rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

variables. The models without engineering cost estimates provide a much clear insight. Suppose that the engineering cost would be calculated based on other independent variables, ' i.e., _COSTt_ = _X t_ θ+ _ut_ . Then, it is found that the equilibrium bid is an increasing function of road length and width, as expected. The estimated coefficient is about 0.25, meaning that an additional 1 km of road costs 0.25 million U.S. dollars (Table 4). It is clear that larger water treatment capacity will be more costly and long water distribution pipes are also costly (Table 5). The cost of procuring electricity generators increases with the size of installed capacity (Table 6).

Based on these estimated bid functions evaluated at the mean values, the average equilibrium bid is calculated. This is the predicted cost of an artificial “average infrastructure contract.”<sup>12</sup> It is worth recalling that there is no actual contract associated with this cost. The contract theoretically involves, for instance in the road case, 49 km of roads with 2.9 lanes, of which 35 percent are new construction work and 30 percent are rehabilitation. For the mean evaluation points, see the summary statistics (Table 3). Roads would cost 0.5 million U.S. dollar per km on average (Table 7). A water and sewage procurement package will cost about 770 U.S. dollars per m<sup>3</sup> of water treatment capacity. Note this package is, roughly speaking, a half-and-half mixture of water supply and sewage facilities and includes treatment plants as well as distribution and/or collection networks. If it is only for water supply infrastructure, it will cost 780 U.S. dollar per m<sup>3</sup> , and a sewage contract will cost 760 U.S. dollars per m<sup>3</sup> . Electricity generation installed capacity and associated facilities cost 0.44 million U.S. dollars per MW.

Table 7. Predicted Unit Bid of “Average” Contract Package <u>(Million US$)</u>

|Bid amount<br>perunit|Std.<br>Err.|
|---|---|
|Road (per km)<br>0.50|(0.12)|
|Water & sewage (per million m3)<br>768.49|(137.01)|
|<br>Only water supply (per million m3)<br>780.05|(143.29)|
|<br>Only sewage (per million m3)<br>759.38|(133.28)|
|<br>Electricity (per MW)<br>0.44|(0.03)|
|Note that the bid functions are evaluated at the mean values.||
|Source: Author’s calculation.||

12 It is assumed that our sample represents a typical composition of relevant sub-components in each sector.
last year of public management (see Table I I and Figure 4 throughi Figure 6 in Appendix 11). This apparent decline was probably due to rigorous pre-privatization audits whichi typically occurred at this time. This would also explain the extremely low percentages of good credits for recent and beginning privatizers in Table 9 -- those banks hiad undergone pre-privatization audits but had not benefited from the creation of a residual entity. Data for beginninig and recent privatizers are, therefore, probably more reflective of the actual situation of the remaining public provincial banks than the data on public banks in Table 9.

**Table 9: Credit Allocation by Industry, 1996**

|Bank<br>Type|Primary<br>Prod.<br>**(%)**|Manuf.<br>)|Const.<br>()|Utilities<br>(|Trade<br>)|Gov't<br>Srvcs.<br>((%)|Other<br>Srvcs.|Family<br>Loans<br>(%)|
|---|---|---|---|---|---|---|---|---|
|**_Prov._**|||||||||
|Avg shr|11.6|20.2|4.0|0.2|12.8|17.6|7.2|15.6|
|%Good<br>|52.2|41.9|53.8|75.2|43.2|93.9|63.8|71.9|
|(n=8)<br>**_EstPrv_**|||||||||
|_Avg_shr|7.2|6.4|5.8|.06|15.4|11.8|6.2|11.8|
|%Good|45.7|63.2|83.3|69.4|57.1|99.9|66.3|77.2|
|(n=8)<br>**_RecPrv_**|||||||||
|Avg shr|15.6|11.4|11.6|.04|20.4|10.2|14.6|6.2|
|%Good|18.8|11.2|22.4|18.4|13.1|82.7|29.6|55.1|
|(n=6)<br>**_!ntPrv_**|||||||||
|Avg shr<br>|11.8|12.6|5.6|.22|28.8|14.8|7.0|15.0|
|%Good|25.7|37.2|25.3|83.8|32.0|80.7|35.1|59.9|
|_(n=5)_<br>**_Private_**|||||||||
|Avg shr|6.8|23.0|4.4|3.2|10.4|4.4|10.8|25.2|
|%Good<br>(n= I0)|90.3<br>__<br>_<br>_<br>_<br>_|90.1<br>_<br>_<br>_<br>_|91.8<br>__<br>_<br>_<br>_|99.4<br>_<br>_<br>_<br>_|83.1|99.6|91.2|84.9|

Data Source: B.C.R.A.

