rafmacalaba/datause-encoder-data huggingface dataset:
changes, representing a successful case of deep integration.<sup>4</sup>

# 3.2 Firm Size and Integration Status

In order to see how the post NAFTA/devaluation affected the performance of Mexican manufacturing firms, we use firm-level data from the Annual Industrial Survey (EIA) covering the period from 1993 to 2002. EIA surveys more than 5,000 firms covering 85 percent of total industrial production. The survey provides plant-level information on characteristics such as number of employees, hours worked, wages, value of production and sales, exports, value of intermediate inputs, inventories, investment, etc. (for more detail see Iacovone (2008)).

As we mentioned before, using the theoretical considerations discussed in Section 2 we allocate firms into one of the following four mutually exclusive groups according to their integration status: (1) exporters, (2) importers, (3) fully integrated, and (4) nonintegrated firms. The first group consists of firms that are exporting into the foreign markets without importing intermediate goods; the second group is made up of firms whose only link with the global markets is via the import of intermediate inputs. The third group is formed by all those firms that sell part of their final production in the foreign markets while importing part of their intermediate inputs. Finally, the last group consists of firms that do not have any direct link with foreign markets.<sup>5</sup> Figures 3 shows information regarding the number of firms and their size by integration status for a given year (1997). In 1997, 2,372 firms, representing more than 40 percent of the total manufacturing firms in Mexico, had no direct linkage with the international markets. In that same year, 10 percent of Mexican manufacturing firms were integrated to international markets via exports, 19 percent via imports, and 28 percent were importing intermediate inputs and exporting their final product (fully integrated).

4 By means of an explicit econometric model linking tariff reduction and household real income, De Hoyos (2005) finds that measuring NAFTA just as the reduction in tariff brought about by the agreement would lead to the conclusion that the agreement had almost no impact on real household incomes in the economy.

> 5Notice that this is not entirely true. For non-integrated firms to be completely isolated from direct linkages with foreign markets they would have to be part of a sector that does not suffer from import-competition and at the same time is not receiving FDI. Even using detailed data such as EIA, it is impossible to define if and to what degree a firm is in an “import-competing” sector. Hence the import competition channel will have an effect on integrated and non-integrated firms according to our definition. Nonetheless, a-priori, trade reform will have a smaller impact on non-integrated firms relatively to integrated firms.

9
# **Appendix 1**

<!-- Start of picture text --> Exporters in Dominican Republic 2002-2009<br>2002 2003 2004 2005 2006 2007 2008 2009<br>single product, single market<br>single product, multi-market<br>multi-product, single market<br>multi-product, multi-market<br>4,000<br>3,000<br>2,000<br>number of firms<br>1,000<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Exports by type of firm 2002-2009<br>2002 2003 2004 2005 2006 2007 2008 2009<br>single product, single market<br>single product, multi-market<br>multi-product, single market<br>multi-product, multi-market<br>5,000<br>4,000<br>3,000<br>2,000<br>exports in US millions<br>1,000<br>0<br><!-- End of picture text -->

# **Appendix 2**

<u>Table : Exports (in US$ million) to the CAFTA markets*</u>

|year<br>El Sal|vador<br>Guat|emala|Honduras|Nicaragua<br>CAFT|A members|
|---|---|---|---|---|---|
|2002|1.18|1.81|23.14|1.65|27.77|
|2003|2.09|5.76|10.79|1.13|19.76|
|2004|1.48|3.68|15.05|1.76|21.98|
|2005|2.62|5.01|10.84|3.05|21.51|
|2006|2.86|8.97|15.52|4.4|31.75|
|2007|3.76|10.5|31.27|3.58|49.10|
|2008|6.82|11.77|32.73|6.34|57.66|
|2009|9.29|9.7|29.64|4.47|53.10|

