rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: The Impact of violent conflict on labor market and livelihood activities**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Any activity|Farm activity|Non-farm<br>business|Wage related<br>activities|
|Round 2|0.049<sup>**</sup><br>(0.021)|0.041<sup>**</sup><br>(0.019)|0.057<sup>***</sup><br>(0.020)|0.067<sup>***</sup><br>(0.023)|
|Round 3|0.053<sup>**</sup>|0.068<sup>***</sup>|0.033<sup>*</sup>|0.049<sup>**</sup>|
||(0.025)|(0.018)|(0.019)|(0.023)|
|Round 4|0.087<sup>***</sup>|0.084<sup>***</sup>|0.022|0.045<sup>**</sup>|
||(0.021)|(0.018)|(0.020)|(0.022)|
|Round 6|0.081<sup>***</sup>|0.062<sup>***</sup>|0.008|0.057<sup>***</sup>|
||(0.020)|(0.017)|(0.017)|(0.019)|
|Round 7|0.086<sup>***</sup>|0.055<sup>***</sup>|0.016|0.063<sup>***</sup>|
||(0.020)|(0.015)|(0.019)|(0.017)|
|Round 8|0.080<sup>***</sup>|0.041<sup>**</sup>|0.011|0.077<sup>***</sup>|
||(0.021)|(0.017)|(0.018)|(0.022)|
|Round 9|0.071<sup>***</sup>|0.036<sup>**</sup>|0.022|0.070<sup>***</sup>|
||(0.023)|(0.017)|(0.017)|(0.021)|
|Round 10|0.083<sup>***</sup>|0.034<sup>**</sup>|0.025|0.072<sup>***</sup>|
||(0.021)|(0.017)|(0.017)|(0.021)|
|Round 2 × Tigray|-0.294<sup>***</sup>|-0.219<sup>***</sup>|-0.184<sup>***</sup>|-0.102|
||(0.083)|(0.072)|(0.060)|(0.075)|
|Round 3 × Tigray|-0.094|-0.047|-0.170<sup>***</sup>|-0.031|
||(0.058)|(0.030)|(0.062)|(0.082)|
|Round 4 × Tigray|-0.112<sup>*</sup>|-0.073<sup>**</sup>|-0.112|-0.038|
||(0.062)|(0.034)|(0.079)|(0.080)|
|Round 6 × Tigray|-0.066|-0.005|-0.058|-0.066|
||(0.047)|(0.028)|(0.080)|(0.080)|
|Round 7 × Tigray|-0.060|0.002|-0.115<sup>*</sup>|-0.053|
||(0.047)|(0.028)|(0.059)|(0.074)|
|Round 8 × Tigray|-0.079|-0.020|-0.180<sup>***</sup>|-0.026|
||(0.058)|(0.034)|(0.060)|(0.078)|
|Round 9 × Tigray|-0.197<sup>***</sup>|0.016|-0.200<sup>***</sup>|-0.193<sup>***</sup>|
||(0.062)|(0.028)|(0.052)|(0.067)|
|Round 10 × Tigray|-0.210<sup>***</sup>|0.044|-0.219<sup>***</sup>|-0.202<sup>***</sup>|
||(0.070)|(0.034)|(0.056)|(0.062)|
|Household fixed effect|Yes|Yes|Yes|Yes|
|R-squared|0.001|0.003|0.002|0.003|
|Mean dep. var (pre-war)|0.801|0.313|0.241|0.417|
|No. observations|21940|28558|28558|21940|

Notes: The outcome variable in the first two columns is a dummy variable for participation in any economic activity. The variables in columns 3-8 are binary indicators assuming a value of 1 for those households participating in these activities. All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

