dataesr/scientific-paragraphs-categorization huggingface dataset:
We have been unable to obtain information on the rollout of RADSL across the UK. 9 ADSL was primarily targeted at households with exchange enablement allowing connection of many more homes than businesses (the mean exchange serves 4,740 households and 255 firms). But this does not satisfy concerns for this instrument. and Criscuolo (2011) amongst others. ITIS provides information on the value of exports by type of service and by destination, allowing us to separate exports into its various firm and service-type extensive and intensive margins. Information on firm-level broadband adoption is contained within the E-commerce Survey, also from the ONS. We combine the firm-level data with information on the telecoms network made available by the Office of the Telecoms Regulator (OFCOM) and a UK-based telecoms data firm, Point-Topic.
From OFCOM we have precise information on the day each local exchange was enabled for ADSL and its location. 10 The large number of exchanges indicates that the spatial variation we exploit is far more disaggregated than regional, local government or even city-level. There are 185 telephone exchanges in London for example. The data from Point-Topic contains information on local-loop lengths for 1. 66 million postcodes (zip codes) in the UK along with the telephone exchange that the postcode is connected to. We find only partial success in providing an explanation for the obvious correlation between service exports and broadband internet use in the UK that motivates the analysis. We find a strong effect of broadband on the extensive margin of firm exports, but only for those exporting businesses services and only for a small part of the population.
In contrast, we find no consistent significant effect when we use total exports, a result which occurs even when we use data on exports from the ARD database, which is not restricted to service exports and is available for a larger number of firms. We can also reach no conclusions regarding the effects of broadband on the intensive margin of exports owing to a problem of weak instruments. Amongst those firms that export, we find that the instruments have the expected relationship with broadband use, but collectively do not have a sufficiently strong relationship. This holds irrespective of whether we use total service or just exports of business services. Bringing these results altogether we estimate that the causal impact of the internet of UK service exports in the period 2000 to 2002 was in the range £1.
For instance,  Haller and Lyons (2012)  use ADSL exchange enablement to predict Irish firms' broadband adoption and the effect on productivity, with firm location at the electoral ward level. In a similar study for German firms, Berthschek et al (2013) use ADSL enablement to predict broadband adoption with firm location at the region level. Akerman et al (2013)  use household adoption of ADSL as a treatment, and find that firms located in areas with higher household adoption experienced faster productivity growth and rising skilled worker wage premiums. 13 The exception to this is  DeStefano et al. (2014 DeStefano et al. ( , 2015) )  who also use ADSL enablement and distance from the telephone exchange to study the effects of ICT on productivity as well as employment, and 11  Breinlich and Criscuolo (2011)  provide a set of stylised facts about service exports for the UK using the same micro data as this paper.
Firm-Level Data i. Sources This paper utilises data from three main data sources. All of the information of firms, their exports, location and characteristics is from the UK Office for National Statistics through the Secure Data Service. These are the same primary sources of UK firm-level data and have been used by other authors including  Bloom et al. (2012)  and  Breinlich and Criscuolo (2011). From the ONS we combine information from four datasets, which we combine using a set of unique identifiers. Further information on each of these datasets is provided in the Appendix to the paper and we provide only brief details here. The primary data source on exports of services is the International Trade in Services Inquiry (ITIS). ITIS contains annual transaction-level data for the services trade of UK firms.
The definition of service trade in ITIS follow the IMF's Balance of Payments Manual (5 th Edition) and feeds into the UK balance of payment statistics. ITIS records service transactions between UK firms and nonresidents, where services are defined as products other than tangible goods. The survey questionnaires explicitly state that non-residents include any individual, organisation or enterprise that is not domiciled in the UK. ITIS data reflects mode 1 and 2, and to a lesser extent mode 4  (Kneller et al, 2010). ITIS does not provide information on mode 3 (commercial presence). ITIS contains almost no information on firm characteristics. We obtain information on ICT, which includes the use of broadband by linking to the E-commerce Survey. This dataset acts as the biggest constraint on the sample size available to us.
