rafmacalaba/datause-encoder-data huggingface dataset:
politically connected. At the same time, the advantage of using stock market data is that it avoids being reliant on the inevitably subjective expert assessments that formed the basis for the Castle ranking of connectedness. Second, it is likely that there are other privately held politically connected firms that are not part of large conglomerates. This measure therefore likely underestimates the prevalence and importance of political connections. This issue is compounded by the fact that only firms that appear in the 1997 manufacturing survey are potentially identified as politically connected; firms that enjoyed connections but exited before 1997 (or lost their connections before 1997) are never identified as being connected in our sample. We also do not know what happened to connections of firms after Suharto’s resignation or whether new connections were formed.

These measurement issues could impact our estimates; by construction the methodology selects overreactive firms, which creates a risk of correlated measurement errors (firms wrongly identified as connected are also those which for some other reason are more likely to be sensitive to political changes while those wrongly identified as non-connected are more likely to be less sensitive to political changes), which could bias upwards the impact of Suharto’s fall. By contrast, if measurement errors are not correlated, this could bias estimates towards zero. To limit these potential biases we primarily focus on firms with family connections, for whom there is no risk that aberrant share price responses result in misclassification and correlated errors.

The number of economically active politically connected firms increased from 191 in 1993 to a peak of 245 in 2000, before decreasing to 178 in 2009. No exit is recorded between 1997 and 2000, the crisis years.<sup>24</sup> As information on connected firms is most accurate in 1997 and 1996 is the last pre-crisis year, in our industry-level analysis we proxy connections with the Suharto regime by the average of the sum of the market shares of politically connected firms within a given five-digit industry in 1996 and 1997. Construction of this de facto time-invariant industry-level presence of politically connected firms also helps alleviate endogeneity concerns related to entry and exit of connected firms and measurement errors. Our preferred measure of connectedness is the aggregate market share of Suharto family firms. The aggregate market share of firms with broad connections to Suharto is used in robustness checks.

> 24 The spectacular survival rate of connected firms documented in the appendix may reflect the fact that some weaker firms may already have been weeded out, that the strategy is more likely to identify larger firms that are part of extended business networks, and that some of the connected firms were “too big to fail.” Another possibility is that the timing of exit of these firms was not accurately recorded in the survey, another reason to discard the crisis years.

37
Several important observations can be made from Table 7. First and most important, this substantial battery of district traits has pretty weak predictive power for industrial specialization and diversity. This was true for the manufacturing sector as well in the longer panel. This weakness is observed in the limited number of regressors that pick up a statistically significant coefficient (often only two out of the 13 regressors modeled) and the low R-Squared value overall. To an important degree, this is the outcome of the metric design that makes these traits more or less independent of factors like district size. Given this independence, we can expect to find broad stability in our upcoming regressions with and without district covariates being modeled (which will hold true).<sup>6</sup> Second, as in our study of the manufacturing sector, worker human capital is important. Districts with high education levels in 2000 display more specialized and diversified industrial bases. Literacy also displays some connection. Literacy was the stronger predictor for manufacturing specialization and diversity in Ghani, Kerr and Tewari (2013), while education plays the sharper role in our new sample that includes services. Either way, these two measures of human capital continue to display the strongest partial correlations, even conditional on other district traits like population levels and consumption per capita. Ghani, Kerr and O’Connell (2013b) also find a connection between local literacy rates and greater use of a wider variety of distinct material inputs in Indian plants (overall and relative to plant size).

In the upcoming growth analyses, we will control for the traits modeled in Table 7, so that we are measuring the impact of specialization and diversity in a district’s industrial base over-and-beyond these correlates. While this approach better isolates the role of specialization and diversity, we should not forget these two basic connections when using stricter econometric frameworks.

By contrast, the absence of some correlations is striking. In the United States, larger cities tend to be less specialized and more diversified (e.g., Black and Henderson, 1998, Duranton and Puga, 2000, Henderson, 1997). Big cities, where firm headquarters and service firms are often based, tend to specialize in business services. Duranton (2013b) also observes this size

> 6 Our earlier study controlled as well for the manufacturing share of the local district work force. We exclude this variable in our current work given the high degree to which our two sectors account for local activity. Our estimations are sensitive to how such a control is constructed.

