rafmacalaba/datause-encoder-data huggingface dataset:
value for this ratio means a lower share of investment in inventories that can be financed by ongoing revenue, suggesting a higher level of liquidity needs that have to be financed externally. The reason for focusing on inventories rather than the overall investment in working capital is to capture the technologically driven exogenous part of liquidity needs. The completion of final goods take longer in certain industries, requiring higher inventory values during the production process. This technological link is arguably weaker for other parts of liquid assets like cash (Raddatz 2006, p. 685).

Following Claessens and Laeven (2003), we measure _Asset intangibility_ as the ratio of the net value of intangible assets (Compustat item 33) to the net fixed assets (Compustat item 8).<sup>11</sup> In Compustat, the intangibles include the value of blueprints or building designs, patents, copyrights, trademarks, franchises, organization costs, client lists, computer software patent costs, licenses, and goodwill. The ratio thus measures for a representative firm in a given industry the relative importance of assets that are less suitable to serve as collateral due to their lack of physical manifestation as opposed to the easily collateralizable fixed assets.

The proxies for our two additional transmission channels also rely on Compustat data on large US firms. Following Fisman and Love (2003), we compute _Trade credit dependency_ as the ratio of accounts payable (Compustat item 70) to total assets (Compustat item 6). The ratio represents the share of total assets that is financed by trade credit, capturing the ability of a representative firm in a given industry to rely on informal credit from its business partners rather than on formal financing from financial markets and intermediaries. We measure _Global growth opportunities_ by the actual growth in real sales for the representative firm in a given industry in the US. This proxy is based on the argument outlined in Fisman and Love (2007) that large publicly traded US firms respond optimally to worldwide industry-specific shocks to growth opportunities, making the actual growth rate of these firms a good proxy for these growth opportunities.

Table 1 shows that all five measures are only weakly and often negatively correlated with each other. The measures thus seem to represent genuinely distinct transmission

> 11 The net fixed assets equal gross property, plan and equipment (Compustat item 7) minus accumulated depreciation, depletion and amortization (Compustat item 196).

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<!-- Start of picture text --> 2000| etal hn 0 2 80000 | Wi o 8000<br>26N 26 N<br>25N 25N<br>24N 24.N<br>23.N C aw* ' 23.N =a ;<br>2 eat<br>22.N ; yA } 22.N b. af.) \<br>Vy “;<br>4<br>“<br>21.N { 21N \<br>89E 9E ME 92E 89 E 90E 91E 92E<br>Arsenic (ug/L) Salinity (as EC in uS/cm)<br>0 100 200 300 400 500 600 700 800 © — 5000 10000 15000 20000 25000 30000<br>15<br>oO<br>Q<br>26 N t :<br>25.N a<br>J<br>24.N<br>23 N 4 é<br>22.N yt, es \<br>Vy<br>21N<br>89E 90E 91E 92E<br>Groundwater depth (m bgl)<br>0 10 20 30 40 50<br><!-- End of picture text -->
# **Figure 12: Scatter plot and average adult equivalent consumption per month in**

# **Manat and factor score, rural administrative units**

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-3 -2 -1 0 1<br>meanadm<br>500000<br>400000<br>mean<br>300000<br>200000<br>100000<br><!-- End of picture text -->

Note: Number of observation=876, Spearman’s rho=0.132 ,

**Figure 13: Scatter plot and average adult equivalent consumption per month in Manat and**

# **factor score, urban administrative units**

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-2 -1 0 1 2 3<br>meanadm<br>500000<br>400000<br>mean<br>300000<br>200000<br>100000<br><!-- End of picture text -->

