rafmacalaba/data-use-mentions-extended huggingface dataset:
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Evolution in the Herfindahl-Hirschman Index between 2000 and 2010**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Herfindahl Index (Base 100)<br><!-- End of picture text --> Source: Wireless Intelligence 2010.

# **Challenges**

Most of the telecommunications network, including the national microwave transmission backbone, was destroyed during the war. All but one telephone exchange at Pointe-Noire were damaged. Since then, Congo Telecom has undertaken a limited infrastructure rehabilitation program. Most of the backbone is using wireless connections through 32 Mbps Microwave links. The Republic of Congo currently has three active mobile companies. As indicated above, GSM population coverage is already approximately 70 percent. Based on consumers’ purchasing power and willingness to pay for mobile services, it is estimated that the private sector could make sufficient investments in the coming year to cover an additional 15 percent of the population on a commercially viable basis (figure 7). To achieve universal access, however, subsidies will be required to provide coverage to the remaining 15 percent of the population.

The cost of international connectivity is high in the Republic of Congo. A mobile call to Washington, DC, costs an estimated $0.96 (Celtel), compared with $0.40 a minute (Celtel and Vodacom) from Democratic Republic of Congo. Broadband and international calls are also more expensive than in the rest of Africa, and Internet access is slower, at 0.3 mpbs compared with 2.7 mbps for the resource-rich benchmark. However, once the country is connected to the submarine cable, these figures should improve. In other African countries that have connected to the submarine cable,
for children. We rely on standardized Demographic and Health Surveys for child health measures as vital statistics in low- and lower-middle-income countries are unreliable.

Allowing for a mortality increase among non-children due to lockdowns would only increase the ratio between the non-COVID-19 deaths caused by the lockdowns and the number of COVID-19 deaths averted, conditional on a given lockdown severity. However, we would expect the overall impact via the elderly mortality rates to be much smaller. One reason is that, as we highlight below, adults above 60 account for a small proportion of the population in developing countries.

> 7These three studies are from middle-income countries with relatively reliable vital statistics data, a rarity in lower income countries. These results are of course in contrast to the literature from wealthy countries that finds recessions to lower mortality rates at adult ages.

16
To account for other factors that might influence the primary balance, the vector **_X_** _k,it_ contains several control variables. First, we include two lags of the primary balance. Following Montiel Olea and Plagborg-Møller (2021), we add an additional lag to address serial correlation in the regression residuals. Specifically, their findings demonstrate that lag-augmented local projections are asymptotically valid across both stationary and nonstationary data and at long horizons (i.e., horizons that are a non-negligible fraction of the sample size). Furthermore, lag augmentation eliminates the need to correct standard errors for serial correlation in the regression residuals.<sup>14</sup> Second, we account for the broader macroeconomic environment. We include the lagged public debt-to-GDP ratio to capture the responsiveness of fiscal policy to debt sustainability challenges. Additionally, we control for real GDP growth, inflation, and the current account balance (all lagged by one period to address endogeneity concerns).

Third, we control for the institutional environment. Amongst others, we incorporate a variable that considers the presence of an election year to account for the potential existence of political budget cycles. Moreover, we control for the strength of political institutions. However, since no single variable fully captures this concept, we employ Principal Component Analysis (PCA) and take first principal component to construct a summary measure. We use data from the International Country Risk Guide (ICRG) database, incorporating variables on the regulatory quality of the government, the preservation of the rule of law, the level of democratic accountability, and the control of corruption. <u>Appendix 2</u> describes the PCA and the outcomes in detail.

