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18
Table 5: Within Sector Correlation of ICT Abundance and Real Income

|||Regr|ession wit|h time effe|cts|
|---|---|---|---|---|---|
||_ρ_|_β_|s.e.|p-value|N|
|Manufacturing|0.562|0.008|0.002|0.000|195|
|Hospitality and Leisure|0.405|0.021|0.006|0.002|195|
|Retail Trade|0.261|0.010|0.006|0.111|195|
|Financial Services|0.268|0.040|0.028|0.173|195|
|Educational Services|0.241|0.003|0.002|0.187|195|
|Energy & Utilities|0.223|0.001|0.001|0.223|195|
|Healthcare|0.187|0.007|0.006|0.247|195|
|Telecommunications|-0.222|-0.061|0.066|0.363|195|
|Construction|0.156|0.005|0.006|0.416|195|
|Government|0.133|0.004|0.006|0.505|195|
|Transportation|-0.096|-0.002|0.005|0.628|195|
|Wholesale Trade|0.073|0.002|0.006|0.791|195|
|Professional Services|0.042|0.000|0.001|0.806|195|
|Natural Resources|0.011|0.000|0.001|0.903|195|

_Notes:_ For each WITSA sector, the first column reports the within-sector correlation of _Xict/K_ and _ln_ ( _GDP/L_ ) over the years 2003-2009 based on 32 countries, where _Xict_ represents sector specific WITSA ICT spending and _K_ is the sector specific capital stock reported in the WIOD. The remaining columns report the results from a regression of _Xict/K_ on _ln_ ( _GDP/L_ ) and a complete set of time effects. Standard errors are clustered on country.

abundance, we take this as an additional piece of suggestive evidence in favor of our results from the previous section.

# 5. Does India Have Too Little ICT? An Illustrative Example

We opened this paper with a comparison of the percent of households that subscribe to broadband internet in the United States and in India, suggesting that internet subscriptions are 30 times more abundant in the United States. Is this 30-fold difference too large or too small? In other words, is ICT under-utilized or over-utilized in India?

Assuming that internet subscriptions are proportional to the physical abundance of ICT, we can construct a benchmark level of ICT capital based on the capital labor ratio, the ICT price and industrial composition using the following decomposition: where _ki_ is the physical stock of ICT capital per-capita;<sup>_<u>p</u>_</sup> _pk_<sup>_<u>iki</u>_is the nominal share of ICT capital out</sup> 28
offshore sector after corporate income tax rates increased.

Third, we perform various checks to address the concern that, given their export orientation, offshore firms might be affected by external shocks differently to onshore firms, thus undermining the identification. First, we interact the log of deflated value of EU imports – the major trading partner of Tunisian firms – with the offshore dummy.<sup>17</sup> Figure A6 shows that our conclusions are robust to this alternative specification. Next, we exploit equation (2) to include interaction terms between the offshore dummy and both i) the Tunisian dinar-Euro exchange rate and ii) Tunisia’s domestic final consumption over GDP ratio.<sup>18</sup> We expect that exchange rate shocks could affect disproportionately offshore firms via export markets, while final consumption would mainly affect the demand for onshore firms, which are predominantly domestic-oriented. Table A2 presents the results which show that accounting for possible differential shocks does not alter the core of our results.<sup>19</sup> The only meaningful changes are the reduction in the negative effect of the reform on entry and the number of firms, particularly in manufacturing, and the positive effect of the reform on the wage bill in manufacturing, which becomes not significant.

The final test aims to ascertain whether the reduced entry is driven by foreign owned firms. To the extent that FDI are more footloose than domestic investors, including that they are also more likely to be registered as _phantom FDI_ (Damgaard et al., 2024), the reform may affect mainly this type of investors. To test this hypothesis, we replicate the analyses excluding foreign owned firms. The results in Figure A7 show that the pattern of entry and exit is similar if we only consider domestic onshore and offshore companies. The results are also similar for employment, wage bill and revenues, which continue to be unaffected by the reform even when excluding foreign firms.

