rafmacalaba/datause-encoder-data huggingface dataset:
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- Daunfeldt, S. O., Elert, N., and Johansson, D. (2010 _). “The economic contribution of high-growth firms: Do definitions matter.”_ Ratio working papers, 151.

- Daunfeldt, S-O., Elert, N and Halvarsson, D. (2014) _“The Economic Contribution of High-Growth Firms: Do Policy Implications Depend on the Choice of Growth Indicator_ ?” Journal of Industry, Competition and Trade, 14(1), 337-365.

- Daunfeldt, S.-O., and D. Halvarsson. (2015) “ _Are High-Growth Firms One-Hit Wonders? Evidence from_ `” –`

- _Sweden_ . Small Business Economics 44 (2), 361 83.

- Daunfeldt, S-O and Halvarsson, D. and Mihaescu, O. (2015a) _“High-growth firms: Not so vital after all?”_ HUI Working Papers 114, HUI.

- Daunfeldt, S-O., Johansson, D. and Halvarsson, D. (2015b). _“A cautionary note on using the EurostatOECD definition of high-growth firms.”_ Journal of Entrepreneurship and Public Policy, 4(1), 5056.

- Daunfeldt, S-O., Elert, N. and Johansson, D. (2016) _“Are high-growth firms overrepresented in high-tech industries?”_ Industrial and Corporate Change, pp. 1-21.

- Daunfeldt, S-O, Elert, N. and Johansson, D., (2014). _The economic contribution of high-growth firms: do policy implications depend on the choice of growth indicator_ ? Journal of Industry, Competition and Trade 25(1), 1—21.

- Decker, R. A., Haltiwanger, J., Jarmin, R. S., and Miranda, J. (2016). _“Where has all the skewness gone? The decline in high-growth (young) firms in the US.”_ European Economic Review, 86, 4-23.

- Deschryvere, M. (2008). _“High growth firms and job creation in Finland.”_ ETLA discussion paper 1144, Research Institute of the Finnish Economy, Helsinki.

- Du, J., and Temouri, Y. (2015). _“High-growth firms and productivity: evidence from the United Kingdom.”_ Small Business Economics, 44(1), 123-143.

- Du, J., Y. Gong, and Y. Temouri. (2013). “ _High Growth Firms and Productivity—Evidence from the United Kingdom._ ” Working Paper 13/04, National Endowment for Science, Technology and the Arts, London.

- Eslava, M., and J. Haltiwanger. (2013). “ _Young Businesses, Entrepreneurship, and the Dynamics of Employment and Output in Colombia’s Manufacturing Industry.”_ Working paper, CAF, Caracas.

- Foster, L., J. Haltiwanger, and C. Syverson. (2008). “ _Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability_ ?” American Economic Review 98 (1), 394–425.

- Fox, J. T., and Smeets, V. (2011). _“Does input quality drive measured differences in firm productivity?”_ International Economic Review, 52(4), 961-989.

- Fujita, M., P. Krugman, and A. J. Venables. 1999. The Spatial Economy: Cities, Regions and International Trade. Cambridge, MA: MIT Press.

25
Figure 6: Growth incidence among rural households in coffee regions, 1995-1999 <!-- Start of picture text --> Growth incidence curve Growth rate in mean<br> Mean of growth rates<br>20<br>15<br>10<br>5<br>0<br>-5<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentiles<br><!-- End of picture text --> Source: Authors’ calculations.

Summing up, from the previous analysis three major observations emerge. First, coffee farmers markedly improve their welfare during the boom period, but other rural households in coffee regions do almost as well. During this same period, rural households’ consumption in non-coffee regions stagnates, which suggests that there are strong indirect effects of the coffee price increase within coffee regions, but no or few links to non-coffee regions. Second, the most important supply response correlated to the coffee price boom (or liberalization) was the decision of many farmers to start growing coffee. Based on the available cross-sectional data, it is difficult to disentangle the poverty and distributional impact of this decision, on the one hand, and of the price boom, on the other. Third, coffee farmers’ were able to sustain consumption growth and poverty reduction even during the second period of falling coffee prices.