In addition to the quality of political institutions, we include variables that control for the effect other macroeconomic policies on the primary balance. Specifically, we incorporate measures that capture the presence of independent fiscal councils and sovereign wealth funds. Furthermore, we account for the presence of an inflation targeting regime, the prevailing exchange rate regime
# **3.2 Customs data**

Our export dataset is generated from raw data files collected by customs authorities containing export flows at the transaction level. The files were provided by the customs authorities of Malawi, Mali, Senegal and Tanzania. Each of them contains information on products exported at the highest level of disaggregation of the HS code used by these administrations: 10-digit for Mali and Senegal and 8-digit for Malawi and Tanzania. In addition to product information, each file contains information on destination market, FOB shipment value, net weight, port used and date of transaction. Original names and tax IDs identifying the individual firm were replaced by ‘dummy’ digital IDs so as to preserve confidentiality. We aggregated transactions up to annual totals at the 6-digit level, the standard level used in cross-country comparisons. Finally, for consistency, we filtered out years with different port coverage. For instance, for Malawi we have information from 2004 onward; however, as fewer ports were covered in 2004 than in other years, we excluded 2004 from our sample for that country. Sample periods are 2005-2008 for Malawi and Mali, 2000-2008 for Senegal, and 2003-2008 for Tanzania.

Table 1 presents some basic descriptive statistics. Tanzania has the largest number of exporters (1,359), followed by Malawi (856), Senegal (715), and Mali (280); however, they are less diversified than those of other countries in our sample in terms of markets. Mali’s exporters are, on average, the most diversified in terms of products.

Our variables of interests are indexed as follows. Let _f_ be a firm, _d_ a destination, _p_ a product (at HS6), _t_ the starting year of an export spell, and _vfpdt_<sup>_c_the dollar value of exports of product</sup><sup>_p_to destination</sup><sup>_d_in calendar year</sup> _t_ by firm _f_ from country _c_ . Because there are no multi-country firms in our
Mato Grosso unknown 2003 1; 250,000 Mato Grosso State Foundation of Primary (paved) and secondary (unpaved) roads in Mato Grosso =<br>the Environmental (FEMA)<br>Distance to roads in Mato unknown = 1: 250,000 Derived from road map (own Bird distance to the nearest road calculated for each pixel of the 100 meter rasterized Map 10<br>Grosso calculation) road map.<br>Agricultural suitability of 1995 2002 1; 250,000 State Secretary of Planing and Agricultural suitability was defined by SEPLAN as part of the socio-economic ecological Map 11<br>Mato Grosso Coordination (SEPLAN) zoning exercise (ZSEE) in Mato Grosso.<br>Land value of Mato Grosso 2003 2003 1: 250,000 derived from INCRA 2003 Geo-referenciation of land values to municipal boundaries of Mato Grosso -<br><!-- End of picture text -->
**Figure 3.9 Results of benchmark regression analysis for production stages (2007‐2014)**

<!-- Start of picture text --> Benchmark against goods firms Benchmark against goods exporters<br>12.3<br>-1.5<br>-5.4 -6.6 -4.4<br>-13.8 -14.0 -14.4<br>-20.2 -18.6 -18.7<br>Establish Pre-man Manu Post-man Post-sales Back-office Establish Pre-man Manu Post-man Post-sales Back-office<br>50 50<br>25 25<br>0 0<br>Percentage difference Percentage difference<br>-25 -25<br>-50 -50<br><!-- End of picture text --> Source: EIS database; author’s calculations using TFP based on Ackerberg et al. (2015). For pre‐manufacturing services in the left‐hand panel, no significant coefficient result was obtained. For back‐office services a negative but only slightly significant coefficient result was found in the left‐hand panel when using ACF TFP only. Similarly, for manufacturing services in the right‐hand panel, only using ACF a slightly negatively significant result was obtained.

# **4. Regulatory Restrictions on Services**

**The previous sections have shown that services are not living up to their potential in Turkey.** The services sector and the interaction between services and the manufacturing sector fall short of what can be expected from an economy like Turkey that is approaching high‐income status. Peer countries tend to have larger services economies and more trade in services both in gross terms and value‐added. An ailing services sector has widespread consequences, since services are used as inputs throughout economy. Moreover, manufacturing firms today do not only produce goods; they are increasingly also producing and selling services. Turkey also shows signs of this servicification process
.7|21.1|15.3|17.1|16.5|18.0|

Based on XDR 30 _low_ ln cointries. ased on Wrld Debt T&blm classification.