17
numerous papers have investigated the internationalization of R&D activities (Guellec and van Pottelsberghe de la Potterie 2001; Patel and Vega 1999; Picci 2010). This paper builds on and extends these two streams of literature by looking at the role of migrant inventors in fostering the internationalization of innovation activities.

Specifically, the paper looks at inventor diasporas and transnational inventive activities between developed and developing countries, which is gaining momentum from a development perspective (Clemens et al. 2014; Montobbio and Sterzi 2013). It also aims to see whether differences emerge across the type of technological internationalization—co-inventorship vs. R&D offshoring. Finally, the extent to which countries’ characteristics govern these potential relations is also investigated—that is, whether the least similar countries, for which informal barriers are more acute, have the greatest potential to benefit from diaspora networks.

The study extends the existing literature in a critical way. Contrary to most migration research, which retrieves migration data from decennial censuses, it exploits inventors’ information listed in patent applications. Inventors make up a specific class of workers at the upper end of the skills distribution.<sup>2</sup> Differently from former approaches to inventor migration that use ethnic name recognition algorithms to infer their migratory background (Agrawal et al. 2011; Breschi et al. 2014; Breschi et al 2015; Kerr 2008), direct nationality information of the inventors is used, allowing for the introduction of a richer range of origin and destination countries. Admittedly, the use of nationality data does not come without its limitations, the most important being

> 2. In this respect, the PatVal survey shows that 76.9% of European inventors underwent tertiary education (26% with PhD) (Giuri et al. 2007). Similarly, the RIETI-Georgia Tech inventor survey shows that 94% of US inventors have college degrees (46% with PhD), with 88% of the cases for Japanese inventors (13% with PhD) (Walsh and Nagaoka 2009).

3
<!-- Start of picture text --> IPV experience and justification<br>—> 15<br>=<br>a | icckieeOL<br>a -1984<br>Ba=7 1965-19695-196" 4999-1994<br>2<br>a 1985-1989<br>S 1 La79 _. , _ 1980-1984<br>o 70-1979965-1969 EAB S69 ASTD AGTENSTS<br>5 158ho$08fo94 of 4995-1999<br>E 5 18 19 50 94 1985-4989<br>3S Holsoat e4g9 -|ere 8604<br>1965-1969<br>2 7 .46pksgpg- LSS 1999<br>co 1990-1994 1995-1999<br>9<br>rsSs|<br>10 20 30 40 50 60 70 80 90<br>Share of women justifying beating (%%)<br>— Aghanistan — Bangladesh — India<br>— Maldives — Nepal — Pakistan<br><!-- End of picture text -->
experimentation and rigorous impact evaluations<sup>28</sup> of these policies ‐ using different combinations of interventions and in different contexts ‐ are critical.

The first ingredient in a policy to support women who wish to cross over is information. A significant majority of the women who have not entered male‐dominated sectors think that they make the same or more as enterprise owners in these sectors when, in fact, they do not. And they recognize that information is a factor and it needs to come early in life. ‐ _“No, they are not different [crossovers], any woman can do that kind of work, provided she is exposed early enough”_ noticed a hair salon owner _._ The provision of information will enable these women, in the first instance, to make better informed choices about their sector of operation. Findings from a 2011 evaluation of the Kenya national vocational training program suggest that this approach may be enough to change minds (Hicks et al., 2011). In the Kenya program, before training enrollment decisions and course selections, some women received information on the difference between expected earnings for graduates of male‐dominated trades versus female‐ dominated trades, as well as a video presentation about successful Kenyan female car mechanics. Women who received the intervention were nine percentage points more likely to express preference for a male‐ dominated course, and five percentage points more likely to enroll in one.

However, as our research shows, information alone is not likely to be enough. A second element of a potential program is to provide supportive engagement with those who can help guide the female entrepreneurs as they seek to enter and then operate businesses in male‐dominated sectors. Ideally, this mentor will be drawn from the entrepreneur's existing network of family and friends, but it may work as well with sympathetic (or empathetic) outsiders. In our qualitative work, both crossovers and non‐ crossovers agreed that they will allow for their daughters to cross over if someone reliable was to introduce them to the sector.

Third, while we do not find skills to be a binding constraint for women who cross over, our analysis suggests that it can be important to be exposed to the sector from early‐on and one way is to work / do apprenticeships, ideally in businesses owned by people within the networks of trust. The qualitative work makes a distinction between active and passive observation. Passive participation characterizes women in female dominated sectors. Active participation, which is key for crossovers, consists of being close to the business, working in a business doing sales or managing the storefront, providing services and inputs

> 28 Given that these policies will involve directly confronting well‐entrenched norms, it is critical to integrate qualitative work in these rigorous impact evaluations.

