rafmacalaba/data-use-mentions-extended huggingface dataset:
Given the large number of potential obstacles to growth that have been identified in surveys, we face a number of difficulties in identifying the obstacles that are truly constraining. First, a potential problem with using survey data is that enterprise managers may identify several operational issues while not all of them may be constraining. Therefore, we examine the extent to which reported obstacles affect growth rates of firms. An obstacle is only considered to be a “constraint” or a “binding constraint” if it has a significant impact on firm growth. Significant impact requires that the coefficient of the obstacle in the firm growth regression be significant _and_ that the enterprise managers identified the factor as an obstacle.<sup>4</sup> Second, to the extent that the characteristics of a firm’s business environment are correlated, it is likely that many perceived business environment characteristics will be correlated with realized firm growth. It is important to sort these into obstacles that directly affect growth and obstacles that may be correlated with firm growth but affect it only indirectly.

Since there is no theoretical basis for classifying the obstacles, we must proceed empirically. To reduce dimensionality in a more structured way, we use the Directed Acyclic Graph (DAG) methodology. The DAG algorithm begins with a set of potentially related variables and uses the conditional correlations between them to rule out possible relations among these variables. The final output of the algorithm is a pattern of graphs listing potential relations between the variables that have not been ruled out and shows (i) variables that have direct effects on the dependent variable or other variables, (ii) variables that only have indirect effects on the dependent variable through other variables, and (iii) variables that lack a consistent statistical relation with the other 7
Figure 2 visually illustrates the use of boat-light detection data. This figure presents a side-by-side comparison between two raw satellite snapshots from VIIRS’ VBD images. In each of the two panels, we process the original raster-formatted data published by the NOAA for the two months of May 2016 (right; i.e. the month immediately after the _Formosa_ breakout) and May 2015, resampling the light maps into 10-mile-square grids covering the entire marine space within the maritime EEZ of Vietnam.<sup>16</sup> The brighter pixels in these figures represent fishing grounds with higher boat density. One could visually notice the impact of _Formosa_ on fishing intensity by contrasting the two snapshots. First, while nearshore fishing boats were densely detected along the coast of all central coastal provinces in May 2015, this region experienced a marked decrease in boat density the first month _Formosa_ took place (May 2016), especially in the near-shore region from Ha Tinh to Hue – the directly affected area. _Formosa_ seemed to also affect major offshore fishing grounds (further out from the 20nm fishing ban zone) of Quang Binh, Quang Tri and Hue, where the brightest cluster of densely-fished area became significantly dimmer. Interestingly, the coastal area north of Ha Tinh seems to experience a significant _increase_ in brightness after the incident, suggesting a level of fishing concentration in this area after _Formosa_ took place. As we will discuss empirically in the next section, this observation on the transition of fishing grounds from within to outside the contaminated zone (to the northern sites) offers a direct explanation to the large geographic distribution of the disaster’s impact.

Figure 4 provides further evidence supporting the parallel pre-trend assumption in nearshore fishing intensity (i.e. boat counts) between the treatment (left) and control zones (right). In this figure, we plot the aggregate monthly boat counts for all the months between 2014 and 2017, separately for the two groups.<sup>17</sup> Even under the apparent existence of fishing seasonality
sophistication than technology measures alone, such as the superior market knowledge, design and logistics present in high-income countries.

Adapting either technology or sophistication based classification of manufacturing activities into a modern and traditional framework is relatively straightforward. High technology trade can be viewed as modern trade, whereas resource-based and low-technology trade is evidence of traditional trade. Because the sophistication measure does not align well with such a discrete division, we rely on country positions relative to a cross-country fit of this export sophistication index and levels of per-capita GDP.

On the services side, Baumol (1985) outlined a taxonomy within the services-producing industries based upon their assumed productivity performance: 1) stagnant personal services, such as education, in which output is highly correlated with labor-time expended; 2) progressive impersonal services, where there is virtually no contact between customers and the production process, an example being telecommunications; and 3) asymptotically stagnant impersonal services, which is an amalgam of the previous two, an example of which is broadcasting. In the discussion that follows, we use a simplified adaptation of Baumol’s services classification that separates out service-activities by industry into “modern” and “traditional” sectors.

Using IMF Balance of Payments data on services trade, we define “modern” services as communications, insurance, financial, computer and information, and other business services; and “traditional” services as travel, transportation, construction, personal, cultural and recreation, royalties and license fees and government services (see World Bank 2009 report on Service Revolution and Mishra et al. 2011 for details). This distinction roughly coincides with the level of international competition and education requirements of the labor force in the service industries. In addition, modern services can be characterized as those activities which can be produced, stored, and transported digitally, and therefore do not need to cross physical borders. For details on the recent transformation in service activities see Box 2.

