rafmacalaba/datause-encoder-data huggingface dataset:
|Mobile|Na|0.7%|
|---|---|---|
|SMS|Na|0.008%|
|Vote turnout (%)|15.1%|14.0%|

* Notes: There were 7,348,996 registered voters in 2012 (data from PROCERGS). Turnout in 2011 is calculated using the 2012 data on the number of voters.

Concerns about the integrity of the voting process are rarely expressed. However, nothing prevents a participant from voting both online and offline, since the online and offline voter lists are not compared at any point in the process, nor is there any protection against voter coercion in the remote voting.<sup>19</sup> It is important to note, however, that among online engagement processes around the world, only a few adopt a more advanced security system that allows the voter to change the vote to discourage improper influence and use stronger authentication procedures (e.g. Estonia, Switzerland). Notably, stronger security e-voting measures are limited in their overwhelming majority to electoral processes. Furthermore, as the literature on election fraud has shown, electoral integrity continues to be a problem even in offline systems (Hyde 2011; Sjoberg 2012).

# 4. Methodology & Data

One approach to investigate the issue of turnout and the effect of digital divide on inclusiveness is to observe changes in aggregate-level turnout when i-voting is introduced. However, since electoral dynamics can change significantly from one election (at time t1) to another (t2), it is very difficult to attribute cause and effect with such a research design. The obvious problem is that there is no data on the counterfactual of no i-voting at t2. Thus in this paper we use an indirect approach. First, we identify online-only voters and then we examine the factors that explain the propensity of individuals to identify themselves as online-only voters. We can calculate the overall turnout effect attributable to the introduction of i-voting by using the proportion of self-reported online-only voters.

On July 4 2012 we conducted an online survey during the Rio Grande do Sul PB vote. All 124,211 voters who participated in the online vote were invited to complete the survey after they had voted. The survey instrument contained 27 questions, ranging from basic demographic details to participation profile and Internet usage (see appendix 3). A total of 22,300 people took the survey. There was substantial item non-response, meaning that the total

> 19 Some of the more sophisticated e-voting systems try to minimize the risk of so-called ‘over the shoulder attacks’ by enabling users to change their vote after they have been cast (e.g. Estonia’s electoral i-voting). Identifying solutions for mitigating the risk of multiple-voting and other factors that may distort outcomes is currently one of the activities supported by the World Bank’s Digital Engagement Unit.

11
socioeconomic resilience to disasters 24

|**District**|**Q1 population**<br>[thousands]|**Per ca**<br>Asset<br> [US$ per|**pita losses**<br>Well-being<br>cap., per year]|
|---|---|---|---|
|national result|4,367|1.42|9.47|
|Rathnapura|236|2.96|37.07|
|Colombo|473|6.08|24.80|
|Trincomalee|82|1.17|19.18|
|Mannar|20|2.32|14.89|
|Kalutara|259|1.74|14.44|
|Polonnaruwa|90|1.83|14.19|
|Puttalam|165|1.68|14.14|
|Batticaloa|114|0.45|7.25|
|Gampaha|479|1.10|6.02|
|Hambantota|133|0.89|5.61|
|Kegalle|185|0.44|4.73|
|Kurunegala|350|0.51|4.26|
|Galle|231|0.22|1.81|
|Badulla|181|0.12|1.68|
|Mullaitivu|18|0.11|1.67|
|Anuradhapura|178|0.20|1.47|
|Ampara|141|0.19|1.39|
|Matale|111|0.11|1.19|
|Kandy|303|0.10|1.03|
|Vavuniya|37|0.09|0.65|
|Moneragala|100|0.04|0.51|
|Nuwara Eliya|154|0.00|0.01|
|Kilinochchi|24|–|–|
|Jaffna|124|–|–|
|Matara|179|–|–|

**Table 4:** Annual _per capita_ asset and wellbeing losses for the poorest 20% (by expenditures) of the population of each district.

poorest quintile give a better sense of their experience of disasters: valued at US$37.3 million per year, the wellbeing losses of the poorest quintile are 32% of total wellbeing losses. It means that on average, and in wellbeing terms, the poorest quintile suffers from losses that are twice as large as the average individual loss in the country.

