rafmacalaba/datause-encoder-data huggingface dataset:
18 company's growth and/or limit their ownership dilution in the event of additional financing rounds.

Finally, investors may require indemnification from the entrepreneurs in the case of any breach of the investment acreement due to causes within the entrepreneur's reasonable knowledge and that were not opportunely disclosed.

# 5. ODeratio.as

One of the peculiarities that differentiates venture capital firms from the more traditional financial institutions is the degree of involvement that the venture capitalist is willing to have with the investee firm after the investment is done. The goal of VCFs is to create capital gains for their investors. The purpose of monitoring is to minimize losses and maximize gains by anticipating possible problems and providing the venture capitalist with timely information on the basis of which sound decisions can be made. This is achieved through following up the operations results of the firm, and providing expert advise. In fact, while the entrepreneurial teams of small firms generally know every aspect of the product or service they have developed, they are generally less knowledgeable about launching the product or service to the market and managing the firm through the growth phase. Venture capitalists, on the contrary, have developed an expertise in these areas and their assistance is extremely valuable to the firm.

A venture capitalist must decide the degree of involvement that he considers desirable and convenient for each individual investment. Some prefer to rely on management reports and periodic company visits; others prefer to take a more active role. Premus' (1984) study found that over 97 percent of the U.S. independent firms and 95 percent of the corporate firms prefer close or frequent involvement with the management team.2 The results of the survey also showed that the area in which venture capitalists prefer to get involved are future financial arrangements and planning development in the first place, followed by marketing and personnel issues (see table II.2).

VCFs' close monitoring of their portfolio firms also benefit the investee firms. In fact, venture capitalists usually have a very strong network which makes them an excellent source of human resources for assisting the entrepreneur in professionalizing the firm; this is especially in the key areas of finance, marketing and sales. In addition, venture capitalists have a strong relationship with other financial institutions, which makes them as well a valuable source for additional capital.

There may be however some problems associated with close monitoring. Among those are the fact that close monitoring can be very time consuming, and venture capital firms tend to have few staff. Also, new firms tend to be run in a very personal way, and a venture capitalist's close involvement may be seen as an outsider intrusion. When the investment is done jointly by several 25 SBICs show a lower preference for management involvement probably because of the type of financial instrument often used by them--i.e., convertible debt--, which makes them less vulnerable to the performance of the firm.
its upward course during the 1990s, coinciding with the second episode of trade liberalization. Overall, there seems to be a correlation between tariff levels and the skill premium. We turn next to an econometric assessment of this relationship.

# 3 The Impacts of Trade

We begin with a simple econometric model in which sectoral tariffs affect industry wages. This would be consistent with a model of interindustry wage premiums (Dickens and Katz, 1986, and Krueger and Summers, 1989) whereby wage premia are attributable to compensating differentials, specific human capital, or more generally, to unobserved worker attributes that may be systematically correlated with industry affiliation. In our model, this wage premia depend on industry tariffs.

We regress the log of the wage of individual i, in industry j, at time t, (ln wijt), on the log of the tariff in industry j at time t, ln τtjt, an indicator of skill level dSigjt (where g indicates whether the worker is classified as skilled, semiskilled, or unskilled), and a number of other individual characteristics (xijt) such as age, gender, and marital status. The model is where Ij is an industry fixed effect, Yt is a survey-period effect, and μijt is the error term. As explained in section 2, we use data on sectoral tariffs at the 3-digit level.

We report findings from four different econometric models. In Model 1, the returns to schooling (δg) and tenure are constant across time; in Model 2, the returns to schooling are allowed to vary from survey to survey (δgt), but the returns to age are not time-varying; in Model 3, both the returns to schooling and the returns to age vary across surveys. In Model (4), we further allow for a sectoral linear trend in the model to control for possible trends in the change in wages that might be a confounding factor for the impacts of tariffs. A nice feature of our study is that the two episodes of trade liberalization that we exploit to identify the effect of tariffs on wages are separated in time by approximately a whole decade.

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sample for the analysis of intergenerational mobility consists of children 18-35 years of age in 2016, and the estimation samples for the investment equation are 6-22 and 8-23 years in the 2010 round of the panel. This age range is chosen so that the age cohorts in the investment equation have substantial overlap with the age cohorts in the mobility analysis. In CFPS-2016 for the 18-35 age cohorts, the mean schooling of fathers is 7.65 in the sons sub-sample and 7.67 in the daughters sub-sample in the urban areas according to Table T1.U. The average schooling of sons is 11.17 years, and 11.18 years for daughters. However, there is a gender gap in the average schooling attainment in rural China: 9.2 (sons) years and 8.7 (daughters) (see Table T1.R, upper panel). The average education expenditure is _higher for daughters_ in the urban estimation samples, but favors the sons in the rural samples.

