rafmacalaba/datause-encoder-data huggingface dataset:
firms. First, cities affect entrepreneurship in the same way they affect wages and innovation. In a comprehensive analysis of the determinants of employment in new manufacturing start-ups across sectors in US cities, Glaeser and Kerr (2009) generate a rich harvest of facts.<sup>17</sup> The first is the existence of scale economies. As a city grows larger, employment in new start-ups in this city increases more than proportionately. Depending on their specification, Glaeser and Kerr (2009) find an elasticity of employment in new start-ups per capita with respect to city scale between 0.07 and 0.22. City population, city-industry employment, and sector effects explain approximately 80 percent of the variation in start-up employment across cities and sectors.

Consistent with a conjecture made initially by Chinitz (1961), Glaeser and Kerr (2009) also find that the presence of many small suppliers is strongly associated with employment growth in start-ups. In addition, they find evidence of mild Marshallian effects associated with labor market pooling and spillovers. Finally, city demographics have only limited explanatory power, such as their measure of ‘entrepreneurial culture’.<sup>18</sup> The ‘Chinitz’ finding about the importance of many small establishments is confirmed by Rosenthal and Strange (2010), who suggest that small establishments matter because they provide a greater diversity of specialized suppliers to local firms.

The analysis of Glaeser and Kerr (2009) has been recently replicated by Ghani, Kerr, and O’Connell (2011) for start-ups in formal manufacturing, informal manufacturing, and services in Indian districts. They find evidence of urban scale effects whereby the number of start-up increases more than proportionately with city population in services, though not in manufacturing. Similar to Glaeser and Kerr (2009) for the United States, the Indian evidence underscores the importance of small firms and broader measures of input-output linkages. Unlike in the United States, Ghani et al _._ (2011) also find that in India, district demographics (education 23
parameters have to be calculated indirectly. For example, **_nd_** is obtained by dividing the amount of total production of rice in Japan by the number of brands of rice listed in the Annual White Paper of the Ministry of Agriculture and Fishery<sup>7</sup> , i.e., _n,=21,_ and _n, (nr)_ is obtained by dividing the amount of total exports of rice from APEC countries by the average amount of production of domestic rice.

In view of the fact that the capacity to export is constrained by the need to feed its own population, we assumed that the amount of rice which could potentially be brought into the Japanese market was limited to the amount already in the international market in 1990-95. Therefore, the calculation of _n,_ and _n,_ was based on the amount of exports rather than on the amount of total production, and we obtained naf1<sup>9</sup> and nr=17.

TIhe value of q (quality premium index of domestic rice over foreign rice) was the most difficult to identify. To obtain this figure we used FAO statistics to calculate the price of rice imported by Japan (56.8 cents per kilogram) and the price of rice imported by Asian developing countries (36.3 cents per kilogram) in 1994, when Japan imported a fairly large amount of rice in order to cope with a possible shortage due to its poor harvest. Thus, the price of rice brought into Japan is 56.5 percent higher than average in the (undistorted) international market. In addition, we tried to incroporate the unpopularity of foreign rice even at the substantially cheaper prices during emergency import in 1994 (due to inferior quality because of foreign objects like stone 7 The brands of rice below the cut-off point in production (15,000 ha) are omitted from the calculation.

51
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term that is the interaction between judicial efficiency and incorporation intensity increases substantially when we instrument for both market size and judicial efficiency, although its statistical significance reduces somewhat.

We find qualitatively similar results when we define incorporation intensity in terms of employment rather than number of firms (not reported). This is hardly surprising given that the correlation between these two measures at the sectoral level is 0.94.

Although we measure the incorporation intensity variable at the country level, this measure may be subject to endogeneity. For example, differences in judicial effectiveness at the country level may explain variation in the share of incorporated firms at the country level.<sup>22</sup> Tables 2 and 5 already showed that the U.S., a country with a more effective legal system than Mexico, has a larger proportion of incorporated firms. We therefore use data from the 1997 U.S. Economic Census to construct an incorporation intensity variable for the same industries based on U.S. data and check for robustness of the results. In doing this, we presume that firms in the United States do not face major obstacles arising from the institutional environment in their decision to incorporate or not, and therefore the sectoral distribution of legal forms of organization in the United States reflects the optimal distribution of legal forms across industries (see Rajan and Zingales (1998) for a similar argument when studying the external financial dependence of firms).

