rafmacalaba/datause-encoder-data huggingface dataset:
# 3.1.2. Power outages in Bangladesh

Load shedding volumes have decreased recently, thus suggesting improved network stability.

The Bangladesh Power Development Board (BPDB) is responsible for the planning, construction, and operation of power generation and transmission in the country. A key challenge for the BPDB is to meet energy demand during peak hours, and frequently resorts to load shedding (Islam and Khan, 2017). Load shedding is a common measure applied to avoid overloading the electricity network, employed by utilities when electricity demand exceeds supply to maintain voltage stability (Taylor, 1992). According to the _Sustainable Energy for All_ initiative, 79 % of connected consumers in Bangladesh suffer power outages caused by regular load shedding.<sup>15</sup> However, the WEF (2018) observes a considerable improvement in Bangladesh’ electricity reliability index in the period between 2011 and 2017 (Figure 1.2). The information derived from daily BREB reports for the divisions Chittagong, Rajshahi, Rangpur, Khulna and Sylhet<sup>16</sup> suggest that load shedding is an almost daily occurrence in Bangladesh. But indeed, the data also show that over the period 2012-2016, the number of days with load shedding decreased continuously in all divisions, where data could be extracted (Figure 3.6). This can be interpreted as a sign of a more efficient power system, as power generation is aligned more accurately with electricity demand.

**Figure 3.6: Decrease of the number of load shedding events and increase of the number of days of power outages caused by storms**

<!-- Start of picture text --> 100 2.5<br>90<br>80 2<br>70<br>60 1.5<br>50<br>40 1<br>30<br>20 0.5<br>10<br>0 0<br>2011 2012 2013 2014 2015<br>Source: authors based on BREB data<br>(moving average)<br>month in the preceding year<br>Average number of outages per<br>Average number of loadsheds per month (12 month moving average)<br><!-- End of picture text --> Storm related power outages are not decreasing during the same time frame.

While falling load shedding volumes suggest that the reliability of power supply has improved overall, resilience to natural shocks has not. During the same time period, storm-induced outages have been stable or even increasing in frequency – at the country level, and in all divisions where data is available (Figure 3.6). Over the period 2000-2017, outages were reported on 575 storm days. Bangladesh

> 15 “Sustainable electricity for all” initiative: SE4ALL database, IEA and World Bank

> 16 Storm-related outages are not available in Barisal. Load-shedding data in Dhaka did not pass robustness checks. Therefore, those divisions were excluded from the comparison between load shedding and storm-related power outages.

13
Table 15: Network regressions: log Leverage Pooled

||(1)<br>logLev|(2)<br>logLev|(3)<br>logLev|(4)<br>logLev|(5)<br>logLev|(6)<br>logLev|
|---|---|---|---|---|---|---|
|SOE|-0.045***|-0.045***|-0.045***|-0.045***|-0.045***|-0.045***|
||(0.014)|(0.014)|(0.014)|(0.014)|(0.014)|(0.014)|
|Connect|-0.079*|-0.079*|-0.017|0.007|0.006|-0.036|
||(0.043)|(0.043)|(0.038)|(0.054)|(0.030)|(0.037)|
|BigI|0.06|0.036||-0.036|||
||(0.048)|(0.052)||(0.077)|||
|HasBetween||0.046||||0.087|
|||(0.045)||||(0.060)|
|logDegree|||-0.012|-0.097**|||
||||(0.022)|(0.044)|||
|BigI_×_logDegree||||0.099*|||
|||||(0.053)|||
|Politician|||||-0.083**|-0.042|
||||||(0.039)|(0.049)|
|Politician_×_HasBetween||||||-0.086<br>(0.080)|
|Constant|-0.932***|-0.932***|-0.932***|-0.932***|-0.931***|-0.931***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|Observations|10,945,643|10,945,643|10,943,381|10,943,381|10,945,643|10,945,643|
|R-squared|0.065|0.065|0.065|0.065|0.065|0.065|
|Sector Dummies|✓|✓|✓|✓|✓|✓|
|Size Dummies|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|
|Country FE|✓|✓|✓|✓|✓|✓|

Robust standard errors in parentheses clustered at the level of firm

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

39
Pindyck, R.S., 1978. "Gains to Producers from the Cartelization of Exhaustible Reaources." Review of Economics and Statistics 60: 238-51.