Notes: Prov covers those eight public provincial banks that have taken no steps toward privatization (Buenos Aires, Chubut, Cordoba, Social de Cordoba, La Pampa, Neuquen, Santafesino de Inversion, and Cofirene Banco de Inversion). EstPrv covers better established privatized provincial banks -- those that had completed privatization as of March, 1996 (Chaco, Corrientes, Entre Rios, Formosa, La Rioja, Misiones, Rio Negro, and Salta). RecPrv covers recently privatized provincial banks, those that completed their privatizations in the last half of 1996 (Mendoza, Prevision Social de Mendoza, San Juan, San Luis, Santiago del Estero, and Tucuman). BegPrv covers those banks that have begun but not completed the privatization process (Catamarca, Juiuy, Santa Cruz, Santa Fe, and Tierra del Fuego). Private includes the ten largest private banks in Argentina in 1996. The columns of each row do not sum to one because we have omitted a catch-all category called "other loans." 21
# Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World<sup>_∗_</sup>

Marie Christine Apedo-Amah (World Bank) Besart Avdiu (World Bank) Xavier Cirera (World Bank) Marcio Cruz (World Bank) Elwyn Davies (World Bank) Arti Grover (World Bank) Leonardo Iacovone (World Bank and Hertie School)<sup>_†_</sup> Umut Kilinc (World Bank) Denis Medvedev (World Bank) Franklin Okechukwu Maduko (Central European University and World Bank) Stavros Poupakis (UCL and World Bank) Jesica Torres (World Bank) Trang Thu Tran (World Bank) Originally published in the Policy Research Working Paper Series on _October, 2020._ This version is updated on _February, 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classification: D22, L20, L25, O10 Keywords: COVID-19, Firms, Employment, Sales, Uncertainty

> _∗_ The authors gratefully acknowledge comments and feedback on the survey questionnaires as well as on earlier drafts of the paper from Caroline Freund, David Mckenzie, Francisco Campos, Bilal Zia, Nick Bloom, Mark Dutz, Ana Paula Cusolito, Jorge Rodriguez Meza, and Silvia Muzi. We also thank Santiago Reyes Ortega for his outstanding research support. We are grateful to all the colleagues and counterparts institutions that have supported the data collection (a complete list of all colleagues, collaborators and institutions that have been crucial in providing support during data collection is provided in the online appendix). We would like to especially thank Tanay Balantrapu, Joao Belivaqua Basto, and Kyungmin Lee for their help during the data collection process. We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. Financial support from USAID and World Bank for data collection is gratefully acknowledged. The views expressed in this article are solely those of the authors and do not necessarily reflect those of the World Bank or its Board.

> _†_ Corresponding author, email: Liacovone@worldbank.org
development (Ro, Jaebong 2002)<sup>15</sup> . This success was facilitated by several factors including: strong leadership, efficient coordination for installing infrastructure facilities, and a well defined focus and priorities on infrastructure development. The government used a variety of public funding instruments to finance the development of infrastructure facilities. The main sources of funding for Korea’s infrastructure development included: taxation, designated funds (special accounts), public pension funds, and private funds. Public financing of infrastructure projects was progressively replaced by private investments as shown in Table 2. As of September 2002, 75% of PPI projects developed in Korea were financed by private investment and government subsidies represented around 25% of these investments. In fact, efforts to develop private investments in infrastructure were initiated in the early 1990s.

Table 2: Financing sources of PPI projects (Central Government) as of September 2002

||Total Investment|Government<br>subsidies|Private equity|Private debt|Others|
|---|---|---|---|---|---|
|KRW|34,438|9,794|7,186|17,972|486|
|billion||||||
|%|100|25.5|20.9|52.2|1.4|

Source: PICKO.