Note: Number of observation=140, Spearman’s rho=0.1752 26
Cc. i <!-- Start of picture text --> B<br><!-- End of picture text --> B <!-- Start of picture text --> Bp<br><!-- End of picture text --> Bp Ki = exp(—fic,,) <!-- Start of picture text --> K,=(+e, y?<br><!-- End of picture text --> K,=(+e, y? o <!-- Start of picture text --> C5 log” +c,) log” +c,) +c,)<br><!-- End of picture text --> <!-- Start of picture text --> Beo<br><!-- End of picture text --> C5 log” +c,) log” +c,) +c,) Beo <!-- Start of picture text --> Log(X,,) = 4,Log(GDP) + a,Log(GDP) = 4,Log(GDP) + a,Log(GDP) 4,Log(GDP) + a,Log(GDP) + a,Log(GDP) a,Log(GDP)<br>SS _ i i<br>I<br>+ bLog(GDPpercap,) + b,Log(Log(GDPpercap,) + b,Log( + b,Log( b,Log( GDPpercap ,,) + c,domestic efficiency variables for i + c,domestic efficiency variables for i c,domestic efficiency variables for i efficiency variables for i variables for i for i i<br>+iO fj<br>tt Sos ro’<br>Ht<br>+d, Log(distance,,) + d,other separation variables + d,bilateral dummies Log(distance,,) + d,other separation variables + d,bilateral dummies + d,other separation variables + d,bilateral dummies d,other separation variables + d,bilateral dummies separation variables + d,bilateral dummies variables + d,bilateral dummies + d,bilateral dummies d,bilateral dummiesbilateral dummies dummies<br>lH YS q_“ ue oq_9~$soewrnN’<br>if<br><!-- End of picture text --> Log(X,,) = 4,Log(GDP) + a,Log(GDP) = 4,Log(GDP) + a,Log(GDP) 4,Log(GDP) + a,Log(GDP) + a,Log(GDP) a,Log(GDP) SS _ i i I + bLog(GDPpercap,) + b,Log(Log(GDPpercap,) + b,Log( + b,Log( b,Log( GDPpercap ,,) + c,domestic efficiency variables for i + c,domestic efficiency variables for i c,domestic efficiency variables for i efficiency variables for i variables for i for i i +iO fj tt Sos ro’ Ht +d, Log(distance,,) + d,other separation variables + d,bilateral dummies Log(distance,,) + d,other separation variables + d,bilateral dummies + d,other separation variables + d,bilateral dummies d,other separation variables + d,bilateral dummies separation variables + d,bilateral dummies variables + d,bilateral dummies + d,bilateral dummies d,bilateral dummiesbilateral dummies dummies lH YS q_“ ue oq_9~$soewrnN’ if
Policy makers, policy analysts, researchers, utilities, and other practitioners in the power sector in SSA are the primary audience of this work. It is also intended to provide insights to inform international financial institutions, including the World Bank, in their policy dialogue with governments in the region, and well as inform sector dialogue and project choices at a strategic level.

Cash collected by utilities might fail to cover operating expenditures (opex), let alone costs that should be incurred (for capacity maintenance, upgrade, and expansion). It is important to note there are several levels of financial viability, while the term is often used in a general sense without recognition of the different levels. Table 1 presents a simplified taxonomy, referencing the basic components of capital expenditure (capex) and opex. Increasing levels of financial viability move closer to costs based on commercial principles where utilities pay all applicable taxes and market-based interest rates, dedicate adequate resources to operations and maintenance, and earn commercially competitive returns on equity capital.

**Table 1: Utility financial viability ladder: a simplified taxonomy**

|**Level of financial viability**|**Comment**|
|---|---|
|Level 1: Not coveringexistingopex.|Financiallyunviable,loss-makingutility.|
|Level 2: Utilitycovers at least existingopex.|Utilitydependent ongovernment for capital investments.|
|Level 3: Utility covers existing opex plus concessional<br>financing costs on new replacement value of existing<br>assets.|Utility dependent on access to concessional financing.|
|**Level 4: Utility covers existing opex and full capex on**<br>**new replacement value of existing assets.**|**Base-case definition used in this study, using a 10-**<br>**percent real discount rate for capex.**|
|Level 5: Utility covering efficient opex and full capex<br>on new replacement value of existing and future<br>assets.|Future assets based on a least-cost expansion plan.|
|Level 6: Utility covers efficient opex and full capex on<br>new replacement value of existing and future assets<br>plus environmental externalities.|Definition of financial viability that may be used in high-<br>income economies.|

_Source_ : World Bank staff.

Many utilities operate at level 1 where they depend on subsidies to operate, either through ongoing subsidies year to year, or through large lump sum bailout transfers and or debt rescheduling / forgiveness. Utilities operating at level 1 may also avoid costs, with maintenance costs often one of the first to be cut. The first step for most utilities in SSA therefore is to achieve cost recovery levels 2 or 3 as a minimum. However, recognizing substantial investments required to meet demand as well as reach universal access, and the extremely limited availability of concessional financing to meet investment needs, this study uses level 4 as the base-case level for QFD estimations.