Priovolos, T., 1987a. Investment Policies for Steel-Producing Countries. EPDCS Division Working Paper No. 1987-2. Washington, D.C.: World Bank.

Priovolos, T., 1987b. An Econometric Model of the Iron Ore Industry. World Bank Staff Commodity Eorking Paper No. 19. Washington, D.C.

- Propoi, A. and I. Zimin, 1981. "Dynamic Linear Programming Models of Energy, Resource and Economic Development Systems." RR-81-14, International Institute for Applied Systems Analysis, Laxenburg.

- Quirk, J., K. Terasawa and D. Whipple, 1982. Coal Models and Their Use in Government Plannin . New York: Praeger Publishers.

- Reutlinger, S., 1976. "A Simulation Model for Evaluating Worldwide Buffer Stocks for Wheat." American Journal of Agricultural Economics 58: 1-12.

- Rose, A., 1984. "Technological Change and Input-Output Analysis: An Appraisal." Journal of Social Planni_g.

- Rose, A. and D. Kolk, 1987. Forecasting Natural Gas Demand in a_ChanginPg World. London: JAI Press, Inc.

- Salant, S., F. Miercort, A. Sanghvi and M. Wagner, 1981. Imperfect Competitition in the International Energy Market. Lexington, MA: D.C. Heath.

- Samuelson, P.A., 1952. "Spatial Price Equilibrium and Linear Programming." American Economic Review 42: 283-303.

- Simpson, J.R., 1980. "Input-Output Modeling and Its Implications for Commodity Planning in Latin America." In W. Labys, M. Nadiri and J.N. del Arco (eds.) Commodity Markets and Latin American DevelopMgnt: eling Approach. New York: National Bureau of Econamic Research.

- Strongman, J.E., W.R. Killingsworth and W.E. Cummings, 1976. "The Dynamics of the International Copper System." Proceedings. The XIV Sympoisum of the Council for the Application of Computers and Mathematics in the Mineral Industries. University Park, PA: Pennsylvania State University.

- Takayama, T. and H. Hashimoto, 1984. A Comparative Study of Linear Complementarity Programming Models and Linear Programming Models in MultiRegional Investment Analysis. EPDCS Division Working Paper No. 1984-1. Washington, D.C.: World Bank.

- Takayama, T. and W.C. Labys, 1985. "Spatial Equilibrium Analysis: Mathematical and Programming Model Formulation of Agricultural, Energy and Mineral Models." In P. Nijkamp (ed.) Hanbook of Regional Economics. Amsterdam: North Holland Publishing Co.

- Tan, C. Suan, 1986. Cuba-USSR Sugar Trade. World Bank Staff Working Paper No. 1986-2, Washington, D.C.

50
# **4.   Allocation between different assets**

Turning to the composition of the household financial portfolio, one distinctive stylized fact is that the rich hold a much riskier financial asset portfolio.<sup>22</sup> In making this judgment, we rely on the conventional classification that treats bank deposits (and other liquid assets largely free of default risk such as mutual funds solely invested in short-term money market assets and certain government-sponsored savings schemes) as largely riskfree.<sup>23</sup> US data (Figure 1) suggests that these display a wealth elasticity of close to unity.<sup>24</sup> Though subject to market risk, government bonds are also often classified with the risk-free; lower wealth deciles do not hold much of these, but even when they are included with the other risk-free assets, this broader category still displays a wealth elasticity little more than unity (constant share of wealth held in this form). Up to a certain level,<sup>25</sup> other risky financial assets have a wealth-elasticity much higher than unity: this reflects the declining share of total wealth held in non-financial form as wealth increases.

