rafmacalaba/datause-encoder-data huggingface dataset:
the first SISBEN from 1994–1997. Eligibility for the SR was based on the Poverty Index Score obtained from the first SISBEN, and we use individual fixed effects to determine whether the informality rates observed in the second SISBEN are higher for eligible individuals.

To construct the panel, we match individual-level information from the first and second SISBEN. We restrict our sample to working-age individuals between 12 and 65 years of age. We match the datasets in two ways. (1) We use the Colombian national identity card number. This is a unique ID number initially assigned to all Colombian citizens at age 7 that changes when they turn 18. This is the more conservative match, which we call the “ID match”. (2) We expand the match using names and dates of birth to include people who may have changed ID numbers when they turned 18. In Colombia, people use two last names; the first name is generally from the father, and the second is from the mother. When we match names, we use the first and second given names and the first and second last names, allowing for spelling mistakes and for phonetic distances no greater than 3 of 10 characters using the Leveinshtein criteria.<sup>18</sup> When we use dates of birth, we allow for the possibility of recording error by using both the exact date (day, month, year), either the same date and month of birth and within four years of birth, or the same year of birth and within two months and two days of birth. We call this the “Expanded match”. We report the results using both matches.

Summary statistics for the panel dataset that we construct using both the ID and expanded matches are reported in Table 2. To find a match, the individual must be in the labor force and have been interviewed for the SISBEN in both periods. Thus, we expect some workers from the first SISBEN (such as older people) to exit the labor force, and we do not see young workers who entered the labor force in the second SISBEN. Because we are observing the same individuals, some of the demographic characteristics, such as age and education, will change (increase) over

> 18Leveinshtein distances measure the similarity between two strings or two vectors of strings. The number indicates the number of characters that need to be changed in one string to equal the other string, normalized by the length of the string.

13
the PTA as bilateral or plurilateral (more than two members), and determine whether it has been notified to the WTO. Unlike the WTO data, which includes agreements on trade in services as well as goods, our database focuses on merchandise trade agreements only. The distinguishing feature of the new database is the inclusion of agreements that have not been notified to the WTO, which outnumber the notified PTAs by 130 to 99 (see Figure 1).<sup>22</sup> <!-- Start of picture text --> 30 300<br>25 250<br>Not notified<br>Notified<br>Cumulative (EU-25)<br>20 Cumulative (EU-15) 200<br>15 150<br>10 100<br>5 50<br>0 0<br>1958 1969 1976 1984 1989 1994 1999 2004<br><!-- End of picture text --> Figure 1: Growth in the number of PTAs _Source:_ WTO and author’s estimates.

The left axis of Figure 1 shows the number of PTAs signed in a given year, using stacked columns

> 22As before, this estimate counts the EU-25 as a single agreement. If EU-15 is treated as a single agreement instead (implying that all PTAs between the EU and the new accession countries, as well as between the accession countries themselves, are included in the estimate), the number of unnotified agreements remains the same, but the number of notified PTAs rises to 155.

13
# **APPENDIX B: ROBUSTNESS TEST RESULTS**

Figure B1: Individual Clinic Treatment Effects for Weeks Pregnant at 1<sup>st</sup> Prenatal Visit Notes: This figure plots individual clinic treatment effects for the outcome of weeks pregnant at first prenatal visit. We run OLS regression of the outcome comparing each clinic assigned to the treatment group to all clinics assigned to the control group pooling the intervention period and the post‐intervention period I ( hence May 2010‐March 2012). One treatment clinic is not included because of its insufficient sample size. This clinic corresponds to one of the two that did not take up treatment. The triangle symbol refers to the clinic that was assigned to treatment but did not take up the treatment. The x‐axis is sorted from the lowest to the highest clinic‐specific impact. The dashed blue line is the intent‐to‐treat effect calculated by pooling the intervention and the first post intervention period. The vertical lines are 95% confidence intervals constructed using standard errors obtained from the Wild bootstrap procedure.

46
employees to align the data set with the survey’s stated intent of sampling firms with five or more employees. The final data set contains information on 52,231 firms operating in 70 countries.