In 1994, the Private Capital Inducement Promotion Act was enacted to promote private participation in infrastructure, primarily Greenfield projects in transportation. This first PPI program was not a success because of substantial risks not properly mitigated. The government targeted 40 primary infrastructure facilities, but was only able to develop

> 15 For instance, immediately after the Korean War, Korea focused on developing light industries and the country’s import substitution capacity. Infrastructure to support these activities included the construction of 275 kilometers of railways and several small highway projects. Furthermore, to stimulate its exports Korea continued the emphasis on infrastructure development by expanding railways, highways and ports. Although the priority devoted to developing modern infrastructure was important during the 1960s, it is only after the country had achieved full employment in the 1970s and when it shifted to heavy and chemical industries that comprehensive policies and plans to develop infrastructure were adopted by the government. These programs involved the construction of major airports, seaports, highways, railways, and telecommunications systems to support the increasingly important manufacturing sector. Because of continuous efforts of the government, investment in infrastructure recorded an average increase of 20% every year during the 1990s prior to the financial crisis. In 2001, the percentage of infrastructure expenditures in the national budget reached 14.6%.

19
would be to increase the immediate costs associated with risky behavior by increasing the possibility of future reward (cash). Several studies of CM interventions have found a positive relationship between the magnitude of the reward and the impact on the target behavior (and laboratory-confirmed health impact) (Sindelar et al., 2007; Stitzer and Bigelow, 1983), at least during the treatment period; the effect appears to weaken during follow up (Higgins et al., 2007). However, the magnitude of incentive required for shaping sexual behavior is unknown. Preliminary assessments can be obtained through focus group discussions, survey data, and discrete choice experiments, but these are no substitute for direct measures of impact achieved by randomly assigning different sized payments to individuals participants.

In the RESPECT study, we chose an incentive amount of approximately $20 at each 4-month testing point. Mean earnings in the study population are approximately $200 per year, thus staying STI negative could increase income by about 30%, which is in a similar ballpark as Mexico‘s Oportunidades program. This amount was discussed extensively with local populations, some of whom argued for much higher amounts (which would not be able to be feasibly scaled-up), but others of whom agreed that $20 sounded reasonable. We then chose to include a second incentivized arm with a reward half that size, in order to test dose-response. One concern about our ability to test this dose response, however, is that randomization is at the individual-level, thus individuals in the $10 group may feel ―cheated‖ and respond less robustly than if they were not aware of the higher group.

In addition, we also chose to assess the acceptability and feasibility of lotterybased incentives. All enrollees were eligible for a $100 village and gender-specific lottery drawing at the 4-month, 8-month, and 12-month testing rounds (thus on average the chances of winning were approximately one in 100 at each 4 month time point). However, if individuals in the cash reward arms tested STI positive, they were made ineligible for the lottery in that round (control enrollees were eligible regardless of their STI status, as long as they had not attrited from the project). This thus equally reinforced incentives for both of the incentivized reward arms. The lottery proved popular, as our ex-ante focus groups had predicted (the national lottery is also popular). Large crowds attended each drawing, providing yet another opportunity to communicate project 19
Figure 6. Costs of crime, power outage, security, and bribe, exporting or non-exporting status <!-- Start of picture text --> 20<br>19<br>16<br>11<br>11 11<br>8 9<br>7<br>6<br>4 4<br>3<br>3 2 3<br>2008 2014 2008 2014<br>non-exporter exporter<br>Bribe Costs of security<br>Costs of crime Costs of power outages<br>Source: Authors' calculation based on Enterprise Surveys<br>20<br>15<br>10<br>Costs(% of sales)<br>5<br>0<br><!-- End of picture text --> Figure 7. Costs of crime, power outage, security, and bribe, by sector of operation <!-- Start of picture text --> 19<br>15<br>14<br>12 11<br>10 10<br>9<br>9<br>8<br>7<br>6 6 6<br>5<br>5<br>4<br>3<br>3 2 3 2 3 2<br>2008 2014 2008 2014 2008 2014<br>Manufacturing Services Construction<br>Bribe Costs of security<br>Costs of crime Costs of power outages<br>Source: Authors' calculation based on Enterprise Surveys<br>20<br>15<br>10<br>Costs(% of sales)<br>5<br>0<br><!-- End of picture text --> 9
# **c. Percentage change in real earnings, by informality status and sector (public vs. private)**

<!-- Start of picture text --> 0.74<br>0.43<br>0.12<br>0.05 0.04 0.00 0.06 0.02<br>Private Public Informal Formal<br>Short-run Long-run<br><!-- End of picture text --> _Source: Own estimates using the 2018 Kosovo LFS._