An alternative context for the distinction in services is given in the WTO Agreement on Trade in Services. Under
As the crisis wore on, job losses mounted. As shown in Table 5-2, Jordan’s formal firms experienced broadly similar conditions by the time of the second CFUWBES wave as they had earlier. They reported only an additional 1.8 weeks of closures since the previous CFUWBES (June-July), when they reported 8.7 weeks on average, and a similar share of firms reported supply disruptions (85 percent). The share of sales comprised of exports fell relative to 2018, in contrast to the rise as of wave 1, and sales declines were of a similar magnitude as the previous round (50 percent). Meanwhile, a lower share of workers was operating remotely (2.8 percent on average relative to 8.7), and the share of firms newly engaging in online or modified (delivery) activities fell. A lower share of remaining firms still in operation was experiencing financial issues, despite the fact that fewer received policy support. By wave 2, we predict the cumulative loss of PFPS jobs to be 25.2 percent, including 9.4 percent from permanent firm closures (Table 5-1).<sup>41</sup> The survey-measured result is 25.5 percent, which is very close to the jobs at risk model prediction.

These estimates are also close to the net observed job loss rate of 23.3 percent in the CMMHH for the most similar group available in that data — formally employed private sector workers in the WBES sectors (PFPS workers) of either Jordanian, Palestinian, or Syrian nationality (“PFPS” workers). According to the CMMHH, 18.2 percent of these workers became unemployed; 2.8 percent were newly out of the labor force, and 2.3 percent had become employed as irregular workers or outside of the formal private sector. Even with the differences in the _Table 5-3 Jordan: Rates of Net Job Loss by Type of Job, Percentage Change in Employment Levels from March to December 2020_

|Employer Type
_growth rate are taken from China Statistic Year book 2017 and UNDESA(2015) with very small modification based on our understanding about labor quality development tendency of China in the future._

# **4. Results from Model Simulations**

In this section, we discuss the allocation of initial allowances, and results of our CGE model on the impacts of ETS to meet China’s NDC. Although we present the detailed results in several tables, the focus of discussions would be on intuition and implications of the results rather than quantitative information provided by the results.

## **4.1 Emission Allowances, Trade and Reduction**

## **4.1.1 Allocation of allowances**

There are many possibilities for allocating the initial allowances across the sectors or emitters, including free allowances based on historical emissions, outputs, emission intensity, or auctioning of emission allowances (Cheng, 2018; Pang and Duan, 2016; Wu et al. 2016). For example, Wu et al. (2016) consider five alternative approaches (GDP based, populationbased, emission based, based on ability to pay and based on government’s five-year plan) to freely allocate national emissions to provinces. In this paper, we do not allocate the emission allowances to the provinces. Instead we directly allocate allowances to emitters (here production sectors) in each province. There are 496 emitters (16 sectors in 31 provinces) participating in the national ETS. We considered two approaches for allocation or distribution of allowances: free allocation (or grandfathering) and auction. Under the free allocation, we considered two criteria for the allocation of the national emission allowances in a given year based on: (i) their baseline emissions, (ii) their baseline outputs. Two cases are also considered under auctioning based on schemes used to recycle the auction revenues to the economy. These are: (i) use of the auction revenue by the government for public consumption or public investment, and (ii)
paid the subsidy amount or less, compared with only 4 percent of the hires by higher-profit firms (p<.01).

To provide context, we can compare these characteristics with two other groups of hired workers. The first is workers who were working for control firms in April 2010, and who were hired between 2009 and 2010.<sup>9</sup> We have more limited data on these employees, but we find that they are slightly older (33.6 years of age) and less likely to be female (9.4 percent). They are much less likely to be related to the owner (9.4 percent) and are paid a higher wage (3,217 LKR per week). A second

> 8 There is no significant difference in reported hours worked between those paid 1,000 LKR (51.6 hours) per week or less and those paid more than this amount (52.8).

> 9 This period encompasses the subsidy period, but is longer so that we have a somewhat larger sample. Among employees at control firms, 32 were hired in 2009 and 21 in 2010. Note that since this sample is workers employed in 2010, it does not include workers hired but no longer working for the enterprise. We note also that this survey provides more limited information about the employees, and that wages are reported by employers (rather than employees and in categories of daily wages, 0-199, 200-399, etc. We estimate average wages from these responses by using the midpoint of the categorical response, which may over- or under-state the level relative to the continuous response given in the survey of hired workers.