Extrapolating these observations to conditions in low-income countries, the share of risky financial assets in the national totals is likely to be smaller in poor countries. This indeed is confirmed by the observation that the ratio of aggregate national bank deposits to market capitalization is negatively correlated with per capita income – though the correlation is rather weak when attention when data is restricted to developing countries, because of outliers such as Israel, Jamaica and Slovenia (Figure 4b).

The pattern of increasing risk as wealth increases appears to be driven mainly by higher participation by rich households in the riskier assets. In other words, a higher proportion

> 22 Carroll (2002) has shown that a strong bequest motive could explain not only higher savings rates for high income households but also a higher risk-tolerance.  Carroll also notes that rich households hold a relatively high share of non-financial business equity.  He suggests that this is due to capital market imperfections inducing owners of technology or other productive assets to invest in their own enterprises An alternative explanation is that true total Haig-Simon income from nonfinancial wealth is higher than measured.  After all, what is wealth but capitalized Haig-Simon income?

> 23 Even though subject to inflation risk as well as (in less advanced economies) a small but sometimes not insignificant default risk.

> 24 Inferred from data on asset multiples of income for deciles of _wealth_ in Kennickell (2003).

> 25 To about the 90th decile of wealth in the US data: non-financial business equity assets represent an increasing share of the portfolios of the very rich.

18
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 19. Benin’s potential gains from greater operational efficiency**

||**ICT**<br>**Ir**|**rigation**|**Power**<br>**Tr**|**ansport**|**WSS**|**Total**|
|---|---|---|---|---|---|---|
|Underrecovery of costs|n.a.|—|50|0|11|61|
|Overstaffing|17|n.a.|—|n.a.|0|17|
|Distribution losses|n.a.|n.a.|11|n.a.|3|13|
|Undercollection|n.a.|—|5|0|3|8|
|Low budget execution|0|—|0|0|0|0|
|Total|17|0|67|0|17|101|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). n.a. = not applicable; —= not available.

Undercharging for power supplied to the distribution company costs Benin about $50 million each year. In the distribution sector, however, end-user tariffs were very close to cost-recovery levels in 2009. Compared with the average low-income, nonfragile country, Benin’s underpricing of power is slight (figure 23).

In the water sector as of 2009, SONEB’s average tariffs stood at $0.72 per cubic meter versus an estimated average cost-recovery tariff of $1.10 per cubic meter. The macroeconomic burden, at 0.17 percent of GDP, is lower than that for power. In comparison to the average low-income, nonfragile country, Benin performs fairly well in the recovery of water costs.

How high would consumers’ utility bills be if cost-reflective tariffs were applied? For power, with a cost-recovery tariff of $0.19 per kilowatt-hour and a monthly subsistence consumption of 50 kilowatt-hours, the associated utility bill would come to $9.50 per month. With a water-recovery tariff of $1.10 per cubic meter and modest consumption of 10 cubic meters per month, the water utility bill would be $11. Based on the distribution of household budgets in Benin, monthly utility bills at these levels would be affordable by less than 30 percent of the population (figure 24). A more limited level of subsistence consumption of 25 kilowatt-hours per month

**Figure 23. Underpricing of power and water in Benin and other low-income, fragile countries**

Financial burden of underpricing as percentage of GDP <!-- Start of picture text -->
1.5<br>1.0<br>0.5<br>0.0<br>Power Water<br>Benin LIC-NoFragile<br>Percenatage of GDP<br><!-- End of picture text -->

_Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

for power and 4 cubic meters per month for water—enough to meet the most basic needs—would cost $4.80 and $4.40 per month respectively and would be affordable for around 70 percent of the population.

