rafmacalaba/datause-encoder-data huggingface dataset:
There is a growing empirical literature documenting the relationship between indicators of various aspects of governance and economic outcomes.5 Using the six aggregate governance indicators described above, we contribute to this literature by providing new empirical evidence of a strong causal relationship from improved governance to better development outcomes. Following the approach of Hall and Jones (1999), we estimate a series of very parsimonious regressions of the log-level of per capita income on each of the six aggregate governance indicators in turn. We correct for reverse causation, omitted variable bias, and measurement error in the governance variables, by using the shares of the population speaking European languages as instruments. We find that govemance matters a great deal for economic outcomes. In particular, a one-standard deviation increase in any one of our governance indicators causes between a two-and-a-half and four-fold increase (decrease) in per capita incomes (infant mortality), and a 15 to 25 percent increase in literacy. Tests of overidentifying restrictions and summary statistics of the explanatory power of the firststage regressions provide support for the validity of this modelling strategy.

The remainder of this paper proceeds as follows. Section 2 briefly describes the governance database, with the details of each source of data and each governance measure relegated to a lengthy Appendix 1. Section 3 summarizes the components of each of the six govemance aggregates and explains the aggregation procedure. The details of the composition of each aggregate are presented in a series of tables in Appendix 2. Section 4 contains new empirical evidence on the relationship between governance and development outcomes.

This literature includes Mauro (1995) on the effects of corruption on economic growth and investment; Loayza (1996) on the determinants of the unofficial economy; Ades and DiTella (1996) on the causes and consequences of corruption; Knack and Keefer (1997) on the importance of institutions for economic growth; Tanzi and Davoodi (1997) on corruption and public investment; Wei (1997) on the effects of corruption on FDI; Rodrik (1997) on the role of instiitutions in the success of East Asia ; Johnson, Kaufmann and ZoidoLobat6n (1998) on the effects of corruption on the unofficial economy; Hall and Jones (1999), on the relationship between levels of per capita income and a measure of what they call 'social infrastructure", and Chong and Calder6n (1999) on the Granger-causality between institutions and economic growth.

3
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**Table 13. Traders' Relationships with Regular Partners in Benin and Malawi**

||Beni|n|Malawi||
|---|---|---|---|---|
||Suppliers|Clients|Suppliers|Clients|
|% Traders that have regular clients or suppliers|79%|69%|74%|88%|
|% Regulars of same origin|90%|87%|67%|92%|
|% Regulars of same ethic group|47%|49%|41%|45%|
|% Regulars of same religion|43%|40%|29%|36%|
|% Regulars with whom meet socially|26%|28%|8%|15%|
|% Regulars that are close relatives|_5%_|6%|4%|7%|
|% Regulars that have exclusive relations|12%|12%|22%|23%|
|% Traders can sell or buy with non-regulars|99%|99%|99%|99%|
|Number of regulars in main market|2.7 (6.1)|6.5 (10.1)|_5(15)_|14(23)|
|Number of regulars in other markets|3.9 (6.4)|0.8 (4.0)|6 (15)|1 (11)|
|% of total trade with regulars|51 (33)|37 (32)|45 (34)|50 (29)|

# **_4.6 Payment Methods and Trade Credit_**

Regarding transaction methods, payment in cash is universal. Payment in foreign currency or in kind occurs very occasionally. Payment by check is unheard of, indicating the lack of sophistication of banking methods by traders and also perhaps the lack of trust among traders.

**Table 14. Payment Methods in Agricultural Trading in Benin and Malawi**

||Be|nin||Mal|awi||
|---|---|---|---|---|---|---|
||Mean|S.d.|N|Mean|S.d.|N|
|Of transactions with suppliers:|||||||
|% Cash, local currency|99.5|_5.1_|662|99.2|6.4|738|
|% Other currency|_0.5_|_5.1_|662|0.4|5.5|738|
|% Wire transfer|0.0|0.0|662|0.0|0.3|738|
|% Check|0.0|0.0|662|0.0|0.0|738|
|% In kind|0.0|0.0|662|0.4|3.2|738|
|Of transactions with clients:|||||||
|% Cash, local currency|99.9|1.9|660|98.1|11.6|738|
|% Other currency|0.1|1.6|660|_0.5_|6.3|738|
|% Wire transfer|0.0|1.2|660|0.0|0.0|738|
|% Check|0.0|0.0|660|1.3|9.5|738|
|%Inkind|0.0|0.0|660|0.1|0.9|738|