> 17Since this specification requires that EU imports from sectors are non-zero, it restricts our sample size – most industries in the service sector drop out of this analysis.

> 18 Since the exchange rate and final consumption vary by year, we cannot use these interaction terms in (1), where the independent variable of interest varies at the same level.

> 19 Collinearity between the independent variable and the additional controls reduces the size of the sample.

25
# **Table 9 Direct price impacts of bilateral air service agreements (percent)**

|_Destination countries_||_Origin cou_|_ntries_||
|---|---|---|---|---|
||_Tunisia_|_EU_|_GAFTA_|_Rest of world_|
|**_Currently_**|||||
|Tunisia|na|15.4|15.9|14.2|
|EU|15.4|0|12.3|16.9|
|GAFTA|15.5|12.3|15.9|14.9|
|Rest of world|14.2|18.2|14.9|18.3|
|**_After Tunisian open skies agreement with EU_**|||||
|Tunisia|na|0|15.9|14.2|
|EU|0|0|12.3|16.9|
|GAFTA|15.5|12.3|15.9|14.9|
|Rest of world|14.2|18.2|14.9|18.3|

_Source_ : Updated from Dee (2005), and based on Doove et al. (2001).

39
- Allen, Franklin, Asli Demirguc-Kunt, Leora Klapper, and Maria Soledad Martinez Peria. 2016. “The Foundations of Financial Inclusion: Understanding Ownership and Use of Formal Accounts.” _Journal of Financial Intermediation_ 27(XX): 1-30.

- Anginer, Deniz, Ata Can Bertay, Robert Cull, Asli Demirgüç-Kunt, and Davide S. Mare. 2022. “Bank Capital Regulation and Risk after the Global Financial Crisis.” _Journal of Financial Stability_ , forthcoming.

- Ardizzi, Guerino, Andrea Nobili, and Giorgia Rocco. 2020. “A Game Changer in Payment Habits: Evidence from Daily Data during a Pandemic.” Bank of Italy Occasional Paper, 591.

- Ashraf, Nava, Dean Karlan, and Wesley Yin. 2010. “Female Empowerment: Further Evidence from a Commitment Savings Product in the Philippines.” _World Development_ 38(3): 333344.

- Banerjee, Abhijit, Esther Duflo, Clement Imbert, Santhosh Mathew, and Rohini Pande. 2015. “Can E-Governance Reduce Capture of Public Programmes? Experimental Evidence from India’s Employment Guarantee Scheme in Bihar.” Harvard Kennedy School Impact Evaluation Report 31. Cambridge, MA.

- Banerjee, Abhijit, Dean Karlan, and Jonathan Zinman. 2015. “Six Randomized Evaluations of Microcredit: Introduction and Further Steps.” _American Economic Journal: Applied Economics_ 7(1): 1-21.

- Barth, James R., Gerard Caprio, and Ross Levine. 2013. “Bank regulation and supervision in 180 countries from 1999 to 2011.” _Journal of Financial Economic Policy_ 5(2): 111-219. <u>https://doi.org/10.1108/17576381311329661</u>

- Beaman, Lori, Dean Karlan, and Bram Thuysbaert. 2014. “Saving for a (Not So) Rainy Day: A Randomized Evaluation of Savings Groups in Mali.” National Bureau of Economic Research Working Paper N. 20600.

- Blumenstock, Joshua, Michael Callen, and Tarek Ghani. 2018. “Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan.” _American Economic Review_ 108 (10): 2868–901.

