rafmacalaba/datause-encoder-data huggingface dataset:
- Fowlie, Meredith, Mar Reguant, and Stephen P. Ryan. 2016. “Market-Based Emissions Regulation and Industry Dynamics.” _Journal of Political Economy_ 124(1): 249–302.

- Gillingham, Kenneth, David Rapson, and Gernot Wagner. 2016. “The Rebound Effect and Energy Efficiency Policy.” _Review of Environmental Economics and Policy_ 10(1): 68– 88.

- Grigolon, Laura, Mathias Reynaert, and Frank Verboven. 2018. “Consumer Valuation of Fuel Costs and Tax Policy: Evidence from the European Car Market.” _American Economic Journal: Economic Policy_ 10(3): 193–225.

- Gruenspecht, Howard K. 1982. “Differentiated Regulation: The Case of Auto Emissions Standards.” _The American Economic Review_ 72(2): 328–31.

- Gwilliam, Ken, Masami Kojima, and Todd Johnson. 2004. _Reducing Air Pollution from Urban Transport_ . Washington, DC: World Bank.

- Intragency Working Group. 2021. _Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990_ . Interagency Working Group on Social Cost of Greenhouse Gases, United States Government. https://www.whitehouse.gov/wp-

- content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitro usOxide.pdf.

- Jacobsen, Mark R., and Arthur A. van Benthem. 2015. “Vehicle Scrappage and Gasoline Policy.” _American Economic Review_ 105(3): 1312–38.

- Jin, Lingzhi et al. 2021. _Driving a Green Future: A Retrospective Review of China’s Electric Vehicle Development and Outlook for the Future_ . International Council on Clean Transportation. https://theicct.org/publications/china-green-future-ev-jan2021 (October 13, 2021).

- Klier, Thomas, and Joshua Linn. 2012. “New-Vehicle Characteristics and the Cost of the Corporate Average Fuel Economy Standard.” _The RAND Journal of Economics_ 43(1): 186–213.

- ———. 2015. “Using Taxes to Reduce Carbon Dioxide Emissions Rates of New Passenger Vehicles: Evidence from France, Germany, and Sweden.” _American Economic Journal: Economic Policy_ 7(1): 212–42.

- Kojima, Masami, and Magda Lovei. 2001. _Urban Air Quality Management: Coordinating Transport, Environment, and Energy Policies in Developing Countries_ . The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/0-8213-4948-1 (September 22, 2021).

- Konishi, Yoshifumi, and Meng Zhao. 2017. “Can Green Car Taxes Restore Efficiency? Evidence from the Japanese New Car Market.” _Journal of the Association of Environmental and Resource Economists_ 4(1): 51–87.

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- CAFI. 2020. Diagnosis of the Epidemic’s Impact on the Financial Health of the Working Class and the -

- MSEs in China & Policy Recommendations. <u>https://www.findevgateway.org/slide deck/2020/03/diagnosis-epidemics-impact-financial-health-working-class-and-mses-china-policy.</u>

- Chetty, Raj et al., 2020, “How did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-time Economic Tracker Based on Private Sector Data”, NBER Working Paper Series.

- Cirera, Xavier; Maloney, William F., 2017. The Innovation Paradox: Developing-Country Capabilities and the Unrealized Promise of Technological Catch-Up. Washington, DC: World Bank

- Corporate Finance Network, 2020. “SMEs’ supply chains feel the strain due to Covid-19” (accessed 30 July 2020).

Dingel, Jonathan I, and Brent Neiman. 2020. How Many Jobs Can Be Done at Home?

Emont, John. 2020. “Retailers Canceling Apparel Orders Amid Coronavirus Torments Clothes Makers.” The Wall Street Journal. https://www.wsj.com/articles/retailers-canceling-apparel-orders-amid- <u>coronavirus-torments-clothes-makers-11588683151.</u>

- Freund, Caroline and Alfonso Garcia Mora. 2020. World Bank Group report “Supporting Firm Resilience”. <u>http://pubdocs.worldbank.org/en/111141593044217205/Supporting-Firm-Resilience-Final.pdf</u>

- Gopalan, Prasad and Oksana Nagayets. 2020. Agribusiness Sector Note from IFC internal publication regarding Government Policy Responses to Support the Private Sector.