# **(4.2) Data for India: IHDS (2012, 2005) and NSS (1995)**

The data for estimating the intergenerational mobility of education in India come from the India Human Development Survey (IHDS)-2012. The construction of matched children and parents pairs are slightly different between sons and daughters. To generate matched son-father pairs, we follow Azam and Bhatt (2015) closely (see the Table 8 in Azam and Bhatt (2015)). The only difference is that we are using wave 2 (2012) while Azam and Bhatt (2015) use wave 1 (2005) of IHDS panel data. For all household heads, their father’s education information is available directly in the household module, irrespective of a father’s residency status at the time of the survey. The comparison between our sample using IHDS2012 to Azam and Bhatt’s sample using IHDS-2005 is documented in the online appendix Table A1.S. To generate matched daughter-father pairs, we follow Azam (2016) closely (see the Table 1 in Azam (2016)). Since Azam (2016) also use IHDS-2012 data, we can compare our sample precisely, as shown in the online appendix Table A1.D.

The summary statistics for our various estimation samples for IHDS-2012 (for intergenerational mobility estimation), and IHDS-2005 and NSS-1995 (for estimation of the investment equation) are reported in the lower panels of Tables T1.U (for the urban plus rural sample), T1.R (for rural sample). The mean education of fathers in urban areas is 6.64 years in the sons sub-sample, and 6.44 years in the daughters sub-sample of the main 16
11 Because these year-by-year sector-quantile growth rates are not directly observed, estimates must be used. What is observed (via survey-based estimates) is each sector-quantile’s cumulative growth in mean household consumption per adult equivalent between the two surveys. This is simply where<sup>_~~c~~_</sup> _tS_ , _Q_ is the mean of household consumption per adult equivalent in year _t,_ and<sup>_T_</sup> 0<sup>and</sup><sup>_T_are</sup> the initial and final years, respectively.

One way to estimate year-by-year sector-quantile growth rates would be to assume constant growth over time within each sector-quantile. The preferred approach, in the spirit of Datt and Walker, is to scale the national accounts year-by-year growth rates by the sector-quantile growth rates. To see how these can be calculated, note that cumulative growth in GDP per capita in the national accounts is simply the product of the series of annual growth rates: An asterisk is used here to refer to overall growth versus growth in particular sector-quantile. Note that the first year for which the growth rate is applied is not the initial year but the following year. Multiply both sides of that equation by (1+ _G S_ , _Q_ ): We carry out a series of algebraic manipulations to find the series of scaled growth rates by sector and quantile: Equation (11), with a structure parallel to that of equation (6), implies a series of year-by-year growth rates for sector S and quantile Q that results in cumulative growth equal to _G S_ , _Q_ . The effect
# **Box 4. Small-scale private sector service provision in Cambodia**

Between 600 and 1,000 small private power systems operate in Cambodia, serving nearly half a million customers or nearly as many households as the national power company. They account for about half of the country’s generating capacity. Most of these businesses were set up by local entrepreneurs before the interim government assumed power in 1991, and were distributed throughout the countryside, effectively filling the void left by the nonfunctioning national utility.

Similarly, small-scale private companies treat and deliver water via piped and metered systems throughout the Cambodian countryside. As with the power companies, these businesses are built almost entirely with a combination of local equity and informal or family borrowing.

_Source:_ Schwartz, Hahn and Bannon (2004).

provide water in rural areas and electricity is now available from private providers in areas where it was not available in the pre-1991 economy.<sup>29</sup> In addition to delivering a wide range of services, the private sector invests in the infrastructure that underpins these services. The flow of investment by the private sector into infrastructure following conflict tends to be counter-cyclical to official aid flows. Post-conflict situations are typically marked by a burst of official aid in the initial years, but this tends to taper off rapidly after about four to five years.<sup>30</sup> Yet it is at this point that significant private investment, largely in infrastructure, starts to flow, after there has been a sufficient period of post-conflict stability to reduce the perceived risks.<sup>31</sup> Through developing infrastructure capacity in this way, the private sector plays an important role in filling the gap in public service delivery.

# **Building Social Capital**

The development of the private sector may, of itself, build public trust and strengthen state institutions in the post-conflict period. Business and investment can be among the few remaining points of contact between two sides during a conflict and can do much to repair societal ties in its aftermath. At the level of individual transactions, entrepreneurs acting in their own self-interest who enter into a deal with an unknown counterpart may end up reaching across invisible barriers and building trust in ways that daily household transactions do not. The web of business transactions that characterizes a vibrant private sector extends across income and social groups, connects the rural and urban economies, and sometimes even bridges borders.

Dialogues organized by the private sector have, by bringing business representatives from both sides of the conflict together, enhanced social capital in some situations. For example, the ‘Turkish-Armenian Business Development Council’ was founded by an Armenian and a Turk in

> 29 Nenova and Harford 2004.