Second, we use data from Orbis, a commercial database distributed by Bureau van Dijk over the period 2004-2011. We focus on those firms that report data on employment. We restrict our analysis to developing countries that had a minimum of 25 firms and furthermore to firms that have a median of five or more employees over the period 2004-2011, to be consistent with the ES sample.<sup>11</sup> Overall, the data we use from Orbis includes information for over one million unique firms operating across 29 developing countries. One advantage of using Orbis is that it includes large, small, listed and unlisted firms. Since most firms are followed through time, the data set also introduces a panel dimension to our analysis.

The use of these two data sets in our estimations aids with the analysis because of their complementary nature. With the ES we get a more comprehensive coverage of developing countries, and the random survey sample is nationally representative. However, the total number of firms included in the analysis is just over 50,000. In contrast, the Orbis data set does not cover as many countries, but provides widespread coverage of both listed and unlisted firms in countries where data are collected. In all, estimations using the Orbis data set include over 4.3 million observations. In addition, as most firms are followed through time in Orbis, we are also able to construct a panel data set following over one million unique firms through time. The ES does include a panel component, but one that is too small for a meaningful cross-country exploration in the context we wish to analyze. As a survey, the ES does offer more insight into firm characteristics which allow the use of more control variables in our regressions analysis.

> 11 We impose the minimum number of firms filter before dropping firms with median number employees less than five over our sample period. Thus two countries Nicaragua and Gabon have less than 25 firms.

8
Hanson and Robertson (2006) explored the impact of the increased supply capacity of China in LAC exports of the top manufacturing industries in Argentina, Brazil, Chile and Mexico (metals, machinery, electronics, transport and industrial equipment). They found that without the increase in Chinese supply of these products, export growth in these products could have been 1 percentage point higher in Argentina and Brazil, 2 percentage points higher in Chile, and 3 percentage points higher in Mexico. Freund and Ozden (2006) undertook a similar exercise, but encompassing all goods, and without disentangling between supply and demand shocks. They found that export growth from China are hurting LAC exports to third markets but only in some industries, namely textiles, electronics and electrical appliances, and telecommunications equipment, which are the industries studied by Hanson and Robertson.

Hence there seems to be sufficient uncertainty about the aggregate trade effects of the rise of China and India to merit further analysis, especially because the aforementioned econometric studies (Hanson and Robertson 2006; Freund and Ozden 2006) focused on intra-industry effects and ignored the potential for inter-industry effects. For example, the existing studies on the threats posed by these Asian economies do not consider the _direct_ effects of rising import demand in China and India as a potential boost for LAC exports. Also, none of the cited studies explore all the potential _indirect_ effects mentioned above. This paper addresses these issues, by examining the potential for complementarities and substitutability between LAC and Chinese exports to third markets at the aggregate level, allowing therefore for both intra-industry and inter-industry effects.

As mentioned, the objectives of this paper are twofold. First, we focus on the opportunities offered to LAC exports by the growth of Chinese and Indian demand. Second, we examine whether the growing presence of China and India in third markets should be seen as a threat or an opportunity for LAC exporters and importers.

definitions are rather loose, because even declining markets shares do not necessarily reflect a direct substitution effect whereby Chinese exports would be displacing LAC exports.

3
Malawi Sustainable Development Network Programme (SDNP), 1998. State of the Environment Report. http://www.sdnp.org.mw Michaelsen, J., David, S. S., Friedl, M. A, Davis, F. W. and Dubayah, R. C. 1994. Regression Tree analysis of satellite and terrain data to guide vegetation sampling and survey. _Journal of Vegetation Science_ , Volume 5, 673-686.

Minde, I.J., G. Kowero, D. Ngugi and J. Luhanga. 2001.Agricultural Land Expansion and Deforestation in Malawi. _Forest, Tress and Livelihoods_ , vol 11: 167-182.

Mukherjee S. and T. Benson. 2003. The determinants of Poverty in Malawi, 1998. World Development vol 31, no. 2: 339-358 R. B. Myneni, J. Dong, C. J. Tucker, R. K. Kaufmann, P. E. Kauppi, J. Liski, L. Zhou, V. Alexeyev, and M. K. Hughes. 2001. _A large carbon sink in the woody biomass of Northern forest_ . Proceedings of the National Academy of Sciences, Volume 98, 14784-14789.

