rafmacalaba/datause-encoder-data huggingface dataset:
14 higher among the rural poor. It was not possible to infer from the questionnaire what explains this odd result. The second largest difference was found in Zambia, where the difference was due largely to use of generators in rural areas. Lastly, in Niger the difference in access between definitions 1 and 5 was larger than 10 percentage points for urban households as well as for the urban poor. The greatest contributing factor was use of electricity as the primary source of energy by those who did not report connection to the grid, followed by use of generators (even among the urban poor); the survey did not ask questions about solar energy. The difference also exceeded 10 percentage points in rural Nigeria and rural Ghana. In rural Nigeria, it was due to generator ownership: nearly one‐fifth of all rural households own generators; the questionnaire did not ask about solar energy. In rural Zambia, the difference was due three‐fifths to generator use and two‐ fifths to solar panels.

Figure 3: Percentage of people using electricity according to different definitions of access <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Definition 1 Definition 2 Definition 3 Definition 4 Definition 5<br>Percentage of population<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : For a descriptions of the five definitions of access, see page 6.

Only four surveys—Madagascar, Mozambique, São Tomé and Príncipe, and Togo—asked about quantities of electricity consumed. Recalling quantities of electricity consumed is even more challenging than recalling the amount spent, and the evidence of this challenge was clear in São Tomé and Príncipe, where 15 surveyed households reported electricity consumption of 1 kWh _a year_ , 11 of them in the top quintile. The results were even more striking in Togo, where average monthly consumption was only 5 kWh, reaching only 6 kWh even in the top urban quintile. Such a consumption pattern seems more indicative of a lack of understanding of kWh as a unit of electricity than actual consumption. In addition, there was one country—Rwanda—that enabled back‐calculation of kWh consumed from spending on electricity because the tariff schedule consisted of a single block with a unit charge applicable to all residential consumers and no fixed charges, and the survey asked about the last electricity bill. Taking the tariff schedule in effect at the time of the survey, kWh consumed was calculated. The results for those reporting non‐zero quantities (and non‐zero spending on grid electricity in the case of Rwanda) are presented in Figure 4 for all these countries other than Togo.
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**Table 5 (continued)**

||(1)|(2)|(3)|(4)<br>Satisfacti|(5)<br>Satisfacti|
|---|---|---|---|---|---|
|||Satisfied|Satisfied|on level|<br><br>on level|
||Utilization|(0-1)|(0-1)|(1-5)<br>|(1-5)<br>|
||Probit|Probit|Probit|Ordered<br>probit|Ordered<br>probit|
|_How often are unofficial payments needed_||||||
|Never||0.254***<br>||0.215***<br>||
|Seldom||(0.0504)<br>0.0444<br>||(0.0410)<br>0.0502<br>||
|||(0.0583)||(0.0451)||
|Sometimes<br>Ull||0215***<br>Refe|rence categ|0202***<br>ory||
|suay||-.||-.||
|||(0.0539)||(0.0440)||
|Alwas||-0342***||-0423***||
|y||.<br>(0.0629)||.<br>(0.0587)||
|Female||0.0624*|0.0731**|0.0176|0.0237|
|||(0.0367)|(0.0372)|(0.0305)|(0.0303)|
|18-34||-0.108**<br>(0.0522)|-0.0720<br>(0.0545)|-0.105**<br>(0.0434)|-0.0657<br>(0.0441)|
|35-44||Refe|rence categ|ory||
|45-54||0.137**<br>|0.0936<br>|0.129***<br>|0.0902*|
|||(0.0599)|(0.0624)|(0.0484)|(0.0487)|
|55-64||0.121**|0.0492|0.133***|0.0723|
|||(0.0605)|(0.0622)|(0.0506)|(0.0506)|
|65+||0.323***<br>|0.240***<br>|0.295***<br>|0.219***<br>|
|_Satisfied with life_||(0.0680)|(0.0705)|(0.0586)|(0.0579)|
|Strongly disagree||-0.292***|-0.239***|-0.300***|-0.258***|
|||(0.0567)<br>***|(0.0591)<br>**|(0.0493)<br>***|(0.0501)<br>***|
|Disagree||-0.142|-0.108|-0.147|-0.111|
|Neither disagree nor agree||(0.0480)<br>Refe|(0.0488)<br>rence categ|(0.0400)<br>ory|(0.0394)|
|Agree||0.0952|0.113*|0.110**|0.129***|
|||(0.0586)|(0.0609)|(0.0473)|(0.0474)|
|Strongly agree||0.278**<br>(0114)|0.260**<br>(0115)|0.192**<br>(00970)|0.163*<br>(00941)|
|_Problems encountered in local public health clinic or h_|_ospital in past 1_|.<br>_2 months_|.|.|.|
|<br>Frequent and unjustified absence of doctors|||-0.321***<br>||-0.246***<br>|
||||(0.0630)||(0.0531)|
|Treated disrespectfully by staff|||-0.482***<br>||-0.455***<br>|
||||(0.0532)||(0.0430)|
|No drugs available|||-0.255***<br>||-0.234***<br>|
|Long waiting times|||(0.0489)<br>-0.353***<br>||(0.0400)<br>-0.355***<br>|
||||(0.0404)||(0.0324)|
|Facilities not clean|||-0.115||-0.0809|
||||(0.0728)||(0.0569)|
|Payments required for services that should be free|||-0.513***||-0.477***|
||||(00500)||(00414)|
|Inverse Mills Ratio (from selection equation)|||.|-0.0719|.<br>-0.0761|
|||||(0162)|(0158)|
|Constant|0.320***|-0.204|0.192|.|.|
|Cut 1|(0.0771)|(0.147)|(0.125)|-1.402***|-1.895***|
|||||(0.114)|(0.114)|
|Cut 2||||-0.331***|-0.725***|
|Cut 3||||(0.113)<br>0.0914|(0.111)<br>-0.261**|
|Cut 4||||(0.113)<br>2057***|(0.111)<br>1821***|
|||||.<br>(0.116)|.<br>(0.113)|
|Observations<br>|28,920<br>|28,248|28,248|19,510<br>|19,510<br>|
|Pseudo R-squared<br>Robust standard errors in parentheses|0.0720|||0.0567|0.105|
|<br>*** p<0.01, ** p<0.05, * p<0.1<br>Country-level dummies not shown||||||
|<br>Columns (2)-(5): with Heckman sample selection corre|ction|||||