As detailed further in the Appendix, this data is available from the year 2000. There is also a substantial increase in the sample size of the survey between 2000 and 2001. The availability of data only from 2000, the increase in the sample size between 2000 and 2001 and our decision to end the sample period at the end of 2002 prevents us from studying the effect of the instruments on firms that switch into using broadband over this period. We note however that is substantial adoption over time. The second key data source used in this study is the ADSL Broadband Database which was made available by the office of the regulator for telecommunications in the UK, OFCOM. The ADSL dataset provides the location (postcode) and date for ADSL enablement for each telephone exchange in the UK. This data is combined with a third dataset purchased by a UK based telecom consultancy firm called PointTopic.
To control for endogeneity we employ instrumental variable estimation. Our instrument captures differences in firms' ability to access broadband internet services by their cable-distance (local loop length) to the exchange, which affects broadband speeds. This instrument in denoted by Local Loop Distance in the first stage regression detailed as equation (  2 ) below. In these IV regressions we compare differences in the probability of firms using broadband based on variations local loop distances within the area of exchanges enabled for ADSL, using pooled cross-section data between 2000 and 2002. 𝐵𝑟𝑜𝑎𝑑𝑏𝑎𝑛𝑑 𝑖𝑙𝑡 = 𝛼 0 + 𝛼 1 𝐿𝑜𝑐𝑎𝑙 𝐿𝑜𝑜𝑝 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑖𝑙 +𝛼 2 𝑌 𝑖𝑙𝑡 + 𝛼 3 𝐹𝐸 𝑡 + 𝜗 𝑖𝑙𝑡 (2) Throughout the analysis we report standard errors that employ the Huber-White correction for heteroskedastic robust standard errors.
i. What is ADSL? Asymmetric Digital Subscriber Line (ADSL) is the dominant form of broadband in the UK. In 2012 over 85% of firms and 55% of households had an ADSL connection  (ONS, 2013a (ONS, , 2013b)). ADSL allows high-speed transmission of data along the pre-existing copper-line telephone network. The asymmetry that the technology generates between the upload versus download bandwidth means that the speeds offered by ADSL broadband were a substantial improvement on those provided by existing narrowband technologies, such as dial-up and ISDN. Dial-up only offered download speeds up to 64Kbits/second, with ISDN 18 offering speeds of up to 128Kbits/second, compared to up to 8,000Kbits/second for ADSL. 16 Notice that using two period lagged values is consistent with using values in 1998 (prior to ADSL diffusion), for observations at the start of the sample period  (2000).
iv. Instrument Construction Using the insights from the ADSL broadband rollout within the UK we create two potential instruments for firm's broadband adoption. The first instrument uses the year of enablement of local telephone exchanges for ADSL broadband. This data, made available by the Office of the Telecoms Regulator (OFCOM), details for every BT exchange in the UK the day that the exchange was ADSL enabled. The second instrument, which is our preferred measure, uses the length of the local loop made available from the Point-Topic database. Here we anticipate that because of lower anticipated broadband speeds, firms further from the exchange are less likely to adopt broadband. The median firm in the sample is 2. 81km from their local exchange, see Table  3. The distances that we exploit are therefore relatively short, with the 95 th percentile of local loop distances just 5.
Pre-ADSL Firm Characteristics Under an assumption that any unobservable geographic factors that make our instrument invalid are unlikely to change quickly across time but likely to be correlated with a range of firm performance variables, not just exports, their potential as an alternative explanation for the power of our instruments can also be tested by taking advantage of the panel data available to us. That is, we examine whether the instruments are correlated with a number of the firm characteristics others have found to predict service exports using data before ADSL rollout took place. If local loop distance or the timing of ADSL enablement is correlated with these characteristics, this would question the validity of our instruments. We can do this for both the timing of enablement and the local loop distance variable.