Nankuni. F. 2004. Environmental degradation, resource scarcity and children’s welfare in Malawi : School attendance, school progress and children’s health. A Ph.D. thesis in agricultural economics and demography. Pennsylvania State University, the Graduate School, College of Agricultural Sciences.

National Economic Council, National Statistical Office, and IFPRI, 2001, The determinants of Poverty in Malawi: An analysis of the Malawi Integrated Household Survey 1997-97 Nerlove, M. 1991. Population and the environment: A parable of firewood and other tales. _American Journal of Agricultural Economcs_ . Vol 73: 1334-47.

Puhr, C. B. and Donoghue, D. N. M. 2000. Remote sensing of upland conifer plantations using Landsat TM data: A case study from Galloway, South-West Scotland. _International Journal of Remote Sensing,_ Volume 21, 633-646.

Shugart, H. H., Chavez, L. B. and Kasischke, E. S., 2000. Determination of stand properties in boreal and temperate forests using high-resolution imagery. _Forest Science_ , Volume 46, 478-486.

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Rubin, D. (1978) Multiple Imputations in Sample Surveys - A Phenomenological Bayesian Approach to Nonresponse. The Proceedings of the Survey Research Methods Section of the American Statistical Association, pp. 20-34. With discussion and reply. Also in Imputation and Editing of Faulty or Missing Survey Data. U.S. Department of Commerce, pp. 1-23.

Rubin, D. (1987). Multiple Imputations. Wiley.

Stifel, D., Christiaensen, L.(2007) Tracking poverty over time in the absence of comparable consumption data. World Bank Econ. Rev. 21(2), 317–341 Tarozzi, A. (2007) Calculating comparable statistics from incomparable surveys, with an application to poverty in India. Journal of Business and Economic Statistics 25(3), 314–336 26
under both screening methods were not offered a loan from our partner bank during the pilot exercise. Appendix table A2 shows the number of loan applicants classified by whether they were rejected or accepted based on each screening method.

# **3. Data**

We obtained data on 1,909 SMEs that applied for a working capital loan with our partner bank between March 2012 and August 2013, from two sources. The first source is an EFL questionnaire that the bank administered at the time of loan application. These data include the EFL score and the date when the entrepreneur was screened by the EFL tool, as well as background characteristics, such as the applicant’s age, gender, and business sales. Our partner bank also shared with us the threshold EFL score it used to determine whether or not to offer a loan, and they indicated whether they would have offered the applicant a loan based on their conventional screening method.<sup>11</sup> In addition, for each loan applicant, the data include the national ID number (DNI) and, if their business is registered under the business name instead of the individual’s name, it also includes the business’s tax payer number (RUC). Out of the 1,909 SMEs in the data, all provided their DNI and 1,327 also provided an RUC. However, for 20 SMEs, the DNIs and RUCs are inconsistent with each other, suggesting typos. We drop these observations from the sample to avoid using wrong information from our second data source. We also drop 6 observations where two DNIs reported the same RUC, that is, three SMEs where two co-owners seem to each have applied for a loan. In these cases, it is not possible to cleanly assign an EFL score to the SME as the unit of observation. Thus, we end up with a sample of 1,883 SMEs.<sup>12</sup> Our second data source corresponds to credit information owned by Equifax Peru, the largest credit bureau in the country. For the DNIs and RUCs in the EFL data, we purchased five years of monthly information on borrowing from Equifax, covering the time period from May 2011 to April 2016.<sup>13</sup> One of Equifax’s main sources of information is the Peruvian Bank Supervisor’s (SBS) _Central de Riesgos_ . SBS collects data directly from all regulated financial institutions on a monthly

> 11 For unknown reasons, this variable is missing for 21 loan applicants in our sample.

> 12 The fraction of the sample dropped is not significantly different below and above the EFL threshold.

> 13 By law, Equifax is not allowed to provide data that is older than five years.

