rafmacalaba/datause-encoder-data huggingface dataset:
come from multi-product firms. These findings are similar to those in Bernard, Redding and Schott (2009) and show that exports are concentrated within multi-product exporters.

Another important characteristic of our data set is that it distinguishes between exporters in Export Zones (EZ) and exporters in the National Territory (NT). This is an important feature as it allows us to control for the firms location in our empirical exercise. In terms of value EZ exporters are particularly important: their exports account for 74% (2009) to 84% (2002) of total exports. As for their number, around 20% (i.e. 545) of the total number of exporters operate in an EZ each year.

Finally, for accuracy purposes we exclude from our analysis, re-exports, ferronickel’ exports<sup>8</sup> , occasional exporters, couriers, as well as firms whose partners were not identified. These exclusions reduce our sample by 18% to 110,702 trade relationships.

# **3. Export trends**

With the purpose of identifying any major change in the export patterns following the DR-CAFTA agreement, we briefly review in this section the evolution of the exports from Dominican firms to the U.S. and the other CAFTA members (i.e. El Salvador, Honduras, Guatemala and Nicaragua).<sup>9</sup> Figure 1 reports the exports from the Dominican Republic by destination from 2002 to 2009 (in US$ millions). The United States is by far the largest partner of the DR with average exports amounting to US$ 2.6 billion each year. Other main destinations for DR exporters’ shipments are Puerto Rico (10.2% in 2008), Haiti (9.9%), Belgium (2.1%), Spain (2%) and the Netherlands (1.9%). As for the exports to the other CAFTA members, they account for only 1% of DR total exports in 2008, and this share has been relatively stable since 2002.

Figure 1: Exports by destination <!-- Start of picture text --> Exports by destination 2002-2009<br>U.S.<br>Other CAFTA<br>members<br>Central America<br>and Caribbean<br>OECD<br>RoW<br>2002 2003 2004 2005 2006 2007 2008 2009<br>5,000<br>4,000<br>in US$ million 3,000<br>2,000<br>1,000<br>0<br><!-- End of picture text -->

> 8 We exclude ferronickel exports since they involve only one exporter and account on average for 13% of DR total exports. Moreover, its export value is heavily dependent on international price fluctuations which could also bias our results. In 2007 its exports accounted for 26% of total exports, while in 2008 the share was of only 9%, as consequence of a price decline.

> 9 For the purpose of our study, we exclude Costa Rica from the CAFTA country group, as it only ratified the treaty in 2009.

6
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**Table 2 ICT and firm size**

|Size|Total<br>employment|Number of<br>computers<br>|% of<br>workers<br>using<br>computers|Internet<br>use|% of<br>workers<br>use<br>internet<br>for work|Has a<br>website|
|---|---|---|---|---|---|---|
|[0,50]|14.3|3.1|32.2|96.6%|29.7|46.5%|
|(50,200]|91.1|16.6|35.7|99.9%|32.6|77.3%|
|(200,1000]|280.0|98.2|42.3|99.8%|36.3|90.2%|
|>1000|650.1|573.7|55.2|100.0%|47.0|93.7%|
|Total|63.8|30.2|34.5|97.7%|31.4|58.3%|

Source: EAM‐DANE 2008‐2014. Author’s calculations. Each column shows the average value of each variable for each bracket of firm size.

The first column of Table 2 shows the average number of employees in each firm size category, to put into context the variables described. The table also shows that the average number of computers at each firm increases with size as does the share of workers that use computers. Column four shows that the average use of internet is basically universal for firms of all sizes. The final column shows that having a website tends to increase with firm size. In smaller firms about 1 in 3 workers uses a computer while in large firms this ratio is about 1 in 2. In the Colombian manufacturing sector, less than a third of workers uses the internet for their work. This percentage is higher for large firms (those hiring more than 1,000 workers) but is just about 42% of workers. In small firms, just under 28% of workers use the internet in their work. This suggests that the impact of internet use or availability on firms’ productivity would be more likely to be observed in larger firms where internet exposition is greater for workers.

