rafmacalaba/datause-encoder-data huggingface dataset:
While limitations of early studies were borne from the measurement of infrastructure, the methodology adopted, and the empirical relationship established, these concerns were addressed with time as the literature evolved.

The key concern in the estimation techniques as well as in the use of production functions to represent the relationship in earlier studies was **_spurious correlations_** , where variables exhibit similar correlations even though they are unrelated. For example, Aschauer's (1989a) original aggregate time-series estimates suggested public capital has a large and positive impact on firms’ output and productivity (a 1% increase in public to private capital stocks yields a 0.39% increase in total factor productivity), though these estimates are likely too large to be credible due to the flawed methodology. That paper investigated output per unit of capital and total factor productivity, where the independent variables were government services. The structure of the data resulted in aggregate time-series data that are subject to common trends, thus yielding spurious correlation. Realizing these flaws, follow-up studies addressed the aforementioned concerns and improved the methodology used (Table 1). This resulted in scholars abandoning the use of the production function approach and instead adopting the use of first differences (e.g., Hulten & Schwab 1991; Evans & Karras 1994), which addresses the identification problem arising from spurious correlation. Targeting spurious correlation, Hulten and Schwab (1991) applied firstdifferencing methods to remove potential common trends. Similar to fixed-effects methods, firstdifferencing controls unobserved time-invariant region-specific fixed effects. Sturm and De Haan (1995) bring up another reason for estimating the model in first differences: nonstationarity of the time-series data used in the early work. Recently, this strand of the literature included updated and further refined empirical techniques. For example, Calderon et al. (2015) used pooled mean group estimators, which unrestrict short-run parameter heterogeneity across countries while imposing (testable) restriction of long-run parameter homogeneity.

Other key concerns raised toward the early literature were **_endogeneity and reverse causality_** , which leads to the use of instrumental variables (e.g., Duffy-Deno & Eberts 1991; HolzEakin 1994; Esfahani & <mark>Ramırez</mark> 2003). A pronounced identification issue that involved this type of research was reverse causality; that is, the causation not only runs from the independent variables to the variable of interest but also runs in reverse from the dependent variable to the independent variable. In addition, although the initial literature used Cobb-Douglas production 10
**Figure 6: Market organisation in ESA (1990, 2008)**

<!-- Start of picture text --> East and southern Africa (1990) East and southern Africa (2008)<br>strong low regulation monopsony strong low regulation monopsony<br><!-- End of picture text --> Competition is ‘strong’ if many firms compete on prices and ‘limited’ if 2 or 3 firms exert price leadership. ‘Regulation’ reflects regional monopsonies or administratively allocated supply.

<!-- Start of picture text --> East and Southern Africa (1990) East and Southern Africa (2008)<br>No private capital Some private capital No private capital Some private capital<br>Only private capital Only private capital<br><!-- End of picture text --> ‘Some private capital’ implies that some of the ginning companies have opened up ownership to private investors. ‘No private capital’ implies that the State is sole owner of all the ginneries and ‘only private capital’ that the state is not part of the ownership of any of them.

<!-- Start of picture text --> East and Southern Africa (1990) East and Southern Africa (2008)<br>Fixed prices Market prices Fixed prices Market prices<br><!-- End of picture text --> ‘Market prices’ indicates that prices exclusively reflect market forces. ‘Fixed prices’ implies that the state or a public agency fixes prices throughout the year and the country.

ESA includes Kenya, Madagascar, Malawi, Mozambique, Sudan, Uganda, Tanzania, Zambia and Zimbabwe Source: Delpeuch and Leblois (2011).

24
# **Figure 4 – Breakdown of virtual-C imports** **<u>to USA (percent)</u>**

<!-- Start of picture text --> USA<br>CHN<br>RoW 29%<br>38%<br>E15<br>11%<br>CAN<br>JPN 11%<br>3% RUS<br>MEX<br>4%<br>4%<br><!-- End of picture text --> Note: virtual carbon imports have been estimated using the BTIO approach. Source: authors 24
Table 3 – Groups, their composition and households’ expenditure descriptive statistics of goods and services included in the QUAIDS

