rafmacalaba/datause-encoder-data huggingface dataset:
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Indicative infrastructure targets for Congo over the period 2005–15**

||Economic target|Social target|
|---|---|---|
|ICT|Fiber optic links to neighboring capitals and<br>submarine cables|GSM voice signal and public access broadband to 100% of the rural<br>population.|
|Power|1,689 MW new generation<br>498 MW inter-connectors|Electricity coverage of 53% (84% within the urban areas)|
|Transport|National and regional connectivity by<br>upgrading 1,052km of existing national<br>roads, 1,318km of regional roads, and<br>305km of railways to good condition.|Rural road network needed to connect 80% of the total value of<br>national agricultural output. This is equivalent to providing access to<br>48% of rural population.<br>Place entire urban population within 500 meters of all-season road|
|Water and sanitation|not applicable|Achieving the MDG for water and sanitation: 85% population with<br>improved water by 2015 and 60% of population with improved<br>sanitation by 2015.|

Meeting the country’s infrastructure needs would cost $946 million per year through 2015. Spending needs are highest in the power sector, which requires an estimated $483 million per year to keep pace with demand by installing 1,689 MW of new hydropower capacity and 498 MW of new cross-border transmission each year. While small relative to the huge power financing needs, water and sanitation needs are also high at $216 million per year, and transport financing needs are substantial at around $163 million per year. Spending needs are comparatively modest in the ICT sector, where the private sector plays a pivotal role. Overall, capital expenditure accounts for 77 percent of spending requirements, with power accounting for $438 million, or 60 percent of total capital needs (table 8). Operations and maintenance account for more than 40 percent of spending needs in the transport sector.

**Table 8. Indicative infrastructure spending needs in Congo for 2005–15**

|||US$ million per year||
|---|---|---|---|
|Sector|Capital expenditure|Operation and maintenance|Total spending needs|
|ICT|40|44|84|
|Power|438|44|482|
|Transport|94|69|163|
|WSS|159|57|216|
|Total|731|215|946|

Note: Figures refer to investment except public sector that also includes recurrent spending. Public sector covers general government and nonfinancial enterprises. Source: Briceño-Garmendia and others 2008.

The Republic of Congo’s spending needs are equivalent 16 percent of GDP. While high, that figure is similar to what the continent as a whole faces (15 percent of GDP) and slightly greater than in the resource-rich peer group (12 percent of GDP) (figure 10).

Existing infrastructure spending in the Republic of Congo amounts to $463 million per year. The public sector accounts for 82 percent ($381 million) of that total, while non-OECD financiers<sup>2</sup> account for 10 percent ($44 million), and official development assistance (ODA) and the private sector account for

> 2 Non OECD financiers include China, India, and the Gulf states. For more information, please visit http://www.ppiaf.org/documents/ebook-BuildingBridges.pdf

26
Table 4: IV estimates of the effect of IT use on firm-level productivity

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable log(sales/worker)|||||||
|Computers-per-worker|2.017***|1.834**|2.175**|2.036**|1.968**|1.999*|
||(0.656)|(0.744)|(0.889)|(0.881)|(0.895)|(1.080)|
|Share of labor with Internet|1.579***|1.755**|1.985**|2.047**|2.091**|2.610**|
||(0.598)|(0.752)|(0.840)|(0.842)|(0.848)|(1.241)|
|Share of labor with computer|2.952***|2.748***|2.783***|2.444**|2.358**|2.150*|
||(0.800)|(0.860)|(0.937)|(1.067)|(1.092)|(1.154)|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * Significant at the 1% level, **Significant at the 5% level, *** Significant at the 1% level All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 18
# **Annex 2: Indexed Indicators**