Panel B in Table 8 reports the results when using the U.S. benchmark data for the incorporation intensity measure rather than Mexican data.<sup>23</sup> The results are qualitatively

> 22 The country’s legal system defines the organizational forms that firms can assume, and partly determines organizational choices (see, for example, North and Thomas 1973).

> 23 These regressions exclude the construction industry. The Mexican economic census is composed of five sub-censuses: manufacturing, services, construction, mining, and financial services. A comparison of data

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peak of 260,000 clinical cases diagnosed in 1998 to just under 26,000 cases in 2008.<sup>4</sup> In Eritrea, the costs of IRS are borne almost exclusively by the government, which conducts spraying campaigns (there is no private market for IRS activities). Similarly, LHM campaigns are organized out by the government with the active involvement of local populations. In contrast, ITNs must be acquired by individuals and then set up above the bed. There exist periodic massive distribution campaigns for ITNs, but use and care of ITNs is still a private decision. Sleeping under a net is perceived as unpleasant, especially in warm weather, and ITNs also need regular re-impregnation, if they are not coated with long lasting insecticide.<sup>5</sup> IRS is an expensive intervention, although generally perceived as effective. Nevertheless, there are no studies of the added benefit of IRS in low-transmission settings over and above ITN use, effective case management and LHM. As such, the National Malaria Control Program (NMCP) decided to conduct an evaluation of the impact of IRS in the context of the existing control program (which promotes LHM and ITN use) with the support of the World Bank. The results of this evaluation are presented in Keating et al. (2011).

The intervention was conducted in the Gash Barka region<sup>6</sup> , one of the six zones that compose the country and the most malarious zone in Eritrea. This zone registered more than half of all diagnosed malaria cases and over 60 percent of all related deaths in the country, for the years of 2007 and 2008. The location of the zone is shown in Figure B1. Gash Barka is mostly a rural/agricultural area, inhabited by one-fifth of the country’s population, which is estimated at 3.6 million. Altitudes range between 500 and 1,500 meters and temperatures are generally associated with hot and dry climatic conditions. Significant variation can be observed across the region in terms of precipitation, leading to marked differences in vegetation and malaria prevalence. The rainy season is concentrated

> 4The information about Malaria clinical cases is provided by the Eritrean National Malaria Control Program (NMCP).

> 5There is limited evidence on the barriers to mosquito net use in malaria-endemic regions (Pulford et al., 2011). However, discomfort, mainly related to heat, is among the main identified reasons for not using the nets.

> 6Gash Barka is composed by 14 sub-zones: Akurdet, Barentu, Dighe, Forto, Gogne, Goluj, Haykota, Laelay-Gash, Logo Anseba, Mensura, Mogolo, Mulki, Shambko and Tesseney. We surveyed only 13 of those sub-zones, because one of the sub-zones, Logo Anseba, was deemed to have a very low malaria prevalence attributable to higher altitude.

6
6 foreign principal before any agency or official of the U.S. government.<sup>8</sup> Each entry in the FARA annual reports contains (i) the name and address of the foreign agent, (ii) the name of the foreign principal (usually a private firm, an industry association, or a government agency), (iii) the purpose of the agency, including any U.S. government entities contacted, and (iv) amount of money paid to the agencies for their services.<sup>9</sup> The results presented in this paper use data taken from the reports that covered calendar years 1990-99.

# **3. Econometric Model and Data**

# **3.1 Econometric Model**

We use a panel data model to study the impact of foreign lobbying on tourism:

# LTOURISTS _it_ = β LLOB _it-1_ + α1 LPCGDP _it_ + α2 LRER _it_ + _ci_ + _uit_ . (1)

In (1), _i_ indexes the 13 destination countries in the Caribbean, and _t_ indexes years 1991-2000. LTOURISTS _it_ is the log of the number of U.S. tourist arrivals in country _i_ during year _t_ and LLOB _it-1_ is the log of tourism-related foreign lobbying spending during year _t-1_ by foreign principals in country _i_ . The coefficient of interest β is the elasticity of U.S. tourist arrivals with respect to foreign lobbying spending by principals in country _i_ .