**Table 3 Internet and firm size**

|Size|Modem|ISDN|ADSL|Cable/Fiber|Wireless|Mobile|0‐256<br>Kbps|254‐1024<br>Kbps|1025‐2048<br>Kbps|>= 2049<br>Kbps|
|---|---|---|---|---|---|---|---|---|---|---|
|[0,50]|6.3%|0.8%|25.3%|39.0%|19.7%|6.5%|7.5%|26.9%|38.7%|24.9%|
|(50,200]|2.2%|0.5%|27.0%|51.2%|13.4%|5.6%|2.7%|18.6%|33.4%|45.2%|
|(200,1000]|1.0%|0.3%|13.1%|72.9%|7.7%|5.0%|1.2%|13.7%|24.7%|60.4%|
|>1000|0.2%|1.1%|5.2%|79.2%|7.0%|7.4%|1.2%|7.7%|16.0%|75.0%|
|Total|4.7%|0.7%|23.9%|45.8%|16.9%|6.2%|5.8%|23.4%|35.7%|33.8%|

Source: EAM‐DANE 2008‐2014. Author’s calculations. Each column shows the average value of each variable for each bracket of firm size.

The leading technology in the Colombian manufacturing sector is cable/fiber with just under 46% of firms using this technology to access the internet. The second most popular technology is ADSL, and this is particularly true for smaller firms. 25% for small firms use ADSL while only 5% of firms with more than 1,000 workers use this technology. The most common contracted speed for internet use is 1‐2 Mbps with just under 36% of firms. However, two‐thirds of large firms report internet speeds over 2Mbps.

TFP estimation **Data Cleaning** 6
(1) a grant of US$12.8 (1000 Kenyan shillings, KSh) paid to the girl’s family; (2) a grant of US$6.4 (500 KSh) intended for the winner’s school fees and paid directly to her school; and (3) public recognition at a school awards assembly organized by the NGO.

Note that there may be benefits for winners’ siblings as well, due to the income transfer, because primary school fees were levied per household, rather than per student, so the cost of schooling declined for them as well, and any within household learning spillovers (we plan to estimate sibling impacts in future research). Given that many parents would not otherwise have fully paid school fees, primary schools with winners benefited to some degree from the award money that paid winners’ fees, and this may have led to increased cooperation with school headmasters.<sup>5</sup> Some of these funds may also have benefited teachers, if they were used to improve the staff room, for example, although these transfers were generally small.

In the two years of the program, two cohorts of Grade 6 girls competed for scholarships. Girls registered for Grade 6 in January 2001 in program schools were the first eligible cohort and those registered for grade 5 in January 2001 made up the second cohort (and they competed for the award in 2002). The NGO restricted eligibility to those girls who were already enrolled in a program school in January 2001, before the program was announced. Thus there was no incentive for students to transfer into program schools, and in fact in all only 5% of sample students transferred schools between January 2001 and January 2002, and incoming transfer rates were nearly identical in program and comparison schools (not shown).

In November 2000, cohort 1 students took end-of-year Grade 5 exams, and these are used as baseline test scores in the evaluation.<sup>6</sup> In March 2001, the NGO held meetings with the headmasters of schools chosen for the program to inform them of program rules and to give each school community the choice to participate. Headmasters were asked to relay information about the program via a school

> 5 Although mandatory school fees were abolished in 2003, as described above, the NGO continued to pay grant money directly to schools with scholarship winners in 2003.

> 6 A detailed project timeline is presented in Appendix Table A.  Unfortunately, there is incomplete 2000 baseline exam data for cohort 2 (when they were in grade 4), and thus baseline comparisons focus on cohort 1.

8
Figue 1 _(continued)_ <!-- Start of picture text --> Decomposmon of not domac svinp (by public and pnvan souce)<br>9<br>6<br>-3<br>-6<br>1970 1973 1976 1979 1982<br>Note: AR an masured as pereentap of Goo.<br>*---- Fedel government budgt sugplus<br>-.-Private sector surplus of svine over investment<br>Trade account balance<br><!-- End of picture text --> <!-- Start of picture text --> Public expenditure and investment (sham in Gat)<br>13<br>3 /<br>1970 1973 1976 1979 1982<br>Public expend van GoCD<br>- -ul tnve$Unc / GDP<br><!-- End of picture text -->
we use the oldest and most recent information on the number of employees in the data to capture change in size over time, i.e., at _t −_ 3 and _t_ . We follow a two-step approach similar to that of Aterido et al. (2011). First, we compute the average size of each firm. Based on average size, we calculate for each country-location-sector-size cluster two proportions: (i) the share of firms facing a bribe demand, and (ii) the share of firms offering a bribe. Next, because the bribery environment may differ for firms that changed size over time, we match the proportions based on initial size. A matching based on current size may leave endogeneity unaddressed, as other characteristics of the current cluster could affect a specific firm’s tax evasion decision through channels other than its own decision to pay a bribe. We consequently match proportions based on initial size, arguing that the owner/manager of a firm continues to practice, in her current situation, the social norms relative to bribery activities that characterized her earlier situation. We thus assume that such norms remain constant over time in a country-location-sector-size cluster, which allows us to use the information on conditions faced, for instance, by microenterprises at _t_ to measure conditions faced by microenterprises at _t −_ 3.

