rafmacalaba/datause-encoder-data huggingface dataset:
**... and there is ample liquidity in the economy ...** Favored by the government’s fiscal efforts, gross domestic savings rose to 19.8 percent of GDP in 2006 (up from 17.1 in 2001). The current account (Figure 6)<sup>13</sup> is in surplus, net capital inflows have been recently on the rise (Figure 7), and broad money (M3) growth has accelerated, driven by the accumulation of net foreign assets in the banking system (Figure 8). Bank balance sheets have strengthened and the lending-deposit spread is the highest recorded in recent years (Figure 9).

**Figure 8. Increased liquidity Figure 9. Lending looks profitable**

<!-- Start of picture text --> 35<br>30<br><!-- End of picture text --> <!-- Start of picture text --> Net foreign assets<br>Net domestic credit (private)<br>Net domestic credit (public)<br>M3 growth<br><!-- End of picture text --> <!-- Start of picture text --> Lending and deposit rates (%)<br>Real lending rate<br>Real deposit rate<br>Lending-deposit spread<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 2006<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 2003<br><!-- End of picture text --> <!-- Start of picture text --> 2004<br><!-- End of picture text --> <!-- Start of picture text --> 2005<br><!-- End of picture text --> <!-- Start of picture text --> 2002<br><!-- End of picture text --> <!-- Start of picture text --> 2003<br><!-- End of picture text --> <!-- Start of picture text --> 2005<br><!-- End of picture text -->

**... but there is still little willingness to invest.** Despite the more favorable environment, the savings-investment gap widened in recent years and the domestic appetite for investment remained stagnant (Table 2 and Figure 10). As a result of present growth and deficits, the “public debt-to-GDP ratio” is declining, but public investment has not picked up. The low and declining lending interest rate (Figure 9) suggests that, in spite of the ample liquidity, the demand for credit is not increasing. Also, despite the favorable lending-deposit spread, banks are cautious in their lending to the private sector (Figure 12), preferring to finance the government in local and foreign currency.<sup>14</sup> In short, the private sector still invests little. Without a deeper reform of the investment climate, businesses are unlikely to lift spending on new plant and equipment (World Bank, 2005c).

> 13 The surplus is largely remittances-driven, see Figure 22.

> 14 Government securities now comprise about a third of the banks’ portfolio.

12
Russian government in 2009 on raw timber appear to be between 7 and 14 times higher than the optimal level. Even the export tax of 25 percent (in effect at the time of this writing) is more than twice the optimum level.

# **References**

- Al-Sahlawi, Mohammed A. (1989), “The Demand for Natural Gas: A Survey of Price and Income Elasticities,” Energy Journal, 10 (1), January, 77-90.

- Aslund, Anders (2009), “Gazprom’s New Weakness offers Opportunity, _Moscow Times,_ May 27. <u>http://www.themoscowtimes.com/article/1028/42/377459.htm</u>

- Aslund, Anders (2008), “Russia’s Energy and the European Union’s Perspectives on Gazprom,” Speech before the European People’s Party, European Parliament, May 15. http://www.iie.com/publications/papers/aslund0508.pdf

- Baranov, Viktor (2008), Rising Gas Exports, Demand for New Supply Guarantees Future of Russia's Independent Gas Producers on the Russian Gas Market, _Oil and Gas Eurasia_ , April. http://www.oilandgaseurasia.com/articles/p/70/article/581/

- Beierlin, J., J. Dunn and J. McConnor, Jr. (1981), “The Demand for Electricity and Natural Gas in the Northeastern United States,” _Review of Economics and Statistics_ , 64, 403-408.

- Blagov, Sergei (2009). “Alternate Gas Suppliers Face Overproduction in Russia,” _European Dialogue_ , September. http://eurodialogue.org/node/679.

- British Petroleum (2000; 2001, 2009), Statistical Review of World Petroleum, 2000, 2001, 2009. Available at:www.bp.com/centres/energy/

- Bresnahan, Timothy (1989), « Studies of Industries with Market Power, » in Richard Sshmalansee and Robert Willig (eds.), _The Handbook of Industrial Organization_ , Amsterdam: North-Holland.