# **Coffee Prices, Growth and Poverty: Can Transmission Channels Be Identified?**

Up to this point, the descriptive analysis of the links between changes of coffee prices and poverty has highlighted strong correlation but has not established causation. This may be difficult to confirm given the limitations imposed by the generally poor quality of the data and by our choice of using cross section versus panel data. However, by further investigating a number of issues focusing on how price shocks may ‘cause’ changes in poverty ratios, we have been able to shed some light on certain relevant structural links.

In more detail, three sets of issues have been considered: 1) What are the characteristics of the farmers who moved into coffee farming during the 1990s, and can the growth process of this 17
**Table 3.2: GMM system estimates. Standardized variables – World sample**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Lagged HH index|0.914***|0.891***|0.863***|0.850***|0.886***|0.907***|0.894***|
||(12.67)|(11.49)|(9.902)|(11.11)|(10.43)|(12.25)|(11.76)|
|Population|-0.00554|0.00588|0.0284|-0.00201|0.00343|0.00682|0.00733|
||(-0.350)|(0.345)|(1.502)|(-0.109)|(0.192)|(0.415)|(0.435)|
|Quality of Institutions|-0.0174|-0.0212|-0.0277|-0.0778|-0.00429|-0.0214|-0.0189|
||(-0.274)|(-0.311)|(-0.428)|(-1.133)|(-0.0617)|(-0.312)|(-0.283)|
|Access to Finance|-0.0495*|-0.0849***|-0.0748***|-0.0729**|-0.0864***|-0.0821**|-0.0858***|
||(-1.764)|(-2.659)|(-2.959)|(-2.503)|(-2.658)|(-2.557)|(-2.736)|
|Income per capita|0.00740|0.116|0.152*|0.120|0.112|0.107|0.118|
||(0.0646)|(1.187)|(1.818)|(1.338)|(1.100)|(1.065)|(1.202)|
|Inflation|-0.0165<br>|||||||
||(-0.548)|||||||
|Openness|0.0265|0.00638|0.0239|0.0136|-0.00674|0.0161|0.00951|
||(0.646)|(0.147)|(0.530)|(0.385)|(-0.139)|(0.397)|(0.220)|
|Human Capital|0.0723|-0.000146|-0.0619|0.500**|0.00698|0.00917|3.44e-05|
||(0.767)|(-0.00184)|(-0.728)|(2.037)|(0.0863)|(0.111)|(0.000414)|
|Terms of Trade (ToT)|0.0857***|0.0729**|0.0916***|0.476***|0.0831***|0.0739**|0.0690**|
||(3.697)|(2.390)|(2.926)|(2.781)|(2.738)|(2.426)|(2.271)|
|FDI inflows|-0.135***|-0.107**||-0.0778**|-0.108**|-0.105**|-0.117**|
||(-2.693)|(-2.332)||(-2.010)|(-2.152)|(-2.301)|(-2.045)|
|Net Commodity Exporter Dummy|-0.0241|-0.0609|-0.0131|-0.0172|-0.149|-0.0689|-0.0364|
||(-0.212)|(-0.484)|(-0.108)|(-0.149)|(-1.250)|(-0.544)|(-0.312)|
|Real Exchange Rate Variation||-1.843***|-1.682***|-0.633|-1.760**|-1.861***|-1.892***|
|||(-2.770)|(-2.838)|(-0.850)|(-2.582)|(-2.787)|(-2.895)|
|Investment|||-0.137|||||
||||(-1.253)|||||
|ToT * Human Capital||||-0.596**<br>(-2.248)||||
|Institutions * Commodity Exporter Dummy|||||-0.0260|||
||||||(-0.642)|||
|Island dummy||||||0.0327<br>||
|||||||(0.729)||
|Island dummy * FDI inflows|||||||0.0438|
||||||||(0.641)|
|Constant|-0.0598|-0.103|-0.114|-0.0645|-0.0721|-0.107|-0.116|
||(-0.837)|(-1.357)|(-1.631)|(-0.825)|(-0.980)|(-1.387)|(-1.607)|
|Observations|338|342|344|340|342|342|342|
|Number of countries|113|111|112|110|111|111|111|

Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 14
<!-- Start of picture text -->
1.80<br>1.70<br>1.60<br>1.50<br>we<br>1.30 =<br>ew 4S<br>1.20 ee =<br>1.101.00 . ee core *. eetwe oe“= S<br>0.900.80 SaRBR8RSRBSS sBsRSssRBARRaSE A<br>a 9 A ananansesescsecsce6eeseaececss So ooecocmUcsccmUCcC8WlUCS8<br>S88 AA 8 § RS SA RRARRRAKRA AANA AN<br>PS —=——=p10 s+see* p50) =====p90 = = p95<br><!-- End of picture text -->

<!-- Start of picture text --> 3.00<br>2.50<br>a ag-a- ae. mem - e- - - -e-—e<br>2.00 a-4<br>Ra<br>S3<br>3CT) 150 ee<br>1.00<br>0.50<br>0.00<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>—@— Formal salaried workers © ——t— Informal salaried workers — m& = Self-employed<br><!-- End of picture text --> <!-- Start of picture text -->
1.20<br>South America Central America and Mexico<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>-0.200.00 LJ LJ LJ = =<br>p10 ps0 p90 p10 p50 p90<br>01995-2002 TM2002-2015<br><!-- End of picture text -->

<!-- Start of picture text --> 60<br>50<br>-. s-* --4- Te Tm mn ee<br>40 e—-e~».i -e<br>_<br>€ ee<br>= 30<br>20<br>10<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>—@—Formal salaried workers ——t— Informal salaried workers — m - Self-employed<br><!-- End of picture text -->
**11 |** P a g e

forms of processing and wood‐based products. The purpose of this note is to assess contribution of wood and wood‐related products to BiH foreign trade performance.

The approach taken to address the role of the wood cluster in BiH foreign trade uses common sense, insights gleaned from various sector studies as well as information on Harmonized System code used by exporters of wood and wood‐based products. We identify the following stages in terms of the degree of processing taking place in wood value‐chain: logging; primary wood processing (wood harvesting including sawlogs and fuelwood); secondary wood processing (joinery, wood boxes, pallets, furniture)<sup>5</sup> ; and other wood based products (paper, cartons, etc.). Annex Table 1 identifies products in terms of six‐digit HS (Harmonized System) items, which allows reproducing their foreign trade flows in terms of the level of processing (Table 5).

**Table 5: Developments in wood cluster trade (forest ‘gate’ and wood‐based products) in 2003‐07 (in million of US dollars and percent)**

||2003|2004|2005|2006|2007|
|---|---|---|---|---|---|
|||Exports in mi<br>|llions of US d<br>|ollars<br>||
|Logs|6.1|12.1|19.2|34.0|40.8|
|Primary wood processing|8.8|12.6|11.0|12.9|13.8|
|Secondary wood processing|197.5|276.5|325.9|485.6|579.6|
|Other wood basedproducts|11.1|34.4|43.7|56.4|71.0|
|Total above|223.7|336.0|400.2|589.3|705.8|
|Share in total exports|21.8|20.8|16.8|17.2|17.0|
|||Composition<br>|of exports(in<br>|percent)<br>||
|Logs|2.7|3.6|4.8|5.8|5.8|
|Primary wood processing|3.9|3.8|2.8|2.2|2.0|
|Secondary wood processing|88.3|82.3|81.4|82.4|82.1|
|Other wood basedproducts|5.0|10.2|10.9|9.6|10.1|
|Total above|100|100|100|100|100|
|||Exports inpe<br>|rcent of impo<br>|rts<br>||
|Logs|8,993|5,993|12,096|22,362|27,830|
|Primary wood processing|772|585|167|471|219|
|Secondary wood processing|251|299|264|378|310|
|Other wood basedproducts|15|35|33|39|39|
|Total above|141|169|145|203|180|
|Total imports of forest 'gate' and wood basedproducts|159|199|276|291|391|
|Memorandum: difference between exports and imports||||||
|in millions of US dollars|65|137|124|299|314|
|Memorandum: share in total exports|22%|21%|17%|17%|17%|

Source: Based on BiH's data from UN COMTRADE Statistics.