All the estimates include country, sector, and year fixed effects to control for omitted variables bias, namely the norm and culture of corruption, and the quality of institutions at the country, location, and sector level. Moreover, long-term relationships between firms and public officials may generate a serial correlation over time through repeated interactions, which are in part captured by year fixed effects.<sup>20</sup>

# **Three-stage least squares method**

As an alternative identification strategy, we employ a simultaneous equation model using the three-stage least squares method (3SLS). This approach has the advantage of employing a structural model in which an equation is specified for each endogenous variable, which allows to exploit the cross-equation correlation of errors. The structural model is specified as follows: where _Xict_ is the set of firm individual characteristics described previously. _Zict_ and _Wict_ refer to the instrumental variables employed in the previous section, i.e., the proportion of firms facing a demand for a bribe and offering a bribe to public officials in each country-location-sector-size cluster respectively.

> 20Given that we have two instruments for two endogenous variables, we are not able to test for overidentifying restrictions (Sargan-Hansen test) in our initial estimations. However, in the analysis below, we check the robustness of the findings using an additional instrumental variable.

13
procedure was implemented using smartphones and an application designed on the SurveyCTO Collect platform. Length, latitude, and altitude information was also collected for monitoring enumerators' work remotely. Enumerators then created a record for each residential unit and households following the protocol described in the 2019 Manual for Enumerators.<sup>14</sup>

# III.3 Selection probabilities and sampling weights

Given the sampling design discussed above, the probability phij of selecting a household in the second-stage stratum hij of sector hi of first-stage stratum h is given by:

## where

- kh is the number of sectors selected in the first-stage stratum h (20 in a stratum of low density, 80 in medium density and 100 in high density),

- nhi is the population in sector hi , as per the 2010 Housing and Population Census;

- Nh is the population in first-stage stratum h , as per the 2010 Housing and Population Census;

- mhij is the number of households selected in the second-stage stratum hij ; and Mhij is the total number of households in the second-stage stratum hij .

Notice, two fractions on the right-hand side of (1) represent the probability of selecting the sector in the first stage and the conditional probability of choosing the household in the second stage, respectively. The first probability is correct regardless of the accuracy of Telefonica's predicted number of Venezuelan phones in each sector.

The data from each household in hij should be affected by a sampling weight (or raising factor) whij , equal to the inverse of its selection probability (i.e. whij = 1 / phij ) to produce unbiased estimates from the sample.

# III.4 Non-response adjustment and calibration

To account for non-response during the listing operation, the total number of households in each secondstage stratum hij (the denominator of the second ratio in (1)) was estimated as: where Yhij is the total number of households listed in hij and Y'hij is the total number of households registered in hij by the 2010 Housing and Population Census.

The prior weights whij are constant for all members of all households in hij . These weights were calibrated considering the household characteristics hijk in hij . For the Ecuadoran population, the survey matches INEC's 2019 demographic projections in three geographic areas (i.e., Guayas, Pichincha, and rest) and five age categories by gender (i.e., 0-4, 5-9, 10-14, 15-64 and 65+ years old). For the Venezuelan population,

> 14 Centre for Evaluation and Development (2019).