To this point in the discussion of results, it is likely that the district-level averages underrepresent the urban poor, who face higher costs of living in search of economic opportunity, and who may not only _not_ receive support from their home communities, but are expected to send remittances home. Indeed, when we limit our analysis to just the poorest quintile in each district, the range of socioeconomic resilience
Table 3. First Stage Indicators

||Effective const|antlegalreserver|equirements|(ECRR)|C|entralbankpolicy|rate (IR)||
|---|---|---|---|---|---|---|---|---|
||Montiel-Pflueger|Critical Fvalue|Partial|Shea's|Montiel-Pflueger|Critical Fvalue|Partial|Shea's|
|Horizon|EffectiveF|at5%|R?|PartialR?|EffectiveF|at5%|R?|PartialR?|
|0|<br>16827.606|<br>37.418|0.748|<br>0.748|<br>2556.396|<br>37.418|0.408|<br>0.408|
|1|15092.741|37.418|0.774|0.773|2885.237|37.418|0.407|0.407|
|2|47075.423|37.418|0.777|0.777|1812.299|37.418|0.410|0.410|
|3|50493.445|37.418|0.776|0.775|574.278|37.418|0.402|0.401|
|4|38463.632|37.418|0.772|0.771|297.545|37.418|0.405|0.404|
|5|13882.624|37.418|0.766|0.767|172.138|37.418|0.448|0.448|
|6|10145.308|37.418|0.765|0.765|185.839|37.418|0.455|0.455|
|7|6260.320|37.418|0.756|0.753|415.819|37.418|0.455|0.453|
|8|409160.290|37.418|0.835|0.810|221.977|37.418|0.461|0.447|

Note: Montiel-Pflueger's (2013) robust weak instruments test calculated for each instrument one at the time. See Section 6 for details. Source: Author's calculation. See Appendix for a detailed description of data sources.

Table 4. Linear predictability of the instruments AECRR’~~

||and|AIR"|||
|---|---|---|---|---|
||PanelA.|AECRRexo|g||
||Argentina|— Brazil|Uruguay|Panel|
|Observations|96|91|98|285|
|R?|0.230|0.315|0.148|0.069|
|R? adjusted|0.074|0.167|-0.020|0.014|
|F|1.074|1.356|0.356|3.450|
|Prob >F|0.394|0.188|0.988|0.225|
||Panel B|. AlRexog|||
||Argentina|Brazil|Uruguay|Panel|
|Observations|89|90|97|276|
|R2|0.170|0.113|0.081|0.048|
|R2 adjusted|-0.015|-0.081|-0.103|-0.011|
|F|1.031|1.241|0.666|1.746|
|Prob>F|0.436|0.259|0.819|0.364|

Note: The table presents the F statistics and the R2 of running a linear regression of our instruments on four lags of real GDP growth, inflation, the change in ECRR, and the change in IR, for each country and all together as a panel. See Section 6 for details. ECRR stands for effective constant legal reserve requirements. IR stands for policy rate. The panel regression includes country fixed effects. Source: Author's calculation. See Appendix for a detailed description of data sources.
that the upstream sector of the market (production of<sup>crudeoil)is competitiveandthat</sup> the government did not choose to promote a more competitive<sup>marketstructure inthe</sup> refining and distribution stages when YPF was privatized,<sup>theSDCyCconcentrated its</sup> study on the contractual relation between oil companies and retail outlets.<sup>Inparticular,</sup> the SDCyC investigated if oil companies were using the exclusive distribution<sup>contracts</sup> subscribed with gasoline stations as a strategic barrier to deter the<sup>entryof a newfirm or</sup> the growth of a small incumbent.

There are different contractual agreements between oil companies<sup>andgasoline</sup> stations. They can be summarized as follows (Shepard,<sup>1993):</sup> _COCO (Company Owned-Company Operated):_ the<sup>oilcompanyownsthegasoline</sup> stations and the manager is employed by the oil company. The oil company<sup>maintains</sup> ownership of the gasoline until it is sold to the consumer and<sup>thereforehas theright to set</sup> the retail price.