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**Figure 3: The competitiveness of local government elections**

<!-- Start of picture text --> Voter turnout in national and local elections Percentage of uncontested seats in union council elections<br>60 45<br>2001<br>2005 40<br>50<br>35<br>1993 2002<br>1997 30<br>40<br>25<br>30 20<br>15<br>20<br>10<br>10 5<br>0<br>0 Punjab Sindh NWFP Balochistan<br>National Assembly elections Local Government elections 2001 2005<br><!-- End of picture text --> _Source_ : DTCE (2005) key local policy-makers. The re-election of an incumbent district or tehsil nazim is dependent not on voters but on a few hundred union councilors and therefore, downward accountability is dependent on the degree to which union councilors act as agents of the public, and the extent to which nazims have an incentive to listen to councilors.

<!-- Start of picture text --> Figure 4: Incumbency disadvantage in local<br>elections<br>Incumbent winning percentage<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Punjab Sindh NWFP Baloch. Pakistan<br>PA elections 1990 PA elections 1993 PA elections 1997 LG elections 2005<br><!-- End of picture text --> One uniform way that district nazims have responded across districts to this dependence on union councilors is by allocating some portion of the district development budget to each individual union nazim to be used at his or her discretion. This is the equivalent of the MPA and MNA funds that have been constituted at the provincial and national levels respectively. However, the district nazim’s relatively greater independent political standing — they are usually from established political families — also implies that the future electoral fortunes of union councilors are as _Source_ : Govt. of Punjab (2007) much linked to their endorsement by the nazim. There are many examples of district nazims campaigning on behalf of union councilors in order to secure his own future re-election from a pliant electoral college (World Bank 2004).

An interesting feature of local government elections is the relatively high degree of incumbency disadvantage of district nazims. As Figure 4 shows, only 24% of the incumbent district nazims were re-elected in the 2005 across Pakistan, as compared to 38% of members of provincial assemblies in 1997.<sup>5</sup> Across the provinces, only in Punjab were district nazims and MPAs equally disadvantaged, with 35% getting re-elected. The incumbency disadvantage of nazims was particularly acute in NWFP and Balochistan, with only 13% and 11% of nazims getting reelected respectively.

A district nazim’s chances of getting re-elected also appears to be unrelated to citizens’ satisfaction with basic services, or views about whether or not these services had improved, as indicated in the household survey data. As Table 1 shows, there is no association between citizen

> 5 The 2002 provincial assemblies were not considered due to the complications caused by the intervening period of military rule.

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from the community survey conducted in each rural district, and the time frame is one year. If there is evidence of a covariate shock in the community survey, we apply it to all households within that rural district. By defining shocks in this fashion, we avoid the reporting problems associated with the apprehension households feel at being identified as victims purposive targeted by armed groups.

Because of the low prevalence of idiosyncratic shocks, as well as the potential report problem, we focus on covariate shocks. Table 4 displays the prevalence of covariate shocks in the sample, both in terms of the percentage of rural districts, as well as the percentage of households in our sample that belong to those districts and hence are affected by the shocks. The most prevalent type of shock is cattle theft—which affects 25 percent of rural districts and 31 percent of households—followed by murder and threats by armed groups. In addition to being a consequence of the violent conflict, cattle theft constitutes a direct economic shock. Land expropriations and kidnapping are less frequent.

# [Table 4 goes about here]

The correlation of shocks within the same municipality or region determines how concentrated they are, and how likely it is for nearby or neighboring rural districts to be affected by covariate shocks. The intra-class correlation of shocks within municipalities is 0.27; within regions it is 0.19. This implies that shocks are to some extent correlated, yet the geographic spreading of conflict activities may be more complex.

Idiosyncratic and covariate conflict shocks are not random. Violent shocks are presumably directed against municipalities and households with particular characteristics. Table 5 shows the differences in terms of who is targeted by violent shocks by status based on household income; also disaggregated based on the source of income: whether derived from agricultural or livestock production, or generated in the labor market. Households affected by violent shocks are significantly wealthier than unaffected households. In particular, households affected by violent shocks report having over 1.4 times the total income and income derived from livestock production than those not affected by shocks.