5 Their aggregate output was valued at around “... 132 million KM in 2003 and the BiH Foreign Trade Chamber claimed that wood and furniture industry products accounted for 21 percent of the country’s export earnings this year.” (USAID 2006, p. 4).
**Table 4:** Worse Economic Situation and Civic Engagement

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Civic Index|Protest|Community Mtg|Volunteer|Party|
|Bad Econ|0.35*|0.17+|0.07|0.10|0.04|
||(0.17)|(0.09)|(0.06)|(0.07)|(0.10)|
|Detained at Border|-0.17|-0.13|-0.01|-0.04|-0.08|
||(0.33)|(0.20)|(0.12)|(0.11)|(0.21)|
|Round 3|-0.11|-0.55***|0.29**|0.16+|-0.14|
||(0.24)|(0.14)|(0.10)|(0.09)|(0.14)|
|Female|0.25|0.24|0.03|-0.02|-0.07|
||(0.51)|(0.26)|(0.17)|(0.18)|(0.26)|
|Age|0.01|-0.00|0.01|0.01|-0.00|
||(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Indigenous|-0.16|0.05|-0.00|-0.21+|0.29|
||(0.31)|(0.18)|(0.12)|(0.12)|(0.19)|
|Years of Education|0.01|-0.01|0.01|0.01|-0.02|
||(0.03)|(0.02)|(0.01)|(0.01)|(0.02)|
|Tattoo|0.52|0.08|0.19|0.26|0.16|
||(0.79)|(0.34)|(0.30)|(0.31)|(0.38)|
|Children US|0.03|-0.15|0.15|0.03|0.26|
||(0.42)|(0.23)|(0.14)|(0.15)|(0.26)|
|Childrent GT|-0.14|-0.17|0.03|0.00|0.28|
||(0.32)|(0.21)|(0.12)|(0.11)|(0.22)|
|Assets|-0.13|-0.05|0.05|-0.13|0.28|
||(0.40)|(0.27)|(0.15)|(0.13)|(0.29)|
|ln(Time in US)|0.18*|0.13*|0.02|0.03|0.01|
||(0.09)|(0.05)|(0.03)|(0.03)|(0.05)|
|Employed|0.26|0.28+|-0.06|0.03|-0.10|
||(0.29)|(0.16)|(0.12)|(0.11)|(0.17)|
|Constant|9.61***|2.89**|3.39***|3.33***|2.48*|
||(1.78)|(0.88)|(0.68)|(0.67)|(0.96)|
|Observations|512|512|512|512|509|

Standard errors in parentheses Robust standard errors clustered by respondent + _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 22
<!-- Start of picture text --> 0 .005 .01 .015 .02 .025<br>Coefficient<br>IV Coefficients OLS Coefficients<br>1500<br>400<br>300 1000<br>200<br>500<br>Density of IV Coefficients<br>100 Density of OLS Coefficients<br>0 0<br><!-- End of picture text --> FIGURE A18: DISTRIBUTION OF ESTIMATED IV AND OLS COEFFICIENTS IN QUADRATIC MODEL Notes: Kernel density plots of coefficients obtained from 100 simulations of the quadratic model estimating an IV and an OLS specification of the effect of aid on conflict. Data are simulated except for NINO3 variable taken from (Hsiang, Meng, and, Cane, 2013).