33
<!-- Start of picture text --> The figure is constructed based on data for establishments Census definition of formality slightly differs across the In Ghana, formal establishments are defined as those<br>Note:<br>In Burkina Faso, an establishment is considered formal it has a registration number (with mobile trade and mobile credit, tax In Cameroon, formal establishments are the ones that prepare a Statistical and Fiscal Declaration In Rwanda, an establishment is considered to operate in the formal sector<br>Establishments with missing employment data and state-owned are excluded.<br> Establishment censuses obtained from the statistical agencies of the select countries; see section 3.<br>Source: in the manufacturing sector. statistical offices of the sample countries. identification number) and uses SYSCOA as an accounting standard. (DSF) or those that do not prepare DSF but keep an operating account and a partial account of balance sheets. that are registered with the Registrar General’s Department (RGD) and keep formal accounts. if it is registered at Rwanda Revenue Authority (RRA) and maintains regular operational accounts.<br>Census definition Census definition Census definition Census definition<br>Employment share in informal establishments<br>Keep formal account Keep formal account Keep formal account<br>Keep formal(SYSCOA) or regular account<br>Employment share in formal establishments<br>Tax registeration Tax registeration Tax registeration<br>Keep formal account (SYSCOA)<br>100+ 100+ 100+ 100+<br>20 − 99 20 − 99 20 − 99 20 − 99<br>10 − 19 10 − 19 10 − 19 10 − 19<br>5 − 9 5 − 9 5 − 9 5 − 9<br>1 − 4 1 − 4 1 − 4 1 − 4<br>100+ 100+ 100+ 100+<br>20 − 99 20 − 99 20 − 99 20 − 99<br>10 − 19 10 − 19 10 − 19 10 − 19<br>5 − 9 5 − 9 5 − 9 5 − 9<br>1 − 4 1 − 4 1 − 4 1 − 4<br>100+ 100+ 100+ 100+<br>20 − 99 20 − 99 20 − 99 20 − 99<br>10 − 19 10 − 19 10 − 19 10 − 19<br>5 − 9 5 − 9 5 − 9 5 − 9<br>1 − 4 1 − 4 1 − 4 1 − 4<br>50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0<br>50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0<br>50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0 50 40 30 20 10 0<br>Burkina Faso (2015) Cameroon (2008) Ghana (2013) Rwanda (2013)<br><!-- End of picture text --> 19
Table 5: Comparison of business environment procedures (days) in lagging and non‐lagging regions

||Spain|Italy|Poland|Romania|
|---|---|---|---|---|
|Starting a<br>avg. in non‐lagging|16.4|6.6|28.2|13|
|business<br>avg. in lagging|14.8|9.8|30.0|17.5|
|difference|‐1.55|3.15*|1.78|4.5|
|diff. t‐stat|[‐1.388]|[2.179]|[0.417]|[1.407]|
|Obtaining a<br>avg. in non‐lagging|169.8|195.4|179.9|274|
|construction<br> <br>avg. in lagging|187.3|252.5|168.8|252.3|
|permit<br>difference|17.526|57.1**|‐11.06|‐21.5|
|diff. t‐stat|[0.492]|[2.522]|[‐0.935]|[‐0.739]|
|Contract<br> <br>avg. in non‐lagging||1273.5|534.6|552|
|enforcement<br>avg. in lagging||1479.5|481.4|539.8|
|difference||206.04|‐53.24|‐12.2|
|<br>diff. t‐stat||[1.422]|[‐1.022]|[‐0.188]|

_Note: Authors’ calculations based on data from Subnational Doing Business_

# 5. Does the regional business environment impact firm performance?

In this section we step away from the focus on lagging and non‐lagging regions and simply test whether there is an association between the regional business environment and the performance of firms. We conduct OLS estimates of the impact of each business environment indicators on five firm performance indicators. Provided that the measure is homogenous across countries, we pool all firms in the four countries and investigate the relationship between the business environment and firms’ performance, regardless of the lagging features.

The dependent variables are calculated as the three‐year average. The explanatory variables include the three SDB indicators and the aggregated DTF, which we present here again in an inverse form for ease of interpretation. We also include a measure of the level of regional market competition, proxied by the Herfindahl‐Hirschman Index (HHI). It is calculated by summing up the squares of the market share of each firm competing in a sector and country. HHI ranges from zero (highest competition) to 10,000 (lowest competition). HHI captures another dimension of the business environment of firms; it is determined by the ease to conduct business and the costs of entry‐exit. Notice that HHI varies substantially across sectors and countries. In order to avoid problems of multicollinearity, the SDB and HHI variables are introduced into the regression separately. All models include country, year, sector, and firm controls.<sup>19</sup> Table 6 reports the results. Starting with the overall (DTF) variable, we find a strong and significant association with all the main performance variables. The relationship is particularly large on the investment rate, but is also strong on sales growth, employment growth, and profits. It is smaller, but still significant, with productivity. The individual indicators for starting a business and construction permits are also significant across the board, with the impact relatively higher for starting a business. This suggests that while the delays to starting a business may confer some benefits to individual incumbents, in terms of capturing market growth and exploiting profit potential, the aggregate effect

> 19 Notice that the main results are unchanged should all SDB variables enter the equations jointly.