8
In Section 6 we argue that the value of non-performing loans relative to GDP prior to the crisis was roughly 0.25. Taking this to be an estimate of the governments implicit liabilities to the financial sector we set 0 = 0.25. Since there is substantial uncertainty about this number we explore the sensitivity of our results to perturbations in Q. The benchmark value of P was chosen to match the increase in consolidated government liabilities that occurred in the period leading up to late October 1997 when the won first lost an unprecedented (by recent standards) fraction of its value. Using data from KIEP and the IFS, we estimate that net liabilities of the consolidated public sector rose to 5.3 percent of GDP as of the end of September 1997. Accordingly we set I = 0.053. Later we explore the sensitivity of our results to perturbations in T.

We now discuss how the benchmark value of _T_ was chosen. According to the IMF World Economic Outlook, the ratio of government revenue to GDP in Korea was roughly 22.0 percent in 1996. Since our model abstracts from investment and from the trade balance, we subtract 27.8 percent from output to ensure that the consumption to output ratio in the model matches the 1996 value in the data, roughly 0.5.

We set the real interest rate, r, to the average ex-post real interest rate in Korea for the period from January 1990 to April 1997, 7.45%. We chose the inverse of the elasticity of intertemporal substitution, cr, to be equal to 1.0. Finally, we assume that the average world rate of inflation, lr*, is 4 percent. This corresponds approximately to the average rate of inflation in the U.S. over the past 10 years. In the theoretical model we assumed that 7r* = 0. Allowing for a positive value of ir* means that the government will collect some revenue during the fixed exchange rate regime. However, this does not change any of our conclusions since those seignorage revenues have already been pledged to financing the old level of the deficit.

18
# **<u>Appendix 2: Variables Definition</u>**

|**Variable**|**Definition**|
|---|---|
|Ln (Age)|Ln of the difference between the year in which the most recent survey is released<br>and the year in which the establishment began operation|
|Ln (Gov own)|Ln of the share of government ownership|
|Main City|Dummy variable that takes the value 1 if the firm is operating in the main business<br>city|
|Small|Dummy variable that takes the value 1 if the number of employees is below 20|
|Medium|Dummy variable that takes the value 1 if the number of employees is between 20<br>and 99|
|Large|Dummy variable that takes the value 1 if the number of employees is greater than<br>or equal 100|
|Female Ownership|Dummy variable that takes the value 1 if the firm has a female owner|
|Female Top Manager|Dummy variable that takes the value 1 if the top manager of the firm is female|
|Ln (Female)|Ln of the number of full-time female employees|
|Ln (Femaleproduction)|Ln of the number of full-time female production workers|
|Ln (Femalenonprod)|Ln of the number of full-time female non-production workers|
|GVC1|Dummy variable that takes the value 1 if the firm is exporting and importing at the<br>same time|
|GVC2|Dummy variable that takes the value 1 if the firm is exporting and importing at the<br>same time and if it has an international quality certification|
|GVC3|Dummy variable that takes the value 1 if the firm is exporting and importing at the<br>same time and if the share of private foreign ownership of the firm is greater than<br>10%|
|GVC4|Dummy variable that takes the value 1 if the firm is exporting and importing at the<br>same time, if it has an international quality certification and if the share of private<br>foreign ownership of the firm is greater than 10%|
|Gender Provisions|The number of gender-related provisions in regional trade agreements.|

Source: Constructed by the authors using the WBES.

31
(2008) emphasizes entrepreneurship for India‘s future, and reallocation can help close India‘s productivity gap (e.g., Hsieh and Klenow 2009).

While focusing on entrepreneurship, we recognize that large firms also play an important role in regional development. Audretsch and Fritsch (2002), for example, discuss the different paths to Germany‘s development. The giant firms of South Asia are becoming globally powerful and growing in efficiency, and they too will shape employment opportunities in the decades ahead. However, the history of regional development shows that big firms are not sufficient forever. An entrepreneurial foundation that provides for local growth and regeneration is essential for long-term success and prosperity. This is evident in the current struggles of Detroit, Michigan, and its car manufacturers. Jacobs (1970) highlights how Manchester, England, and its giant textile mills in the 1800s were a model of short-term efficiency and power but also were ultimately insufficient for long-term regional growth. Likewise, the very dynamic times ahead for South Asia require that entrepreneurship be embraced and supported at the regional level.

The plan of this paper is as follows. The next section discusses our entrepreneurship data and spatial differences in entry across India. Section 3 reviews the determinants of spatial locations for firms and our metrics. Sections 4 and 5 provide our empirical results for unconditional and conditional estimations, respectively, and the last section concludes.<sup>2</sup>

# **Spatial Entrepreneurship Rates in India**

This section discusses our two primary datasets and describes the spatial variation on entrepreneurship in India. We first define how we will measure entrepreneurship.