19
experienced a banking crises in a given year. We use the banking crisis definition and the data provided in Leaven and Valencia (2010). is a vector of country level controls, includes GDP per capita, GDP per capita growth, stock market capitalization over GDP, bank deposits over GDP, liquid assets ratio, capital ratio and log of the number of banks. These variables are described in detail in the appendix. In the regressions, we exclude countries with less than 7 banks. The regressions include country fixed effects ( ) and we report robust standard errors clustered at the country level.

||**M1**|**M2**|**M3**|**M4**|**M5**|**M6**|**M7**|**M8**|**M9**|**M10**|
|---|---|---|---|---|---|---|---|---|---|---|
|Stock mkt Cap / GDP|0.127*|0.136*|0.152*|0.228**|0.257**|0.213*|0.246**|0.139*|0.104|0.279*|
||(1.727)|(1.764)|(1.869)|(1.963)|(2.433)|(1.938)|(2.258)|(1.804)|(1.347)|(1.891)|
|Bank Deposits / GDP|-0.05|0|0.035|0.258|0.199|-0.027|-0.116|-0.043|-0.002|0.102|
||(-0.187)|(0.001)|(0.118)|(0.891)|(0.627)|(-0.114)|(-0.511)|(-0.147)|(-0.007)|(0.43)|
|Bank Crisis Dummy|0.273***|0.299***|0.294***|0.264***|0.286***|0.202**|0.195*|0.302***|0.273***|0.327***|
||(2.696)|(2.949)|(2.836)|(2.62)|(2.745)|(1.99)|(1.896)|(3.054)|(2.719)|(3.063)|
|Log # of Banks|-0.691***|-0.807***|-0.807***|-0.729***|-0.696***|-0.704***|-0.721***|-0.680***|-0.829***|-0.882***|
||(-3.692)|(-3.951)|(-4.002)|(-3.849)|(-3.299)|(-8.008)|(-7.905)|(-3.440)|(-4.354)|(-2.771)|
|Bank Capital / Assets|0.039*|0.019|0.022|0.021|0.026|0.012|0.013|0.013|0.016|0.018|
||(1.826)|(0.828)|(0.942)|(0.856)|(1.121)|(0.555)|(0.576)|(0.59)|(0.675)|(0.553)|
|Liquid Assets Ratio|0.029**|0.035**|0.034**|0.039***|0.036**|0.039***|0.036***|0.041***|0.036**|0.039***|
||(2.259)|(2.483)|(2.36)|(2.685)|(2.561)|(3.002)|(2.782)|(2.965)|(2.529)|(2.688)|
|log GDP/cap|-0.007|0.025|0.02|-0.049|-0.169|-0.077|0.045|0.022|0.041|-0.062|
||(-0.067)|(0.222)|(0.166)|(-0.337)|(-1.092)|(-0.242)|(0.155)|(0.191)|(0.409)|(-0.456)|
|GDP/cap growth|-0.033**|-0.039**|-0.040**|-0.049***|-0.039**|-0.058***|-0.059***|-0.048***|-0.037**|-0.048**|
||(-2.283)|(-2.435)|(-2.095)|(-2.946)|(-2.543)|(-4.015)|(-4.028)|(-3.384)|(-2.306)|(-2.196)|
|**_Global integration and Financial O_**|**_penness_**||||||||||
|Stock mkt turnover|0.130***||||||||||
||(2.933)||||||||||
|Chin-Ito Financial Openness||0.100**|||||||||
|||(1.971)|||||||||
|Trade / GDP|||-0.001||||||||
||||(-0.707)||||||||
|KOF Social globalization||||0.015|||||||