Based on the Ibrld **_Bsiks_** stnctural adjustat lendiv camtries. Chile, Calaobia, Coe d'Ivoire, Jaulca, Kenya, Korea, Malawi, llritius, Mmdco, Mbrocco, Psm, Philippies, Seg1, Tailad, and lnbiey. e/ _Basd_ on WIR 90 amuntries.

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The same analyses used in this paper were conducted using the 2016 Labor Force Survey (the latest available data at that time) and were intended to inform the debate in 2018 when the government was considering an increase between 230 and 250 euros for all employed. The estimates presented here have been updated with the 2018 LFS.

> 6 Poverty-level wages are the wages required to lift a family out of poverty in Kosovo. Poverty-level wages are computed as the national poverty line (per adult equivalent) multiplied by adult equivalents in the household, divided by the number of household members who work full time. Given that part-time work in Kosovo is prevalent, an adjustment is also made to include part time-work.  In 2017, the poverty line per household was 302 euros per month. Considering that about 0.93 household members on average work full time (LFS estimates) and 0.98 work part-time, this is equivalent to a poverty-level wage of 212 euros per month.

> 7 As per Administrative Instruction No. 09/2013.

6
Figure 2: SMEs have Shorter Survival Times than Large Firms to Sales Disruptions <!-- Start of picture text --> Survival if sales stopped (weeks) Coverage of fixed costs through retained<br>earnings (weeks)<br>30 30<br>25 25<br>20 20<br>15 15Large<br>SME SME<br>10 10<br>Large<br>5 5<br>0 0<br>Weeks<br>Zimbabwe Chad Albania Italy Russia Georgia Moldova Guinea Cyprus Greece Zambia Togo Niger Colombia Greece Italy Jordan Kazakhstan Kenya Morocco Peru Portugal Russia Turkey Ukraine<br><!-- End of picture text --> Source: World Bank Enterprise Follow-Up Surveys and Bosio et al. (2020) _Note_ : Mean estimates using the World Bank Enterprise Surveys on the left panel use stratification weights adjusted for the follow-up enterprise surveys according to median eligibility.were used to produce the estimates. The weights account for non-response, assuming that businesses that could not be re-contacted (unobtainable) have exited the market.

Without any sales, the survival time they self-report is on average 4-5 weeks across countries, which is not very different to large firms. An analysis of the distribution across countries shows that, without sales, half of them will have shut down at the end of the first month, and 3 out of 4 by the end of 10 weeks (see Annex Fig A1). A protracted downturn can therefore result in an exodus of firms at these turning points.

# 2.1 Prevalence of SMEs in affected countries and sectors

The vulnerability of SMEs to the crisis depends in part on their prevalence in more crisis-exposed countries and sectors. Measuring exposure to the crisis is difficult because countries and sectors are interconnected through trade linkages, which can magnify the effects of demand and supply shocks. For instance, in February, Chinese lockdowns led to shortages of key inputs and disrupted production worldwide. European consumers cutting back on clothing demand led to order
area, while ‘Urban’ is a dummy for location in any urban area. ‘Above Mean Wealth’ is a dummy for whether and index of household wealth, based on housing and asset ownership, is above the sample mean. The dependent variable is total working hours over the last 7 days, with individuals not working coded as working 0 hours. Observations include data from May to November 2020, and include treatment households with children in grades 4 or 8 (indicated by ’Treat’), control households with children in an adjacent grade, and ‘mixed’ households with both (results not shown). ’Post’ is a dummy for being observed on or after the partial school reopening on October 12. Regressions include household and county by month fixed effects, and additional household and individual controls. SEs clustered at household level. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 of the pandemic on mothers’ labor supply relative to fathers’ and other women’s, pointing to school closures as the mechanism. But this result aligns with the data on childcare hours in Kenya: responsibilities prior to the pandemic were less gendered than expected prior to the pandemic and both women’s and men’s hours increase by over one-third during school closures, though women’s hours increase by more in absolute terms. A childcare shock in this context could thus affect both parents’ labor participation. Different mechanisms may also underlie the similar increases for women and men, which we discuss further in subsection 4.2.