78
<mark>between corporates across the two types of countries suggests that changes in the investor side during the post-GFC environment were much more relevant for emerging market corporate bond issuers than for developed country investment grade issuers.</mark><sup>18</sup> <mark>Table 2 reports the statistical significance of the differences in means for yields and issuances, before and after 2008, for emerging economy issuers and investment grade developed market issuers. Panel A shows that yields fell after 2008 for both bonds with face value in the [400:500) range and those in the [500:600) range (expressed in millions of U.S. dollars). But they fell much more for emerging market issuances in the [500:600) range. The triple difference test is statistically significant and shows a differential of almost 100 basis points in the decline in yields between emerging and developed markets. The table shows analogous comparisons in the issuance activity (Panel B), which reacted positively to the yield decrease, again especially in emerging markets in the [500:600) range.</mark><sup>19</sup>

# _<mark>4.2. Regression Analysis</mark>_

<mark>We next use regressions to estimate how yields and issuances of bonds of different issuance size categories changed after 2008 for emerging market firms. These regressions allow us to control for observable and unobservable characteristics that can predict yields and issuance size. As before, we include both emerging market issuers and investment grade developed market issuers in our analysis. We estimate the following type of regression for bond yields:</mark>

> 18 Results for high-yield developed country issuers’ yields and issuances are very similar to those for emerging market firms (Appendix Figure 2). These two sets of firms share two important characteristics. First, they are inherently riskier than investment grade developed market firms. Furthermore, these high-yield developed economy firms also can be included in special indexes that are similar to the CEMBI and EMBI. The Bloomberg Barclays High Yield Very Liquid Index is an important benchmark for these firms that only includes high-yield dollar-denominated debt from developed market firms, with a minimum issue size of $500 million.

> 19 <mark>Appendix Table 3 reports similar results using narrower bins.</mark>

19
provided by the British government UK [2016]. Most Indian names in Maharashtra follow a traditional naming convention. Their full name usually consists of three names. Personal name + Middle name + Family name. Men and unmarried women traditionally take their father’s personal name as their middle name. For instance, Sanjav Bharat Vadgama. On marrying, a woman traditionally drops her father’s name and family name and takes on her husband’s personal name and his family name in its place. There are some exceptions (e.g. for people who reject the caste system) which we took into consideration when matching.

Our census survey asked for the full names of every household head and his spouse. If there was no spouse available, then we asked for the full name of the eldest female member in the household. After census, we matched the full names provided in the census survey to the list of full names of female MF clients and their husbands provided by the implementing MFI. From the matched list of clients, we randomly drew a sample of clients for our study. The baseline and endline survey of sampled clients included a household roster, where the first and last name of all household members were listed. Those clients whom we did not match using census survey (either because the household had not been listed - given that in larger villages we only listed random segments - or because the client was not the head or the spouse of the head) were matched to the endline sample using the first and last names provided in the household roster.

Table 15 investigates the determinants of matching success, showing the marginal effects of a Probit regression of an indicator of having been matched on a set of household level and village level characteristics. These results indicate that the matched client sample is not representative of our study sample.

41
For Vietnam our universe includes 179,725 establishments. We collected data on 1,499 establishments randomly selected from the GSO’s census. Table A.4 and Table A.5 provide the information on the distribution of firms in the population and the sample for Vietnam.