*excluding U.S. and Costa Rica

26
# Table 3: Benefits of Locating on Large (and Polluted) Roads

||(1)<br>log(Rev/Worker)|(2)<br>log(Rev)|(3)<br>log(Proft/Worker)|(4)<br>log(Proft)|(5)<br>log(Salary)|(6)<br>log(Salary)|(7)<br>log(Rent)|(8)<br>Man. Score|
|---|---|---|---|---|---|---|---|---|
|Med. Road Size/Cell|0.0672<sup>_∗∗_</sup><br>(0.0317)|0.136<sup>_∗∗∗_</sup><br>(0.0316)|0.0815<sup>_∗∗_</sup><br>(0.0327)|0.148<sup>_∗∗∗_</sup><br>(0.0325)|0.0308<sup>_∗_</sup><br>(0.0158)|0.0260<sup>_∗_</sup><br>(0.0152)|0.106<sup>_∗∗∗_</sup><br>(0.0287)|0.0542<br>(0.0375)|
|Man. Score|0.191<sup>_∗∗∗_</sup>|0.292<sup>_∗∗∗_</sup>|0.132<sup>_∗∗∗_</sup>|0.237<sup>_∗∗∗_</sup>|0.0881<sup>_∗∗∗_</sup>|0.0844<sup>_∗∗∗_</sup>|0.0745<sup>_∗∗_</sup>||
||(0.0276)|(0.0296)|(0.0278)|(0.0310)|(0.0191)|(0.0191)|(0.0296)||
|log(Size Premises)|||||||0.0499<sup>_∗∗_</sup><br>(0.0212)||
|N|977|976|967|967|2272|2272|654|950|
|R2|0.418|0.450|0.483|0.537|0.316|0.392|0.475|0.187|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sub-county FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Level of Observation|Firm|Firm|Firm|Firm|Employee|Employee|Firm|Firm|
|SE clustering|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|
|Employee Controls|||||No|Yes|||

Notes: Standard errors are displayed in parentheses (* p _<_ 0.10, ** p _<_ 0.5, *** p _<_ 0.01). Man. Score is a standardized index of managerial ability constructed using our survey (see Appendix A.1 for details). We control for log distance to the main city in the region and whether the grid cell contains any road. We also control for whether the grid cell is incomplete (i.e., _<_ 500m x 500m), its area, as well as a dummy for whether it is in our main surveyed area. Employee controls include education, age, age squared, vocational training (dummy), cognitive ability (measured through a Raven matrices test), employee tenure. We control for missing managerial score (dummy) and missing employee controls (dummy). The top and bottom one percent of all monetary dependent variables are trimmed. Road size goes from 1 (Trail/Track) to 5 (Highway).
4 CDFs allocate budgetary resources directly to individual legislators to spend on public works in their constituencies. The data for this analysis are from the Indian CDF, the Member of Parliament Local Area Development Scheme (MPLADS). Using a measure of voter attachment based on whether constituencies are party strongholds or not, we find robust evidence that legislators substantially reduce effort to provide public works to their constituents when their constituency is a party stronghold. This directly demonstrates that voter attachment to parties reduces legislator incentives to cultivate a personal vote– that in the presence of strong parties, even legislators in single member constituencies, electoral institutions that give legislators strong incentives to attract a personal vote, nevertheless often “pass on pork.” Our research also sheds light on the effects of political affirmative action on legislator incentives. Some electoral constituencies for the national legislature in India are “reserved” exclusively for candidates belonging to lower castes in the Indian caste system. Such seat reservations are among a broad set of electoral remedies, including the Voting Rights Act in the United States, intended to offset the historic and social disadvantages of particular groups in society. However, the extent to which reservations improve welfare of scheduled castes or the constituency as a whole, or whether they increase targeted services at the expense of overall public good provision because of reduced responsiveness, is unclear.

To the extent that legislator behavior is influenced by their affinity for their own social group, seat reservations might increase the government benefits that flow to this group. In fact, research suggests that seat reservations in India may shift government spending towards goods that particularly benefit scheduled castes (Pande, 2003). However, seat reservations may limit electoral competition and interact with voter attachments to party or candidate identity in ways that reduce legislator incentives to exert effort on behalf of
Both the incidence and the amount remitted appear to be even higher among the most highly-skilled migrants from countries with high levels of brain drain. Gibson and McKenzie (2010) report that between 68 and 93 percent of the developing country high-skilled migrants in their sample remit, with an average amount remitted of around $5,000. Clemens (2011) also finds an annual remittance level of about $5,000 in his survey of African physicians in the U.S. and Canada. Existing empirical evidence therefore does support the idea that high-skilled migrants remit, particularly back to lower-income countries, and that the level of these remittances can be sizeable relative to per capita income in their home countries.