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efficiency or sales. This is not only unrealistic, but also would have invalidated the moment conditions needed to identify the coefficients of the production function, as the productivity shock would not have been orthogonal to factor choices. In other words, if TFPR is a function of digitization, the PF elasticities will be biased. The sign of the bias is ambiguous, depending on whether digitization is factor-augmenting or factor-saving. If business digitization is factor-augmenting, then TFPR would be underestimated. By contrast, if TFPR is factor-saving, TFPR will be overestimated.

There are important reasons to make TFPR a function of business digitization. Using email to connect with clients and suppliers or having a business website to gain online presence can affect TFPR through various channels. On the demand-side of the market for an enterprise’s goods and services, reductions in search and transaction costs affect firm profitability at the extensive and intensive margins, by facilitating access to new clients or expanding the volume of transactions online. Dynamically, the scale-up of the demand for a firm’s products or services increases profits, allowing it to pay the fixed cost of investing in TFPR-enhancing activities like innovation, managerial upgrading, or technology adoption (Bustos 2011). On the supply-side, using email to connect with suppliers helps improve production efficiency, by enlarging the potential set of input providers in non-relationship specific investments. Alternatively, it reduces the number of suppliers in relationship-specific investments but enlarges the fraction of repeated interactions, thus addressing contract incompleteness and guaranteeing access to specific assets needed to produce more sophisticated goods (Aral, Bakos, and Brynjolfsson 2018). Because adoption of digital business solutions is not exogenous, we lagged the corresponding variables used to estimate their effects on TFPR.

Since WBES data are not census data, a key question is whether to do weighted estimations by using sampling weights to estimate the coefficients of the production function and TFPR. Following Cameron and Trivedi (2005), sampling schemes such as stratification lead to the conditional density of any variable in the sample differing from that in
In Equation (1), can include household variables that capture not only household socio- jj demographic characteristics, but also preferences and attitudes that may shape household xx consumption patterns; can include household assets or incomes; and is the error term (Elbers jj jj _et al._ , 2003; Ravallion, 2016).xx<sup>12</sup> consists of the two components, a cluster random effects μμ jj ( ) and an idiosyncratic error term (μμ ), which are assumed independent and normally distributed given υυccjj , such that 2 ) and εεjj 2 ). jj ccjj jj υυjj jj jj εεjj xx υυ |xx ~NN(0, σσ εε |xx ~NN(0, σσ

The consumption data exist in the base survey (i.e., _j_ = 1, or survey 1) but are not available in the other survey(s). Our goal is to estimate consumption data from the base survey (j=1) for use in poverty estimates in the target survey (j=2), where the consumption data may not be available or of poor quality. The estimation process relies on two assumptions that must be met. Assumption 1 states that the sampled data in survey 1 and survey 2 are representative of the same population in each respective time period, and Assumption 2 assumes that changes in the distributions of the explanatory variables between the two periods can capture the change in the poverty rate in the jj next period. xx Given Assumptions 1 and 2, to obtain the imputed consumption for survey 2 we can replace 1 with 2 in Equation (1): 1 xx xx 2 1 2 1 (2) cc1 Put differently, Equation (2) applies the model parameter yy = ββ ′xx + υυ + εε 1 and the distributions of the error terms and 1 from the base survey to the 2 characteristics in the target survey to obtain ββ cc1 1 estimates of household consumption υυ εε 2 in the target survey (with the
**Figure 29 The Margins of Export, Annual Export Growth**

<!-- Start of picture text --> 50<br>40<br>30<br>2002-2003<br>2003-2004<br>20<br>2004-2005<br>2005-2006<br>10<br>2006-2007<br>2007-2008<br>0<br>2008-2009<br>2009-2010<br>-10<br>Firm Entry Firm Exit Country Country Sector Sector Exit Product Product Intensive Intensive<br>Entry Exit Entry Entry Exit Positive Negative<br>-20<br>-30<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

The key results of the decomposition along export margins are intriguing as it can be grasped easily by the visual inspection of Figure 29 , which plots in a chart the results from the export growth decomposition in Table 12. Firm churning, i.e. entries and exits of firms from exporting activity and their entry and exit from markets and products, seems to be associated with a positive selection effect: new entrants more than offset the loss in exports by exiting firms. This is a largely expected result, in particular for developing countries. Several papers have indeed demonstrated that export growth in developing countries takes place mostly at the extensive margin, as the reallocation of resources from less to more efficient firms and products is a main engine. Yet, a closer inspection to the various sub-components of the extensive margin suggests that performance has been rather disappointing.