# **Accountability Index (including indicator sources)**

|**Ad**|**justed Accountability Index**|
|---|---|
|•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•<br>•|Accountability, Transparency & Corruption in the Public Sector (AfDB/WB)<br>Accountability, Transparency & Corruption in the Public Sector (AfDB)<br>Accountability, Transparency & Corruption in the Public Sector (WB)<br>Corruption & Bureaucracy (WB)<br>Corruption in Government & Public Officials (EIU)<br>Diversion of Public Funds (WEF)<br>Accountability of Public Officials (EIU)<br>Public Sector Corruption Investigation (GI)<br>Public Sector Corruption Bodies (GI)<br>Corruption Investigation (GI)<br>Prosecution of Abuse of Office (BS)<br>Accountability, Transparency & Corruption in Rural Areas (IFAD)|
|**Sub**|**-Indicators removed for endogeneity**|
|~~•~~<br>~~•~~<br>~~•~~|~~Access to Information (GI)~~<br>~~Public Information (GI)~~<br>~~Access to Legislative Processes & Documents (GI)~~|

_Source: Mo Ibrahim Index of African Governance Indicators, 2017_

# **Service Delivery Indices (including indicator sources)**

||**Education Service Delivery**|**Health Service Delivery**|
|---|---|---|
|**Ov**<br>(pro|**erall Education Index**<br>vision + outcomes)|**Overall Health Index**<br>(provision + outcomes)|
|**Ed**|**ucation Provision Sub-Index**|**Health Provision Sub-Index**|
|•<br>•|Education Provision & Quality (BS)<br>Education System Quality (WEF)|•<br>Basic Health Services (AFR)<br>•<br>Public Health Campaigns (GI)|
|•<br>**Ed**|Ratio of Pupils to Teachers in Primary School<br>(UNESCO)<br>**ucation Outcomes Sub-Index**|•<br>Immunization (WB/WHO)<br>•<br>Immunization against Measles (WB)<br>•<br>Immunization against DPT (WB)<br>•<br>Immunization against Hepatitis B (WHO)|
|•<br>•<br>•<br>•|Primary School Completion (WB)<br>Secondary School Enrolment (UNESCO)<br>Tertiary Education Enrolment (UNESCO)<br>Literacy (UNESCO)|•<br>Antiretroviral Treatment (ART)<br>•<br>Provision (UNAIDS)<br>•<br>ART Provision (UNAIDS)<br>•<br>ART Provision for Pregnant Women (UNAIDS)<br>~~•~~<br>~~Access to Sanitation (WHO/UNICEF)~~<br>~~•~~<br>~~Access to Improved Sanitation (WHO/UNICEF)~~<br>**Health Outcomes Sub-Index**<br>•<br>Child Mortality (IGME)<br>•<br>Maternal Mortality (MMEIG)<br>•<br>Undernourishment (WB)<br>•<br>Disease (WHO)<br>Malaria (WHO)<br>•<br>Tuberculosis (WHO)<br>•<br>~~Open Defecation Sanitation (WHO/UNICEF)~~|