32
Other external costs of traffic accidents include non-fatal injuries, property damage and medical burdens borne by third parties, and the tax revenue component of productivity losses. However, for lower and middle income countries these additional costs are fairly modest relative to those from fatalities. Following detailed estimates for Chile in Parry and Strand (2010) we scale up external costs by 12 percent to make some adjustment for these broader effects, to leave an overall cost of $270 million.

Based on Parry and Small (2010) we assume that external accident costs per _vehicle_ mile are the same for cars, buses, and microbuses. Buses are larger, and therefore pose greater damage risk in a collision for a given speed. However, offsetting this is that they typically are driven at slower speeds than cars, and crash less often as they are driven by professionals. Dividing the above cost figure by total road vehicle mileage gives an external cost for all vehicles of 8.7 cents per vehicle mile.

_Summary of External Costs_ . Relating the above discussion to externalities that determine the optimal transportation prices in (A1)-(A6), we have the following. _EXTG_ = 72 + 9 = 81 cents per gallon. _EXTA_ = 9.7 + 8.7 = 18.4 cents per vehicle mile. _EXTMB_ = 29.2 + 8.7 = 37.9 cents per vehicle mile. _EXTBM_ = 1.3 and _EXTRM_ = 0 cents per passenger mile (after adding external costs per vehicle mile and dividing by occupancy).

_Elasticities_ . There is a large empirical literature on gasoline price elasticities for advanced industrial countries, especially the United States. Surveys by Goodwin et al. (2004) and Glaister and Graham (2002) put the long-run elasticity at around –0.6 to –0.7, while assessments by US DOE (1996) and Small and Van Dender (2006) suggest an elasticity of around –0.4. Limited evidence for middle and lower income countries is broadly in line with these estimates (e.g., Eskeland and Feyzioglu 1994, Galindo 2005).

The empirical literature also suggests that about a half to two-thirds of the elasticity comes from long-run improvements in vehicle fuel economy, and the remainder from reduced vehicle use. We might expect a somewhat larger mileage–fuel price response for GCMA than in the United States, given the wider availability of transit alternatives to private car use, and the greater feasibility of walking to destinations in the (compact) GCMA.

34
<!-- Start of picture text --> 120<br>o 100<br>#a<br>G 380<br>i<br>a<br>ER 60<br>E 6 40<br>aE<br>eo<br>8 * 320<br>e<br>y 0<br>a 2003 2009 2003 2009<br>Bottom 40 (persistent group) B40 to T60 T60 to B40<br>(graduates) {entrants}<br><!-- End of picture text -->
Table 3: Description of Main Data and Sources