- Cala, Andres (2009),  Eurasia Geopolitics: Iran Making Inroads On Caspian Gas, _Global Research_ , February26. http://www.globalresearch.ca/index.php?context=va&aid=12468

- Carlton, Denis and Jeffrey Perloff (2000), _Modern Industrial Organization_ , 3<sup>rd</sup> edition, Harper Collins.

- Chaudhary. Mohammad Ali et al. (1999), “Industrial Sector Input Demand Responsiveness and Policy Interventions,” _Pakistan Development Review_ , 38 (4), Winter, 1083-1099.

- Estrada, Javier and Ole Fugleberg (1989), “Price Elasticities of Natural Gas Demand in France and West Germany,” _Energy Journal_ , 10 (3), 77-90.

- Gaskins, Darius (1971), “Dynamic Limit Pricing: Optimal Pricing under Threat of Entry,” _Journal of Economic Theory_ 3, September, 306-322.

- Heinrich, Andreas and Julia Kusznir (2005), “Independent Gas Producers in Russia,” KICES Working Paper, September.  Available at: http://www.kices.org/downloads/KICES_WP_02.pdf

- Hsing, Yu (1992), Interstate Differences in Price and Income Elasticities: The Case of Natural Gas, _Review of Regional Studies, 22 (3),_ Winter: 251-59

17
As shown in Box 2, information on the evolution of variance can be obtained from the time series' characteristics of the variable, or from the estimated range.<sup>7</sup> Computationally, _INFRISK_ works in conjunction with Microsoft Excel and supports both the construction and the operation phases of a capital investment project. The input to the simulation exercise includes data on projected revenues, operating costs, and other risk variable inputs which are part of the standard forecasting and cash flow analysis.

# **_INFRISK_ Analytics**

At the heart of _INFRISK_ is a generic financial model, describing the year to year uses and sources of funds in the context of the project's initial capitalization, its incomeexpenditure flows, cash flows, as well as certain specific accounts established for servicing of debt (debt service reserve), operations and maintenance, tax payments, and general accounts (see Table 3 in the Annex). The drawdown of funds during the construction period and the distribution of cash flows during the operation phase are governed by a hierarchy of claims embedded in the contracts and loan covenants. If a project, for instance, takes three years to be constructed and the distribution of total capital expenditures is given by 25% (1st year), 50% (2nd year), and 25% (3rd year), it is assumed that equity funds are also drawn according to the same pattern. _INFRISK,_ however, has the flexibility of incorporating a different pattern of capital expenditure disbursement, as well as equity drawdown, depending on the particular project at hand.

Driving the financial model are project specific sub-models determining operating revenues and costs, as functions of tariffs, capacity, output, and input prices and quantities. In a power project, for instance, operating revenues could consist of payments for electric generating capacity and energy, and associated steam (in a cogenerated plant), as provided under the Power Purchase Agreement, and operating costs dependent on fuel usage and prices as well as the operation and maintenance expenses.

Currently, _INFRISK_ operates on an annual, year-by-year basis. Work, however, is underway to introduce calendar time (day, month, year) as the basis for analysis in line with the actual functioning of financial markets and contracts.

# **_Box 2: Probabilistic-based simulation_**

Technically, the stochastic process { Y, _I_ t1. _T_ } can represent the possible realization of a risk variable, Y, in an infrastructure project, over the project's lifetime, where T is the life of the project, i.e. concession period. It is useful to represent the value of Y in year t ( Y, ) as the sum of its projected value, Au, and a random variable, u,, as follows: 7 In much of the finance literature, risk is modeled to evolve monotonically with time, through the dominant application of diffusion process of Brownian motion, where variance increases through time.

6
used by scholars investigating inter-state cooperation over water (Brochmann and Hensel 2009; Tir and Ackerman 2009; Dinar et al. 2011). Most recently, scholars have also considered the role of water variability across time on hydro-political relations between states (e.g. Dinar et al. 2010; De Stefano et al. 2012). Given some of its advantages (e.g. ability to predict natural runoff with a higher accuracy and high significance levels than precipitation and measure the magnitude of extreme events particularly when precipitation and gauge data are limited), the BWI, therefore, provides another measure of water flows that scholars can utilize in their socio-political studies. In particular, the data provided by the BWI befit recent calls by scholars for better measures of ―dynamic scarcity‖ which make better use of satellite imagery and meteorological data to account for time-varying measures of water availability (Buhaug et al. 2008).