30
with similar socio-economic characteristics) was associated not only with “ability to pay” but also strongly correlated with the following three variables:

- (1) Trust in the local government to use revenues to provide expected services

- (2) Trust in the authorities to establish fair procedures for revenue collection and distribution of services

- (3) Trust in other citizens to pay their share.

# **2.  Main Survey Findings**

Overall 12% of formal businesses reported operating at least for a while before registering for tax. When informal businesses were asked how likely it is for them to register for tax in the next two years 66% of valid responses suggest that it is at least to some extent likely<sup>4</sup> .

Figures 1 and 2 below focus on the data by province. It appears that businesses in Gauteng and Western Cape are the ones most oriented toward the formal sector: Almost 80 % of informal firms in Gauteng report they are at least somewhat likely to register for tax, while only 5% of formal businesses reported they had ever operated informally before registering for tax. By contrast, in Kwazulu Natal (which, like the previous two mentioned, is a relatively urbanized province), only 56% of informal businesses say they are likely to register for tax and 15% of formal businesses reported they had operated informally before registering for tax. Western Cape has low number of formal SMMEs who reported operating informally, but roughly an “average” proportion of informal firms reporting likelihood to register for tax. In the less urbanized provinces (“Other”), 20% of formal businesses reported operating informally before registering.5

**Figure 1: Likelihood for registering for tax by province**

**Figure 2: Operated before registering for tax by province**

<!-- Start of picture text --> 44 85<br> Kw aZulu Natal 56 Kw aZulu Natal 15<br> Western Cape 34 66 Western Cape 10 90<br>79<br> Gauteng 21 Gauteng 5 95<br>63<br>Others 37 Others 80<br>20<br>66<br>Total 34 Total 88<br>12<br>0 20 40 60 80 100<br>Percentage of informal businesses likely or not likely to register for tax in the next 2 0 20 40 60 80 100<br>years Percentage of formal businesses (not) operating before registering<br>Not likely/Neutral Likely Operated before registering for tax Did not operate before registering for tax<br>Province<br>Province<br><!-- End of picture text -->

> 4 The number of non-valid responses is 84 and they mostly refer to answer ‘I don’t know’. If we include these answers, the share of respondents claiming that it is at least to some extent likely that they will register for tax in the next 2 years is 60.3%.

> 5 Survey data was not conducive to a strict urban/rural analysis.

5
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41
# **II. INFORMAL OVERVIEW OF RECENT TELECOMMUNICATIONS EVOLUTION IN AFRICA**

# **_Quantity_**

Many African countries have considerably improved access to telecommunications services with the regional average of teledensity increasing from 19 per 1,000 people in 1995 to about 130 in 2004.<sup>11</sup> However, cross-country _unconditional_ convergence appears to have been weak in the region, even if the long-run data is used (Figure 3).<sup>12</sup> In addition to Ethiopia and Guinea-Bissau as mentioned above, Guinea, Liberia, Sierra Leone, São Tomé and Principe, Zambia, and Zimbabwe are among the countries that have suffered from poor initial penetration and relatively low growth in the long run.

Figure 3. 25-Year Evolution of Telephone Penetration Rates in Africa, 1981-2005 <!-- Start of picture text --> 23 TCD<br>MLIGNQMRT<br>18 BDISOMBFACMRCPVNGALSOGMBSEN MYTBWA<br>RWAUGATTZASDNBEN G OHAKEN GAB MUS<br>13 NERCAFMDGMWICOMMOZAGOCOGCIV SWZ SYC<br>STP<br>GIN ZMB ZAF<br>8 SLE ZWE NAM<br>ETHGNB<br>3<br>LBR<br>-2<br>0 10 20 30 40 50 60<br>Telephone penetration rate (per 1,000) in 1981<br>Annual average growth rate (percent)<br><!-- End of picture text --> Source: _International Telecommunication Union_ .