||**Groups**|**Items**||**Obs**|**Mean**|**Std.**<br>**Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|---|
|1|FOOD|Fragrant plain rice,<br>specialty rice<br>Sticky rice<br>Pork<br>Beef<br>Buffalo meat<br>Chicken meat|Other types of meat (goats,<br>dogs, etc.)<br>Eggs of chickens, ducks<br>Beans of various kinds<br>Fruits<br>Duck and other poultry meat|1,461|1371.46|1075.25|64|11720|
|2|FOODSTUFF|Processed meat<br>Other fish and seafood<br>Sugar, molasses|Confectionery<br>Outdoors meals and drinks<br>Other meals and drinks|1,461|1442.74|1373.72|60|22995|
|3|BEVERAGES AND<br>TOBACCO|<br>Alcohol of various<br>kinds<br>Beer of various kinds<br>|Bottled, canned, boxed<br>beverages<br>Dried tea<br>|1,461|633.268|552.195|10|6660|
|||Coffee|Cigarettes, tobacco||||||
|4|HOUSEHOLD<br>EQUIPMENT|Video player(s), DVD<br>player(s)<br>Electricity<br>generator(s)<br>Printer(s)<br>Sewing machine(s)<br>Electric fan(s)<br>TV(s)<br>Music rack<br>Radio/radio-cassette<br>player(s)<br>Disk player(s)<br>Computer(s)<br>Water heater(s)<br>Refrigerator(s)<br>Air conditioner(s)<br>Washing/drying<br>machine(s)|<br>Cupboard(s), cabinet(s),<br>wardrobe(s)<br>Vacuum cleaner(s),<br>dehumidifier(s)<br>Gas/magnetic cooker(s)<br>Electric cooker(s)<br>Trolleys of various kinds<br>Pumping machine(s)<br>Bed(s)<br>Desk(s), chair(s), long<br>bench(es)<br>Microwave/baking oven(s)<br>Piano(s), keyboard(s)<br>Others (Specify)<br>Juice extractor(s)<br>Camera(s), video recorder(s)|1,461|1392.96|1340.02|16|12120|
|5|CLOTHING AND<br>ACCESSORIES|<br>Garment (including<br>underwear)<br>Fabrics<br>Hats, umbrellas|Shoes, slippers, wooden<br>underfoot<br>Kerchiefs, turbans, scarfs<br>Watches, eyewear, jewelry<br>Other garment accessories|1,461|4198.15|3847.99|0|38100|
|6|TRANSPORT|Automobile(s)<br>Motorbike(s)<br>Ship(s), boat(s)|<br>Other means of travel<br>Bicycle(s)|1,461|827.674|1292.85|10|11900|
|7|POST AND<br>COMMUNICATION|Fax machine(s)<br>Mobile telephone(s)|Landline telephone(s)|1,461|2747.64|2523.01|10|21472|
|8|EDUCATION|<br>Expenditure on educatio<br>compulsory school|n over the past 12 months for|1,461|7568.12|9136.31|50|75337|
|9|MEDICAL AND<br>HEALTH|<br>Medical checks and<br>treatments||1,461|4700.75|8573.86|10|80092|
|10|ELECTRICITY|Expenditure on electrici<br>12 months|ty for daily activities in the last|1,461|3728.25|3113.22|100|36000|

13
# **Table 7: Regression coefficients of individual nutritional outcomes on the DHS household wealth index and household consumption per person**

||BMI|DHS<br>Height-for-<br>age|Weight-for-<br>height|BMI|LSMS<br>Height-for-<br>age|Weight-for-<br>height|
|---|---|---|---|---|---|---|
|Benin|0.194***|0.189***|0.080***||||
|Burkina Faso|0.279***|0.281***|0.097***|n/a|0.377***|-0.016|
|Burundi|0.242***|0.505***|0.115***||||
|Cameroon|0.285***|0.451***|0.257***||||
|Congo, Rep.|0.265***|0.292***|0.051**||||
|Côte d'Ivoire|0.203***|0.279***|0.043||||
|Congo, Dem. Rep.|0.276***|0.378***|0.073***||||
|Ethiopia|0.352***|0.374***|0.229***|n/a|0.037|0.025|
|Gabon|0.182***|0.397***|0.056**||||
|Gambia|0.208***|0.297***|0.061**||||
|Ghana|0.385***|0.299***|0.070***|0.120***|0.178**|0.172|
|Guinea|0.284***|0.285***|0.023||||
|Kenya|0.331***|0.257***|0.210***||||
|Lesotho|0.263***|0.182***|0.091*||||
|Liberia|0.182***|0.183***|-0.024||||
|Malawi|0.197***|0.224***|0.072***|n/a|0.103**|0.004|
|Mali|0.025***|0.039***|0.002|n/a|0.044|0.006|
|Mozambique|0.344***|0.372***|0.156***||||
|Namibia|0.289***|0.323***|0.217***||||
|Niger|0.268***|0.254***|0.119***||||
|Nigeria|0.291***|0.566***|0.042***|n/a|0.450***|0.200***|
|Rwanda|0.211***|0.395***|0.026||||
|Senegal|0.157***|0.110***|-0.025||||
|Sierra Leone|0.189***|0.253***|0.027||||
|Swaziland|0.174***|0.305***|0.115***||||
|Tanzania|0.295***|0.303***|-0.022|0.213***|0.111**|0.036|
|Togo|0.323***|0.319***|0.073**||||
|Uganda|0.364***|0.278***|0.175***||||
|Zambia|0.284***|0.255***|0.070***||||
|Zimbabwe|0.311***|0.147***|0.151***||||