39
33 TABLE 1 Qualitative <!-- Start of picture text --> General - Dependent Economy model<br>Equilibrium<br>Partial - Sectoral analysis<br>-<br>Equilibrium Poverty profiles<br><!-- End of picture text --> Quantitative

- SAM/CGE models

- - Cross-country analysis

- - Reduced-form econometrics - Optimizing models

- Sector specific models

- - Time series analysis

- Fiscal incidence studies

- - Macromodel output used as input for micro analysis

- - Microdata analysis
||(3.248)|
|---|---|
|Maternity leave|0.934**|
|(logs)*Number of workers||
|(logs)||
||(0.404)|
|Maternity leave|0.289|
|(logs)*Female owner present||
|Y:1 N:0||
||(0.639)|
|Maternity leave|-4.428|
|(logs)*Fertility rate||
||(2.786)|
|Maternity leave|-0.003|
|(logs)*Gender parity in||
|workplace laws||
||(0.048)|
|Maternity leave|28.696|
|(logs)*Gender parity in||
|primary education enrollment||
||(39.037)|
|Females: 20-29 yrs. old|0.798|
|(%)*GDP per capita (logs)||
||(0.908)|
|Females: 20-29 yrs. old|-0.001|
|(%)*Number of workers||
|(logs)||
||(0.236)|
|Females: 20-29 yrs. old|-0.436|
|(%)*Female owner present||
|Y:1 N:0||
||(0.433)|

63
allow us to investigate if migrant networks constitute an important source of market and ...rm-speci...c heterogeneity in entry costs and demand.

Portugal further provides an interesting setting for re-examining the e¤ect of common language/colonial ties on export participation and intensity. Portuguese-speaking nations belong to three di¤erent continents and di¤er markedly in terms of size. They are also at di¤erent stages of economic development. Brazil was the ...rst country to become independent in 1822. The other nations became independent in the wake of the Carnation Revolution of 1974. Table 1 reports the independence dates, o¢ cial languages, and number of Portuguese migrants in each of these countries. Table A.2 in the Appendix displays the distribution of Portuguese migrants across the full set of export destinations.

# 4 Data

Our empirical analysis draws on a rich ...rm-destination data set that combines information from several sources: Export ‡ows. Our main data source is the Foreign Trade Statistics (FTS) of Portugal for 2005. This is the country’s o¢ cial information source on international trade statistics, gathering the shipments of virtually all exporting ...rms to each destination market. The FTS data are collected in two di¤erent ways. Data on trade with countries outside the EU (external trade) are collected via the customs clearance system, which covers the universe of external trade transactions. Data on the transactions with other EU member states (internal trade) are obtained via the Intrastat system, where the information providers are companies engaged in internal trade and registered in the VAT system whose value of annual shipments exceeds a legally binding threshold (85,000 Euros in 2005). Export values in these data are "free on board", thus excluding any duties or shipping charges. The 2005 FTS data set comprises information on 16,541 exporting ...rms and 220 destination markets. Despite the above-mentioned constraint, the export transactions included in these data aggregate to 97 percent of the total value of merchandise exports reported in the o¢ cial national accounts.<sup>6</sup> Firm characteristics. Data on additional characteristics of exporting ...rms, namely labor productivity (gross value added per worker), employment, age and geographical location, come from 2005 Enterprise Integrated Accounts System (EIAS). This is a census of ...rms operating in Portugal run by the National Statistics Institute.<sup>7</sup> These data are

> 6 In the FTS data, Serbia, Montenegro, and Serbia and Montenegro are considered as three di¤erent destinations. We treat them as such in the analysis, but none of our results is in‡uenced by this decision.

> 7 In both the FTS and the EIAS, ...rms are uniquely identi...ed by their tax identi...cation number (NPC). Hence the mapping of the two data sets was straightforward. After merging the two datasets, the Statistics Institute applied a transformation of the NPC to the data that were made available to researchers in order

7
**Table 4: MFI Membership and Propensity to Borrow from Informal Sources**

|||**DID-FE**||**MDID-FE**<br>|**MBDID-FE**<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Control group: Never members**||||||
|**MFI member**|**-0.041**|**-0.044**|**-0.047**|**-0.043**|**-0.065**|
||**(2.64)*****|**(2.76)*****|**(2.97)*****|**(3.09)*****|**(2.57)*****|
|No. of Observations|1223|1223|1223|1223|1223|
|**Control group: Dropouts**||||||
|**MFI member**|**-0.059**|**-0.054**|**-0.053**|**-0.049**|**-0.083**|
||**(2.63)*****|**(2.39)****|**(2.34)****|**(1.82)***|**(2.20)*****|
|No.ofObservations|521|521|521|521|521|
|**Control group: Never members &**|**Dropouts**|||||
|**MFI member**|**-0.044**|**-0.046**|**-0.049**|**-0.044**|**-0.059**|
||**(3.27)*****|**(2.90)*****|**(3.09)*****|**(3.32)*****|**(2.81)*****|
|No.ofObservations|1365|1365|1365|1365|1365|
|Household Controls|No|Yes|Yes|Yes|Yes|
|Region Controls|No|No|Yes|Yes|Yes|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute t statistics in parentheses.