_CODO (Company Owned - Dealer Operated):_ the land and the immobile<sup>capitalare</sup> owned by the oil company. The manager is self-employed.<sup>The oilcompanysetsthe</sup> wholesale price and the manager has the right to set the retail<sup>price.</sup> _DOCO (Dealer Owned -Company Operated):_ the dealer<sup>isthe owner of theland and the</sup> oil company rents the property. These arrangements are the<sup>leastfrequentingasoline</sup> markets. Oil companies are willing to operate under this<sup>arrangement becausethe</sup> location of the gasoline station is important for commercial<sup>reasons.</sup> _DODO (Dealer Owned - Dealer Operated):_ this contractual arrangement varies between countries. In the United States, for instance, the oil company<sup>hasnoinvestmentinthe</sup> gasoline station and decision rights over service and retail price<sup>areallocatedtothe</sup> station owner. The only substantive constraints on manager<sup>behaviorarewithrespectto</sup> product purity and labeling. In Argentina, oil companies usually<sup>17provideinvestment</sup> capital (in the form of loans) to refurbish the gasoline station<sup>andtheylendthepumps</sup> and other equipment for the duration of the contract. To receive<sup>these loans,dealers must</sup> follow strict rules about the appearance of the retail outlets.

> 17 According to the Secretariat of Energy (Resolution 25/00)<sup>oil companiesgaveloans(data until2000) to</sup> approximately 25% of the retail stations operating under DODO: YPF<sup>(26%),Shell(20%), Esso (41 %) and</sup> EG3 (26%).

16
Table 2: Marginal Effect of Climate Change on Crop Choice in Latin America

||Maize|Potato|Rice|Soybean|Squash|Wheat|Fruits|
|---|---|---|---|---|---|---|---|
|Baseline|19.5%|6.8%|4.8%|7.9%|8.0%|14.4%|38.6%|
|Temp|-0.2%|0.5%|0.4%|0.2%|0.7%|-2.3%|0.8%|
|Prec|-0.3%|0.2%|0.1%|0.0%|0.1%|-0.1%|-0.2%|

20
<!-- Start of picture text --> der de<br>7. 7.<br>1996 - 2000 country average 2000 - 2010 country average<br>growing on protected areas growing on protected areas<br>0.0 - 2.3 % 0.0- 2.7%<br>2.3 -6.3% 2.7-7.6%<br>MTM 63-90% TM7.6-147%<br>M 9.0-15 % @ 14.7-211%<br>15 -56 % Wi 21.1-55.4%<br>TM no data 1 no data<br>City Size Vs. Spatial growth<br>25 3.0<br>— Area_km2_1996:Q1 — Area_km2_2000:01<br>—— Area_km2_1996:Q2 25 —— Area_km2_2000:Q2<br>—— Area_km2_1996:Q3 , —— Area_km2_2000:Q3<br>20, —— Area_km2_1996:Q4 —— Area_km2_2000:04<br>2.0<br>1:5<br>1.5<br>1.0<br>1.0<br>0.5 05<br>0.0 0.0<br>0 1 2 3 a 5 6 0 1 2 3 4 5 6<br>p_growth_km2_2000 p_growth_km2_2010<br><!-- End of picture text -->
**Table 8: Factor Analysis on Institutional Indicators (2000-2006)**