**Table 4. Simulated change in the wage bill by sector and firm size**

|Firm size|Sector|Percentage<br>change in wage<br>bill, short-run|Percentage<br>change in wage<br>bill, long-run|
|---|---|---|---|
|Between 1 and 10 persons|Private|1.34|0.34|
|11 to 19 persons|Private|0.67|0.18|
|20 to 49 persons|Private|0.43|0.16|
|50 or more persons|Private|0.01|-0.08|
|Unknown but less than 11 persons|Private|0.55|-0.04|
|Unknown but more than 10 persons|Private|0.14|0.00|
|Total|Private|0.48|0.06|
|Between 1 and 10 persons|Public|0.34|0.06|
|11 to 19 persons|Public|0.14|0.06|
|20 to 49 persons|Public|0.01|-0.02|
|50 or more persons|Public|0.01|0.00|
|Unknown but less than 11 persons|Public|0.10|0.01|
|Unknown but more than 10 persons|Public|0.01|-0.02|
|Total<br>_Note: Kosovo LFS 2018 is used. Wages are imputed a_|Public<br>_s described in Section 2._|0.14<br>_Labor demand elasticity_|0.01<br>_value is set at -0.5 (-_|

_Note: Kosovo LFS 2018 is used. Wages are imputed as described in Section 2. Labor demand elasticity value is set at -0.5 (- 0.8) for skilled labor and -0.7 (-1) for unskilled labor. Only employees with the wage between the old and the proposed minimum wage are assumed to be affected. Aged 15-64._

**Increases in the wage bill are expected to be small.** Table 4 shows the simulated increase in a wage bill due to the policy for the public and private sectors. For the public sector, simulation results suggest no increase in the government wage bill. The fiscal rule that came into effect in 2018 establishes that the overall wage bill of the government is capped with the increase in the last available year’s nominal GDP growth rate. An increase

25
sharecropping, transferring part of their harvest to the landlord. When it comes to efficiency, the question to ask is which arrangements lead to a less skewed land-use distribution and increase productivity. The extent to which fixed-rent and sharecropping are to be preferred over one another depends on the degree of asymmetric information between farmer and landlord.<sup>10</sup> Under perfect enforceability of effort, fixed-rent and sharecropping contracts should lead to the same productivity for a given farmer. But moral hazard can reduce the effort of the tenant under a share-cropping contract if the effort is not directly observable and contractible. Therefore, in terms of effort, fixed-rent contracts are preferred. If tenants are risk-averse, they might be unwilling to enter into a fixed-rent contract because the risk burden of a bad harvest would be completely shifted to them. In this case, sharecropping is the second-best solution in terms of efficiency. Empirical studies show that even if share-cropping reduces productivity compared with fixed-rent contracts, adjusting for the land quality of shared plots, the loss is small (Otsuka and Hayami 1988). One way landlords try to induce higher effort levels by tenants in sharecropping arrangements is interlinked contracts in which the landlord supplies credit and inputs to the tenant. For example, if the landlord provides cheaper credits while at the same time claiming a higher output share, tenants might be encouraged to borrow more. If the consequences of default are severe, higher borrowing will induce them to employ higher effort (Braverman and Stiglitz 1982). Another explanation for such interlinked contracts is credit constraints faced by the tenant (World Bank 2003). Laffont and Matoussi (1995), for example, find a positive relation between the tenant‘s own working capital and her crop share.

In Asia and Latin America, tenancy usually reduces farm size (Eastwood et al. 2010). Mearns (1999) shows that in India plot fragmentation is reduced, which increases productivity by reducing costs of production. In terms of tenancy reforms that try to strengthen tenants‘ rights, Besley and Burgess (2000) find that in India such tenancy reform is negatively related to agricultural productivity. However, Ghatak and Roy (2007) disaggregate the analysis to the regional level in India and conclude that in West Bengal, where the reform was implemented rigorously, there has been a positive relation between tenancy and productivity. They do not, however, find any effect on land distribution. Despite such positive findings, tenancy regulation

> 10  Binswanger et al. (1995) contains a detailed discussion.

25
also shows the result of the same analysis for the preferred design. For our preferred design, security, the dimension least correlated with monetary poverty has the largest impact on the fraction of omitted poor.