Note: The table gives coefficients from a regression of standardized nutritional outcomes on the wealth index or standardized consumption per capita. Robust standard errors are used; * prob.<.10 ** prob.<.05 *** prob.<.01.

44
**whose** names were on tho ZJC entry forms for each of the 48 sampled schools were included.) As most secondary schools have a record of their students' Grade 7 scores, we sent the ZJC entry form print-out to each of the head teachers of the 48 schools sampled and requested that they fill in each 1989 Form II studentle Grade 7 marks. The Grade 7 examination scores allow a control to be made for intake ability ln each of the subjects, enabling us to model the increment in achievement that could be attributed to the secondary school, rather than the child's previous schooling. Whilst we would have liked to have had a measure of each student's socio-economic background, something which we could have obtained through a detailed questionnaire, along with other, student-level information, it was thought unrealistic to expect the head teachers to administer such questionnaires in the time available. Besides the four grades for each pupll, the two subjects for the two examinations - the other pupil-level variables which we were able to Lnclude in the study were the sex and age of the pupils, which were obtained from the ZJC entry forms.

It was also possible to match each pupil with her/his Form II English and Math teachers as well as classmates by using information from the school heads on the deployment of the Form It English and Math teachers in each class. This enabled us to carry out a three-level analysis, including the class as well as the school and pupil levels.

?/in a 1985 study of 'O' level results in English and Mathematics (Riddell 1988), both an intake variable and a pupil-level background index variable were included, but the influence of the intake variable was much larger than the socio-economic variable. In fact, while modelled separately, it was questionable to what extent the intake variable covered for some of the influence of the background variable.

11
# Table A1: Sample size description

||_Panel A: Rider repor_|_ts_||
|---|---|---|---|
||Number of riders<br>% of riders|Total number of rides|Average number of rides per rider|
|Demographic survey answered|263<br>72.5|||
|Rides phase started||||
|1. Revealed preference|363<br>100.0|17,591|48|
|2. Random assignment to reserved space|261<br>71.9|3,806|15|
|Exit survey answered|229<br>63.1|||
||_Panel B: Platform survey a_|_nd IAT_||
||Women<br>Response rate (%|)<br>Men|Response rate (%)|
|Platform survey||||
|Approached|555|523||
|Accepted|500<br>90.1<sup>1</sup>|448|85.7|
|Finished|448<br>89.6<sup>2</sup>|423|94.4|
|IAT||||
|Approached|429<br>85.8|391|87.3|
|Accepted|163<br>38.0<sup>1</sup>|170|43.5|
|Finished|145<br>89.0<sup>2</sup>|146|85.9|

_Note:_ On Panel A, the percent of riders is calculated among riders who started the revealed preference phase. 1 Among those approached. 2 Among those who accepted.

Table A2: Individual pipeline of rides and payments per for riding public and reserved space per rides phase

|Ride phase<br>Payment for<br>public space task (USD)|Payment for<br>reserved space task (USD)|Median number of rides|
|---|---|---|
|Phase 1 - Revealed preference||37|
|_Ride block 1_<br>4.50|4.50||
|_Ride block 2_<br>4.70|4.50||
|_Ride block 3_<br>4.55|4.50||
|_Ride block 4_<br>4.60|4.50||
|_Ride block 5_<br>4.55|4.50||
|Phase 2 - Random assignment to a space||11|
|_Ride block 6_<br>4.70|4.70||