|**Variable**|**Size**|**Reach**|**Efficiency**|**Uniqueness**|
|---|---|---|---|---|
|**Log private credit via deposit money banks to GDP**|**0.86**|**0.19**|**-0.18**|**0.19**|
|**Log deposits to GDP**|**0.88**|**0.08**|**-0.28**|**0.14**|
|**Log total assets of deposit money banks to GDP**|**0.95**|**0.07**|**-0.19**|**0.06**|
|**Log net interest margin**|**-0.48**|**-0.02**|**0.70**|**0.28**|
|**Log interest spread**|**-0.09**|**-0.03**|**0.48**|**0.76**|
|**Log overhead to total assets**|**-0.54**|**0.13**|**0.65**|**0.26**|
|**Log assets of institutional assets to GDP**|**0.77**|**-0.05**|**0.02**|**0.40**|
|**Log ATMs per 100,000 people**|**0.07**|**0.67**|**0.23**|**0.48**|
|**Log deposits per 1,000 people**|**0.14**|**0.73**|**-0.14**|**0.44**|
|**Log branches per 100,000 people**|**0.10**|**0.59**|**0.06**|**0.63**|
||**Number of o**|**bservations: 36. Numb**|**er of parameters: 27.**||
|**Variable**|**Size**|**Efficiency**|**Uniqueness**||
|**Log private credit via deposit money banks to GDP**|**0.84**|**-0.15**|**0.27**||
|**Log deposits to GDP**|**0.84**|**-0.21**|**0.25**||
|**Log total assets of deposit money banks to GDP**|**0.94**|**-0.15**|**0.09**||
|**Log net interest margin**|**-0.23**|**0.80**|**0.30**||
|**Log interest spread**|**-0.20**|**0.50**|**0.71**||
|**Log overhead to total assets**|**-0.16**|**0.81**|**0.32**||

**Number of observations: 113. Number of parameters: 11.**

- As regards reach, the deposit account indicator has the highest loading, branches the lowest. While this would argue in principle in favor of deposits, notice that all three have high uniqueness factors, suggesting the presence of substantial “unexplained” components. Notice also the significant positive loading of ATMs per capita in the efficiency column, reflecting again the cost of providing access.

- Going now to Table 9, the size and liquidity dimensions appear clearly, with market cap being the main size indicator and turnover the main liquidity indicator. Interestingly, market cap is unrelated to liquidity. Not surprisingly, value traded is instead the main bridge between size and liquidity (turnover being the key liquidity indicator).

- The third dimension in the equity market PCA analysis yields a somewhat blurred picture. It brings together measures of efficiency (as determined by price synchronicity), reach (as determined by market concentration), and size (as determined by new issues). As would be expected, a higher price synchronicity (less efficiency) is related to smaller size (less issues) and a higher share of closely held shares. However, less efficient markets also appear to be less concentrated in terms of the top ten capitalization share, which is somewhat

27
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The gap between tariffs and the current cost of electricity is one of the causes of SENELEC’s financial problems and of the sector’s as a whole. An increase in tariffs from 17.2 cents in June 2007 to 22.7 cents in 2009 (table 14) was insufficient to bridge this gap. In January 2009 tariffs dropped by 12 percent and in July 2009 they went up 8 percent. Moreover, although SENELEC’s financial gap is expected to be met by the public sector, government transfers are insufficient and always late. As a result, the revenue gap for 2009 reached FCFA 8 billion as transfers fell short by FCFA 36

**Figure 15. Prevailing tariffs in line with operational costs, but embedded is an important subsidy of capital costs**

<!-- Start of picture text --> 35<br>30 Capital<br>25 Subsidy<br>20<br>15<br>10<br>5<br>0<br>Existing costs Existing Average tariff Average Existing<br>operating revenue capital costs<br>Costs<br>US$ cents per kWh<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and Shkaratan 2010. _Note_ : Light gray area represents capital subsidy.

billion of the expected total of FCFA 43 billion for the year. The delays also created additional financial fees for the company, as electricity generation, transmission, and distribution facilities further deteriorated due to a lack of maintenance and renewal.

**Table 14. Evolution of hidden costs associated with SENELEC**

||Electricity<br>produced<br>(GWh/year)|System losses<br>(%)|Collection ratio<br>(%)|Average total<br>cost ($/kWh)|Average<br>effective tariff<br>($/kWh)|Total hidden<br>costs ($ million/year)|Total hidden<br>costs (%<br>revenues)|
|---|---|---|---|---|---|---|---|
|2003|1,826|21.5|98.8|0.24|0.14|182.4|90.1|
|2004|1,952|21.1|99.6|0.24|0.15|176.4|74.9|
|2005|2,171|21.2|99.7|0.24|0.15|202.3|79.3|
|2006|2,192|20.6|100.0|0.29|0.17|273.4|88|
|2007|2,306|22.5|100.0|0.29|0.21|204.3|52|

_Source:_ Eberhard and others 2008; Briceño-Garmendia and Shkaratan 2010. _Note:_ GWh = gigawatt-hour; kWh = kilowatt - hour.