To illustrate the utility of the BWI for the cooperation and conflict analysis, we show how actual past water related political events are distributed over time relative to the extreme weather events (particularly floods) which the BWI identifies in this study. To that extent, we utilize the Basins at Risk Data Base (TFDD website n.d.), which contains a database of historical water relations (such as events) documenting historical incidents of international water cooperation and conflict, and comb the databank for relevant flood-related events that transpire around the flood periods identified by the BWI. This exercise is not intended to confirm any type of causation between hydro-political events and extreme weather events as this will require a much more robust econometric approach utilizing control variables. We simply wish to demonstrate the value of the BWI (among the other measures of water variability and supply) for systematic and empirical research.

Various studies have already used the TFDD Basins at Risk database to make certain arguments about conflict and cooperation over water (Wolf, Stahl & Macomber 2003; Yoffe at al. 2004; Stahl 2005) and the application of the BWI can prove useful for ascertaining how physical phenomenon (such as droughts and floods) interact with political, economic, and social variables to explain some of these political events. An analysis of Mekong and Zambezi Treaties with regards to types of complaints and analysis of Mekong and Zambezi Treaties with regards to the water regulation mechanisms and existing institutions is provided in Tables 7 and 8, respectively. Below we begin with the Mekong and proceed to the Zambezi.

In regards to the flood events pertaining to the Mekong, the BWI predicted heavy flooding in August through October 1988, August and October 1989, and August through 21
**Table 3:  Place of residence by town of respondents before the crisis**

|**Town**|**IDPs in**<br>**Bamako (%)**|**Returnees in Gao,**<br>**Timbuktu & Kidal**<br>**(%)**|**Refugees in**<br>**Niger &**<br>**Mauritania (%)**|
|---|---|---|---|
|Timbuktu Town|31|70|5|
|Gargando Town (Timbuktu Region)|-|-|12|
|Goundam Town (Timbuktu Region)|12|**-**|-|
|Gao Town|27|29|2|
|Outagouna Town (Gao Region)|-|**-**|10|
|Kidal Town|-|1|1|
|Other|30|**-**|70|
|**Total %**|**100**|**100**|**100**|
|**Total n**|**100**|**220**|**181**|

Source: Listening to Displaced People Survey, 2014.

The majority of displaced people left in April 2012 when the towns of Gao, Kidal and Timbuktu were taken over by rebels (Figure 2). There was another wave of displacement in June of the same year when major fighting broke out between the MNLA and Jihadist factions. A third wave occurred in January 2013 following the French intervention.

**Figure 2: Patterns of Displacement (percent)**

<!-- Start of picture text --> 77<br>62<br>58<br>IDPs<br>25<br>Refugees<br>13 16<br>3 6 1 2 3 2 3 13 1 1 5 5 Returnees<br>January: March: April: May: June: August: Application of Sharia January: French<br>Massacre Coup Taking of Douentza Confrontati intervention<br>at Aguel d’état Kidal, falls on b/w<br>Hoc Gao and MNLA and<br>Timbuktu Jihadists<br>by rebels<br>2012<br>2013<br>Jan-12 Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

8
**Table 3. Estimated Mean Annual Out-of-Pocket Healthcare Expenditures** **<u>by NHIS Membership Status, Cedis</u>**

|Welfare decile|Nonm|embers|Me|mbers|
|---|---|---|---|---|
||Mean<br>(1)|Std. dev.<br>(2)|Mean<br>(3)|Std. dev.<br>(4)|
|Overall|344|525|289|523|
|1<sup>st</sup>(poorest)<br>|162|169|91|108|
|2<sup>nd</sup><br>|209|199|150|153|
|3<sup>rd</sup>|250|273|177|189|
|4<sup>th</sup>|214|241|175|217|
|5<sup>th</sup>|216|232|226|281|
|6<sup>th</sup>|278|294|242|269|
|7<sup>th</sup>|283|320|308|335|
|8<sup>th</sup>|393|467|295|329|
|9<sup>th</sup>|396|495|406|488|
|10<sup>th</sup> (richest)|877|1,084|678|1,202|

_Note_ : Welfare = household consumption per adult equivalent. Std. dev. = standard deviation.