_Source: Mo Ibrahim Index of African Governance Indicators, 2017_ 37
- 43 -

# **Annex V.  An example of risk matrix for an ESCO project**

**<u>Table 11. An example of risk matrix attached to RFP in the prefecture of Niigata</u>**

||Type of risk|Description|City|ESCO|
|---|---|---|---|---|
|Throughout all|Typo in RFP|Significant error in RFP|X||
|stages|Error in proposal|Failure to meet the agreed energy savings target||X|
||Safety|Safety in design, construction and O&M||X|
||Environmental<br>protection|Environmental protection in design, construction<br>and O&M||X|
||Institutional change|Change in VAT|X||
|||Other taxes||X|
||Cancelation and delay|City's order and decline by the city council|X|X|
|||Manifestation of neighboring residents|X|X|
|||Delay of issuance of construction and other permits||X|
|||Delay of issuance of construction and other permits|X||
|||because of city's mismanagement|||
|||Contractor's default||X|
|Planning/design|/ Force majeure|Design change, cancelation or delay by weather<br>conditions|X|X|
|construction|Price changes|Rapid inflation or deflation|X|X|
||Design change|City's order|X||
|||Contractor's order||X|
||Proposal cost|Cost of preparing proposal||X|
||Financial risk|Assurance of necessary financial resources||X|
|Construction|Project delay|Project delays and late delivery||X|
||Cost overruns|Cost increases by city's order|X||
|||Cost increases by contractor's mismanagement||X|
||Performance|Mis-specification||X|
||Damage before delivery|Damage of facilities before their delivery||X|
|Payment|Change of service fees|Administrative cost of changing service fees|X|X|
||Delay of payment|Delay of payment because of city's delayed process|X||
|||Delay of payment due to modification of revenue||X|
|||Delay of payment because of delay of monitoring<br>and verification||X|
|||Delay of payment of penalty||X|
||Interest rate|Change in commercial interest rates||X|
||Warranty against defect|Warranty against hidden defects||X|
|O&M|Change of facility use|Change of facility use by city's order|X||
||Increase in O&M cost|Increase in O&M cost for other reasons||X|
||Entry permission|Failure of O&M due to the lack of entry permission<br>in the facility|X||
||Damage of assets|<br>Damage by fire or accidents on purpose||X|
|||Damage by fire or accidents (force majeure)|X||
|Monitoring/|Quality of equipment|Failure to operate at the designed full capacity||X|
|Verification|Monitoring/Verification|Mis-report of monitoring and verification results||X|
|||No or late access to city's information of monitoring<br>and verification results|X||
||Utility cost|Change of utility unit costs|X||
Although these measures have improved the budget management, yet there is a need to rigorously implement an enhanced flow of expenditure and the reliability of financial monitoring. The budget execution remains highly centralized. This high degree of centralization should in theory enable the Ministry of Finance to have access to detailed data on budget execution. However, the existing data are not systematically reviewed, the statements produced by different services are not cross-checked, and information on expenditure execution is not systematically made available to the managers who initiated the expenditures. The central controls are numerous and cumbersome. Various factors explain these difficulties: persistence of complex procedures in the budget execution circuit; lack of clear documentation and/or strict application of the procedures; weaknesses of the technical infrastructure; and inadequacy of human resources.

The budget execution is slow in priority sectors which is attributed to a set of various factors and specifically in delays observed in budget notification and funding delegation. In order to improve budget implementation it is important to accelerate funding delegation at the beginning of the fiscal year. The rules governing the stages of the expenditure circuit are not fully complied with in Chad, which means that commitment often occurs excessively late in the expenditure process, instead of in the beginning. Delays in commitments in priority sectors are partially due to: (i) undue delays in making the budget officially available, especially delegated funding to noncentral levels (which concern priority sectors specifically); and (ii) delays for ministries to prepare and launch procurement processes. Steps are being taken to strengthen newlycreated Administrative and Financial Directorates (DAAFM) to address these issues.

<!-- Start of picture text --> Share of the Priority Sectors in the Budget Voted and Executed<br>2002 - 2006 (in percentage of the total budget)<br>70<br>60<br>50<br>40 Voted Budgets<br>30 Executed Budgets<br>20<br>10<br>0<br>2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: Chadian Authorities and the World Bank.

With the starting of the oil exploitation and the application of the provisions of the law regulating management of the oil revenues, the budget allocated to the priority sectors has substantially increased since 2004. Hence, the share of the budget allocated to priority ministries (including the subsidy in Cotontchad) based on interior resources and budgetary supports, excluding the debt servicing, stabilization account’s contribution; reached 52 percent in 2004, more than 57 percent in 2005, but dropped to 49 percent in 2006. It should be added to these amounts, the external assistance related to projects, 21
still predominant in commercial activities that can be viable for the private sector<sup>51</sup> such as fiber and textile manufacturing (e.g., the Arab Republic of Egypt), manufacture of food and cardboard (e.g., Bolivia), meat production (e.g., Botswana), groundnuts production (e.g., The Gambia), and even financial intermediation firms and digital payments (e.g., Russia, Vietnam) that could be served by the private sector. State participation in these sectors could potentially crowd-out private sector participation and thus private sector competitiveness, and as such, warrants further attention from policy makers.<sup>52</sup> Nonetheless, it is important to note that the BOS database collects only the primary activity of the SOEs, even though some companies could be multi-product. Since the BOS database captures the different legal entities even if under a conglomerate group, the risk of misclassification of the main activity is limited, but for the policy agenda and assessment of options of reforms this can be complemented with other tools (e.g., MCPAT).