SENELEC’s financial health is also severely impacted by persistent operational inefficiencies. Losses in transmission and distribution have amounted to about 20 percent of produced electricity during the past decade; the accepted engineering norm is 10 percent of electricity produced. When combined with the implicit cost-tariff deviation, the hidden costs of SENELEC amount to 60 percent of its revenues (box 1, figure 16).

29
# **Measuring Time Use in Development Settings**

Greg Seymour<sup>1</sup> , Hazel Malapit<sup>2</sup> , and Agnes Quisumbing<sup>3</sup> Keywords: time use, recall error, measurement error JEL codes: C8, J22, O12, Q12

> 1 <u>g.seymour@cgiar.org, International Food Policy Research Institute, CGIAR Research Program on Policies,</u> Institutions, and Markets

> 2 International Food Policy Research Institute, CGIAR Research Program on Agriculture for Nutrition and Health 3 International Food Policy Research Institute, Poverty, Health, and Nutrition Division
experience faster productivity growth than non-ICT sub-sectors. For these countries, the high productivity growth rates documented in Section 3 for financial intermediation and transport, storage, and communications can be rationalized by the introduction of costreducing ICT. In Latvia, ICT producers exhibit the highest productivity growth in 19972000 and in 2000-2004. Finally, note that the findings for the services sub-sectors in the ECA region match those for manufacturing sub-sectors for which better performance is associated with the production or use of ICT (Anos-Casero, 2007).

**_Figure 12: Average KLEMS Labor Productivity Growth and ICT in Services Sub-Sectors_**

<!-- Start of picture text --> EU-8 Countries Average<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>ICT Producers ICT Users Non-ICT<br>1997-2000 2000-2004<br>Czech Republic Estonia Hungary<br>20% 20% 12%<br>15% 15% 8%<br>10% 10%<br>4%<br>5% 5%<br>0%<br>ICT Producers ICT Users Non-ICT<br>0% 0%<br>ICT Producers ICT Users Non-ICT ICT Producers ICT Users Non-ICT -4%<br>1997-2000 2000-2004 1997-2000 2000-2004 1997-2000 2000-2004<br>Latvia Lithuania Poland<br>10% 15% 10%<br>8% 8%<br>6% 10%<br>6%<br>4%<br>2% 5% 4%<br>0% 2%<br>ICT Producers ICT Users Non-ICT<br>-2%<br>0% 0%<br>-4% ICT Producers ICT Users Non-ICT ICT Producers ICT Users Non-ICT<br>1997-2000 2000-2004 1997-2000 2000-2004 1997-2000 2000-2004<br>Slovak Republic Slovenia<br>10% 6%<br>5%<br>4%<br>0%<br>ICT Producers ICT Users Non-ICT 2%<br>-5%<br>0%<br>-10% ICT Producers ICT Users Non-ICT<br>-15% -2%<br>1997-2000 2000-2004 1997-2000 2000-2004<br><!-- End of picture text --> Source: KLEMS database 2007.

Note: For each country and ICT-related category, the figure shows the simple average taken across all sub-sectors in that category of their geometric averages of labor productivity growth in 1997-2000 and 2000-2004.

27
17 Table 3

# Spearman Rank Correlation Coefficients Between Revealed Comparative Advantage Indices and Ton Explanatory Variables

|:xpl|anatory<br>Variable 1/|All Developing<br>Countries|Asian Exporters<br>of manufactures|Other South<br>Asian Countries|
|---|---|---|---|---|
|1)|Labor intensity<br>variable|-0.542*|-0.499*|-0.559'|
|(2)|Developed country imports (Smill.)|-0.119|-0.078|-0.021|
|(3)|Capital per worker|-0.547^|-0.494*|-0.492*|
|(4)|Skill variable|-0.338*|-0.254|-0.377*|
|5)|Average per capita wage|-0.617*|-0.5840|-0.615*|
|:)|Scale variable|-0.271*|-0.248|-0.259|
|7)|Consumer good ratio|0.476*|0.492'|0.427*|
|8)<br>|First trade date<br>|-0.2890|-0.276|-0.226|
|(9)|Product differentiation<br>variable|-0.326'|-0.259|-0.286'|
|(10)|Resource based product dummy2/|-0.183|-0.017|-0.159|

Statistically significant at the 99 per cent level.