14
**<u>Table 6. Impact of a one dollar increase in per capita woreda block grants on service outcomes</u>**

|Indicators|National<br>Pooled OLS|Bottom 20%<br>–Pooled OLS|
|---|---|---|
||Coeff(t-ratio)|Coeff(t-ratio)|
|Net enrollment rate 1-8|0.3833***|0.4225***|
||(12.84)|(6.34)|
|Nt llt t 58|0.6630***|0.6413**|
|e enromen rae -|(17.71)|(5.65)|
|Ctti t t|0.0281|-0.2482**|
|onracepve accepance rae|(0.79)|(-2.37)|
|Ctti t t ld|0.1321***|-0.2580**|
|onracepve accepance rae (agge one year)|(3.11)|(-2.29)|
|Al|0.0776**|-0.0878|
|ntenata care|(2.56)|(-1.47)|
|Pti f bith ttdd b SBA|0.2533***|0.4732***|
|roporon o rs aene y s|(4.00 )|(2.65)|

Note: *** p<0.01, ** p<0.05, * p<0.1 33
85 Kerosene and heavy oil fractions are sometimes added to diesel as fuel extenders, but they impair the quality of diesel fuel.

Other fuel considerations for diesel engines center around fuel purity and its relationship to diesel particulate control. Impurities in diesel fuel are a source of concern, particularly metals like chromium, because of the potential for direct emission in the exhaust. Fuel additives may also be important with respect to diesel particulate control. A number of fuel additives, mostly derivatives of barium, calcium or managanese that act as catalysts to convert carbon particles to CO2, have been used to suppress smoke emissions. A class of trap oxidizer systems includes self regeneration by means of metallic fuel additives. Such additives could be a source of unregulated pollutants. Environmentally benign materials such as cerium are less problematic in this respect than metals such as lead or copper.

# **Alternative Fuels**

Petroleum supply disruptions and cost increases in the 1970s and early 1980s accelerated interest in alternative fuels for motor vehicles. The need for these fuels became a high priority for many countries and investigations were initiated into possible use of coal, oil shale, natural gas, uranium and biomass. As oil prices dropped, interest in these alternatives from an energy conservation standpoint dwindled. From an environmental standpoint, alternative fuels hold promise for solving the diesel particulate problem (especially in city buses), reducing the overall toxic emissions problem from vehicles, and helping to reduce urban CO and ozone levels. A comprehensive technological and environmental assessment of alternative fuels for road transport is provided in IEA [1990].

# **Compressed Natural Gas (CNG)**

It has been estimated that the use of CNG would reduce HC emissions by 40% in the exhaust and virtually eliminate HC emissions through evaporation; at the same time CO emissions would be reduced by about 50%. NOx emissions, however, could increase by about 40% [U.S. EPA 1988]. In the aggregate, U.S. EPA estimates CNG would substantially lower tropospheric ozone (the HC reductions more than offsetting the NOx increases) and CO, both of which are greenhouse gases. Recent studies indicate that the NOx increase may not be as high as previously estimated [Bruestch 1988; IEA 1990]. It seems likely that further optimization of emission controls will enable CNG vehicles to achieve low levels of CO, HC and NOx as well as toxic pollutants. However, it should be noted that the use of CNG has certain adverse safety and vehicle performance implications that need to be carefully evaluated. These and other technological aspects of alternative transport fuels from natural gas have been assessed by Moreno and Bailey [1989].
recovery rates and there was no clear trend of improvement for these utilities. A second group (GEPCO, IESCO, LESCO, FESCO and MEPCO) reported high or almost perfect recovery rates.

The privatized utility (KE) showed a billing loss of about 10 percent throughout the period with no trend improvement, unlike its experience with T&D losses during the same period. This was worse than the higher performing group of DISCOs and does not directly support the argument that privatization leads to improved performance.

**Table 12: Recovery  percentages for DISCOs 2008 – 2016 ( percent)**

||**PESCO**|**IESCO**|**GEPCO**|**LESCO**|**FESCO**|**MEPCO**|**HESCO**|**SEPCO**|**QESCO**|**KE**|
|---|---|---|---|---|---|---|---|---|---|---|
|**2017**|89|92|96|99|97|96|94|110|44|90|
|**2016**|88|91|99|99|100|100|72|55|72|88|
|**2015**|88|100|97|96|100|102|78|58|33|90|
|**2014**|86|120|96|98|100|96|79|59|42|91|
|**2013**|84|94|98|98|99|92|81|54|32|89|
|**2012**|83|96|99|96|100|97|69|51|36|91|
|**2011**|82|94|99|98|100|98|59|-|41|86|
|**2010**|85|96|96|93|97|94|60|-|76|100|
|**2009**|87|97|95|96|97|96|68|-|80|100|
|**2008**|92|98|98|98|99|97|77|-|86|-|

Source: State of Industry Reports, various years.