Mobile telecommunications can explain most of the recent telecommunications developments in Africa. While the number of mobile subscribers increased by 90 percent per annum over the last 10 years, the average growth rate of fixed-line teledensity was merely 6 percent a year (Table 2). In 2005, mobile telecommunications accounted for some 70-80 percent of total telephone subscribers in the region. In addition, such rapid growth in mobile telecommunications network was largely associated with prepaid subscription, rather than monthly contracts. In the region, notably, fixed-line and mobile

> 11 The tentative figure for 2005 is about 220, when 19 countries whose data are available are considered.

> 12 For several countries whose recent data are unavailable, the annual average growth rates are calculated on a shorter period basis. This treatment is applied to Liberia and Sierra Leon. Eritrea is omitted from the figure because of lack of data in the 1980s.

9
functions of wind and water stress, in the supporting information appendix we show that our results are also robust to following Nordhaus (2006) suggestion of an eighth-power relationship between maximum wind speed and damages.

## **Storm Surge Prone Area**

The _f_ damage index provides an informative measure of wind damages and potentially of overall damages because it is also correlated with damage from excess rainfall and storm surge. Nonetheless, because storm surge is often considered one of the most damaging aspects of hurricanes, we further investigate whether coastal lowlands are disproportionately affected by hurricanes. Specifically, we create a coastal lowland indicator variable identifying contiguous areas along the coast which are less than 10 meters above sea level.<sup>4</sup> This storm surge prone area is composed of 7,758 cells (3.6% of all cells). For each of these cells, we additionally calculate the shortest path (Euclidean distance) from the centroid of the cell to the coast. The average distance to the coast is 5.41 km.

# **2.3 Mangrove**

Data on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats. The second source is Giri et al. (2011), who use 1997 to 2000 Landsat data, together with supervised and unsupervised digital image classification, to construct a 30 square meter resolution map of the global distribution of mangrove. We use this second data set to precisely measure the presence of mangrove at the beginning of our sample period.

To rule out that any protection benefits of the mangrove are derived because mangroves grow in areas that are naturally more sheltered, for example, areas that lay in a steeper continental shelf, we begin by excluding from the analysis areas that have not historically supported mangrove habitats. Specifically, for every cell in the storm surge prone area we exclude cells that have no mangrove, as defined by Spalding et al. (1997), in their

> 4Nordhaus (2006) considers areas with elevation less than 8 meters as vulnerable to storm surge. We use a less stringent definition because Shuttle Radar Topography Mission (SRTM) elevation estimates below 10 meters are not considered reliable (McGranahan et al., 2007). We thank Eric Strobl for providing us with GIS boundaries for global coastal lowlands constructed from STRM data.

7
about their sales and employment for both the survey year and three years prior to it, which allows us to construct annualized growth rates.

We include data collected after 2006, although some WBES were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. A complete list of variables and data sources is shown in Table 1; the summary statistics for our key variables are in Table 2. Our final sample consists of up to approximately 80,000 firms covering 709 cities in 128 countries.<sup>7</sup>

# **Empirical specification and estimation strategy**

We focus on firm growth, including both employment and labor productivity growth, for two reasons. First, individual firm growth provides a key source of overall national economic growth, and a country in which firms are failing to grow is unlikely to develop. Second, focusing on firm growth allows us to more cleanly identify the contribution of the BE to growth. Static measures of performance such as labor productivity or total factor productivity (TFP) are more likely to be subject to persistent sources of measurement error and to be influenced by firm-specific market power than are measures of firm growth. By using firm growth and taking a difference of static firm performance, we essentially filter out time-invariant measurement errors. Our base estimation equation is: Here, _i, c, k, j,_ and _t_ index firms, countries, cities, industries, and year respectively. We examine two different outcome variables (������) , namely, a firm’s employment growth and its labor productivity growth, where labor productivity is measured as sales over the number of employees expressed in constant U.S. dollars. Since growth rates are heavily influenced by outlier issues,<sup>8</sup> we follow Davis and Haltiwanger (1999) by calculating mid-point growth rates by dividing the change in employment (or labor productivity) between the survey year and three years earlier by

> 7 For each dependent variable, the number of observations differs, and the figure of 80,000 is for the dependent variable of employment growth which has fewer missing observations. The 709 cities in 128 countries refers to the sample where the missing data issue does not arise.

> 8 To see this, consider a firm whose employment grows from 10 to 100 workers compared to one whose employment grows from 100 to 200 workers.  Both firms increase employment by 100 workers.  However, whereas the former shows a growth rate of 900 percent, the latter shows a growth rate of only 100 percent.

8