9
**Table 4.5. Sectoral GDP shares in 2012 and by simulation for 2012-2030 (Percent of nominal GDP at factor cost)**

||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|
|**Mining**|3.6|14.3|21.3|13.9|14.2|14.2|
|**Non-mining**|96.4|85.7|78.7|86.1|85.8|85.8|
|**Agriculture**|40.6|34.5|31.3|34.5|34.6|34.6|
|**Non-mining industry**|7.5|4.5|4.2|4.5|4.6|4.6|
|**Private services**|35.6|32.0|28.7|33.5|32.4|33.6|
|**Government services**|12.6|14.7|14.5|13.5|14.2|12.9|
|**Human development services**|6.2|6.5|6.6|6.5|7.9|6.0|
|**Infrastructure services**|1.8|2.0|2.1|2.7|2.0|1.8|
|**Othergovernment services**|4.7|6.1|5.8|4.3|4.3|5.1|
|**Total**|100.0|100.0|100.0|100.0|100.0|100.0|

**Table 4.6 Real GDP at factor cost by sector: level in 2012 (constant US$ million) and growth 2013-2030 by simulation (Percent per year)**

||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|
|**Mining**|59.8|12.0|18.2|11.9|12.0|12.0|
|**Non-mining**|1,578.9|5.8|6.4|6.6|6.0|6.4|
|**Agriculture**|665.2|5.0|5.6|5.6|5.1|5.2|
|**Non-mining industry**|122.8|4.0|4.9|5.0|4.3|4.6|
|**Private services**|583.8|6.7|7.2|7.9|7.0|7.5|
|**Government services**|207.1|6.4|7.5|6.4|6.5|7.3|
|**Human development services**|101.3|6.7|8.2|7.1|8.3|8.1|
|**Infrastructure services**|28.8|5.8|7.7|9.6|5.8|7.0|
|**Othergovernment services**|77.1|6.1|6.3|3.2|3.2|6.2|
|**Total**|1,638.7|6.2|7.6|6.9|6.3|6.7|

**Note: Constant US$ are at 2009 prices.**

28
respectively. Depth seems to have a much stronger average effect on trade in goods than trade in services. Signing the deepest PTA increases members’ trade in goods by 69 percent and trade in services by 20 percent. The larger effect for trade in goods could be explained by GVC trade, in which intermediate goods cross the borders many times before they are assembled into final products. We come back to the link between deep PTAs and GVCs in the next subsection. The interaction with the dummy UK reveals an interesting result. While the depth of the UK’s trade agreements did not increase trade in goods more than the average country, it strongly increased trade in services. The UK more than doubled its trade in services as a result of its EU membership and its participation in deep PTAs signed by the EU with third countries.

So far, the analysis has not distinguished the gains for the UK as an importer or an exporter. The second three columns of Table 4 explore if the UK was affected by deep PTAs depending on being an importer or an exporter. We split the dummy UK into two variables that identify if the UK is an importer or an exporter and we include in the regression the interactions of these dummies with depth. Columns 4 and 5 show that the depth did not increase UK imports and exports of goods more than the average country. Column 6 instead, shows that the UK both imported and exported more services than the average after signing deep agreements. The comparison of the coefficients of the two interaction terms suggests that the UK’s services exports increased more than its imports.

_Table 4: Trade and depth_

||(1)<br>Total|(2)<br>Goods|(3)<br>Services|(4)<br>Total|(5)<br>Goods|(6)<br>Services|(7)<br>Total|(8)<br>Goods|(9)<br>Services|
|---|---|---|---|---|---|---|---|---|---|
|Depth|0.353***|0.522***|0.182***|0.353***|0.522***|0.182***|0.326***|0.483***|0.184***|
||(0.022)|(0.036)|(0.037)|(0.022)|(0.036)|(0.037)|(0.039)|(0.05)|(0.056)|
|Depth*UK exp/imp|0.017|0.128|0.693***|||||||
||(0.098)|(0.106)|(0.145)|||||||
|Depth*UK exp||||‐0.002|0.129|0.712***|0.062|0.211|0.530***|
|||||(0.102)|(0.111)|(0.16)|(0.118)|(0.144)|(0.161)|
|Depth*UK imp||||0.037|0.126|0.670***|0.09|0.051|0.665***|
|||||(0.102)|(0.118)|(0.149)|(0.124)|(0.161)|(0.134)|
|Depth*founding EU imp|||||||0.073|0.062|‐0.021|
||||||||(0.056)|(0.074)|(0.084)|
|Depth*founding EU exp|||||||0.011|0.058|0.02|
||||||||(0.056)|(0.073)|(0.089)|
|Depth*UK exp*founding EU imp|||||||‐1.286|1.544|3.529***|
||||||||(1.04)|(0.994)|(1.299)|
|Depth*UK imp*founding EU exp|||||||‐1.206|1.767*|3.208**|
||||||||(1.013)|(0.988)|(1.261)|
|Observations|27,200|27,200|27,200|27,200|27,200|27,200|27,200|27,200|27,200|

15
The results are reported in the fourth column of Table 4A. The group variables have the expected signs. Firms acquired by foreigners are more likely to belong to debt-ridden groups and less likely to belong to groups with large assets. This suggests that firms belonging to capital constrained groups are more likely to have foreign ownership.