We wish to exploit within-country variation in lobbying and tourism and therefore choose a fixed-effects model specification. The country fixed effects are denoted _ci_ . They are intended to pick up unobserved effects including, among other things, government policy towards tourism, multiple measures of natural “sun, sea and sand” amenities (for example, opportunities for scuba diving), and attractiveness of the island’s unique culture. In fixed-effects models, the unobserved effects may be correlated with other regressors without inducing bias.

> 8 From the FARA document at http://www.usdoj.gov/criminal/fara/q_A.htm.

> 9 Often the exact dollar amount of spending by the FARA agent is left blank. For example, of the 1500 individual FARA entries in 1990, about 1000 reported a dollar amount of transactions.  We proceed by using the reported dollar amounts, and presume that the unreported amounts are either negligible or are randomly missing.
Table 9. India’s urban area and urban population by global urban layer products

||Urban area<br>(percent of land area)||Urban p<br>(percent o|opulation<br>fpopulation)||
|---|---|---|---|---|---|
|||Census|GHSL|LandScan|WorldPop|
|Prediction|3.2%|29.9%|39.6%|28.8%|22.7%|
|GHSL|2.9%|-|54.7%|29.5%|23.1%|
|BEAM|1.9%|-|29.0%|26.9%|20.1%|

Combining the GHSL’s classification of urban cores with population data from LandScan yields an urbanization rate that is less than half a percentage point apart from our preferred estimate (29.9 percent). Similarly, applying the same approach to BEAM urban areas leads to an urbanization rate that is 3 percentage points lower than our preferred estimate. Therefore, when relying on the same population data the urbanization rates predicted by other recent studies are quite similar to the one we obtain with our methodology.

Discrepancies between our proposed methodology and global urban layer products become much wider when using other population data. All predicted urbanization rates decline when relying on WorldPop data and increase when using GHSL data instead. But regardless of the population data used our predicted urbanization rate falls in between the BEAM-based and GHSL-based estimates. We interpret this as further evidence that our predicted urbanization rate is not an outlier.

# **6. Conclusion**

When assessing the urban extent there is value in relying on what one “sees,” especially in countries where urbanization is messy in nature. Subjective assessments can capture the multifaceted nature of cities—relatively large spaces with a higher density of construction, better access to transportation networks, and greater availability of residential amenities. In this paper, we develop what we believe is a credible methodology for the subjective assessment of urban status across very diverse places.

Human judgment could be dismissed as a tool on the grounds that much rests on the eyes of the beholder. However, our methodology convincingly addresses this potential shortcoming. It shows indeed that the classification of places only changes at the margin when involving assessors with different backgrounds or following different protocols.

The increased availability of satellite imageries, of crowdsourcing tools and of computing capability also makes human judgment a scalable methodology. Physically visiting thousands of places to assess their status would be costly and time-consuming. But Google images make a remote assessment possible in a growing number of countries. Crowdsourcing makes it efficient to collect judgments for a representative sample of places. And the availability of remote sensing data, especially of built-up cover and nighttime lights, makes it viable to predict the urban status of thousands of other places.

31
# CONTENTS

|Bankers and the Unbanked in South Africa ................................<br>,.1|
|---|
|Services for Low Income Customers<br>.3|
|A Narrative History of the E Plan Product and Delivery System<br>.4|
|_Physical Infrastructure_<br>_.5_|
|_Savings Instruments_<br>_.6_|
|_Transaction Record_<br>_.7_|
|_Performance Premiums and Bonuses_<br>_.7_|
|_Fees_................................<br>8|
|_New Products_<br>_.8_|
|Cost Considerations / Supply<br>.8|
|Merging Delivery Channels to Achieve Economies of Scale<br>.9|
|Market Performance and Plans for the Network .11|
|Marketing and Innovative Product Design .16|
|Components of the Successful Experiment .19|
|Concluding Comments.22|
and sector‐year fixed effects and identify from variation across counties in regions and across industries within sectors.

We control for finer geographic heterogeneity with microregion‐year fixed effects (Table 9). This set of fixed effects can capture very fine local demand or supply shocks. This change does not affect the results substantially. Still, it is possible that the results are identified from industry‐specific demand shocks. To capture this, we include county‐sector‐year level fixed effects to find that our results are robust to different ways of controlling for local demand shocks.