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|<br>|Table 10. Do SME Exchanges Allow Private Placements?<br>|
|---|---|
|Exchange|Private Placement|
|NSE India Emerge|Yes|
|JSE AltX|Yes, without any restriction except in the Companies Act.<br>Requirements: 50% of the shareholding of each director and the Designated<br>Adviser held in trust from the date of listing until the date the financial results are<br>released; 50% of the shareholding in trust released in the first year thereafter and<br>the remainder after that.|
|GreTai|Yes. Shareholder restriction: 35 shareholders maximum and 3 years lock-up<br>period.|
|Bovespa Mais|No. CVM likely to introduce a restricted offering, (set number, qualified<br>investors,lower disclosure requirements)in late 2014.|
|WSE NewConnect|Yes. Maximum of 149 shareholders. Informational document approved by an<br>Authorized Adviser and information memorandum replacesprospectus.|
|BIST ECM|Yes. Maximum of 100 shareholders. No obligation to issue prospectus and<br>circular, instead trading information form.<br>Only new shares allowed for private placement, and sales by a shareholder after<br>listingare not allowed.|

_Source:_ WBG compilation of exchange information.

# _iii. Reducing Disclosure Requirements_

It is typically thought that to achieve an attractive cost structure for issuers and facilitate issuance, disclosure content for SMEs needs to be significantly reduced relative to the main board requirements. On the contrary, most SME exchanges do not reduce these requirements because of the inherently riskier nature of investing in SMEs. Investors need adequate and clear information. In fact, some SME boards, such as ChiNext, have higher disclosure requirements than the main board (see Table 11).

Although content disclosure is not often reduced, other disclosure requirements can be amended to lower costs in time and money. Examples found in many SME exchanges are the following:

- _Allowing longer time to prepare reports:_ In addition to making the preparation less intense, this can reduce costs because it moves financial report verification out of high peak periods for auditors and accountants. Brazil plans to give SMEs more time to prepare reports, 60 days for SMEs compared with 45 days for main board companies. TSXV allows 15 days longer for quarterly reports and an extra 30 days for the production of annual reports.

- _Reducing the frequency of reporting:_ NSE Emerge requires half-year reporting for SMEs compared with quarterly for the main market; NewConnect is semiannual compared with the main board’s quarterly requirement. To compensate for reduced reporting, listed companies should be encouraged to increase their communication with investors.

- _Requiring only online reporting through exchange websites,_ and remove requirements to print reports. Websites can also be used to reduce the cost of investor relations and communications more generally.

25
# **References**

Altonji, J., Elder, T., & Taber, C., 2005. Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. Journal of Political Economy. 113, 151-184.

Ani, A.O., 2003. Taking Farm Decisions and Socioeconomic Characteristics of Rural Women Farmers in Southern Ebonyi State, Nigeria. International Journal of Agriculture and Biology. 5(4), 645-649.

Blinder, A., 1973. Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources. 8, 436–455.

British Council, 2012. Gender in Nigeria Report 2012: Improving the Lives of Girls and Women in Nigeria. British Council, Nigeria Carletto, C. & Savastano, S. & Zezza, A., 2013. Fact or Artefact: the Impact of Measurement Errors on the Farm Size - Productivity Relationship. Journal of Development Economics. 103, 254-261.

Croppenstedt, A., Goldstein, M., & Rosas, N., 2013. Gender and Agriculture: Inefficiencies, Segregation and Low Productivity Traps. The World Bank Research Observer. 28, 79-109.

Damisa, M. A., & Yohanna, M., 2007. Role of Rural Women in Farm Management Decision Making Process: Ordered Probit Analysis. Trends in Applied Science Research. 2(3), 241-145.

Ezumah, N., & Di Domenico, C., 1995. Enhancing the Role Of Women In Crop Production: A Case Study of Igbo Women in Nigeria. World Development. 23(10), 1731–1744.

Firpo, S., Fortin, N. M., & Lemieux, T., 2009. Unconditional Quantile Regressions. Econometrica. 77(3), 953–973.

Fortin, N., et al., 2011. Decomposition methods in economics. Handbook of labor economics. 4, 1-102.

Fortin, N. M., 2006. Greed, altruism, and the gender wage gap. Working Paper, Department of Economics, University of British Columbia. Retrieved from http://faculty.arts.ubc.ca/nfortin/Fortinat8.pdf Jann, B., 2008. The Blinder-Oaxaca decomposition for linear regression models. The Stata Journal 8(4), 453-479.

Kilic, T., Palacios-Lopez, A., & Goldstein, M., 2013. Caught in a Productivity Trap: A Distributional Perspective on Gender Differences in Malawian Agriculture. Policy Research Working Paper, 6381. The World Bank.