The impact of partial reopening on work hours is over four times as large for adults in agricultural households (61%), defined as households with any agricultural activity. This is consistent with effects on total hours driven largely by household agriculture.<sup>12</sup> Adults in non-agricultural households do not significantly increase work hours, likely reflecting constraints on increase
the 2000 survey). With this unique set of data, we are able to judge if there was a recall bias. As it turns out, there is almost none. Household participation in the off-farm labor market in 2000 was estimated to be 34.5 percent in 2008 CNRS survey; the off-farm labor market in 2000 was estimated to be 35.4 percent when using the 2000 survey itself.

5 Whether the counterfactual method overestimates the quantitative impact of the global financial crisis largely depends on how to isolate the shock of the global financial crisis from other domestic and exchange 37
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**<u>Table 4. Electricity tariff structure in Albania</u>**

|Year|1st block<br>rate|2nd block<br>rate|Threshold<br>(kWh)|Norma<br>consumption|tive<br> (kWh)1/|
|---|---|---|---|---|---|
||(lek/kWh)|(lek/kWh)||Urban|Rural|
|2002|4.0|8.0|300.0|356.9|244.4|
|2003|4.0|8.1|300.0|356.9|244.4|
|2004|4.5|8.9|220.0|363.8|237.0|
|2005|4.5|9.0|210.0|363.8|237.0|

1/ Average across districts for the summer period. Source: Electricity Regulatory Entity, Albania.

> How much people would respond to changes in marginal prices, _MP_ , depends on the price elasticity of demand. In the literature the estimates vary significantly, depending on country, period and data being analyzed, and methodology adopted. According to a classical literature review (Taylor, 1975), the price elasticity is estimated at –1.02 to –2.00 for residential users and –1.25 to –1.94 for industrial consumers. A more recent meta analysis shows that the average price elasticity of residential electricity demand is –0.35 in the short run and –0.85 over the long run (Espey and Espey, 2004). With regional aggregate data, Bernstein and Griffin (2005) find that the residential electricity elasticities in the United States are –0.24 and –0.32 in the short and long run,
>0%<br>0<br>0 5 10 15 20 25 30<br>LRMC, trade expansion, US cents/kWh<br>level of LRMC recovery<br>cents/kWh<br>percentage of countries<br>Effective Residential Tariffs US<br>cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

27
The analysis of road sequences’ gains, efficiency, and importance across solutions may help policy makers identify the key roadworks in potential plans. These strategies may further help compensate for the delays and inefficiencies inherent to Nepal’s capital infrastructure planning bureaucracy, by focusing policy makers’ attention on the roadworks most critical to success within an overall optimized portfolio.

# 5.5. Limitations

We must note certain limitations to our analysis. The most significant is that due to resource and data constraints, we present the accessibility gains as a simultaneous phenomenon instead of staggered over the provincial government’s 5-year road construction window. A deeper analysis would specify a yearly budgetary max within the overall budget max and stagger the solution roads across the time period, with gains appropriately discounted. Additionally, we employed flat per-kilometer road costs per geographic area due to lack of data, and to better align and compare with Nepali practitioners and academics (Shrestha, 2018); more specific road cost data would improve our method’s accuracy. Finally, this analysis treats population as spatially fixed, while in reality population is dynamic and new road construction can induce population movements; for example, in Nepal new markets often spring up at newly built roads heads. This could create additional unobserved objectives if the government speculatively selected roads to create new market hubs or connect underpopulated remote villages to reduce population outflows.