We use the panel structure of the data to measure firm characteristics in 1998, just one year before the start of ADSL rollout. 25 As firm characteristics we include labour productivity, 26  measures of firm size (gross value added, turnover, employment, having multiple plants), foreign ownership and (contemporaneous) firm age. 27 Note that for these regressions we do not restrict ourselves to the sample used in the main regression analyses (those who have broadband adoption data for example). Instead, we use all firms for which the data is available. The evidence in Table  6  suggests that the timing of ADSL enablement is not completely orthogonal to exante firm characteristics. Ex-ante, firms connected to enabled wave 1 exchanges were on average no larger (in terms of employment, turnover or gross value added) than firms connected to non-enabled exchanges.
Firms connected to enabled exchanges are also not consistently ex-ante no more likely to be foreign owned, have multiple plants, be older/younger or be located closer to the exchange. There is some evidence that firms are more productive however, which suggests that the application of some caution in using this instrument is warranted. For the cable-distance instrument we restrict the data to only areas with enabled exchanges since the cabledistance effects are only present once ADSL is available to the firm. In Table  7  we find no evidence for systematic ex-ante differences between firms' performance characteristics and how far they are located from their local (enabled) telephone exchange. There are no significant differences in their size, in terms of employment, turnover and value added, or their productivity.
In each set of three regressions we rotate the measure of services trade to the total services trade data drawn from ITIS, exports data from the ARD and finally exports of business services again drawn from ITIS. We include lagged firm controls and year fixed effects in all of the regressions, while the OLS regressions 25-27 additionally include a full set of region and industry dummies. In terms of unreported firm covariates, as one would expect, being foreign owned is positively correlated with the propensity to export and the value of exports, which is consistent with the findings of  Breinlich and Criscuolo (2011)  using broadly similar UK data. Foreign owned firms are 11% more likely to export services than domestically owned firms in regression 25. We find no significant correlations between export propensities and firm age or having multiple plants, except for regression 27 where age is significant.
In the OLS regressions we find that service exports are highly correlated with firms' use of broadband. According to the results reported in regression 25, firms with broadband connections are 10% more likely to export services than those without such connections. This falls to 7. 2% in regression 26 when we use data drawn from the ARD and 4. 4% when we concentrate on the probability of exporting business services in regression 27. Nevertheless the significant positive relationship is in line with the aggregate data presented in Figure  1  and with regression evidence presented elsewhere in the literature. In the IV regressions in Table  9  we find more mixed evidence regarding the effects of broadband on exporting decisions. The first-stage results mirror those in section 4; the instruments are strong predictors of broadband access, with F-statistics around (in the case of regressions 28-30) the rule-of-thumb of 10 suggested by  Staiger and Stock (1997)  and well above this in our preferred specification in which we use local loop distance as a single instrument (regressions 31-33).
Robustness That we find an effect from broadband only for business service exports would again seem to indicate that potential confounding factors that are an alternative explanation for our findings are not a problem, or at least the list of potential variables that it could contain is much diminished. While one can never rule out such a possibility, this difference across types of service exports restricts the set of confounding factors that this might be to exclude common shocks, agglomeration or other common geographic factors. In regressions 35 to 37 we consider these possibilities further. In regressions 35 and 36 we consider the role of agglomeration effects. We consider this possibility by adding to the regression the log of the number of households (regression 35) and businesses (regression 36) connected to each exchange using information drawn from the PointTopic database.
In principle there may also be an effect on the variable costs of exporting and therefore the volume of exports for a firm. In the final table of results (Table  11 ) we explore whether the use of broadband also impacts on the firm-intensive margin. In Table  11  we present evidence for total exports and business service exports using the ITIS dataset and report OLS (regressions 39 and 42) as well as estimations using both instruments (regressions 40 and 43) and a local loop length as a single instrument (regressions 41 and 44). Here the analysis is restricted to firms that export, explaining the drop in the number of observations compared to earlier tables. According to OLS estimates the effect on the intensive margin is strong and significant for both total service exports and business service exports.