Less empirical evidence is available about the extent to which high-skilled emigrants invest or engage in knowledge flow or return investment, especially for the types of countries for which brain drain rates are highest. At the cross-country level, Kugler and Rapoport (2007) and Javorcik et al. (2011) find a positive relationship between the number of skilled migrants a country has in the United States and the level of foreign direct investment from the U.S. economy to that country. However, their dataset contains only two countries with population rates below one million, making it difficult to see whether this relationship holds in the smallest countries for which brain drain rates are highest. At the micro-level, Gibson and McKenzie (2010) find very low incidence rates of high-skilled migrants being involved in trade facilitation or investment in business start-ups in their home countries: they estimate a small country like Tonga or Micronesia might gain at most $500-$2,000 per high-skilled migrant from trade and foreign direct investment. This contribution is a positive one, but unlikely to have large effects on development.

Reductions in the cost of air travel and improvements in information technology make it significantly easier for high-skilled migrants today to continue to stay in contact with people in their home countries. In principle this opens up many opportunities for knowledge flow. However, again the types of knowledge flow that occur back to large and vibrant economies are likely to differ from those to smaller countries where brain drain rates are higher. Gibson and McKenzie (2010) find the main forms of knowledge flow of high-skilled migrants from Ghana, Micronesia, Papua New Guinea, and Tonga are information about educational and work opportunities abroad, with few migrants providing advice to home country companies or governments.

16
# **TABLE A6:  Business Environment Risk Intelligence (BRI, QLM)**

## **A6: Business Environment Risk Intelligence (BRI)**

http://www.beri.com BERI S.A. is a private source of analysis and forecasts of the business environment in developed and developing countries. The firm was founded in 1966 and is headquartered in Geneva, Switzerland.

BERI has two services that include variables of interest for the purpose of this paper: The Business Risk Service, and the FORELEND or Lender Risk Rating. Both services are supervised by Dr. F.T. Haner, founder and senior editor. A number of analysts review various data sources and produce initial draft reports, relying on an international network of sources for intelligence in the field. BERI convenes two permanent panels of about 105 experts from all over the world. These panels provide country ratings and qualitative observations on the basis of these initial reports. One panel assesses political conditions, and the other offers perspectives on the business operating environment. These ratings are constructed using the Delphi method, in which panelists are also supplied with the ratings they produced in previous assessments as well as the panel average score for each measure.

BERI monitors 50 countries three times per year, assessing 57 criteria separated into three indices. The Political Risk Index (PRI) focuses on sociopolitical conditions in a country. Diplomats and political scientists rate the present condition of eight causes and two symptoms of political risk, using a scale from 7 (no problem) to 0 (prohibitive problem). The Operation Risk Index (ORI) identifies major bottlenecks for business development, rating 15 criteria on a scale of 0 (unacceptable conditions) to 4 (superior conditions). The R factor assesses a country’s willingness to allow foreign companies to convert and repatriate profits and to import components, equipment and raw materials. It is composed of 4 sub-indices, one of which assesses the quality of legal framework in terms of statutory laws and actual practice.

BERI also produced a different set of indicators, the Quantitative Risk Measure in Foreign Lending (QLM), measurings the qualitative risk factors in credit exposure in 115 countries using a scale from 0 (high risk) to 100 (low risk). In the table below we list the variables included in each of the governance indicators. We use BERI's data for the last quarters of 1996, 1998, 2000, and 2002-2005.

51
**5**

conclusive that IPO are on average unde:priced.<sup>9</sup> In this context, however, if venture capital firms are viewed as financial intermediaries with specialist skills regarding the financing, monitoring and management of new firms, it would be expected that the average firm brought to the market by a venture capitalist is in some rense better than non-venture capital backed new issues. This in turn should have an impact on the degree of underpricing of the venture backed new issue.