- Grover Goswami, Arti; Medvedev, Denis; Olafsen, Ellen. 2019. High-Growth Firms : Facts, Fiction, and Policy Options for Emerging Economies. Washington, DC: World Bank

IFC. 2017. MSME Finance Gap. https://www.ifc.org/wps/wcm/connect/03522e90-a13d-4a02-87cd- <u>9ee9a297b311/121264-WP-PUBLICMSMEReportFINAL.pdf?MOD=AJPERES&CVID=m5SwAQA.</u> IFC. 2018. “IFC Reach Data.” https://www.smefinanceforum.org/data-sites.

International Trade Centre, 2020. “SME Competitiveness Outlook 2020” (accessed on 1 August 2020).

- “ ’ ”

- Intrum, 2020. <u>One in five of Europe s SMEs say late payments are a threat to their business . (accessed 1</u> August 2020).

- Leer, John. “Analysis: Divergent Paths for Consumer Condence in U.S., EuropeUnderline Need to Control Coronavirus Spread.” Morning Consult. https://morningconsult.com/2020/07/29/analysis-consumer- <u>confidence-us-europe-spain/ (July 29, 2020).</u>

- Li, Lei, Philip E Strahan, and Song Zhang. 2020. Banks Are Lenders of First Resort: Evidence Form the COVID-19 Crisis.

Long, Heather. 2020. “Small Business Used to Define America’s Economy. The Pandemic Could Change - That Forever.” Washington Post. https://www.washingtonpost.com/business/2020/05/12/small <u>business-used-define-americas-economy-pandemic-could-end-that-forever/.</u> OAG (Open Access Government). 2019. World Bank blockchain pilot sows fresh narrative for Haiti’s farmers. https://www.openaccessgovernment.org/world-bank-blockchain-haitis-farmers/61205/ 24
Given the difference in educational systems throughout the world, the implementation of similar policies to stimulate the commercialization of public research should be approached with caution. Other scholarship has highlighted technology field differences, recognizing that licensing is not effective to the same degree in all fields and arguing that the Bayh-Dole Act did in fact lead to shifts in university patenting towards fields where licensing is an effective mechanism to transfer knowledge and generate licensing revenue (Scott 2004). Academic study on the impact of the Bayh-Dole Act suggests that technology field differences, the research focus and institutional structures of universities, and the extent of collaboration between public and private actors all influence the extent to which universities can monetize patent assets via licensing. Policy makers in the developing world must appreciate these subtleties when considering similar approaches.

_Figure 5. Middle-income States: Licensing and Royalties Balance of Payments, 1980-2009_ Middle-income states: licensing & royalties balance of payments, 1980-2009 <!-- Start of picture text --> $1<br>$0<br>($1)<br>($1) Botswana<br>($2) Poland<br>($2) South Africa<br>($3)<br>Brazil<br>($3)<br>South Korea<br>($4)<br>Russian<br>($4)<br>Federation<br>($5)<br>Billions<br><!-- End of picture text --> Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files (Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files) Figures 5 and 6 show that Balance of Payments for IP royalties in middle-income nations have moderately decreased over time and low-income countries have experienced more rapid decline. 1995 marks a clear turning point for most countries in the sample. While the high-income countries began experiencing a positive increase in Balance of Payments, middle- and lowincome countries moved from a static neutral position to an increasingly negative one.

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CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Atlantis 2 cable, and plans to connect to a second cable, the WACS (West African Cable System), in 2011.

This report will now review the main achievements and challenges observed in Cape Verde’s major infrastructure sectors (see table 1 for a summary of key findings). It will then turn to the problem of how to finance Cape Verde’s outstanding infrastructure needs.

**Table 1. Achievements and challenges in Cape Verde’s infrastructure sectors**

||Achievements|Challenges|
|---|---|---|
|Air transport|Well-functioning network of airports<br>Attained international security standards|Continuing subsidies to TACV|
|ICT|Market liberalization completed<br>Rapid growth in coverage and penetration<br>Access to submarine cable|Reducing prices through enhanced competition|
|Ports|Well-functioning network of ports|Completing institutional reform agenda<br>Expanding capacity|
|Power|High rates of electrification|Maintaining tariffs at cost-recovery levels<br>Reducing cost of power generation<br>Improving efficiency of ELECTRA|
|Roads|High-density road network Establishment<br>of road fund|Improving road network conditions<br>Securing adequate funds for maintenance<br>Clarifying roles of sector institutions|
|Water and sanitation|Good rates of access to utility water|Improving efficiency of ELECTRA<br>Raising access to improved sanitation|

_Source:_ Authors’ elaboration, based on findings of this report. _Note:_ ICT = information and communications technology; TACV = Cabo Verde Airlines.