These findings lend weight to the argument that unbundling alone is not likely to improve performance, but it would be desirable to examine other indicators of performance to see whether they present a similar picture. NEPRA collected data on nine indicators for its performance evaluations. T&D losses and recovery rates have been described above. For SAIDI and SAIFI, NEPRA commented in its 2015-2016 report<sup>38</sup> the data appeared so inaccurate for these measurements that it was excluding these indicators from its overall assessment score for each of the DISCOs. The overall indicator did include the average time to make new connections, and the number of fatal accidents, but omitted the average duration of load shedding, the number of complaints, and the system faults occurring within the distribution system. It is notable that on the method of scoring adopted, KE came fifth out of ten. Although this calculation was carried out for a single year, and full details of the calculation of the scores used for ranking were not provided, this result does not speak strongly to the superiority of private sector ownership versus state ownership.

A further level of analysis can be provided for LESCO and KE the two utilities whose performance was examined in greater detail. In particular, the T&D losses in excess of an efficiency benchmark of 5 percent and collection losses for these two utilities are quantified in monetary terms and expressed as a percentage of utility revenues, allowing their financial magnitude to be compared. Data for LESCO are limited to only six years (2009-2015), however throughout this period the

> 38 Performance Evaluation Report of all Distribution Companies for the Year 2015-2016. NEPRA.

37
Aggregating urban population projections with data on the spatial distribution of cyclones and earthquakes described earlier shows that population exposure to those hazards is likely to more than double by 2050 (Figure 10 and Figure 11). The largest urban cyclone exposure is expected to be in South Asia while the largest earthquake exposure will be found in East Asia and the Pacific. Figure 12 shows the data for individual cities in map form.

**Figure 10: Population in large cities exposed to cyclones (1970-2050)**

<!-- Start of picture text --> 800<br>700<br>OHIE<br>600<br>OECD<br>500 SSA<br>400 SAS<br>MNA<br>300<br>LAC<br>200<br>ECA<br>100 EAP<br>0<br>1970 1980 1990 2000 2010 2020 2030 2040 2050<br><!-- End of picture text --> Note: OHIE=Other high income economies, OECD=Organization for Economic Cooperation and Development, SSA=Sub-Saharan Africa, SAS=South Asia, MNA=Middle East and North Africa, LAC=Latin America and the Caribbean, ECA=Europe and Central Asia, EAP=East Asia and the Pacific.

**Figure 11: Population in large cities exposed to earthquakes (1970-2050)**

<!-- Start of picture text --> 1000<br>900<br>800 OHIE<br>700 OECD<br>600 SSA<br>500 SAS<br>400 MNA<br>300 LAC<br>200 ECA<br>100 EAP<br>0<br>1970 1980 1990 2000 2010 2020 2030 2040 2050<br><!-- End of picture text --> 36
Table 2: Growth of Agricultural Production and Population, 1970-91.

|Country|19|70-91|
|---|---|---|
||Agriculture|Population|
|Pakistan|3.2|3.1|
|Indonesia|3.6|2.1|
|India|2.5|2.2|
|Egypt|2.6|2.3|

Source: World Development Report, 1993.

The concern about sustainability arises because growth has generally come from extensive, not intensive, agriculture. One way to illustrate this is to decompose the growth in output of major crops into area and yield effects. An increase in output can be broken into (1) expansion in area cropped at the old yield level, (2) an increase in yields on the old cropped area, and (3), a cross-produc **t** erm representing the increase in yields on the expansion in cropped area. Such a decomposition is presented by Mehmood et al (1992) for the major crops in Pakistan for selected periods (Table 3).