**Table 5: MFI membership and propensity to borrow from informal sources: Land-poor households** **<u>(Less than 1 acre of agricultural land)</u>**

||**DID-FE**||**MDID-FE**|**MBDID-FE**|
|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|
|**Control group: Never**<br>**members**|||||
|**MFI member**<br>**-0.045**|**-0.044**|**-0.050**|**-0.050**|**-0.061**|
|**(2.32)****|**(2.24)****|**(2.50)****|**(2.78)*****|**(1.70)***|
|No. of Observations<br>749|749|749|749|749|
|**Control group: Dropouts**|||||
|**MFI member**<br>**-0.078**<br>**(2.72)*****|**-0.074**<br>**(2.54)****|**-0.074**<br>**(2.55)****|**-0.074**<br>**(2.07)****|**-0.066**<br>**(1.39)**|
|No. of Observations<br>363|363|363|363|363|
|**Control group: Never members &**<br>**Dropouts**|||||
|**MFI member**<br>**-0.050**|**-0.051**|**-0.056**|**-0.053**|**-0.055**|
|**(2.59)*****|**(2.55)****|**(2.83)*****|**(3.1)*****|**(1.73)***|
|No.ofObservations<br>842|842|842|842|842|
|Household Controls<br>No|Yes|Yes|Yes|Yes|
|Region Controls<br>No|No|Yes|Yes|Yes|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute t statistics in parentheses

34
Argentina, Brazil, China, India, Indonesia, Malaysia, Mexico, Philippines, Republic of Korea, and Thailand.

**Table 7:** Cumulative Private Investment Commitments from 1990 to 2002 (2002-US$ billion) (*)

|**Region**|**Telecom**|**Electricity**|**Natural**<br>**Gas**|**Airports**|**Railways**|**Seaports**|**Toll-roads**|**W&S**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|East Asia & Pacific|56.2|68.3|6.8|2.8|10.3|11.2|26.8|17.0|199.4|
|Europe & Central Asia|68.1|21.1|11.3|1.5|0.3|1.8|2.6|3.5|110.2|
|Latin America and the<br>Caribbean|182.9|100.4|19.5|7.5|18.3|6.9|40.6|21.3|397.4|
|M. East and North Africa|10.6|8.4|3.9|0.9|0.2|1.2|-|1.3|26.5|
|South Asia|19.7|22.6|0.2|0.2|-|2.1|0.8|0.2|45.8|
|Sub-Saharan Africa|18.5|5.0|1.3|0.4|0.3|0.4|2.0|0.2|28.1|
|Total|355.9|225.7|43.0|13.2|30.3|22.6|72.8|43.6|807.4|

(*) Numbers might not exactly add up, due to rounding Source: World Bank, PPI Project Database

# **5. Progress on policy reform**

The 1990s saw an impressive array of reforms in the way the sectors were being managed ranging from corporatization to full divestiture. They also included many efforts to promote competition whenever possible and to create independent sector regulators when necessary. Reliable data on infrastructure policies are, however, no easier to come by than data on access and quality. Still, available indicators show that infrastructure policy and regulation have been improving—though much remains to be done. The picture varies enormously across regions, countries, and sectors.

A 1998 “scorecard” compared the electricity sectors in 115 countries by asking experts in each country whether a variety of reforms had been completed, including commercialization, restructuring, regulation, legal reform, and private investment. The experts were also asked to grade reforms on a scale of 1 to 5 (from low to high progress).