Lim and saundera (1990) examine this problem through a model based on optimal dynamic new issue strategies adopted by good firms relative to bad firms. In their model, the "events" taking place at each stage of the process are as follows: (i) at the IPO, the venture backed firm--i.e. the good firm-underprices more precisely to signal that it is a good firm<sup>1</sup> o; (ii) at the second stage, the venture backed firm gets better market reception--i.e. more analysts track the stock, there is higher institutional ownership, the shares appreciate faster towards their true market value in the aftermarket: (iii) once the true value is revealed, the venture backed firm comes to the market with new seasoned offerings of stock at an earlier date and raises larger amounts, recouping the cost of underpricing at the IPO. Empirical tests performed on data for 925 non-venture backed IPOs and 382 venture backed IPOs offer some support to the first two stages of the dynamic strategy model. As to the third stage, venture capital backed firms were found to come out with seasoned issues earlier and at higher dollar amounts than non-venture capital backed firms, as predicted by the model; yet, the differences were not statistically significant.

# C. A Stylized Model of the Investment Activity of the Venture Capital Firms

# 1. Basic Staces

The investment activity of the venture capital firm can be modelled as consisting of three basic stages: (i) In the first stage a decision must be made as to the entry of a venture capital firm in terms of embracing a particular investment. The key aspects to be analyzed are the following: (i.l) how do venture capital firms choose among different projects, that is: (a) how is the process of screening done?, (b) how are individual projects evaluated and what are the factors affecting the decision (for example: (b.1) sharing rules for risk taking, (b.2) operational decision-making in selecting projects)?

9 Underpricing is the difference between the offer price of the initial public offering and the first available market bid price. The degree of underpricing is measured by the percentual ratio of this difference to the offer price.

10 Since in the aftermarket the bad firm's true value is revealed, it could not recoup the costs associated to the degree of underpricing assumed by the good firm, and therefore the signal cannot be imitated.
Table 9: Regression results from baseline specification for manufacturing only

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐TFP Ac|kerberg_et al_|. using count|ry specific IO‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TF|P Ackerberg|_et al_. using EU|‐wide IO‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.377||||||‐1.113||||||
||(0.440)||||||(0.128)||||||
|Entryt‐2||‐0.349|||‐1.921***|‐1.936***||‐0.508|||‐1.403*|‐1.379*|
|||(0.418)|||(0.000)|(0.000)||(0.449)|||(0.064)|(0.067)|
|Conductt‐2|||0.222||0.335|0.323|||‐0.627||‐0.378|‐0.455|
||||(0.473)||(0.284)|(0.303)|||(0.236)||(0.457)|(0.372)|
|FDI Overallt‐2||||‐0.059||‐0.675||||‐1.510**||‐1.327**|
|||||(0.904)||(0.114)||||(0.031)||(0.021)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|1417951|1523104|1440022|1618931|1417951|1417951|1407773|1512926|1429844|1601608|1407773|1407773|
|R2A|0.985|0.985|0.985|0.987|0.985|0.985|0.984|0.984|0.984|0.987|0.984|0.984|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.128|0.131|0.127|0.132|0.128|0.128|0.128|0.131|0.127|0.131|0.128|0.128|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

38
As indicated in table 21, most of the people whose applications for a bank loan were denied believed that they were denied due to bank concerns about the risks associated with the loan. These risks were caused by a lack of a credit history, insufficient or unsteady earnings, and the lack of collateral or an outside guarantee.

|**Table 21. Why your credit application was rejected?**|
|---|
|**Total**<br>|
|**Didn't have Credit background**<br>**20.7%**|
|**Didn't have enough earnings**<br>**17.2%**|
|**Other**<br>**17.2%**|
|**Didn't have guarantees**<br>**13.8%**|
|**Civil status, sex or age**<br>**10.3%**|
|**Didn't have collateral**<br>**6.9%**|
|**Didn't have steady income**<br>**6.9%**|
|**High amount**<br>**3.4%**|
|**Credit propose**<br>**3.4%**|
|**Total**<br>**100%**|
|**Source: ENDESFI**|