- / The explanatory variables are defined as follows:

- (1) Labor intensity average per capita value added in the industry relative to that for all United States manufacturing activity.

(2) Developed country imports - a measure of the size of the market for the product. Measured in 1985 US dollars.

(3) Fixed plant and equipment immediately employed in making the commodity. Measured in US dollars on a per capita basis.

(4) Percentage of the industry's labor force xccounted for by professional, technical and scientific personnel.

(5) wage bill divided by total employees immediately occupiad in making the commodity. A measure of human capital.

(6) The exponent in the regression equation V = KNa, where V is the ratio between value added in plants employing N persons and the average value added for the industry.

(7) Percentage of industry output (and imports) directly purchased by final consumers.

(8) Hufbauer's (1970) estimate of the first date (year) that the product was traded internationally.

(9) Measured by the coefficient of variation in unit values of industry goods destined for different countries. Differentiated goods have higher coefficients of variation.

(10) Taken from UNIDO (1982). Products dependent on available natural resources take a value of one. Other greods take a value of zero.

Since more than 85 per cent of the NBER labor intensive products are not considered to be natural resource based, and hence take a value of zero for this dummy, the rank correlation coefficient is a very weak statistical test for causality. A dummy variable produces a very weak ranking as it merely separates the data into two alternative levels.
# **Services Reform and Manufacturing Performance: Evidence from India**

Jens Matthias Arnold* Beata Javorcik** Molly Lipscomb*** Aaditya Mattoo**** Keywords: services reform, manufacturing productivity, foreign direct investment JEL Codes: L8, F2, D24

## **_______________________________________________**

* OECD Economics Department, 2 rue André Pascal, 75116 Paris, France. Email: jens.arnold@oecd.org. ** Department of Economics, University of Oxford and CEPR, Manor Road Building, Manor Road, Oxford OX1 3UQ, United Kingdom. Email: beata.javorcik@economics.ox.ac.uk. *** Department of Economics, University of Notre Dame, Notre Dame, IN 46556, USA. Email: molly.lipscomb.5@nd.edu **** World Bank, 1818 H Street, NW; MSN MC3-303; Washington, DC 20433, USA. Email: amattoo@worldbank.org. The authors are grateful to Ann Harrison, Giovanna Prennushi, Arvind Subramanian and participants of the 2011 Annual Bank Conference on Development Economics, CEPR GIST workshop, DEFI conference and seminars at DFID and the University of Nottingham for useful comments. This paper is part of a World Bank research project on trade in services supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID).  The views expressed in the paper are those of the authors and should not be attributed to the OECD, to the World Bank, its Executive Directors or the countries they represent.
_COTE D’IVOIRE: Volatility, Shocks and Growth_

**Figure 6: Per Worker Output, Capital Stock, & Real World Cocoa Prices - Deviations from Trend**

<!-- Start of picture text --> 0 .1 2 5<br>O u t p u t p e r W o r k e r S h o c k s<br>0 .1 0 5 ( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .0 8 5<br>0 .0 6 5<br>0 .0 4 5<br>0 .0 2 5<br>0 .0 0 5<br>- 0 .0 1 5<br>- 0 .0 3 5<br>- 0 .0 5 5<br>- 0 .0 7 5<br>0 .1 2<br>C a p it a l P e r W o r k e r S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .0 8<br>0 .0 4<br>0<br>- 0 .0 4<br>- 0 .0 8<br>0 .7<br>C o c o a P r ic e S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .5<br>0 .3<br>0 .1<br>- 0 .1<br>- 0 .3<br>- 0 .5<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->