Many other studies estimate the effect of NHIS membership on mean OOP healthcare expenditures, usually based on cross-sectional data such as our sample. Garcia-Mandicó et al. (2021) is unique among these studies as the authors use data from the 2005/06 round of the GLSS, whose fielding straddled the rollout of NHIS at that time. Thus, they use a difference-indifferences strategy comparing survey respondents in the same districts, some of whom gained access to NHIS during the survey-fielding period and some of whom did not. The survey data they use, GLSS 2005/06, are very similar to the GLSS 2016/17 data we use, though it is likely that all OOP expenditures for medicines are reported in one variable in GLSS 2005/06, while they are separated between OOP expenditures in the context of a consultation and other OOP expenditures on medicines in GLSS 2016/17. They find a mean decrease in OOP healthcare expenditures of 18 percent.

Powell-Jackson et al. (2014) investigate a randomized allocation of free healthcare to children ages 6–59 months in one district of Ghana, Dangme West, and find that this comprehensive insurance reduced OOP healthcare expenditures by 27 percent on average. By comparison, we estimate a mean reduction of 16 percent for the overall Ghanaian population, 20 percent for children ages 6–59 months across Ghana, and 46 percent for children ages 6–59 15
# **<mark>Local Financial Development and Growth</mark>**<sup>**1**</sup>

<mark>Jake Kendall</mark><sup>2</sup> The World Bank

**<mark>Keywords:</mark>** <mark>Banking; Financial Development; Human Capital; India; Regional Growth.</mark>

**JEL Classification Numbers:** I2, I3, O1, O3, O4, R1, F4, G2

> 1 The findings, interpretations, or opinions expressed in this paper do not necessarily reflect those of the World Bank, its Board of Executive Directors, or its member organizations. I would like to thank Nirvikar Singh for his help in obtaining this data and his generous advice. I would also like to thank my advisor Joshua Aizenman for his critical support in my research. Further thanks go to Carl Walsh, Yin-Wong Cheung, Mike Dooley, Micheal Hutchison, Alan Spearot, Jon Robinson, Lori Kletzer, Lauren Malone, Jonathan Conning, Bob Flood, Thorsten Janus, Simeon Djankov, Alejandro Ponce Rodriguez, and seminar participants at UC Santa Cruz, Oxford, Vasar, Georgetown, Hunter College, the World Bank, and U. Richmond, for valuable comments

> 2 **Author Contact Information** : The World Bank, 1818 H Street NW, Washington, DC, 20433. E-mail: <u>jkendall1@worldbank.org</u>
linked to increased marriage and fertility rates (Kutinova 2009, Hanson 2011). While stress may affect later life health directly, in the form of induction risk, stress will also affect health through an increased likelihood of marriage and having children. Specification 3 additionally includes years of education past high school, and log income, which are endogenous variables but possible mechanisms through which induction risk may affect health (see e.g. Card and Lemieux 2001).

We also investigate the effect of stress in early adulthood on the risk of mortality, using a restricted-use version of the NHIS that contains data on mortality. Using a Cox proportional hazards model, we estimate time to death for men who never served during the Vietnam War. This equation takes the form: �(���) = ��(���) �<sup>����������������,���������������������</sup> (6) where �(���) is the hazard of dying by 2011, the year in which the NHIS-Mortality linked file follows individuals. Induction risk and other covariates are exactly the same as described above and we perform the same three specfications.

As a falsification test, we also estimate our models for male veterans. Men who served in the military during the Vietnam War at some point joined the military (we do not know whether they voluntarily enlisted or were drafted, or at what age they joined the military) and were then no longer at risk of being inducted. Moreover, those who served in the military during the Vietnam War faced many other stressors during these ages (e.g. combat, separation from loved ones, injury, deaths of fellow soldiers); we have no way of quantifying this stress but there is no reason to believe that it is correlated with induction risk for others of the same age who did not serve.