_Note:_ The order in which the ride blocks were offered was the same for all riders. The number of rides per block for blocks 1-5 were scheduled to be 5 rides each, however in practice there is some variation in actual number of rides taken in each block. The assignment of tasks by the app was done on a daily basis, riders are only moved to the next block at the end of that day and once quality checks are performed. In addition, some riders dropped out before finishing a block. Sampling weights are applied in the analysis to adjust for this variation in number of rides.
# **Tables**

Table 1: Chamber-level caseload summary statistics

|||1st Com-<br>mercial|2nd<br>Commer-<br>cial|3rd Com-<br>mercial|4th Com-<br>mercial|1st Civil|2nd Civil|3rd Civil|
|---|---|---|---|---|---|---|---|---|
|Average number|2012|11.0|13.5|18.7|.|13.3|13.7|12.3|
|of incoming cases|2013|11.5|13.4|12.0|13.2|14.6|4.9|15.7|
|per hearing|2014|21.2|19.2|24.4|9.1|19.0|.|23.9|
||2015|19.5|21.8|26.8|.|15.1|.|25.8|
|Average number|2012|142.9|188.9|149.2|.|228.0|166.7|37.0|
|of ongoing cases|2013|116.0|208.7|109.3|63.2|195.8|85.4|89.7|
|in pre-trial|2014|151.8|200.8|140.0|69.3|156.3|.|119.1|
||2015|178.0|269.6|163.8|.|154.0|.|136.0|
|Average number|2012|25.5|26.8|46.9|.|52.9|50.1|3.0|
|of ongoing cases|2013|26.8|49.1|44.6|16.8|69.0|33.8|31.1|
|in decision stage|2014|50.3|97.5|86.0|28.0|99.4|.|49.9|
||2015|62.8|118.7|127.0|.|96.7|.|72.5|

<u>Notes:</u> The table shows yearly descriptive statistics at the chamber level over the 2012/15 period. The first panel reports the average incoming number of cases per hearing. The second panel reports the average number of cases undergoing pre-trial stage per hearing. The third panel reports the average number of cases undergoing decision stage per hearing.

40
(2) The main controls included in **_Xit_** are indicator variables to identify microenterprises (fewer than nine employees), age, sectors, and a female-leadership. We also control for country and time-fixed effects, the severity of the lockdown (using the Google mobility indicator),<sup>5</sup> and a dummy variable indicating if the firm is a panel firm to control for recall biases in the case of retrospective questions. **_Zit_** are additional controls to account for the size of the shock. For example, when we analyze uncertainty, we control for the level of change in sales reported against the level of 2019 because a big shock can lead to higher uncertainty. In the case of investment in digital technologies, we control for the score of digital readiness, as used in previous papers (Cirera et al., 2021; Avalos et al., 2023; Grover and Pereira-López, forthcoming) based on the BPS, because this is a factor that could condition the need to invest in digital technologies.

The following are the firm outcomes, _yit_ : : _Financial fragility_ This is measured based on whether the responding firm is already in arrears or expects to fall into arrears in any of its outstanding liabilities (Apedo-Amah et al., 2020; Cirera et al., 2021). On average, over the three waves of data collection, 48% of the observations have a value of one in this dichotomous variable (Table B1), with some variation over time.<sup>6</sup> In addition, we measure liquidity constraints with self-reported data on the number of weeks that the firm can cover its operating costs. The average in our sample is around 14 weeks (3.5 months) of costs covered, and half of the observations exhibit less than one month of liquidity.<sup>7</sup> _Uncertainty_ : Following Altig et al. (2022), we rely on a measure of uncertainty computed using firm owners’ and directors’ forecasts about their own outcomes over different scenarios (e.g., normal, optimistic, and pessimistic) as well as the probability distributions associated with these scenarios. This information is used to construct two indices: the first

> 5The Mobility Index is based on Google mobility reports around transit stations. Specifically, and following Apedo-Amah et al. (2020) and Cirera et al. (2021), we construct a smoothed index as the weighted average of 30-day periods from the start of the pandemic until the date of the survey. The 30-day period average just before the survey has a weight of 1, the average from day 31-60 has a weight of 1/2, the average from day 61 to 90 has a weight of 1/3, and so on until the start of the pandemic.

> 6In the first wave of data collection, the share of firms that reported falling or expecting to fall into arrears was 74%.

> 7Across the two rounds where this variable was collected, the variation is high. In the first round, firms reported around 8 weeks, while in the second, it increased to around 16.