As discussed earlier, labor markets may constitute an alternative for hedging against _exante_ and _ex-post_ risks if labor markets are flexible and non-agricultural wages have a 19
WPS3795 with regard to the comparative advantages of large versus small institutions, foreign- versus domesticallyowned institutions, state- versus privately-owned institutions, and market competition.

# **A. Large versus small institutions**

For a number of reasons, large institutions may have a comparative advantage in transactions lending and small institutions may have an advantage in relationship lending. Large institutions may be able to take advantage of economies of scale in the processing of hard information, but be relatively poor at processing soft information because it is difficult to quantify and transmit through the communication channels of large organizations (e.g., Stein 2002). Under relationship lending, there may be agency problems created within the financial institution because the loan officer that has direct contact over time with the SME is the repository of soft information that cannot be easily communicated within the financial institution. This may give a comparative advantage in relationship lending to small institutions with fewer layers of management (e.g., Berger and Udell 2002). Finally, large institutions may be disadvantaged at relationship lending because of Williamson-type (Williamson 1988) organizational diseconomies associated with also providing transactions loans and other wholesale services.

The empirical literature generally does not identify the lending technologies, but in some cases draws conclusions from the characteristics of the SME borrowers and their relationships with financial institutions. In most cases, the research is based on data from U.S. banks and SMEs. Large institutions are found to lend to larger, older SMEs with stronger financial ratios, and small institutions are found to rely more on soft information and lend to SMEs with which they have stronger relationships (e.g., Haynes, Ou, and Berney 1999, Cole, Goldberg, and White 2004, Scott 2004, Berger, Miller, Petersen, Rajan, and Stein 2005). The strong financial ratios and SME size and age findings are consistent with a comparative advantage for large institutions in using financial statement lending based on financial ratios for relatively transparent SMEs (which tend to be larger and older), given that financial statement lending is the only lending technology generally used for transparent borrowers. The strong relationships and use of soft information by small institutions is consistent with a comparative advantage for small institutions in relationship lending. There is very little evidence on comparative advantages by financial institution size in the other transactions lending technologies, although it is likely that large institutions have comparative advantages of differing degrees in the other transactions technologies due to economies of scale in processing hard information.

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# REEERENCE

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in Bangladesh on seven household outcomes<sup>6</sup> by household fixed-effects method. For the two projects under consideration, road development allowed to significantly reduce the price of fertilizer. Transport costs also decreased significantly. Controlling for soil fertility (and thus for non random placement of roads), Minten and Stifel (2008) show that crop yields for the three major staple items in Madagascar (rice, maize, and cassava) are lower in isolated<sup>7</sup> relative to non-isolated areas. Fan, Rao and Zhang (2004) provide a more macro example on Uganda in which shortened distances to feeder roads significantly increase the agricultural labor productivity.

The improved access to output markets leads to a rise in income thanks to greater opportunities of sales or higher prices. Gibson and Rozelle (2002) provide simple correlation between access to roads and prices that farmers receive for their crops: the rate of price decline is around seven percent for each extra hour to the nearest transport facility. Escobal and Ponce (2002) assess the impact of roads projects in Peru by propensity score matching techniques and demonstrate that rehabilitation entails an income increase. Khandker et al (2009) prove that road development entails higher agricultural production, higher wages, and higher output prices. Jacoby and Minten (2008) estimate the willingness-to-pay for a reduction in transport costs on cross-sectional data collected in a small region of Madagascar. As this region is relatively homogenous but faces great variations in transport costs to the same market, the problem of non random placement of roads is solved. They found that “A road that essentially eliminated transport costs in the study area would boost the incomes of the remotest households—those facing transport costs of about $75/ton—by nearly half, mostly by raising nonfarm earnings”.

It is also worth noting that Ruijs et al. (2004) find out that the direct effect of transport costs reductions on food prices, such as cereals, requires some nuance and tempered expectations in the case of Burkina Faso, notably due to the organization of markets.

> 6 Household daily transport costs, Input price: fertilizer (taka/kg), Daily agricultural wage (men), Laspeyres Price Index, Laspeyres Quantity Index, Monthly employment hours: adult men, Monthly employment hours: adult women, HH per capita expenditure, Boys’ schooling, 5-17 years: HH average, Girls’ schooling, 5-17 years: HH average.

> 7 Isolation is here defined as the travel time to nearest city.

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