The Global BOS database shows that state ownership and relationships among firms with state participation can be very complex. For the first time, the Global BOS database provides an in-depth view of the ownership relationships between the government and the corporate sector. It allows to identify countries where there are large conglomerate groups (e.g., Egypt and Vietnam) with strong upstream and downstream relationships. It also provides new tools to assess the degree of vertical integration of some firms across markets, which can also be relevant to understanding potential implications in terms of competition and firm dynamics.

Last, the Global BOS database has the potential to inform key reform agendas in areas such as corporate governance, anticorruption, debt management, among others.<sup>53</sup> The rich information contained in the database has been used to inform country-level analysis around SOE reform, but its uses go beyond corporate governance. For instance, the SOE agenda is directly relevant to macro-fiscal management given SOEs potential impact on direct and contingent public sector liabilities. SOEs are also important employers in certain markets, with implications for the jobs and poverty agenda. Finally, SOEs have become more relevant in the context of the COVID-19 pandemic, as both critical elements in the production of emergency protective and medical equipment in the case of manufacturing firms, but also as recipients of state support, for example in the airline industry (Sanchez-Navarro, Martinez-Licetti, & Perrotet, 2020).

> 51 (Sanchez-Navarro, Goodwin, & Kikeri, SOE CPSD Knowledge note, 2021). CPSD SOE Knowledge note (2021). 52 Who’s the BOSs : Shedding New Light on Businesses of the State, (World Bank, 2022). 53 Ibid.

34
|dummy for full in-house (0) and full outsourcing|-0.186|0.106|7.9%|
|---|---|---|---|
|Guateng provincial dummy|0.366|0.086|0.0%|
|Manufacturing|-0.241|0.119|4.3%|
|PAYE|0.537|0.140|0.0%|
|Adj. R2|0.276|||
|Mean of the dependent variable|9.421|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance**|**(-10% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|6.348|0.377|0.0%|
|LN full-time employees|0.170|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.143|0.030|0.0%|
|dummy for full in-house (0) and full outsourcing|-0.086|0.106|41.7%|
|Guateng provincial dummy|0.367|0.086|0.0%|
|Manufacturing|-0.241|0.119|4.3%|
|PAYE|0.536|0.140|0.0%|
|Adj. R2|0.268|||
|Mean of the dependent variable|9.335|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance**|**(-20% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|6.230|0.377|0.0%|
|LN full-time employees|0.170|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no|0.143|0.030|0.0%|
|answer")||||
|dummy for full in-house (0) and full outsourcing|0.027|0.106|80.2%|
|Guateng provincial dummy|0.368|0.086|0.0%|
|Manufacturing|-0.242|0.119|4.2%|
|PAYE|0.534|0.140|0.0%|
|Adj. R2|0.262|||
|Mean of the dependent variable|9.240|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance**|**(-30% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|6.095|0.377|0.0%|
|LN full-time employees|0.169|0.039|0.0%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.143|0.030|0.0%|
|dummy for full in-house (0) and full outsourcing|0.154|0.106|14.5%|
|Guateng provincial dummy|0.370|0.086|0.0%|
|Manufacturing|-0.243|0.119|4.1%|
|PAYE|0.531|0.140|0.0%|
|Adj. R2|0.257|||

47
_t_ ; _WageBankAaccmt_ is the log of wage bank accounts in municipality _m_ in month _t_ ; and the rest of the variables are defined as in Equation (1). All models also include municipality fixed effects, time fixed effects, and linear municipality trends. The event study window is defined relative to December 2019 ( _τ_ = 0). The main coefficient of interest is _δτ_ , which captures the relationship between transaction bank accounts and distance relative to December 2019.