Among those individual who reported that they did not apply for bank credit, 71% said that they did not need any bank loans or that they preferred to finance their purchases out of their income flows (Table 22). Much smaller percentages cited factors that suggested a fear of being rejected. This suggests that many people never or rarely borrow or have other sources of credit so that a lack of access to bank credit does not cause a hardship.

|**Table 22. Why you didn't apply for a**|**ny credit?**|
|---|---|
||**Total**|
|**Didn't require it**|37.0%|
|**Prefers to use own income**|33.7%|
|**They have other options**|0.9%|
|**A lot of requirements**|6.6%|
|**Didn't trust in the financial institutions sector**|1.4%|
|**Didn't have a job**|3.7%|
|**Didn't have a job**|6.9%|
|**Didn't have a collateral**|1.4%|
|**High amount**|0.3%|
|**High interest rate**|4.2%|
|**High fees**|0.7%|
|**Didn't know who to get it**|0.9%|
|**Didn't have enough information**|0.8%|
|**Didn't have guarantee**|0.1%|
|**Other**|1.6%|
|**Total**|**100.0%**|
|**Source: ENDESFI**||

Finally, as shown in table 23, of the individuals who reported that they had taken out a bank loan, 65% were still making payments on the loans. The survey did not ask whether or not they had experienced periods of delinquencies with their payments.

45
# **The Exposure of Workers to Artificial Intelligence in Low- and Middle-Income Countries**

Gabriel Demombynes,<sup>1</sup> Jörg Langbein,<sup>2</sup> and Michael Weber<sup>3</sup> JEL classification: J24, J21, O33 Keywords: Jobs and Development, Human Capital and Growth, Digital Economy Strategy

> 1 World Bank, Email: gdemombynes@worldbank.org. 2 World Bank, Email: jlangbein@worldbank.org.

> 3 World Bank and IZA, Bonn. Email: mweber1@worldbank.org.

> We are grateful to John Giles and Omar Arias for useful comments and discussions.
# **2.2 Arsenicosis and children’s development**

For various reasons, arsenic exposure is a public health concern for children and infants as well. Newborns and infants are especially susceptible as arsenic may be passed from mother to child during pregnancy or through breastfeeding. Claudia et al. (2003) find that moderate arsenic exposures from drinking water during pregnancy leads to lower average birth weight. Children who are exposed to high levels of arsenic in their drinking water are seven to 12 times more likely to die of lung cancer and other lung diseases in young adulthood (Smith et al. 2006).

The existing health literature on children is however weak regarding the heterogeneity in the impact of arsenicosis. For adults, there is suggestive evidence that the symptoms of arsenicosis vary by an individual’s demographic and socio-economic attributes. For instance, Hadi and Parveen (2004) studied the correlation between the prevalence of arsenicosis and various individual and household level factors such as age, sex, education and the economic status of the household<sup>1</sup> . Two findings are noteworthy here. First is the genderdifferentiated impact -- the prevalence of arsenicosis was highest among middle-aged males<sup>2</sup> compared to their wives who drank water from the same source and who showed no visible signs of poisoning. A possible explanation for the gender difference in prevalence of arsenicosis is that males are more involved in physical activity and consume more fluid. Hence, they are more prone to suffer from Arsenicosis than females. Nonetheless, evidence from nation-wide data on arsenic patients is contradictory. According to Bangladesh Arsenic Mitigation Water Supply Project (BAMWSP), there are about 38,000 arsenicosis patients people in 61 out of 64 districts in the country and most of the victims are women.

A second important finding emanating from Hadi and Parveen (2004) is the negative association between economic status and prevalence of arsenicosis. Poor people, who earn their living from physical labor, were found to drink more fluid than the non-poor. On the

> 1 The authors drew upon a dataset of 1654 individuals all of whom reside in a single village in South-west Bangladesh.

> 2 The author argues that the first manifestation of arsenicosis occurs with a lag of 5–10 years (from initial exposure). The incubation period may vary with the amount of arsenic ingested, and the individual’s nutritional and other immunity levels. This explains higher prevalence among old people than young people.

5