The complexity of the analysis routines described here places them effectively out of reach of even national government planning agencies in Nepal, let alone provincial or municipal ones. Thus government will need to employ universities and private sector companies with specialized expertise to replicate this analysis in new areas. Given that the studied investment planning exercise happens on a 5 year basis this seems a manageable extra expense. As stated above, care was taken to use free global or common national data sets to ease adoption and replication by other parties.

A further limitation is the difficulty of communicating our results to policy makers compared to traditional instruments. Welfare-based evaluation methods ease the cognitive burden on decision
**Figure 1: Average Actual Expenditures vs. Allocated Budget per Municipality, 2009**

<!-- Start of picture text --> 8000<br>Actual<br>Budget<br>6000<br>4000<br>2000<br>0<br>Capital Capital Current Current<br>Ordinary Defined Ordinary Defined<br><!-- End of picture text --> Note: Data are in thousands Nuevo Soles. As of December 31, 2009, 1 Peruvian Nuevo Sol = 0.35 US Dollar. Source: Authors’ calculations based on data from Ministry of Economics and Finance (MEF).

There is also considerable variability in municipalities’ spending patterns (Figure 2), which do not display any geographic trends: municipalities that are (not) able to spend their budget appear to be scattered all across the territory, from the Amazon basin, to Lima, and from North to South. The lack of a clear-cut pattern is all the more surprising given the geographic clustering of poverty and other factors such as natural resources that may affect governance and political factors.

In studying determinants of spending ability, we draw both from existing literature and the Peruvian context. We classify constraints to spending into four broad categories: the budget size and allocation process; local capacity; local needs; and political economy constraints. Both the academic and policy literature have widely documented the extent to which capacity constraints and political economy considerations can significantly hamper the ability and willingness of local administrations to address the needs of their electorates. Poor capacity of local administrations has long been identified as an important bottleneck for successful 5
<br>313.5<br>224.4<br>200<br>0 11.8 1.9 30.6<br>EAP LAC SAS SSA Global<br>Region<br>(million, 2030)<br>People in extreme poverty<br><!-- End of picture text --> Figure 1: People in extreme poverty, without climate change in 2030 Figure 1 also shows that the total number of people in extreme poverty exceeds 590 million in more than 250 scenarios. There are therefore many futures among our scenarios in which extreme poverty remains close to its current levels. These scenarios tend to be associated with the SSP4, which assumes high population growth and slow progress in education and productivity.

On the other hand, there are also 250 scenarios with fewer than 170 million poor people. In the bestcase scenario there are only 9.3 million people in extreme poverty globally. This shows that approaching the eradication of extreme poverty by 2030 requires all our assumptions to be among the most optimistic, confirming that this objective is very ambitious at this point. Introducing the impacts of COVID-19 would make this objective even more difficult to achieve.

## **3.2 Climate change impacts on extreme poverty**

We now turn our attention to the poverty headcount under scenarios where all climate impact channels are simultaneously included. Figure 2a shows the distribution of the number of additional people falling 4
Policy Research Working Paper 10272

# **Abstract**

This paper studies the effect of the 2018 drought on household consumption and poverty in Afghanistan, a semi-arid and conflict-affected country. The paper combines geolocated household data with remote-sensing weather data on precipitation, vegetation, and temperature. The findings show that drought-like conditions decreased monthly per capita consumption expenditures and hence increased poverty, with a highly nonlinear relationship between consumption and weather shocks. When forced to cut back, households reduced nonfood consumption to maintain their food consumption; only under severe stress did they reduce food consumption. Households that owned agricultural land were more resilient to the 2018 drought. Based on the historical distribution of weather shocks, estimates of vulnerability to poverty suggest that 62.5 percent of people have a one in four probability of falling into poverty due to weather shocks. Given that climate change will exacerbate the frequency and severity of future droughts, these findings highlight the importance of investments in resilience and shock-responsive social protection to supplement urgent humanitarian assistance.

This paper is a product of the Poverty and Equity Global Practice. 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 authors may be contacted at nkochhar@worldbank.org and eknippenberg@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