In the final column of Table 4A, we include the export share of the firm's output in the regression equation. There are two reasons why export share might positively impact foreign ownership. First, firms with a large export share may benefit more from foreign ownership if foreigners have greater access to global markets. This follows the case in the theoretical section where the foreign parent adds more value to the target than a domestic parent would add. Second, foreigners may have better information about firms which also serve their home market. This is akin to a lower transaction cost because less information about the target needs to be acquired by the foreign parent, which implies that a Korean exporting firm is more likely to be acquired than an otherwise similar Korean firm. We find that firms that export a large share of output are more likely to attract foreign investment.

Next we use the 1999 data to test the robustness of these findings and further distinguish portfolio investors from merger and acquisition activity. First, we examine how firm characteristics affect the likelihood of receiving positive foreign investment, i.e., the dependent variable is 1 if foreign ownership is greater than 0. Second, we estimate the likelihood of receiving foreign portfolio investment, defined as foreign investment between 0 and 10 percent. Third, we use the same binomial variable defined above (foreign investment greater than 10 percent) on the 1999 ownership data. And finally, we evaluate a more restrictive binomial variable, which is 1 only if the firm is known to have been a target of foreign acquisition in the past or has foreign ownership share exceeding 25 percent.

The results are reported in Table 4B. The first column reports the results for the likelihood of 15
Table 14 Evaluainn of Labor Retraining Proram Snumary of Program Effects

|||||j <;, :,; :.; < . ... ... ''f:.|0 .... :**.**'**.**=;' ...**.**.**.....**...**.**.**.**......................<br>|**_**~~~|............||
|---|---|---|---|---|---|---|---|
|AUS<br>|4.0<br>|6.5<br>|6.9|0.4|.27'|642|490|
|Wob Prior Wk Exp.<br>No PtkorWfkExp|3.5<br>7.2|6.5<br>S9|6A.<br>101|4o.1<br>42|_3S-_<br>|66d<br>|507<br>|
|<br>||.|.|.|.27T|541|413|
|APg < =25<br>J4Be > 25|4.7<br>32|6.1<br>69|7.6<br>|I.S<br>|.37<br>|609|421|
|<br>|.|.|6.1|-0.8|.22|719|577t|
|Moc irg Edhucation<br>Scl>oionCrtoist|3.9|6.1|6.8|0.7|.28to37T|632|460|
|AU_<br>|S.9<br>|7._|8.8|1.0|.02|444|435|
|Wnh P4or Wk Ep.<br>No Ptior Wk Exp|S.0<br>82|9.0<br>59|7.9<br>111|-1.1<br>|.11<br>|467<br>|457|
|<br>|.<br>|.|.|4.3|.02|401|394|
|_Age_<=25<br>Apg > 25|6.0<br>.7|7.0<br>94|8.9<br>86|1.9<br>|.16<br>|475<br>|404<br>|
|<br>||.|.|-0.8|.04|44|5436|
|Meeing Education<br>Selction Citerial|S.9|7.8|8.8|0|-.13 to.10|432|460|

*SigniSe n at 10% leve

Sifieant at l levd Individuals rWth at conplce pdn y education but no 9.0 tn complete higher s4conda7y.7.9ore MeAvengc duration of traning pedod-2.9 mond. ' Equals the coefficient on a dummy for trainee status fiom regression of log monthly wages on experience, experience squared and schooling. The regression also includes quarter dummies, a dummy for self-employment, duration of unemploymentprior to time tO, a dununy for pfior work experience, and interactions between the training dummy and schooling status, and between the trining dununy and age (see main text and tables 10 and 11 for detailed description of resuls). I Predicted monthly wages at sanple means from same regression.