- Blumenstock, Joshua E., Michael Callen, Tarek Ghani, and Lucas Koepke., 2015. “Promises and Pitfalls of Mobile Money in Afghanistan: Evidence from a Randomized Control Trial.” ICTD '15: Proceedings of the Seventh International Conference on Information and Communication Technologies and Development. Article No.: 15. Pages 1–10. https://doi.org/10.1145/2737856.2738031

- Breza, Emily, Martin Kanz, and Leora Klapper. 2020. “Learning to Navigate a New Financial Technology: Evidence from Payroll Accounts.” NBER Working Paper 28249, National Bureau of Economic Research, Cambridge, MA.

27
# **Annex 1: Disaggregate scores for each question in the management survey**

Data driven performance monitoring is composed of the average score of questions 1 to 5. Russian firms lag considerably in all of them with respect to US firms. In particular the worst scores are the ones relating to display board location, review of KPIs by nonmanagers and the number of KPIs followed. These results show that, overall, firms in Russia tend not to track very well their own performance -44% track no KPI at all- and employees know little about how their own firm is doing -within those firms that do track KPIs, in 32% of them nonmanagers never review them. In fact, firms in the sample also lag considerably on the employees’ awareness of operational targets (question 8, pertaining to incentives and targets): in the US, firms adopted 71% of the most structured management practice in that dimension -all managers and most workers are aware of the firm’s targets- while in Russian firms that were surveyed that share was 48% - meaning that even a portion of managers are not usually aware of operational targets.

With respect to bonus policies, Russian firms perform as well as US firms, but they lag considerably on promotion criteria and management of underperformance. Among Russian firms, 38% do not promote non-managers at all, and 47% of them do not promote managers. This last figure is particularly striking and represents one of the biggest differences in management practices between the US and Russia. With respect to underperformance, 65% of the surveyed firms stated that they rarely or never dismiss an underperforming non-manager, and 72% of them rarely or never dismiss an underperforming manager. What emerges from this summarized comparison is that Russian firms have a rather stagnant body of employees: any kind of employee, either non-manager or manager, is both not dismissed and not promoted. While this may reflect overall preference for job stability in Russia,<sup>38</sup> it still reveals how Russian firms lag considerably in their human resources management practices with respect to US firms.

> 38 According to the second round of the Life in Transition Survey (2010), 64% of Russian respondents preferred a job with an average salary, not much chance of promotion but safe in the long term, to a job with high salary, a lot of chance of promotion but less job security. Moreover, 45% of the respondents preferred a job in the public sector, 20% preferred self-employment and only 25% preferred private sector employment.

27
Adding to these global and multi-country systematic studies are reviews from the nine countries, again showing wide variation in resistance between different drugs (see Annex 2).

In conclusion, the evidence presented above and in Annex 1 indicates significant variation in AMR rates across different bacteria/viruses, across different first line drugs and across different countries and sub-regions. However, given that the analysis is not conducted by different types of HAI, an average rate is needed for the present study. Rates of AMR are seen below 50%, well above 50%, and also around 50%. Hence, in the base case analysis, an average rate of 50% AMR is used. The sensitivity analysis uses alternative AMR rates of 25% and 75%.

# 3. Annual hospital admissions

In most studies reporting HAI, rates are typically expressed as number of HAIs as a proportion of hospital admissions. Hence, this present study estimates the total HAIs in each country by multiplying the average HAI rate by the total number of hospital admissions in the latest year of data. Data from national statistical reports have been provided for Ethiopia, Kenya, Mozambique, Tanzania and Uganda, mainly relating to 2022 data. These data are presented in Table 6. For countries that did not report recent health sector statistics the number of hospital admissions per year is estimated using an average of 4 inpatient admissions per 100 population.