## _Definitions of Entrepreneurship_

Defining entrepreneurship is hard and controversial, even in advanced countries.<sup>3</sup> One approach, dating back to Cantillion (1730), is to describe entrepreneurship as the number of people leading independent businesses. Evans and Jovanovic (1989), Blanchflower and Oswald (1998), and many others have accordingly used self-employment rates as their metric of 2 For brevity, we do not provide an extensive background on the entrepreneurship literature, instead highlighting relevant studies throughout the paper. Ghani et al. (2011) provide a short introduction, and Parker (2009) provides a complete review. Storey (1994) and Storey and Greene (2010) overview small businesses and their connections to entrepreneurship specifically. Deichmann et al. (2008) survey prior work on firm locations in developing economies. Sections 2 and 3 draw on Ghani et al. (2011) and Glaeser and Kerr (2009) for describing our entrepreneurship data and metrics, respectively.

3 See, for example, the work of the OECD Entrepreneurship Indicators Programme in 2008 and 2009.

5
**Table 4.1b (continued): Coverage bias in Ethiopia HFPS by Urban-Rural domain**

||**Ethi**<br>|**opia(ESS -**<br>|**Urban)**<br>|**Eth**<br>|**iopia(ESS -**<br>|**Rural)**<br>|
|---|---|---|---|---|---|---|
||**All**|**With Co**|**ntact info**|**All**|**With C**|**ontact info**|
||||_Coverage_|||_Coverage_|
||_Mean_|_Mean_|<br>_bias†_<br>_(2) -(1)_|_Mean_|_Mean_|<br>_bias†_<br>_(5) -(4)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|**_Housing_**|||||||
|Improved roof|0.954|0.961|0.008|0.562|0.583|0.022|
|Improved floor|0.482|0.497|0.015|0.030|0.040|0.010|
|Improved toilet|0.593|0.606|0.013|0.179|0.199|0.020|
|Improved water source|0.976|0.978|0.002|0.637|0.650|0.013|
|Access to electricity|0.905|0.919|0.014|0.393|0.433|0.040|
|**_Asset ownership_**|||||||
|Solar|0.051|0.051|0.000|0.202|0.217|0.015|
|Generator|||||||
|TV|0.505|0.525|0.019|0.021|0.030|0.009|
|Refrigerator|0.213|0.223|0.010|0.004|0.006|0.002|
|Bicycle|0.032|0.033|0.002|0.002|0.003|0.001|
|Motorbike|0.019|0.020|0.001|0.004|0.007|0.002|
|Car or other vehicle|0.028|0.030|0.001|0.000|0.000|0.000|
|Mobile Phone|0.866|0.904|0.038**|0.403|0.568|0.164***|
|**_Other activities_**|||||||
|Owns land||||0.962|0.957|-0.006|
|Cropfarming||||0.876|0.899|0.022|
|Owns anylivestock||||0.882|0.895|0.013|
|Operates nonfarm enterprise|0.380|0.381|0.001|0.157|0.174|0.017|
|Anymember has financial account|0.773|0.799|0.026|0.305|0.370|0.065**|
|Income from remittance (int'l &<br>domestic)|0.215|0.213|-0.001|0.111|0.124|0.013|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

# <mark>4.1.2 Sample representativeness</mark>

The Nigeria HFPS included a sample selection performed on the frame (GHS-Panel households with a phone number) to reach the phone survey sample (see Section 3.1.1 above for more details on the Nigeria sample selection). This is in contrast to Ethiopia, Malawi, and Uganda where the entire frame served as the phone survey sample in those countries. As a result of this additional sampling step in Nigeria, it is important to examine just how representative the sample selected is. Unlike the issues of coverage and response, no distortions in the representativeness of the sample are expected from this sampling selection, but it is nonetheless useful to confirm this is the case. Table 4.2 presents the mean characteristics for the full GHS-Panel sample, the frame (households with contact information), and the selected sample for the Nigeria HFPS (columns 1, 2, and 3, respectively). Column 4 also contains the difference between the mean characteristics observed in the sample and the frame (with significance 17