# Roads

## **Achievements**

Compared with similar countries, Cape Verde has a dense road network. Cape Verde’s main national network amounts to a little over 1,000 km, increasing to 1,600 km when urban and other unclassified roads are taken into account. There are only three islands with road networks in excess of 100 km: Santiago, Santo Antão, and Fogo. Nevertheless, given the small size of the country, road density (measured as a percentage of arable land)<sup>1</sup> is 3.5 in Cape Verde, higher than the comparator countries (for instance, Maldives, 2.2; Mauritius, 2.0; Indonesia, 1.6). This dense road network allows the entire population access to health, education, and other basic services. It also serves to interconnect the domestic market.

Almost three-quarters of the national road network is paved. The rest of the network divides evenly between gravel and earth roads. The extent of paving varies significantly across islands, from 100 percent paved in San Nicolau and San Vicente to little more than 50 percent paved in Santiago. Cape Verde uses a

> 1 According to the World Bank Indicators, in 2003 the total land in Cape Verde was equal to 4,033 square kilometers (km<sup>2</sup> ), of which 11.4 percent was considered arable land (460.3 km<sup>2</sup> ). Thus, road density is the ratio of road length to arable land (in km<sup>2</sup> ): 1,607 km/460.3 km<sup>2</sup> = 3.5.

5
where<sup>_u_</sup> _ir_<sup>is the level of utility that person</sup><sup>_i_thinks is implied by ―making ends meet.‖ This is</sup> assumed in turn to be a strictly increasing function of actual utility: Equation 3 can now be interpreted as the solution of (5) and (6) for<sup>_y_</sup> _i_ min . The SSPL in the income space is then found as the solution to: But now we see that there is also a SSPL in welfare space,<sup>_u_</sup> _z_<sup>, at which point:</sup>

> (The solution is unique if  ( _u_ 0<sup>)</sup>  _u_ 0 and  ( _u_ 1<sup>)</sup>  _u_ 1 , where utility is bounded below by<sup>_u_</sup> 0<sup>and</sup> above by<sup>_u_</sup> 1<sup>.) Comparing (7) and (8) we see that</sup> _uz_  _v_ ( _pi_ , _zi_ , _xi_ ) . The SSPL—the point at which min _yi_  _yi_ —also identifies those who just reach the implicit welfare poverty line<sup>_u_</sup> _z_<sup>and the set of</sup>

> people who are welfare poor ( _ui_  _uz_ ) is the same as the set of income poor ( _yi_  _zi_ ). Thus, under these conditions, the SSPL approach can be said to be consistent with the other two approaches, in that they identify the same people as poor and deliver the same poverty rate.

# **4. Methods and examples**

The most common method of empirical investigation in this literature has been a regression of the survey responses on subjective welfare on individual and household characteristics, including age, gender, income, education, employment status and household demographics.<sup>17</sup> Typically the researcher has data on subjective welfare ( _SW_ ) across _N_ survey

> 17 Examples include van Praag (1968), van de Stadt et al. (1985), Clark and Oswald (1994, 1996), Kapteyn et al. (1998), Easterlin (1995), Oswald (1997), Winkelmann and Winkelmann (1998), Pradhan and Ravallion (2000), McBride (2001), Ravallion and Lokshin (2001, 2002, 2010), Graham and Pettinato (2002), Senik (2004), Luttmer (2005), Ferrer-i-Carbonell (2005), Herrera et al. (2006), Bishop and Luo (2006), Kingdon and Knight (2006, 2007), Fafchamps and Shilpi (2009), Knight and Gunatilaka (2010) and Castilla (2010).

10
4 availability of skilled labor, access to land, foreign ownership and/or export status. We find that these variables do have some explanatory power but cannot fully explain the slower growth of employment in African firms. Going forward, the paper is divided into four additional sections. First, we present the data. Second, we describe our model and present the main results. Third, we discuss the possible reasons behind the “stunted growth” of African firms. Finally, we conclude with a summary of key findings and some policy implications.