13
# **A Tables and Figures**

Table A1: Definition of Variables and Parameters

|Variables|Defnition|
|---|---|
|**Production variables**||
|GVP|Value of annual gross output.|
|PY|Total value of production minus intermediate inputs.|
|WL|Wages and salaries plus fringe benefts..|
|K|Capital stock is defned as the book value of fxed assets.|
|M|Annual consumption of intermediate inputs - materials,<br>utilities and purchased services.|
|**Other frm covariates**||
|L|Total number of paid and unpaid workers plus proprietors.|
|Age|Years since the establishment started its operation.|
|Ownership|Private and government-owned frms.|
|Location|Establishment located in the capital (Addis Ababa, Accra, Nairobi)<br>or outside from capital.|
|**Parameters**||
|_αs_|The capital share in the corresponding 4-digit US industry.|
|_σ_|Elasticity of substitution, set at 3 in the baseline calculation.|
|_R_|The rental rate of capital, set at 10%.|

Table A2: Data Sources.

|**Country**||**Provider**||**Size Threshold**|**Year**|**# Ob**|**servations**|
|---|---|---|---|---|---|---|---|
|Cˆote d’Ivoire|National<br>(INS): R<br>for the <br>tors.|Statistics<br>egistrar of C<br>modern enter|Institute<br>ompanies<br>prise sec-|_Census_ of all registered frms.|2003-2012|Raw<br>6,746|data:|
|Ethiopia|Central<br>(CSA):<br>Scale<br>Electrici|Statistical<br>Large<br>and<br>Manufacturi<br>ty Industries|Agency<br>Medium<br>ng<br>and<br> Survey|_Census_<br>of<br>frms<br>employing<br>more than 10 workers.|2011|Raw<br>1,936|data:|
||Central|Statistical|Agency|Survey of frms employing less|2011|Raw|data:|
||(CSA): <br>turing In|Small-Scale <br>dustries Sur|Manufac-<br>vey|than 10 workers and use power-<br>driven machinery.||3,882||
|Ghana|Ghanaia<br>(GSS).<br>Census|n<br>Statistica<br>National<br>|l<br>Service<br>Industrial|Census of more than 10 work-<br>ers and representative sample<br>of establishments engaging less<br>than 10 workers.|2003|Raw<br>3,302|data:|
|||||||Continued|on next page|

34
In order to refine our investigation, we specifically examine how different types of rules influence the cyclicality of fiscal policy. We differentiate between expenditure rules (ER), budget balance rules (BBR), debt rules (DR) and revenue rules (RR). Table 2 reports the estimation results for expenditure rules, budget balance rules and debt rules. Specifically, we find that while expenditure rules and budget balance rules seem to be associated with fiscal pro-cyclicality, debt rules and revenue rules seem to favor more budgetary discipline.

|Dependent vari|able: Cyclica|lity of fscal|policy<br>ˆ<br>_βit_||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|BQ|-0.028***|-0.029***|-0.058***|-0.041***|
||(0.087)|(0.071)|(0.062)|(0.088)|
|ERR|0.008***|0.008***|0.007***|0.008***|
||(0.011)|(0.010)|(0.011)|(0.010)|
|ERD|0.235**||||
||(0.066)||||
|BBRD||0.021***<br>(0.081)|||
|DRD|||-0.155***<br>(0.102)||
|RRD||||-0.130**<br>(0.117)|
|IT|-0.427***|-0.437***|-0.420***|-0.435***|
||(0.054)|(0.054)|(0.055)|(0.053)|
|ELECTIONS|0.003***|0.004***|0.004***|0.005***|
||(0.003)|(0.003)|(0.003)|(0.003)|
|POLARIZ|-0.096***|-0.099***|-0.097***|-0.100***|
||(0.010)|(0.009)|(0.010)|(0.010)|
|GDPPC|-0.204***|-0.183***|-0.147***|-0.174***|
||(0.033)|(0.023)|(0.032)|(0.032)|
|OV|19.208|19.636|17.990|18.513|
||(6.865)|(6.915)|(5.841)|(6.149)|
|RENT|-0.029***|-0.029***|-0.029***|-0.029***|
||(0.003)|(0.003)|(0.003)|(0.003)|
|DEBT|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Obs|912|912|912|912|
|Countries|68.000|68.000|68.000|68.000|
|Hansen J|0.736|0.728|0.733|0.735|

Bootstrapped standard errors in parenthesis (100 replications) * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Table 2: Results for ER, BBR, DR, RR 18