Table A.4: Population Distribution, Vietnam

|Region|Size|Agri-<br>culture|Food<br>Processing|Wearing<br>Apparel|Other<br>Manuf.|Wholesale<br>& Retail|Other<br>Services|Total<br>Region|
|---|---|---|---|---|---|---|---|---|
|Region 1|Small|29|91|82|2074|4183|3155|13417|
||Medium|12|38|61|1225|491|923||
||Large|4|28|103|623|44|251||
|Region 2|Small|39|49|29|691|1270|897|4354|
||Medium|10|22|54|408|208|308||
||Large|0|5|60|222|18|64||
|Region 3|Small|85|95|46|1001|2330|2547|8572|
||Medium|24|31|50|452|327|1042||
||Large|7|27|81|167|31|229||
|Region 4|Small|117|78|14|164|539|716|2162|
||Medium|28|43|7|70|76|206||
||Large|12|13|7|12|10|50||
|Region 5|Small|89|145|127|3699|4278|2978|17942|
||Medium|33|101|134|2494|589|798||
||Large|16|100|204|1937|48|172||
|Region 6|Small|7|143|31|868|1048|781|4595|
||Medium|8|92|46|656|154|253||
||Large|0|54|60|340|13|41||
|Region 7|Small|279|669|578|9597|34466|22025|77462|
||Medium|35|100|126|1463|2954|3064||
||Large|7|33|90|536|338|1102||
|Region 8|Small|204|564|854|7453|18024|11661|51221|
||Medium|36|200|433|2364|3376|3445||
||Large|6|111|365|820|469|836||
|Total||1087|2832|3642|39336|75284|57544|179725|

Note: Vietnam regions are as follows: Region 1 (Bc Ninh; Hi Ph`ong; Ninh B`ınh), Region 2 (Th ́ai Nguyˆen; Bc Giang), Region 3 (Thanh Ho ́a; H`a T ̃ınh; B`ınh nh), Region 4 (Kon Tum; Lˆam ng), Region 5 (B`ınh Dng; ng Nai), Region 6 (Long An; V ̃ınh Long), Region 7 (H`a Ni), and Region 8 (H Ch ́ı Minh).

52
by tracing the evolution of firm characteristics, including firm capital structure, investment, and profitability, after firms issue debt and equity securities in domestic and international markets. These time-series patterns for a broad array of firms from around the world provide new evidence regarding the motivation for security issuances. As shown above, we also find that firms issue debt and equity securities in both domestic and foreign markets following internationalization, suggesting that future research needs to account for these corporate financing patterns.

To analyze the firm-level patterns associated with international capital raisings, we construct a new database. The dataset includes 168,514 equity and debt issues in domestic and international capital markets, conducted by 45,969 firms from 116 countries, and covers the period 1991-2005. We match these data with comprehensive information on firm balance sheets and income statements for 23,366 firms.

The remainder of the paper is organized as follows. Section 2 describes the data. Section 3 documents the extent of internationalization of securities markets and analyzes the characteristics of those firms that raise capital abroad. Section 4 analyzes the evolution of firm characteristics and performance following capital raisings in international markets and compares these patterns to firms that only raise capital in domestic securities markets. Section 5 examines the international and domestic capital raising activity of firms that have accessed international markets. We conclude in Section 6.

# **2. Data**

To document patterns of international capital raisings and analyze the characteristics and performance of firms that raise capital through security issues in international capital markets, 7
**Figure 3: Conditional Average Treatment Effects (CATEs)**

<!-- Start of picture text --> 15 15<br>10 10<br>5<br>5<br>0<br>0<br>-5<br>-5<br>-10<br>-10<br>-15<br>-15<br>0 10 20 30 -.4 -.2 0 .2 .4 .6<br>Asset index Lagged GS-SPEI (reversed)<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>0 20 40 60 80 9.5 10 10.5 11 11.5 12<br>In situ adaptive capacity Total consumption (per capita)<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>0 .1 .2 .3 .4 -.5 0 .5 1<br>Sh. of members with tertiary education GS-SPEI (reversed)<br>Treatment effect (%) Treatment effect (%)<br>Treatment effect (%) Treatment effect (%)<br>Treatment effect (%) Treatment effect (%)<br><!-- End of picture text --> _Note_ : the triangles represent average CATE values for each decile of the treatment effect modifier reported on the X- axis. Treatment effect is the change in the probability of a household sending at least one migrant associated with a one standard deviation increase in the reversed GS-SPEI. All the treatment effect modifiers are lagged variables from the preceding wave. Variable 'GS-SPEI (reversed)' shows heterogeneity in treatment effects depending on treatment intensity levels.

23