7
consider a quadratic term of the instrument in all our IV specifications. Table 2 presents the first stage of the IV regression. Column 1 is our basic specification, which only includes fixed effects and uses sampling weights to derive standard errors. Our most complete specification, including the full set of controls, corresponds to column 4. The estimated coefficient for the quadratic term of the shift-share instrument is always positive and statistically significant at the 1% level. Column 5 presents the coefficient with standard errors clustered at the PSU level. In all cases the F-statistic is greater than 10, which is the cutoff value for considering the instrument as weak.<sup>23</sup> Overall, the instrument is strong at explaining the likelihood of being exposed to an incident in Wave 2 across these specifications among households in the main IV sample.

Table 2: First stage of the instrumental variables approach

|Urban areas w<br>|ith exposed a<br>|nd control ho<br>|useholds in W<br>|ave 1 and 2<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Instrument (θ)|0.0001***<br>(<0.001)|0.0001***<br>(<0.001)|0.0001***<br>(<0.001)|0.0001***<br>(<0.001)|0.0001***<br>(<0.001)|
|Fixed effects|Region|Region|Region|Region|Region|
|Characteristics of household<br>& head|No|Yes|Yes|Yes|Yes|
|Dwelling characteristics|No|No|Yes|Yes|Yes|
|Drought affected status|No|No|No|Yes|Yes|
|F-statistic|34.9|34.6|37.7|49.8|10.7|
|Standard errors|Sampling<br>weights|Sampling<br>weights|Sampling<br>weights|Sampling<br>weights|Clustered<br>by PSU|
|Observations|2,272|2,249|2,241|2,241|2,241|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from the 1st stage of the IV regression. The dependent variable corresponds to exposure to incidents in Wave 2. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Humanitarian assistance is not included due to collinearity with the region fixed effects. Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

In terms of statistical inference, the estimates from both the difference-in-difference and instrumental variables approaches are obtained with standard errors that consider the sampling weights of the surveys in all specifications, mainly due to the different sampling frame and design between Wave 1 and 2. One of the key differences is that the sampling strategy of Wave 2 included an oversampling of households in Mogadishu (Pape and Wollburg 2019b). Hence, using sampling weights to derive the standard errors is

> 23 Staiger and Stock (1994) proposed a rule of thumb cutoff of 10 for the first-stage F-statistic of an IV regression, below which the instrument is considered as weak.

14
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pronounced split _within_ exporters between the handful of large firms that drive trade volumes, trade growth, trade patterns, and diversification, and the rest.

This paper is organized as follows. In Section 2 we introduce the data utilized in the paper. Section 3 highlights the role of superstars in comparative advantage and characterizes those exporters we consider "Superstars". In Section 4 we analyze superstars’ contribution to export growth and diversification. Section 5 delves into the origin of superstars. Finally Section 6 concludes.

# **2. Data on Firm Exports in 32 Countries**

The data used for this paper are exporter-level information on non-oil exports from 32 countries in different regions of the world, mostly for the period 2004-2008.<sup>5</sup> This information has been gathered as part of the World Bank Export Dynamics Database.<sup>6</sup> In most cases, the data have been collected directly from Customs Authorities, Ministries of Finance or Commerce and National Statistics Institutes. In other instances, the data have been collected indirectly through think tank institutions (Egypt) or purchased from a private company based on inputs from customs authorities (Chile, Colombia and Ecuador).

> 5 Although exporter-level data is available for years before and after this period we focus on these years for two main reasons. First, this period is covered in all countries in our sample (whether fully or partially). Data for years before 2004 are scarce across countries and for years after 2008 they include the trade collapse in 2009. Second, for consistency purposes in our cross-country comparisons, it is important to have a similar period across all countries. However, in the analysis of the origin of superstars, which requires a longer period, we also use data from 2000 to2010 for three countries with available data.

> 6 See Cebeci, Fernandes, Freund and Pierola (2012) for a detailed description of the data and the cleaning process operated in it. This cleaning process includes the use of a “consolidated” product classification that takes into account the transformations made to product codes according to the HS classification throughout the years. In addition, in order to mitigate the risk of including transactions that correspond to the shipping of samples or personal belongings, we dropped the observations corresponding to exporters that, in a given year, had total sales below $1,000. We also dropped all the observations belonging to Chapter 27 according to the HS classification –Mineral fuels, oils and product of their distillation; etc.

7