**Table 6. Estimated number of HAIs based on HAI rate and hospital admissions**

|**Variable**|**Angola**|**Congo,**<br>**Dem.**<br>**Rep. **|**Ethiopia**|**Kenya**|**Madagascar**|**Mozambique**|**Sudan**|**Tanzania**|**Uganda**|
|---|---|---|---|---|---|---|---|---|---|
|HAI rate (% of<br>admissions)|12.0%|12.0%|17.0%|12.0%|12.0%|12.0%|12.0%|12.0%|12.0%|
|Total inpatients<br>peryear|1,779,400|2,760,000|2,976,000|3,200,000|888,000|3,607,172|1,874,960|1,630,442|2,069,310|
|Estimated<br>number of HAIs|213,528|331,200|505,920|384,000|106,560|432,861|224,995|195,653|248,317|
|AMR rate (% of<br>HAIs)|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|50.0%|
|Estimated<br>number of AMR<br>infections|106,764|165,600|252,960|192,000|53,280|216,430|112,498|97,827|124,159|

Note: Figures in red are estimated based on 4 admissions per 100 population.

For more detailed future analysis, breakdowns between different hospital wards would be possible if specific HAI rates are available. In Kenya, for example, admissions data are commonly provided by hospital level (HC III level, General Hospitals, HC IV, and Regional Referral Hospitals), by region, by age category (typically under five years of age and over five years of age), and by cause.

# 4. Average hospital cost per day

Recently, a cost categorization of HAI and AMR has been comprehensively provided by Morel et al (2020). However, their framework contains a large number of variables and there are no reports yet of studies that have applied their framework.

More than ten years ago, a review of economic studies that assessed the costs of illness (18 studies) and the cost-effectiveness of interventions (12 studies) for patient safety was 32
evidence consistent with the theory. Section 5 concludes with several suggestions for future research.

# **2 The measure of connectedness**

Our proposed measure of the connection of a financial sector (or a financial institution) to the real economy is based on the composition of its assets. While the exact formula depends on the type of financial institution analyzed, the _Measure of Connectedness_ is conceptually the share of the credit market instruments represented by claims whose _direct counterparts_ belong to the non-financial sectors (households, non-financial firms and government): where _CREDITit_ represents the total amount of credit market instruments of a given institution _i_ at time _t_ , while _CREDIT_ _~~R~~ EALit_ represents the credit market instruments whose _direct counterparts_ are households (i.e. mortgages and consumer loans), non-financial firms (i.e. commercial loans) or the government (i.e. treasuries). Such a measure can be computed for each player in the U.S. financial system using data from the Flows of Funds.

Probably the most important drawback of using flow of funds data is that we are not able to say much about non-balance sheet items, such as derivatives. Since derivatives are typically used as the main example of the disconnectedness between the financial sector and the real economy, we are aware that we are missing an important piece of information, and we therefore consider our results suggestive of an _upper-bound_ to the level of connectedness. However, an advantage of using flow of funds data is that the measure can be computed for a long time series and hypothetically for different financial systems. Moreover, a conceptually similar measure could be computed also for a single financial institution using balance sheet data.

We stress that we try to measure the share of credit market instruments whose _direct counterparts_ are in the non-financial sector. As many commentators observed, the transfor6
Table 11. Reduced Disclosure Requirements

|Exchange|Content Requirements|Frequencyof Reporting|Form of Publication|
|---|---|---|---|
|NSE India<br>Emerge|Similar to main board|Half yearly vs. quarterly||
|JSE AltX|Similar to main board|Same as main board,<br>biannually|Announcements must be<br>published on SENS, the<br>issuer’s website (where<br>one exists) and on the<br>AltX website. This is only<br>a minimum, and the JSE<br>encourages voluntary<br>publication in thepress.|
|GreTai|Same as main board|Same as main board|In both paper and<br>electronic form|
|Bovespa Mais|Same as main board|Same as main board|Exemption from<br>publishing in printed<br>newspaper for companies<br>with market cap <R$700<br>million (approximately<br>$350 million)|
|WSE<br>NewConnect|Similar though smaller<br>range of information<br>required|Semiannual vs. quarterly<br>Longer period to prepare<br>information|In electronic files through<br>stock exchange Electronic<br>Information Base system|
|BIST ECM|Semiannual financial<br>statements do not need to<br>be independentlyaudited|Semiannual vs. quarterly|In electronic files through<br>Public Disclosure<br>Platform|

_Source:_ WBG compilation of exchange information.