21
definition of the hemoglobin cut-off value for anemic women<sup>31</sup> , I replicate the same graph using the subsamples of women who are not currently pregnant or breastfeeding (cut-off values are not different for lactating women). Appendix figure A.2 shows that the patterns are found also in these subsamples. This suggests that differential incidence of anemia between women with a first-born girl or boy may be driven by short birth spacing (or past pregnancies).

Figure 8 shows a graph for BMI. Overall, BMI increases with women’s age (within the range of 19 to 22).<sup>32</sup> There are no apparent differences in BMI by the sex of the first-born for women below age 40. It is also worth noting that the BMI index excludes women who are pregnant at the time of the survey and who gave birth less than three months before the survey (IIPS and Macro International, 2007). Among older women, those with a girl as first-born child show a slightly higher nutritional status.<sup>33</sup> Lastly, figure 9 reports a graph for height. Height reflects nutritional status in childhood and adolescence and therefore should be affected by fertility behavior to a lower extent than other nutritional indicators that are not predetermined to first birth. Yet, mortality selection might also entail higher survival possibilities for taller women who have been better nourished during childhood (similarly to Bozzoli, Deaton, and Quintana-Domeque, 2009). Moreover, taller women are more likely to be from a higher socioeconomic status and thus enjoy better access to prenatal diagnostics. The figure shows that, as expected, there are no height differences among younger women with a first-born girl or boy, while there is some weak evidence that older women with first-born girls are taller (especially above age 45).

# **5 Empirical strategy and results**

This section first discusses the identification assumption, then explores how the sex of the first-born affects fertility behavior, and lastly examines the age structure and anemia status of women using parametric analysis.

## **5.1 Identification assumption**

As shown in table 2, women with a first-born girl or boy do not exhibit significant differences in terms of observable predetermined characteristics close to the time of first birth or when they are

> 31A woman is defined as being anemic if the measured hemoglobin count is of less than 12 grams per deciliter (cut-off for all women) and less than 11 grams for pregnant women.

> 32The BMI is defined as weight (kg) divided by height (m2). A cut-off point of 18.5 is used to define chronic energy deficiency and a BMI of 25 or above indicates overweight (IIPS and Macro International, 2007).

> 33While the association between fertility behavior and anemia status is widely documented in the medical literature, BMI is not among the outcomes mentioned in a review of the literature on the effects of short spacing (Conde-Agudelo, Rosas-Bermdez, and Kafury-Goeta, 2007). Evidence that mother’s malnutrition at conception might affect the sex ratio is discussed in Section 6.1.

14
finance ministry<sup>4</sup> that is consistent with macroeconomic stability and government policy priorities (a ‘top-down’ component); (ii) an estimate of the medium term resource needs (or costs<sup>5</sup> ) of the existing activities of spending ministries (a ‘bottom-up’ component); and (iii) an iterative process of budget decision making that reconciles the cost of existing and new activities with the resources available over the medium term (ODI 2003, 5 and World Bank 2013, 17-18).

The sequence for implementing an MTEF commonly starts with the first element and progresses to incorporate the second and third elements. The first element is dependent on the quality of macroeconomic forecasting methodologies, and the second and third elements are, as discussed later, reliant on the quality of budget program definition and the application of consistent costing methodologies.

# **2.2 Objectives of an MTEF**

Consistent with a multiyear planning horizon for budgeting, an MTEF is usually associated with the three key technical objectives of public budgeting: (i) aggregate fiscal discipline, or better control over budget totals; (ii) allocative efficiency, or more strategic allocation of resources between priorities; and (iii) technical efficiency, or more efficient use of resources.

These objectives receive detailed treatment in many places (see, for example, World Bank 1998, Allen and Tommasi 2001; Schick 2009). In summary we can note two points. First, that when integrated with the annual budget process an MTEF can contribute to these objectives in various ways:

- Aggregate fiscal discipline can be improved by signaling to decision makers the sustainability of existing spending programs, setting out the multiyear impact of a new program prior to its adoption and establishing multiyear expenditure ceilings that help to contain expectations about future total expenditure.

- Allocative efficiency can be strengthened by providing decision makers with more detailed program cost and performance information as well as the flexibility of a longer timeframe to reallocate resources between lower and higher priority programs.

> 4 E xecutive government central agency with primary responsibility for the fiscal framework, budget process and financial management systems

> 5 The monetary value of the resources (human, physical or financial) used to achieve a particular objective; costs usually relate to a cost object (an activity, program, project, product, service, client or organization); costs can be distinguished from expenditure which is the amount of money that has been spent during a defined period

7
TABLE 3: Microenterprises

|Subsector|No. of<br>Different<br>Activities|No. of<br>Enterprise|Distribution<br>s of Enterp.ise|Average Aniuat<br>Turnover<br>s (USS'OOO)|
|---|---|---|---|---|
|Commerce|16|466|29_|7.4|
|Bars/Restaurants|<br>3|91|6X|5.4|
|Other Services|12|358|23X|6.6|
|Workshops|11|318|20X|N.A.|
|Food Processing|7|161|10X|7.4|
|Other Manufacturin|g<br>6|194|12X|22.7|
|6 Subsector Total|55|1,588|100X|9-4|

SOURCE: GANA Center/CECPS Note: All data appties to the sample in the CANA survey, which is not a census of all firms.