# **_The Data_**

This analysis is based on firm-level data from the World Bank's Enterprise Surveys. The Enterprise Surveys collect qualitative information on a country's perceived business environment as well as quantitative firm performance measures, predominantly in the formal service and manufacturing sectors of the economy. The data set integrates all surveys that have been conducted after 2006, which use a common global methodology. As such, we analyze data from 119 countries, of which 41 are in Sub-Saharan Africa.<sup>4</sup> The surveys cover formal firms in the following sectors--manufacturing, construction, retail, wholesale, hospitality and other services--that are classified according to ISIC Rev 3.0 and cover the ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72. Interviewers conduct face-to-face interviews using standardized questionnaires for the manufacturing and service sectors. Topics covered include information on firm characteristics, the business environment, access to finance, annual sales, costs of inputs and labor, workforce composition, and basic performance measures. These data are representative at the size, sector and location levels.

We divide our sample into five age quintiles, using the age distribution of the entire sample of firms (i.e. the global data set). This yields the following quintiles: age quintile 1 contains firms that are between 0 and 6 years old, age quintile 2 is from 7 to 11 years, age quintile 3 is from 12 to 16 years, age quintile 4 from 17 to 26 years and age quintile 5

> 4 See <u>www.enterprisesurveys.org/Methodology</u> for more information.  We exclude firms in which the government holds at least 50 percent of shares.
**Table 3: National Characteristics, FDI Spillovers, All Domestic Firms, OLS**

|Dependent varia|ble: ln_lpirst_|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|_FDI_<sup>_Y_</sup>_sct_|-0.0927|-1.7358*|-0.8046***|-0.0069|-0.0178|0.9202|-0.2461**|-2.7521**|1.1780|-0.1221|-0.1388|
||(0.929)|(0.072)|(0.003)|(0.961)|(0.893)|(0.175)|(0.027)|(0.026)|(0.411)|(0.172)|(0.749)|
|_FDI_<sup>_Y_</sup>_sct*laborct_|0.0087<br>(0.972)|||||||||||
|_FDI_<sup>_Y_</sup>_sct*financect_||0.4316*<br>(0.080)||||||||||
|_FDI_<sup>_Y_</sup>_sct*educ1ct_|||3.9472**<br>(0.013)|||||||||
|_FDI_<sup>_Y_</sup>_sct*educ2ct_||||-0.1922<br>(0.680)||||||||
|_FDI_<sup>_Y_</sup>_sct*rdct_|||||-35.1017<br>(0.141)|||||||
|_FDI_<sup>_Y_</sup>_sct*investmct_||||||-0.2561<br>(0.140)||||||
|_FDI_<sup>_Y_</sup>_sct*trade1ct_|||||||0.5713**<br>(0.031)|||||
|_FDIYsct*trade2ct_||||||||0.6309**<br>(0.029)||||
|_FDI_<sup>_Y_</sup>_sct*businessct_|||||||||-0.3019<br>(0.384)|||
|_FDI_<sup>_Y_</sup>_sct*hhisct_||||||||||0.2488<br>(0.316)||
|_FDI_<sup>_Y_</sup>_sct*incomect_|||||||||||0.0113<br>(0.848)|
|ln_capintirst_|0.2707***|0.2709***|0.2772***|0.2754***|0.2717***|0.2706***|0.2706***|0.2705***|0.2706***|0.2701***|0.2707***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|15,884|15,884|11,696|14,330|10,356|15,884|15,788|15,884|15,884|15,936|15,884|
|R-squared|0.62|0.62|0.49|0.61|0.46|0.62|0.62|0.62|0.62|0.62|0.62|

Note: p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). All regressions include sector, subnational region, and year fixed effects. Standard errors are clustered at the country-sector level.

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To measure poverty we use the Foster, Greer and Thorbecke’s (FGT) poverty measure Pα (see Foster, Greer and Thorbecke (1984)): where α is a poverty-aversion parameter, N the total number of households in the survey, yit household i’s income or consumption in period t, and zt the poverty line in period t. α = 0 yields the headcount ratio, that is, the percentage of poor households. α = 1 yields the poverty gap index, that is, the average distance between income and the poverty line (where for nonpoor households this distance is set to zero) as a fraction of the poverty line. These measures can be calculated for each household category j, as well as for the total population.

To measure inequality we use the Gini coefficient and the Theil index.<sup>4</sup> The Gini coefficient is given by: where households are ranked in ascending order of yit and y ̄t is mean household income or consumption in period t.

The Theil index is given by: Figure 1 summarizes the whole procedure, where each step is represented by a box. This approach to micro-macro linkage is “top-down” as there are no feedback effects from the household survey to the Macro-RHG model, i.e. market equilibria are entirely simulated on the macro-side without accounting for any further heterogeneity in behavior within groups.

