covered for establishment _j_ .

# **3.2 An Illustration**

Before studying the general patterns of technology use in establishments, it is useful to become familiar with the grid and the measures of technology sophistication by exploring some examples from FAT.

To begin appreciating the level of detail in the grid, we examine two medium-sized establishments in apparel retail: one in India (establishment 1) and the other in Vietnam (establishment 2). Figure 3 plots the _MAXf,j_ index in each business function for both establishments. For instance, establishment 1 uses a dynamic pricing system that automatically adjusts prices based on demand conditions, while establishment 2 uses an automated markup technology that collects information on costs and applies a uniform markup. The pricing business function includes five technologies (Figure A.7). From least to most sophisticated, these are: manual pricing (prices set without a formal account of the costs), automated markup, automated promotional pricing (prices adjusted based on seasonal factors), dynamic pricing, and personalized pricing (prices adjusted at the individual customer level using data analytics such as data mining and machine learning). The value of the MAX index for establishment 1 (4) indicates that it is one notch below the technology frontier in pricing, while the value for establishment 2 (2) shows that it is three notches below the 17
# **Table A.6**

# **What Determines Participation in the Urban Residents’ Pension Program?**

||Probi|t Marginal E|ffects||||
|---|---|---|---|---|---|---|
|Controls|(1)|(2)|(3)|(4)|(5)|(6)|
|Age|0.009***<br>(0.001)|0.009***<br>(0.001)|0.008***<br>(0.001)|0.008***<br>(0.001)|0.008***<br>(0.001)|0.007***<br>(0.001)|
|Age-Squared|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Schooling|-0.000|-0.000|0.002***|0.000|0.000|0.002***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Post-Secondary Education|-0.012***|-0.012***|-0.009**|-0.005|-0.005|-0.007|
||(0.005)|(0.005)|(0.004)|(0.005)|(0.005)|(0.004)|
|Married|0.003|0.003|0.003|0.003|0.003|0.003|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Rural Hukou Migrant|-0.039***|-0.040***|-0.047***|-0.045***|-0.046***|-0.048***|
||(0.003)|(0.004)|(0.003)|(0.003)|(0.004)|(0.003)|
|Urban Migrant w/ Other Urban|0.015**|0.021**|-0.005|0.005|0.010|-0.007**|
|Hukou|(0.006)|(0.009)|(0.004)|(0.005)|(0.008)|(0.003)|
|Female|0.001|0.002|0.000|0.001|0.001|0.002|
||(0.003)|(0.004)|(0.003)|(0.003)|(0.004)|(0.003)|
|Female x Rural Migrant||0.001|||0.004||
|||(0.008)|||(0.008)||
|Female x Urban Migrant||-0.008<br>(0.007)|||-0.007<br>(0.007)||
|Participate in Employer-Provided|||-0.055***|||-0.057***|
|Pension|||(0.004)|||(0.004)|
|_Work Unit Type_|||||||
|Collective Enterprise||||0.022|0.022|0.014|
|||||(0.022)|(0.022)|(0.019)|
|Private Enterprise||||0.020|0.020|0.004|
|||||(0.017)|(0.017)|(0.012)|
|Foreign-Invested Enterprise||||-0.003|-0.003|-0.003|
|||||(0.013)|(0.013)|(0.011)|
|Self-Employed||||0.033*|0.033*|0.002|
|||||(0.018)|(0.018)|(0.012)|
|Other||||0.023|0.023|-0.003|
|||||(0.021)|(0.021)|(0.011)|
|Includes Industry Controls|no|no|no|yes|yes|Yes|
|Observations|11,769|11,769|11,769|11,749|11,749|11,749|
|Average Participation|0.0466|0.0466|0.0466|0.0467|0.0467|0.0467|

Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes all working age adults in 2010, regardless of work status. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables.

45
# **Table A2 – Bank Comparison, Given Exposure to the Energy Sector**

This table displays the 5-digit NAICS energy-related sectors as well as their descriptions. NAICS = North American Industry <u>Classification System.</u>

|**5-digit NAICS sector**|**Description**|
|---|---|
|21111|Oil and gas extraction|
|21211|Coal mining|
|21311|Support activities for mining and oil and gas extraction|
|23712|Oil and gas pipeline related structures construction|
|32411|Petroleum refineries|
|32419|All other petroleum and coal products manufacturing|
|32511|Petrochemical manufacturing|
|48311|Marine oil and natural gas transportation|
|48611|Transportation of crude oil through pipelines|
|48621|Transportation of natural gas through pipelines|
|48691|Pipeline transportationof refined petroleumproducts|

38
Integration is measured as a ratio between the outcome variable of Venezuelans and the outcome of Brazilians. Most commonly used measure, used by Abramitzky et al. (2020), Carneiro et al. (2020), OECD (2015) and many others, is the relative probability of the outcome variable of Venezuelans compared to Brazilians. To measure integration in the education sector, this paper calculates the relative probability of Venezuelans, aged between 4 and 17 years old, the mandatory school age,<sup>16</sup> enrolled in regular school compared to the Brazilian cohort. This paper also calculates the relative probability of Venezuelans in the fundamental and high school level of schooling, with the schooling age of 6 to 14 years and 15 to 17 years, respectively.

To measure integration in the formal labor market, this paper calculates the relative probability of Venezuelans, aged between 15 and 64 years old,<sup>17</sup> employed in the formal labor market compared to Brazilian cohort.

To measure integration in the social protection program, this paper calculates the relative probability of Venezuelans registering in _Cadastro Unico_<sup>18</sup> compared to Brazilian cohort and the relative probability of registered Venezuelans to be _Bolsa Familia_ (PBF)<sup>19</sup> beneficiaries compared to their Brazilian counterpart.

This relative probability index has an easy interpretation. A _Ri_ of 0.5 means that Venezuelans are half as likely as Brazilian to be found in sector _i_ . Abramitzky et al. (2020), Carneiro et al. (2020) and Fryer and Levitt (2004) point out that the relative probability index is sensitive to outliers and advocates the use of F-Index , which is a monotonic transformation of the relative probability index. The _F − Index_ is measured by the following expression: where _i_ can be _e_ , _f_ , _c_ and _p_ . This paper reports the F-Index in the main body of the paper. The relative probability index is reported in the appendix. The _F − Index_ runs from 0 to 100, with higher number signalling more integration. A F-index of 0 means that Venezuelans are not present at all, while a F-index of 50 means that Venezuelans are as likely to be present as Brazilians and F-index of more than 50 means that Venezuelans are more likely to be present than Brazilians. Although there is no upper bound of relative probability, there is an upper limit of the F-index.

> 16Art.4,l Law n. 9394/96.

> 17Working-age population.

> 18The Unified Registry of Social Programs ( _Cadastro Unico_ ) is a database that collects details about low-income families in Brazil, which can be used to identify vulnerable people in the society and develop appropriate benefits for them.

> 19The flagship conditional cash transfer program for the poor in Brazil.

11
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23