Results from Specification (3) are presented in Fig. 3. First, we find no relationship between transaction bank accounts and distance to the border before February 2020, precisely when the first restrictive travel policies were implemented.<sup>22</sup> After that period, we find that the elasticity between distance to the closer border crossing and bank accounts was negative. This indicates that municipalities closer to the border increased their bank accounts during COVID-19 with respect to municipalities farther away from the border. This is consistent with the idea that recipient households at the border opened bank accounts at the outset of COVID-19 to receive remittances through formal channels that previously might have been sent via informal ones (such as through a border crossing).

# **7 Discussion**

Remittances are a vital source of income for developing countries. Remittance flows to low- and middleincome countries are larger than foreign direct investment flows and overseas development assistance combined. The economic recession caused by COVID-19 was expected to reduce remittance inflows. Indeed, households reported a decline in remittance inflows from May through June 2020. Yet, formal remittances to several developing countries—including Mexico—increased substantially during the first months of the pandemic.

This article produces evidence to explain this paradox. It contends that a sudden acceleration in the formalization of remittances partly explains the increase in recorded remittances observed in several developing countries during the first period of the COVID-19 pandemic. This stemmed from a prohibitive increase in the costs of sending informal remittances due to mobility restrictions imposed to control contagion. We use subnational data from a developing country, Mexico, to show that the increase in formal remittances was driven by municipalities closer to a US border crossing that had been more likely to receive informal remittances before March 2020. Consistent with this hypothesis, these areas also experienced a disproportionate increase in transaction bank accounts during this period. These accounts were probably opened in order to receive formal remittances via electronic transfer that would have come through informal channels prior to the pandemic.

> 22On January 31, 2020, the White House 2019 Novel Coronavirus Task Force announced the implementation of new travel policies effective on February 2, 2020. For a detailed timeline of the different events since December 2019, see the CDC museum timeline in the following website.

12
robustness to long-difference estimation, comparing investment in a future period to the year before the AIA policy, which also removes any previously treated firms by definition.

The measures of cloud, big data and AI are available only for a few specific years, so we estimate as long-differences, comparing technology adoption in a future period to the year before the AIA policy. When exploring these outcome variables our treatment is then determined as those firms for whom the marginal incentives to invest (in capital) fell at some point between the start of the AIA (in 2007) and the given future period (2013/15/17/19 depending on available digital technology measure), while control firms are firms who marginal incentive to invest did not change over this period. We do this by measuring treatment status as the maximum of the year-by-year treatment dummies described above - i.e. firms that were treated in any year. For the continuous measure we construct the mean value over the years between 2007 and the given future period (2013/15/17/19). Finally, we note that as we measure outcomes only in a single pre-treatment period and a single post-treatment period concerns over staggered treatment does not apply and we can estimate Equation 1 using OLS.<sup>29</sup> The validity of difference-in-differences rests on parallel trends in the absence of treatment. We follow common practice in the literature and test this by comparing the treatment and control groups in the periods before treatment takes place. To do so we show graphically how the treatment effects of changes in the AIA evolves over time using the approach of Callaway and Sant’Anna (2021). For completeness, we note that such test for pre-trends is not possible for the firm digital technologies as they had not been invented by the start of the AIA policy.

In Figure 1, we present the event study plot for total investment against its associated treatment-year dummies. It is clear that in the periods leading up to the different AIA changes, the treatment and control groups share similar pre-treatment trends. There is a

> 29With few periods, the degree of the treatment stagger is inherently limited, and in any case, without a semi-balanced panel it is not possible to employ new staggered treatment estimators, such as Callaway and Sant’Anna (2021).

18