This method is not entirely satisfactory to the extent that per capita consumption and income in the household sample are adjusted to the corresponding levels in the macro model, but the employment structure is not.

> 4For details on these indicators, see for instance Cowell (1999). Whereas the Gini coefficient is most sensitive to income differences around the middle (or, more precisely, the mode) of the distribution, the Theil index is most sensitive to income differences at the top of the distribution.

8
growth in rural areas.<sup>21</sup> Table _5_ illustrates that almost 400/D of the households in the sample have, durinlg the period under concern, either started up a new non-farm enterprise or expanded on an existing one. At the same time, mortality of these enterprises was bigh as Mwell; almost 19% of households closed down an existing enterprise. The underlying hypothesis is Ihat individuals who have been affected by civil strife in the past will reduce their exposure to the non-fann econornmy. Results from estimating a probit regression for equation (7) where the dependent variable is a dummy for expansion or disappearance of non-farm enterprises are reported in table 5 where the coefficients reported are inarginal probabilities at the mean of other variables. We first discuss the results for non-farm enterprise startups, comparing them to the investment equation, and finally shift to a brief consideration of the factors underlying shrinkage and disappearance of enterprises.

In general, results are consistent with expectations as to the variables not referring to civil conflict. As noted in the investment equation, higher levels of education, higher endowments with assets, and lower age all increase the propensity to start non-farm enterprises. Sinmilarly, the propensity to do so is significantly higher, by more than 20 percentage points, in urban than in rural areas, possibly reflecting the scope for accessing informal finance and to lhnk into trading networks. Having been affected by civil strife reduces the propensity to establish such an enterprise by almost 10%. Comparing the estimated coefficient to those of other variables helps to illustrate the importar ce of this effect; to compensate one would have to increase the head's level of education by at least 4 years or to increase the initial value of asset endowments by more than double the mean observed in the sample. Again, the estimates suggest that it is only civil strife but not theft or physical violence vhere such an impact is observed. Also, it is of interest to note that, finding an impact of violence on asset accumulation mainly at the community level and on non-agricultural enterprise startups at the individual level is consistent with the hypothesis of civil

> **21** In fact, the household survey data allow us to confirm that, as emphasized in a number of rcent contributions to the literature (Reardon et al, 2001), non-farm enterprises make an increasing contribution to the livelihood of the rural poor. Non-farm enterprises are quantitatively more important than wage income of the rural poor and, in contrast to overall stagnation of rnual wE.ge earnings during the 1992 to 1999 period, Shave shown a considerable increase, from about 9% of household income to more than _15%_ (Deininger and Okidi, 2001).

26
instrument to capture income disparities among countries sharing a basin – the basin-level coefficient of variation of GDP in 2009.

Column 4 reports results from a 2SLS regression using all six instruments. The signs of the downstream country and treaty interaction coefficients are consistent with our hypotheses, but statistically insignificant; they vary little in comparison to the single-instrument model in column 2. The cluster-robust first-stage F-statistic remains small, suggesting weak instruments, although it is slightly improved with the additional instruments.<sup>28</sup> We repeat the models in Table 8 using limited-information maximum likelihood (LIML) estimation instead of 2SLS; LIML may perform better than 2SLS in the presence of weak instruments (Hahn et al. 2004). The results are qualitatively similar to Table 8: the coefficient on downstream country is positive, the coefficient on the interaction between treaty and downstream country is negative, and neither is statistically significant, for any combination of the instruments.

The question of whether treaties can mitigate free-riding in water diversion and impoundment in international river basins is a difficult one to answer econometrically. Of the 382 major river basins in our analyses, we code 116 as international and thus eligible for a treaty. Eighty basins have at least one international river management treaty, and 48 basins have at least one such treaty that explicitly addresses water allocation.<sup>29</sup> We exploit all available global data in the analysis, but the amount of 28 An Anderson-Rubin-Wald test of the validity of overidentifying restrictions rejects the null hypothesis in column 4 – additional evidence that the instruments are weak.

29 Of the 80 basins with treaties, 8 are not coded as international by our methods. Visual inspection suggests that 7 of these basins are not international, so the functions of these treaties are unclear. For the remaining basin, the geographic extent of the GRDC basin and TFDD basins differ. In any event, these 8 treaties do not influence our results because the treaty variable only appears interacted with the “some downstream country” dummy.

20