> 30 See, for example, World Bank 2004, where a 2002 study in Cambodia showed that government resources were able to cover less than one-third of the total annual cost of infrastructure rehabilitation and maintenance. 31 Schwartz, Hahn and Bannon 2004.

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# **1. Introduction**

Exporter characteristics and dynamics vary significantly across countries. The average Chilean exporter is almost three times as large as the average Moroccan exporter, and Chile has one-third more exporters than Morocco. There are twice as many exporters in Bulgaria as in Bangladesh, but a Bulgarian firm exports on average half as much as a Bangladeshi firm. There is also wide variation in exporter dynamics across countries. Firms from Cameroon and Malawi have high entry rates into exporting but only 25 percent of entrants survive after the first year. In contrast, firms from Brazil and South Africa have lower entry rates, but among entrants, about half survive for more than one year. This paper shows that these measures of exporter behavior vary in systematic ways with country size and stage of development and considers potential explanations.

Our first goal is to introduce the Exporter Dynamics Database and use it to characterize export behavior at the firm level across countries. We use exporter-level customs information from 38 developing and 7 developed countries as input to build the _“Exporter Dynamics Database”_ (henceforth referred to as the Database). The Database contains the number of exporters, average size of an exporter, exporter concentration, rates of entry and exit and entrant survival among exporters for each country at various levels of disaggregation. Using these measures, we examine how exporter behavior varies with country size and stage of development. Many previous studies use data from individual countries or a small group of countries in a region to study how firms export.<sup>1</sup> However, their focus has been on the size distribution of exporters and the process of resource reallocation in response to changes in trade costs within a country. In the absence of

> 1 See Eaton, Kortum, and Kramarz (2008) for France; Eaton et al. (2008) for Colombia; Amador and Opromolla (2013) for Portugal; Iacovone and Javorcik (2010) for Mexico; Andersson, Lööf, and Johansson (2008) for Sweden, Albornoz et al. (2012) for Argentina; Freund and Pierola (2010) for Peru; Manova and Zhang (2012) for China, Masso and Vahter (20151) for Estonia, De Lucio et al. (2011) for Spain, Ekholm, Moxnes, and Ulltveit-Moe (2012) for Norway, Fabling and Sanderson (2012) for New Zealand, Mayer and Ottaviano (2008) for several EU countries, among others. See also Bernard et al. (2007, 2012) for the U.S. and a review of the empirical literature, respectively.

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in aggregation.<sup>10</sup> Throughout this paper, we use a threshold of zero, implying that we sum over all pixels with a value greater than zero.<sup>11</sup> Third, satellite sensor recalibration and decay may affect the nighttime lights captured across countries. For example, a recalibration of satellite sensors in the first quarter of 2017 reduced background light and resulted in a worldwide increase in recorded nighttime lights.<sup>12</sup> Countries across the planet all show an increase in nighttime lights, albeit to different extents. Since sensor recalibration and decay pertain to all countries, controlling for time and country fixed effects helps to mitigate the issue.<sup>13</sup> Last but not least, despite all the noise removal, nighttime light growth rates still exhibit large fluctuations. For example, year-on-year quarterly nighttime light growth can exceed 200 percent in some countries in some quarters. Those countries are either very poorly lit because of low electricity access or their nighttime lights are poorly captured because of atmospheric conditions and interference by the sun. A small change in the sum of nighttime lights can then lead to large percentage changes. To mitigate this issue, we winsorize the data by removing the tails of the nighttime light growth distribution. In the following, we report results after removing the top and bottom 5 percent of nighttime light growth rates.<sup>14</sup>

# **3.2 GDP Growth and Other Data**

# _GDP Growth, GDP per Capita, and Sectoral Shares_

For quarterly GDP growth, we use all the available data from the World Economic Outlook database from the International Monetary Fund (IMF), which covers mostly advanced economies. We complement these data with national accounts data for EMDEs compiled by Haver Analytics. We remove the top and bottom 1 percent of

> 10Panel (a) in figure B.1 in the appendix shows that the threshold of zero reduces the volatility of the sum of nighttime lights in Sierra Leone and ensures that the sum is always positive.

> 11Threshold values greater than zero yield very similar results.

> 12As shown in Panel (b) in figure B.1 in the appendix. The calibration occurred at 14:18 Coordinated Universal Time on January 12, 2017. For more details, see `https://ncc.nesdis.noaa.gov/ VIIRS/` .

> 13Since changes in recorded nighttime lights are at the pixel level, there is a stronger increase in lights of larger countries. However, this is of no concern for growth rates of nighttime lights.

> 14We find that not winsorizing nighttime light data at all leads to poor correlation between nighttime light growth and GDP growth. However, winsorizing the tail end of the distribution from 1 to 10 percent yields similar results.