An additional concern is that unobserved firm‐level heterogeneity may bias the results. Recall, however, that firm‐level heterogeneity is already handled in the original specification by differentiating the dependent variable and controlling for key firm characteristics. Also, the explanatory variables only vary at a more aggregated level. Still, we report specifications with firm fixed effects to control fully for unobserved firm‐level heterogeneity (representing “fixed trends”). Results are somewhat sensitive to this, but we still find a significant positive relationship between the share of HGFs in buyer industries and productivity growth.

**Table 9 Robustness checks of the specification**

||**La**<br>|**P**<br>**borproductiv**<br>|**roductivity (**<br>**ity**<br>|**3‐year chang**<br>|**e)**<br>**TFP**<br>||
|---|---|---|---|---|---|---|
|HGF Downstream|0.034***|0.028***|0.044***|0.044***|0.045***|0.199***|
|share|||||||
||(7.811)|(5.123)|(3.576)|(8.252)|(7.552)|(12.419)|
|HGF Upstream|0.030***|0.067***|‐0.032**|0.023***|0.045***|0.065***|
|share|||||||
||(6.240)|(7.940)|(‐2.486)|(4.031)|(4.781)|(3.987)|
|HGF Intra‐industry|0.010***|0.015***|‐0.011**|0.021***|0.030***|‐0.026***|
|share|(2.890)|(4.197)|(‐2.075)|(5.177)|(6.878)|(‐4.241)|
|Region‐Year FE|yes|yes|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|no|yes|yes|no|
|Microregion‐Year FE|yes|no|no|yes|no|no|
|County‐Sector‐year|no|yes|no|no|yes|no|
|Firm FE|no|no|yes|no|no|yes|
|Firm controls|yes|yes|yes|yes|yes|yes|
|Observations|140,594|140,569|121,559|140,594|140,569|121,559|
|R‐squared|0.224|0.225|0.286|0.178|0.182|0.290|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. The explanatory variables show the share of HGFs in different industries at the same microregion and county, by using the 2‐digit input‐out matrix from OECD STAN. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year Birch employment definition. Small firms are firms with less than 5 employees A relevant concern about our empirical specification is that the share of HGFs can also be correlated with other industry characteristics. For example, the share of HGFs in an industry may be strongly correlated with the share of foreign firms. If foreign firms generate productivity spillovers, it is possible 18
more open. Whatever the dominant effect, it is useful to allow agreements to have differentiated effects based upon their age.

**_Control data_** . In order to identify the impact of policy on traffic we must also control for nonpolicy determinants of international traffic.  Since our primary interest is in city-to-city traffic, we include a number of controls at the city and the city-pair levels.  The primary city-specific data we include is city population.<sup>12</sup> City-to-city distances are included as further controls; specifically, we employ great-circle distances as calculated from the geographic coordinates of each city.  Logged distances are included in the regression as are squared log distances.  We also include a measure of the absolute number of hours of difference in time between the two cities as such time differences might affect air travel demand.

There are several control variables at the country and country-pair levels. Country level measures of GDP per capita, population, and land area are taken from the World Development Indicators, and are included as potential determinants of the overall demand for air travel. Country-pair control measures include dummy variables indicating that a) one country colonized the other, b) the two countries share a common language, c) the countries share a land border, d) are both democracies, e) are both WTO members, and f) are both members of a common regional trade agreement.<sup>13</sup> Continuous variables on country pair relationships include a) annual average temperature difference, an indicator for tourism demand;<sup>14</sup> b) total bilateral trade, and c) the share of total trade that involves face-to-face interactions (that is, trade that falls under the “differentiated products” classification in Rauch (1999)).

# **III. Descriptive Statistics**

In this section we provide a brief description of patterns of passenger travel and policy.

> 12 The population data are taken, in the first instance from UN Department of Economic and Social Affairs, Population Division.  This data set does not report populations for some smaller cities.  We supplement this data with data from the web site www.citypopulation.de, an aggregator of national population statistics. In each case the city populations are reflective of larger urban areas, not simply the city boundaries.

> 13 The colonial, language and common border dummy variables are taken from _CEPII_ , and described in Head, et al (2010).  The classification of governments as democracies or not is done by The Polity IV project.  Data available here  http://www.systemicpeace.org/polity/polity4.htm.

> 14 The underlying data for the average monthly temperature differences are an indicator for tourism demand, and are taken from the _World Bank’s Climate Change Knowledge Portal: Historical Data,_ at _<u>http://data.worldbank.org/data-catalog/cckp_historical_data.</u>_

8