# _iv. Governance Requirements_

Governance requirements for SME exchanges are often reduced compared with the main board. For example (see Table 12), JSE AltX companies are not required to comply with all aspects of the King Code of Corporate Governance, Bovespa Mais has reduced board requirements, and AIM has no formal corporate governance requirements. Others, such as TSXV and NSE Emerge, where issuer governance requirements are strong, do not reduce their standards compared to the main board.

An important question is what elements of corporate governance are investors willing and not willing to forfeit for SMEs? Lowering requirements, such as removing the need for an audit committee or the number of independent directors, might not substantially harm investors in SME equity, but other reductions, for instance, related to mandatory tender offers or the need to notify about substantial and insider holdings on related party transactions, may affect the value of a company and minority shareholder rights.

Table 12. Governance Requirements

|Exchange|Governance|
|---|---|
|NSE India Emerge|Similar to main board|
|JSE AltX|Similar to main board except:<br>•<br>Need at least 3 (or 25%) nonexecutive directors<br>•<br>Not required to have separate CEO and chairman<br>•<br>Must appoint audit committee and invite DA to attend committee meetings as<br>an observer|

26
# **Table 6: Reasons for Avoiding Wage Cuts: The role of worker characteristics and firm size**

||Reg./Agreement|Reduced<br>Effort/Morale|<sup>Reputation</sup>|Best<br>employees<br>leave|Hiring/training<br>cost|Hiring<br>difficulty|Implicit<br>contract|Employees<br>compare<br>wages|
|---|---|---|---|---|---|---|---|---|
|<br>_(Reference: high skilled_<br>_white collar)_|||||||||
|% Low skill blue collar|0.440***|-0.065|0.004|-0.221***|-0.196***|-0.003|0.035|-0.014|
||(0.000)|(0.171)|(0.937)|(0.000)|(0.000)|(0.954)|(0.467)|(0.768)|
|% High skill blue collar|0.186***|-0.044|0.140***|0.016|0.042|0.113**|0.027|0.039|
||(0.001)|(0.422)|(0.007)|(0.778)|(0.432)|(0.033)|(0.622)|(0.480)|
|% Low skill white collar|0.241***|-0.039|-0.016|-0.079|-0.122*|-0.114*|0.065|-0.095|
||(0.000)|(0.563)|(0.804)|(0.250)|(0.062)|(0.081)|(0.334)|(0.160)|
|% Temporary|0.083|0.018|0.149***|0.143**|0.074|0.125**|0.107*|0.147**|
||(0.187)|(0.766)|(0.009)|(0.021)|(0.193)|(0.028)|(0.069)|(0.013)|
|(_Reference: 5–19_|||||||||
|_employees_)|||||||||
|Size=20-49|0.134***|0.011|0.148***|0.128***|0.085***|0.076***|-0.023|0.023|
||(0.000)|(0.708)|(0.000)|(0.000)|(0.003)|(0.008)|(0.435)|(0.430)|
|Size=50-199|0.323***|-0.001|0.183***|0.126***|0.107***|0.146***|-0.050*|0.039|
||(0.000)|(0.977)|(0.000)|(0.000)|(0.000)|(0.000)|(0.076)|(0.165)|
|Size=200+|0.474***|-0.018|0.278***|0.105***|0.101***|0.230***|-0.105***|0.031|
||(0.000)|(0.573)|(0.000)|(0.001)|(0.001)|(0.000)|(0.001)|(0.304)|
|Observations|13335|13685|13402|13529|13255|13431|12869|13002|

Note: Ordered probit regressions. Robust p-values in parentheses. Country and sector effects not reported. p<0.01, ** p<0.05, * p<0.1 27