Table 3: Decomposition of Crop Growth into Area and Yield Effects for Selected Periods

|||Whea|t||Rice|||Cotto|n|
|---|---|---|---|---|---|---|---|---|---|
|Period|Area|Yield|Multiple|Area|Yield|Multiple|Area|Yield|Multiple|
|1961-67|111.4|<br>-9.9|<br>-1.5|<br>44.5|46.2|<br>9.3|<br>45.8|39.9|<br>14.3|
|1967-76|<br>13.7|<br>75.7|<br>10.6|<br>18.6|<br>73.6|<br>7.8|<br>1.7|<br>91.2|<br>7.1|
|1976-89|40.0|47.3|12.7|78.5|_16.5_|<br>_5.0_|19.1|58.4|<br>22.5|

Note: The decomposition is constructed as follows. Let Q be output, A be area, and Y be yield. Then identically, Q _ A.Y. The change in output between any two years t and u, t'u, is AQ a Qt - Q. and can be broken down as AQ = YuA + AuY+AYAA. The effects sum to 100 for each crop in each period. As an example, for wheat in the period 1961-67, area expansion at old yield levels account for more than total output growth. This is because yields actually fell, subtracting from the contribution of area expansion.

The three periods are chosen to capture the sources of growth before, during, and after the Green Revolution. The striking finding is the importance of area effects before and after the Green Revolution for wheat and rice; not surprisingly, during the Green Revolution, yield effects predominate. Following the Green Revolution, yield effects have remained important for cotton, for reasons explained earlier, and for wheat. The continued reliance on area expansion for growth in rice and to some extent in wheat indicates that technological progress has been slow in recent years in these key crops, a point on which we expand further in the next section.

5
In Table 4 we first regress _Access to loan_ on _CB reform_ and find that the introduction of a CB is associated with an increase in access to finance. These regressions are estimated using a linear probability model (LPM) and standard errors are clustered at the survey level. Specifically, controlling for country and year fixed effects,<sup>23</sup> in the years after which a CB is introduced in the country, there is a 6 percentage point increase in the average firm’s access to a loan in the overall sample. We find similar point estimates in the MSME and SME sample. The association between CB reform and access to finance is not statistically significant in the large firm sample as seen in column 4 of Table 4. While we expect MSMEs and SMEs, which are potentially the most financially constrained firms in an economy, to benefit the most from reforms that improve the credit information environment in the country, we are not able to reject the null hypothesis for the equality of the estimates for MSMEs and SMEs against large firms.

Having established the effect of CB reform on access to finance, we next re-estimate equation (2) but replace _Access to loan_ with the more exogenous _CB reform_ . Using the Enterprise Survey data, Table 5 shows that the introduction of CBs leads to 1.28 percentage point increase in employment growth for the overall sample. MSMEs and SMEs again respond more to the supply shock (1.34% and 1.79% increase in employment growth respectively), while the larger firms have a lower increase in employment growth (1.11%) that is not statistically significant. Statistically, we confirm using t-tests that the estimates for MSMEs and SMEs are higher than those for large firms at the 99% confidence level.

In Table 6, we confirm the impact of CB reform on employment growth using the ORBIS sample but the magnitudes we find are larger. Introduction of CBs leads to an over 5 percentage point increase in employment growth both for the overall sample (5.29%) and for MSMEs

> 23 We are unable to use survey (country-year variation) fixed effects as in Table 3, because CB reform is a countryyear variable.

22
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- ———. 2009e. ―Niger Enterprise Surveys 2009‖. Washington, DC: World Bank. http://www.enterprisesurveys.org/ExploreEconomies/?economyid=142&year=2009.

- ———. 2010a. ―Getting Electricity: A Pilot Indicator Set from the Doing Business Project‖ _._ Washington, DC: World Bank. http://doingbusiness.org/data/exploretopics/getting-electricity.

- ———. 2010b. ―Niger: Modernizing Trade during a Mining Boom Diagnostic Trade Integration Study for the Integrated Framework Program.‖ Poverty Reduction and Economic Management. Africa Region, World Bank, Washington, DC.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Enhanced Integrated Framework. 2008. ―Niger: Modernizing Trade during a Mining Boom.‖ Diagnostic Trade Integration Study for the Integrated Framework Program, February 2008. Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- Teravaninthorn, Supee and Gael Raballand. 2009. ―Transport Prices and Costs in Africa: A Review of the International Corridors.‖ Africa Region, World Bank, Washington, DC.

- World Bank. 2008c. _Project Appraisal Document on a Proposed Grant in the Amount of SDR 18.8 Million ($30 Million Equivalent) to the Republic of Niger for a Transport Sector Program Support Project_ . Report No: 43162-NE, World Bank, Washington, DC, April 3.

# Water supply and sanitation

Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2008a.―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, Africa Region, World Bank, Washington, DC.

47