To account for the endogeneity of broadband adoption, we construct novel instrumental variables that have not previously been used in the literature on ICT and service trade. In particular, we use precise geographic data on firm location to capture fine geographical differences in the timing of ADSL broadband availability and, in our preferred specification, likely broadband speeds based on cable distances from the exchange. The instruments perform well, strongly predicting firm broadband adoption and we perform a battery of (indirect) validity tests that support the robustness of the identification. Could the growth in international services trade be caused by firms' use of broadband technology? Our analysis suggests the answer is yes, at least for a rather small compliant population who exported business services.
Business Structure Database (BSD) The Business Structure Database is utilised to provide our main measure of firm location as well as firm age as a control variable. The BSD provides demographic information, such as age, for virtually every local unit (plant-level) and enterprise unit (group-level) in the UK, but does not provide any information at the reporting-unit level. Unlike the other databases which are linked at the reporting-unit level, the BSD can only be completely matched to other data at the enterprise level, with unique enterprise reference numbers permitting matching to the ARD and via the ARD to the other datasets 35.
Annual Respondents' Database (ARD) The ARD is one of the primary sources of firm-level information in the UK. It provides information on firmlevel covariates. The ARD is a statutory survey that since 1997 covers all sectors of the economy (aside from finance), prior to 1997 the ARD did not cover the service sector. The ARD is a stratified random sample of all firms in the UK economy, with the strata defined by employment, industry and region and again the sample is drawn from the IBDR. The ARD is disaggregated into two levels of information, the reporting-unit (firm-level) and local-unit (plantlevel). I rely on the reporting unit data for additional firm covariates, such as foreign ownership and having multiple plants. Reporting-unit level information comprises of two elements a selected and non-selected sample.
Figure4shows that in 2000, only 38% of firms had broadband access, by 2001 it was 63%, 73% by 2001 and 96% by 2005. 14 Finally, we obtain location and other covariates from the Business Structure Database (BSD) and Annual Respondents' Database (ARD) respectively. These include the age of the firm, its ownership status (domestic or foreign), size, sales and whether it has multiple plants. Since 2000 the ARD also contains a question on exports and we use this as an alternative measure of firm exports. 15 Table 1 describes the sample size of the merged data. The merged data commences in the year 2000, the first year of the E-commerce survey and finishes in 2005. Beyond 2005 ITIS has missing data and is difficult to use. The E-commerce survey has around 6,000 to 9,000 firm-level observations each year.
The vast majority of these firms are also present in the BSD, with more than 94% of firms being matched in a given year. Only around 30% of the firms are also present in the ITIS data, with substantially fewer (14%) present in 2000. The difference has been observed by other researchers, as noted in ONS (2014), and occurs when linking the E-commerce survey to other databases. The resulting merged sample is fairly small, 13,099 firm-year observations spread over the six years. Despite the small sample size in the merged data, the data represents approximately 40% of the trade in raw ITIS data. Both the E-commerce survey and ITIS tend to sample large firms more frequently than smaller firms, meaning the union of the datasets will be directed towards larger firms. For reasons laid out in more detail below we do not use all of the observations available to, instead using the years 2000-2002, and in the estimation sample we have 3,362 firm-year observations.
One strength of the algorithm proposed in this study, hereafter referred to as OLCIPAR algorithm, is to consider exact radiative transfer calculations of the atmospheric and oceanic radiation to enhance the quality of the daily PAR product under a great range of aerosol models. The paper is organized as follows. First, the Frouin's algorithm is briefly reminded since this method will be used in this study for comparisons. Then, the different steps of the OLCIPAR algorithm are detailed. Validation of OLCIPAR is carried out using both the full MERIS data archive and a time series of in situ measurements of daily PAR. Finally, the influence of aerosols on daily PAR are analyzed in the context of the subtropical Atlantic Ocean.