21
In the importing country, the consumer surplus increases but producer surplus decreases, the total welfare increased as the gain in c onsumer surplus is greater than the los s in producer surplus. However, in the exporting country, economic waste is created because the cost of increasing o utput to ex pand export exceeds th e revenue earned from the export. (If this is not the case, manufacturers would prod uce more even without the subsidy.) Consumers /households lose signifi cantly because (a) the consum er surplus declined, and (b) consumers have to bear the b urden of additional taxes that are required to finance the export subsidy. In the cas e where the country does not have the comparative advantage in a product which, for e xample, is resource-intensive, this will lead to f urther inefficiency due to m isallocation of resources and depletion of na tural resources.

In the cases where countries are relatively small and cannot influence world prices, the consequence would be different. Exporters are price takers in this case. Providing export subsidies will not influence the price in the world market, but, still, the consumer surplus increases but producer surplus has no change in im porting country; consum ers in exporting countries would have to bear the fiscal burden of export subsidies.

**Production Subsidies.** Now assume the exporting country provides a per-unit production subsidy on a tradable good. We assume that two large countries trade a particular product in a partial equilibrium model. The expor ting country is assum ed large enough to influence the world price in this particular product. Initially, consumption and production 0 0 0 0 in country A are q d , qs , and consumption and production in country B are q* d , q*s , respectively. After the expor ting country introduced a pe r-unit production subsidy, the domestic supply curve will shift down to the right from S to S’ by the amount of subsidy, reflecting the lower costs the industry now faces. This shif t increases the export and drives down the prices in both expor ting and importing countries from P w0 to P w1.  See Figure 2.

Figure 2. Partial equilibrium model of a production subsidy with trade <!-- Start of picture text --> Country A (exporter) World Country B (importer)<br>WS 0 S*<br>S 0<br>0 WS 1<br>Pw S<br>Pw1 S 1<br>D*<br>WD0<br>D<br>qd0 qd1 qs0 qs1 X 0 X 1 q*s1 q*s0 q*d0 q*d1<br><!-- End of picture text --> Source: Kathy Baylis, 2005.

10
# Table 4: Welfare indicators

||(1)<br>Food<br>consumption<br>score<br>(food security)|(2)<br># household<br>durable goods<br>owned|(3)<br>Education<br>expenditures|(4)<br>Psychological<br>well being<br>index<br>(z-score)|
|---|---|---|---|---|
|Panel A. Pooled Estimates|||||
|Treatment (ITT)|0.79|0.03|-5388.59|0.07|
||(0.99)|(0.17)|(3410.01)|(0.06)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|52.57|3.69|48798.88|-0.00|
|Observations|2,618|2,618|2,617|2,618|
|Panel B. Treatment Arm Estimates|||||
|VSLA (T1) (ITT)|0.22|0.04|-2669.95|0.03|
||(1.16)|(0.20)|(3816.59)|(0.07)|
|Cash Grant with repayment (T2) (ITT)|1.01|-0.05|-6082.24|0.07|
||(1.19)|(0.20)|(3974.22)|(0.06)|
|Cash Grant (T3) (ITT)|1.48|0.17|-9409.15**|0.14|
||(1.70)|(0.25)|(4559.82)|(0.10)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|52.57|3.69|48798.88|-0.00|
|p-value T1=T2|0.50|0.67|0.33|0.50|
|p-value T2=T3|0.79|0.39|0.43|0.46|
|p-value T1=T3|0.47|0.62|0.11|0.26|
|p-value T1=T2=T3|0.69|0.69|0.26|0.50|
|Observations|2,618|2,618|2,617|2,618|

Robust standard errors clustered at locality level. Expenditures are in CFA franc. Expenditures and number of goods are winsorized at 99%.

The _Food consumption score_ (FCS) provides a measure of food security that captures the dietary diversity. It is commonly used by humanitarian organizations such as the World Food Programme.

The _Psychological well-being index_ includes the CESD Positive Affect scale, the Rosenberg self-esteem scale and a self-assessment of life satisfaction based on a Cantril ladder.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01

40