Virtually all microentrepreneurs report paying taxes (although the survey data do not illuminate exactly what type of taxes they pay). Those with activities requiring fixed premises most often reporting payment of ficensing taxes. Their annual turnover averages over US $9,000 equivalent, while their costs average close to 50% of tumover. Net incomes (which vary more with activity than with education, gender, or nationality) appear, on average, to be above the level cf the minimum wage (SMIG).

TABLE 4: Microentrepreneurs

||Percent<br>of Saptle|Illiterate|Primary|Secondry/I<br>Nigher|Total|
|---|---|---|---|---|---|
|Ivoirian<br>Female|(21)|52X|33X|15X|100l|
|Other Female|(7)|43X|43X|14X|100X|
|Ivoirian<br>Male|(31)|36X|392|23X|1001|
|OtherMale|(42)|521|30X|10X|100X|
|All Hicroentrepreneur|s (100)|482|38X|14X|1001|

Source: GANA Center/CECPS 12
Policy Research Working Paper 9970

# **Abstract**

Infrastructure is critical to economic development. When infrastructure companies are owned and operated by the government, however, they create significant sources of fiscal risk. These fiscal risks can be sizable, but they are often preventable with proper planning, risk assessment, and strict rules and procedures for corporate and fiscal governance. This paper examines fiscal risk stemming from state-owned enterprises (SOEs) in the infrastructure sector in a sample of 135 firms in 19 countries from an original database of SOE financials for 2009–18. The paper develops a typology of fiscal risks and their determinants, builds new measures of fiscal injections to SOEs, and documents them using the novel database. The results show that governments support SOEs through a remarkably wide range of fiscal instruments. The fiscal cost of supporting infrastructure SOEs is usually below 1 percent of gross domestic product. Support is more prevalent and frequent than previously thought. The findings show that fiscal risk stems not only from “tail risk,” but also from the everyday operation of infrastructure SOEs. The paper calculates the Altman Z” score (a measure of default risk) and shows that it can be used to forecast the need for fiscal injections in SOEs.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at mdappe@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 4. The economic life of power plants (Year)**

|Technology|Lazard(2019)|IEA(2015)|IRENA(2018)|EIA(2020)|NREL(2019)|
|---|---|---|---|---|---|
|PV(Utility)|20|25|25|30|20-30|
|CSP|35|25|25|30|20-30|
|Wind Onshore|20|25|25|30|20-30|
|Wind Offshore|20|25|25|30|20-30|
|Gas CC|20|25|n.a.|30|20-30|
|Gas GT|20|25|n.a.|30|20-30|
|Geothermal|25|40|25|30|20-30|
|Hydro|n.a.|80|30|30|20-30|
|Coal|40|40|n.a|30|20-30|
|Nuclear|40|60|n.a.|30|20-30|
|Biomass|n.a|40|20|30|20-30|

We find that solar PV, CSP, offshore wind, and biomass are relatively more sensitive to their economic lives keeping the values for other input variables constant. If the economic life of solar PV increases by one year, its LCOE decreases by 1.7%. Similarly, if the economic lives of CSP, offshore wind, and biomass are increased by one year, their LCOEs decrease by, respectively, 1.6%, 1.5% and 1.1%. Fossil fuel technologies and nuclear power plants are found relatively less sensitive to their economic lives. The LCOEs of gas GT, gas CC, nuclear, and coal decrease by, respectively, 0.2%, 0.3%, 0.4% and 0.5% for a one-year increase in their economic lives. The corresponding values for onshore wind, hydro and geothermal are, respectively, 0.6%, 0.8% and 0.9%. The interdependency between the variables (e.g., capital costs and economic life) and the mix of various cost components in LCOEs causes the different levels of sensitivity of LCOEs with economic lives across the technologies. For example, economic life affects the distribution of investment costs to its annuity. It affects more the technologies which are capital intensive (i.e., have higher shares of capital costs in their LCOEs) such as CSP and offshore wind. For fossil fuels, the share of fuel costs in LCOEs is relatively higher. As the fuel cost is independent of the economic life of a technology, LCOEs of fossil fuel-based technologies are relatively less sensitive to their economic lives.

27
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