Brief overview of Frouin's algorithm Frouin's algorithm was initially dedicated to process data acquired by SeaWiFS (NASA) satellite sensor. The method is based on the quasi-single scattering approximation to compute the atmospheric irradiance at ground level. For clear sky conditions, operational processing makes use of a given climatology to obtain the aerosols optical properties in order to calculate the atmospheric transparency in order to ensure operational efficiency and facilitate common data distribution. More specifically, the values of the aerosol optical depth that are used for each pixel of a satellite image are based on a climatology of aerosol relying on a two years averaging (namely, 1997-1998) of SeaWiFS observations. Recently, Frouin's algorithm was applied to other satellite sensors such as GOCI/COMS (KORDI) and Global Imager/ADEOS II (JAXA)  [28, 29].
It is important to mention that the Frouin's algorithm considers that the parameters T a , S a and A s of Eq. (  2 ) are modeled based on the quasi-single scattering approximation (see Eqs. (8) and 9 in  [29] ). The integration of the flux Φ surf over the visible spectral range (400-700 nm) and over the course of the day provides the daily PAR from Frouin's algorithm. Knowledge of the aerosol optical depth and aerosol phase function is required to calculate the atmospheric transmittance T a and the spherical albedo S a. Knowledge of ozone and water vapor transmittance is necessary to determine the term T g. The gaseous absorptions are usually taken from ancillary data, typically from ECMWF database or satellite data measured by other sensors. Knowledge of the measured top of atmosphere radiances and atmospheric reflectance are necessary to calculate the term A.
Performances of daily PAR algorithms The validation of the daily PAR is carried out using satellite data acquired by the MERIS instrument (since data from the OLCI sensor are not routinely distributed yet) and comparisons with time series of in situ measurements. For that purpose, the high frequency measurements (typically every 15 minutes) of PAR measured at the BOUSSOLE buoy were used to perform comparisons with the daily PAR retrieved from MERIS data using OLCIPAR algorithm. BOUSSOLE buoy is a permanent mooring which is located at [43°22′N; 7°54′E] in the western Mediterranean Sea at 60 km off the coast (between Nice and Corsica, France)  [39]. BOUSSOLE buoy is equipped with many optical sensors, including above-water irradiance and PAR sensors. Note that BOUSSOLE database is publicly available (http://www.
First, it is readily visible that the variation of the daily PAR is mainly driven by the solar elevation over the course of the year. The OLCIPAR product is significantly impacted by the aerosol optical thickness relatively to NASA product. This can be easily explained by the fact that the standard NASA processing makes use of an aerosol climatology where the aerosol load is quite low (i. e. , for aerosol optical thicknesses typically smaller than 0. 3). Second, the OLCIPAR product exhibits a good overall agreement with the in situ data. Note that some of the BOUSSOLE data show dramatically low values for certain days which indicates that all the cloudy condition data were not totally filtered out. Nevertheless, this time series comparison illustrates qualitatively the good representativeness of the OLCIPAR retrievals shedding light on the role of aerosols in clear to moderately turbid atmosphere on modulation of daily PAR.
Aerosol retrievals Retrieval of aerosol abundance by OLCIPAR is evaluated against ground-based measurements provided by the AERONET network for the Capo Verde site. For matchup comparison, we followed the Bréon et al. [43]  recommendations for averaging ground-based and satellite data. All aerosol optical thickness data (AERONET level 2) acquired within the time window of plus or minus 30 minutes the satellite overpass time were used for averaging. The space window considered to extract the ocean pixels corresponds to a square of 50 km side length centered on the AERONET site location. Before averaging, any pixel masked as "high glint" by the NASA processing or "cloudy" based on the MODIS processing criteria is removed. Only averaged values exhibiting low variability in space and time were used for comparison based on the criteria of a relative and absolute standard deviations lower than 20% and 0.