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# **4 Data and descriptive statistics**

## **4.1 Data sources**

The empirical analysis in this paper combines and examines several sources of panel data for the period 1995-2015, which we can link through a consistent definition of industries. In this section, we provide a brief description of each data source, while giving further details in Appendix B.1.

The stock of industrial robots by industry, country and year comes from the International Federation of Robotics (IFR). These data are based on yearly surveys of robot suppliers, and cover about 90 percent of the industrial robots market. The IFR measures both deliveries and stocks of “multipurpose manipulating industrial robots” based on the definitions of the International Organization for Standardization.<sup>11</sup> The data are disaggregated roughly at the three-digit level for manufacturing and roughly at the two-digit level for non-manufacturing activities. The IFR data are regarded as the best source of information on industrial robots (Graetz and Michaels, 2018; Acemoglu and Restrepo, 2017b). Nonetheless, these data have some limitations. First, about 30 percent of industrial robots are not classified into any industry. Following Acemoglu and Restrepo (2017b), we allocate these unclassified robots to industries in the same proportion as observed in the classified data. Second, the IFR data do not cover “dedicated industrial robots”, which are defined as automatically controlled machines for only one industrial application. Third, the IFR data have incomplete coverage for a number of countries; as a result our panel is unbalanced. We construct the robot stock for each country-sector-year using the perpetual inventory method assuming a depreciation rate of 10%. We prefer this measure over the measure of the robots stock reported by the IFR because that measure assumes that robots do not depreciate for a period of 12 years, yet lose all their economic value after exactly 12 years. We believe that gradual depreciation is a more plausible description of reality, but use the IFR measure of the robots stock in our robustness tests.

We complement the IFR data with information on labor hours, material inputs, IT capital, and non-IT capital by industry-country-year from EU KLEMS. These data were originally reported for 28 industries, which were mapped into the 16 sectors we use in the analysis. Table A1 in the Appendix provides details on the exact correspondence we employed.

> 11According to this definition, a machine is considered to be an industrial robot if it is an “Automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications”. Each element of the definition is essential for a machine to be considered an industrial robot. Common applications of industrial robots include assembling, dispensing, handling, processing, and welding (all of which are common in manufacturing).

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Cambodia’s level of development (Figure 5), although this is owed partly to exports of tourism services. Cambodia also achieved a high level of foreign investment, dominated by investors from Asia.

19. **Third, growth also benefited from Cambodia’s demographic transition** (see above). With fertility rates decreasing, demographic growth slowed down and the dependency ratio (ratio of the young and elderly to those working) started to decline. This contributed around 2 points of GDP per capita growth. This is unusual in the sense that most other SRG countries saw this transition

**Figure 5: Cambodia has higher exports per capita than other SRG countries at its level of development**

<!-- Start of picture text --> 100,000<br>10,000<br>Cambodia<br>1,000<br>100<br>10<br>1<br>100 1,000 10,000 100,000<br>GDP per capita (constant 2000 US$)<br>Brazil Botswana China Hong Kong, China<br>India Indonesia Japan Korea, Rep.<br>Malaysia Malta Oman Singapore<br>Taiwan, China Thailand Vietnam Cambodia<br>Source: World Bank, WDI.<br>Export per capita (constant 2000 $)<br><!-- End of picture text --> happen only after they had reached US$1,000 in GDP per capita.

20. **This dimension – history and geography – stresses the vulnerability of Cambodia’s achievements** . In a global environment characterized by considerable uncertainties, this dimension reveals that growth is very fragile and dependent on the global environment. Growth outcomes have been largely dependent on the availability of foreign savings (see the negative correlation between growth and the current account deficit which suggest the supply of foreign savings has been a constraint, Figure 6).<sup>4</sup> This highlights the

**Figure 6: Growth tends to accelerate when more foreign savings are available**

<!-- Start of picture text --> 0.0<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br>‐1.0<br>‐2.0<br>Correlation in the 2000s<br>‐3.0<br>‐4.0<br>‐5.0<br>‐6.0<br>‐7.0<br>GDP growth (%)<br>Source: World Bank, WDI. Data for 1994-2007.<br>Current Account Balance (% GDP)<br><!-- End of picture text --> importance of product and export diversification for sustaining growth and reducing these vulnerabilities to external shocks. Even encouraging achievements in the area of regulations and institutions are not always anchored in a long tradition of business in Cambodia, but rather often imported from other countries: this explains why several of these achievements are only _de jure_ , not _de facto_ . Finally, this dimension highlights that Cambodia went through a unique window of opportunity with the establishment of peace coinciding with a favorable external environment.

> 4 This also highlights the low level of financial depth and limited capacity to mobilize domestic savings (see below). It should be noted that Cambodians’ trust in the financial system had been seriously eroded by the Khmer Rouge regime – which abolished the currency – and is only gradually being rebuilt.

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