Churning along the destination-country margin, i.e. entries and exits of incumbent exporters from specific destination markets was relatively stable over the 2002-2010 period. While there was evidence of exits over the same period of time, the net effect on the export growth rate was positive (21.1%). This notwithstanding, the performance is far from positive given the focus of Pakistan’
tend to oversample large and, therefore, the results should be primarily interpreted in relation to firms of larger size. An additional advantage of the survey is that it collects substantial balance sheet and other information regarding the investment climate, which enables the linkage of innovation efforts to performance and potential obstacles.<sup>8</sup> The standard Enterprise Survey covers topics such as characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. A number of the survey questions objectively ascertain characteristics of a country’s business environment and assess the respondents opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

The innovation survey differentiates between product and process innovation, and two nontechnological innovations, marketing and organization. However, there is significant confusion when identifying the different types of innovation outcomes by firms in the survey. For example, new marketing processes such as discounts, new packaging or new client segments are sometimes identified with process or product innovations. The fact that interviewees provide a recorded description of the product and process innovations allows us to verify the identified product and process innovation and clean the wrongly attributed cases, or the cases that do not constitute an innovation at all (the detailed methodology to clean the data is described in the appendix). Overall the cleaning exercise results in a decrease in both product and process innovation rates due to cases of either not an innovation or a misclassification.<sup>9</sup> The appendix also contains a description of how we inferred whether process innovations could also be categorized as automation.

including manufacturing and services. In total our estimation sample is based on over 15,000

> 8Sector breakdown is usually manufacturing, retail, and other services. For larger economies, specific manufacturing sub-sectors are selected as
Yes|Yes|Yes|
|Obs.|17,958<br>17,958|17,958|17,958|
|Number of Banks|1,280<br>1,280|1,280|1,280|
|Mean Dep. Var.|299,076<br>969,361|223,475|53,715|
|SD Dep. Var.|771,046<br>1,528,938|677,795|126,821|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank, month, state, year, and state _×_ year fixed effects are included. Standard errors are clustered at bank level. Deposits is a variable reporting the overall deposits at bank level; it is continuous and measured in USD. Safe assets is the amount of liquid assets held by banks in cash, bank-to-bank notes, real estate assets, and other private assets; it is continuous and measured in USD. Loans and Discounts embodies all lending operations at bank level; it is continuous and measured in USD. Profits and Losses is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in USD. All variables are in real dollars at 1800 prices, and estimates of the consumer price index for these years are from FED Minneapolis, which are based on the Index of Prices Paid by Vermont Farmers for Family Living for 1800 to 1851 and the Consumer Price Index by Ethel D. Hoover for 1851–1890. The means and standard deviations of these variables are reported in the final two rows. The following states are included in the sample because they are treated in some year by the policy: CT, GA, MA, MD, ME, NC, NH, NJ, NY, PA, RI, and VT. However, because of data limitations we observe a bank balance sheet before and after the policy only for the
(1903) and Roome (1924) is 0.31. One reason for the low correlation could be that missionaries started penetrating the African inland only after first settling on the coast. The 1924 data indeed show a higher concentration of mission stations farther from the coast. Therefore, this study takes a conservative approach, and the analysis is conducted using the two sources separately. The results of virtually all the analyses, using either one of the two atlases to construct Mission _ik_ , are quantitatively similar and qualitatively identical. Results from the baseline regression, equation (1), are reported for both sources, but for ease of exposition only results obtained using data from 10
# **Figure 12: Direct and indirect job content of exports across refined sectors, South Africa 2001 vs. 2011**

<!-- Start of picture text --> 2001 2011<br>achinery, Metal Prod, Metal, Ferrous Metal achinery, Metal Prod, Metal, Ferrous Metal<br>Agr, Forestry, Fisheries Minerals nec<br>Trade and Transport Services Energy Ext, Chem/Rub/Plast<br>Energy Ext, Chem/Rub/Plast Trade and Transport Services<br>Minerals nec Transport Equipment<br>Processed Food, Bev and Tob Agr, Forestry, Fisheries<br>Manufactures nec Processed Food, Bev and Tob<br>Transport Equipment Other Private Services<br>Paper/Pub, Wood Products Paper/Pub, Wood Products<br>Other Private Services Manufactures nec<br>Apparel, Textiles, Leather Apparel, Textiles, Leather<br>Mineral Products nec PubAdmin/Defence/Health/Educat<br>PubAdmin/Defence/Health/Educat Mineral Products nec<br>Electricity, Gas, Water Electricity, Gas, Water<br>Construction Construction<br>Dwellings Dwellings<br>0 200 400 600 800 0 200 400 600 800 1,000<br>Thousands Thousands<br>Direct JOBX Indirect JOBX Direct JOBX Indirect JOBX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX and South African labor force surveys.

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