_Table 3: Impact of each non-monetary dimensions on other-dimensions poverty rates_

|||**Nationalpoverty headcounts**<br>||
|---|---|---|---|
||**Other-dimensions poor**|**Omitted poor**|**Consistently poor**|
||**(%)**|**(%)**|**(%)**|
|||**Baseline Scenario**<br>||
|All dimensions kept|27.9|11.4|16.5|
|Health|18.1|6.5|11.6|
|Housing|14.9|6.9|8.0|
|Security|21.4|7.6|13.8|
|||**Preferred Scenario**||
|All dimensions kept|34.9|15.7|19.1|
|Education|30.5|14.0|16.5|
|Health|27.0|11.2|15.8|
|Housing|22.8|11.4|11.4|
|Security|27.7|10.9|16.8|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table shows, other-dimensions poverty rates when all non-monetary dimensions are taken into account and when one of them is removed, for the baseline and the preferred scenario. The overall other-dimensions poverty rate is decomposed into omitted poverty and consistent poverty.

Second, we look at the impact of considering both extreme and moderate deprivation statuses for nonmonetary dimensions, as suggested by the theory presented in section 2.1. To do this, we compute counterfactual poverty rates by converting any extreme deprivation into a moderate deprivation. More precisely, we compute the fraction of other-dimensions poor when counterfactually assuming that individuals who are extremely deprived in a non-monetary dimension are in fact moderately deprived in that non-monetary dimension. For these counterfactual estimates, individuals can be other-dimensions poor only if they cumulate deprivations in at least two non-monetary dimensions. The results are presented in Table 4 and reveal that these fractions are decreased by slightly more than one-third in the baseline design and by about a quarter in the preferred design. The smaller decrease for the preferred design reflects the fact that the preferred design features four non-monetary dimensions, instead of just three for the baseline design.<sup>19</sup> Overall, this significant reduction suggests that the extreme deprivation status, which allows accounting for the depth of deprivation, plays an important role in our methodology to identify the (multidimensionally) poor.

> 19 The more numerous are the non-monetary dimensions, the more numerous are the opportunities for individuals to cumulate deprivations in at least two non-monetary dimensions. At one extreme, with only one non-monetary dimension, the fraction of other-dimensions poor individuals would fall to zero when removing the extreme deprivation status (at least in our baseline design for which no individual is moderately monetary deprived).

24
hypothetical unleveraged financial structure. (They are then called _unleveraged_ or _unlevered betas._ ) All the betas in Table 2 are unleveraged. To account for the extra risk embedded in companies’ leveraged capital structure (making them _leveraged_ or _levered betas_ ), they must be releveraged using the formula (3): where _ßL_ is the leveraged beta, _ßU_ is the unleveraged beta, _D_ is outstanding long-term debt, _E_ is total equity, and _T_ is the corporate income tax rate. In this analysis, unleveraged betas were transformed into leveraged betas using a capital structure typical for each sector estimated as the average leverage of our sample companies which are summarized in Table 3.

**Table 3: Average leverage by sector**

||**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|
|**Electricity**|87%|87%|87%|86%|97%|
|**Water & Sanitation**|103%|112%|83%|79%|145%|
|**Railroad**|43%|45%|46%|50%|76%|
|**Ports**|104%|93%|106%|122%|90%|

Source: own calculations based on sample data The **country risk premium** (Crp) is the extra return that investors require to invest in stocks of companies in a country deemed riskier than a less risky country used as benchmark (often the United States). The premium reflects the potential volatility of investments in a given country due to defaults associated with political or other events. Country risk premiums are usually estimated as the average spread over U.S. Treasury bonds (assumed to be riskfree) of U.S. corporate bonds with a credit rating equivalent to that of the country under consideration (called the _default spread_ ). To estimate these spreads, we used default spreads estimated by Reuters for a large number of utilities worldwide.<sup>9</sup> However, Reuters does not calculate this default spread for a number of developing countries such as, but not limited to Mozambique, Cameroon, Georgia and Estonia. As a result, for these countries, we utilized Fitch, Standard & Poor or Moody’s ratings where available and then, proxied these default spreads using rating equivalences published by Moody’s.

The country risk premium is the most discriminating factor among countries, ranging from less than 1 percent in Chile and South Africa to 13-15 percent in Argentina, Kenya and Venezuela. It is also highly volatile—varying, for example, from 8 percent in 1998 to 13 percent in 2002 in Argentina. This is because it is influenced by many factors subject to frequent shocks and variations, including exchange rate risk, political risk, and regulation risk. It is for this reason that one needs to make sure the calculation compares investors’ expected

> 9 Some authors argue that the country risk premium is likely to be higher than the country’s default spread. Instead, they multiply the default spread by the ratio of the volatility of the equity market to that of the bond market in the country under consideration (sometimes proxied by the same ratio globally of 1.5). To be as conservative as possible, we have not made such an adjustment

7