|Variable|Description|Source|
|---|---|---|
|Log of Cost|Log of unit cost of a particular road work<br>activity (1984–2008)|ROCKS dataset, World Bank|
|Estimate|=1 if estimated costs|ROCKS dataset, World Bank|
|Contract|=1 if contracted costs|ROCKS dataset, World Bank|
|Actual|=1 if actual costs|ROCKS dataset, World Bank|
|Flat|=1 if terrain is fat|ROCKS dataset, World Bank|
|Hilly|=1 if terrain is hilly<br>|ROCKS dataset, World Bank|
|Mountainous|=1 if terrain is mountainous|ROCKS dataset, World Bank|
|Rolling|=1 if terrain is rolling|ROCKS dataset, World Bank|
|Foreign frm or JV|=1 if the work activity was carried out by a<br>foreign frm or joint venture|ROCKS dataset, World Bank|
|World Bank|=1 if the work activity was fnanced by the<br>World Bank|ROCKS dataset, World Bank|
|Bilateral Donor|=1 if the work activity was fnanced by a<br>bilateral donor|ROCKS dataset, World Bank|
|Log of Ruggedness|Log of Terrain Ruggedness Index, represent-<br>ing the average ruggedness of a country<br>measured as hundred of meters of elevation<br>difference for grid points 926 meters apart|Nunn and Puga (2012)|
|Log of Distance to<br>the nearest ice free<br>|Log of average distance to nearest ice-free<br>coast (1000 km)|Nunn and Puga (2012)|
|coast|||
|Log of Rainfall|Log of yearly precipitation in 100s mm,<br>2000–2008|Dell et al. (2012)|
|Population<br>Den-<br>sity|Population Density (100 people per square<br>km), 1960-2012|World Development Indicators|
|Log<br>of<br>Surface<br>Area|Log of Surface Area (1,000 square kilome-<br>ters)|World Development Indicators|
|Log of GDP|Log of GDP per capita (1984–2008), con-<br>stant 2000 US$|World Development Indicators|
|ACD Confict|=1 if country is in a confict|Armed Confict Dataset|
|WGI Instability|Index of political instability and violence<br>from World governance Indicators (1996–<br>2012), redefned to: -1.26 (lowest) to 2.21<br>(highest)|World Governance Indicators|
|TI Corruption|Corruption index from Transparency Inter-<br>national, survey 2008, rescaled to 0.1 (low-<br>|Transparency International|
||est corruption), 5.6 (highest corruption)||
|WGI Corruption|<br>Index of corruption from World Governance<br>Indicators (1996-2012), redefned to: -1.45<br>(lowest corruption) to 1.6 (highest corrup-<br>tion)|World Governance Indicators|
|PIMI|Public<br>Investment<br>Management<br>Index,<br>2011, measured on scale from 0 (worst) to<br>4 (best)|Dabla-Norris et al. (2012)|
|Log of DB Contract|Number of days it takes to enforce a con-|Doing Business Indicators|
||tract,from DoingBusiness Indicators 2007||

_Source_ : Datasets as outlined in table.

32
must prove homeownership prior to receiving _Piso Firme_ . Neighborhoods are surveyed door-to-door, and housing units that meet these criteria are offered up to 50 square meters (538 square feet) of cement valued at about 1,500 Mexican pesos (approximately 150 US dollars). The program covers the cost of the cement, while the household supplies the labor input needed to lay the floor. The cement is delivered by large cement trucks that roll through these neighborhoods, spreading the cement, house-by-house. Beneficiaries are informed of the delivery date and are asked to prepare the rooms following a set of technical specifications. The cement is then poured, and each family smoothes out the floor according to the instructions they received.

One of the primary objective of the program is to improve the physical environment in which families live. Replacing dirt floors with cement floors improves the cleanliness, warmth and aesthetics of the home environment. As a result, family members may enjoy spending time in the home with cement floors more than they did with dirt floors. Therefore, we hypothesize that one would be happier and less depressed living in a cleaner, warmer and more aesthetically pleasing environment.

Another primary objective of the program is to improve child health by reducing the presence of intestinal parasitic infestations. Most parasites live and breed in feces and are transmitted to humans when ingested. Fecal matter enters the house through various modes of transmission, including on the shoes of people, through animals, spillage of unclean water, and from young children with inadequate diapers. Dirt floors provide a vector for parasites to infest people, especially young children, since fecal matter tends to remain on the floor because it is less easy to spot and dirt floors are not easy to clean. Young children who live in houses with dirt surfaces are much more likely to ingest fecal material than children who reside in houses with cement floors.

Parasitic infestations pose serious threats to young children, as more than 3 million children die from parasitic diseases each year (World Bank, 2002). Among those who survive, parasitic infestations are associated with diarrhea and micronutrient malnutrition, which often leads to iron-deficiency anemia, protein-energy malnutrition, and enlargement of the liver and spleen (see, among others, Anderson and May, 1991; and Hesham et al., 2004). Currently anemia, which leads to slow cognitive development, is a 6
ethnic minority men and Spanish-speaking men is 5.1 percentage points. Ethnic minority women face an _additional_ 7.3 percentage point disadvantage because of the intersection of gender and ethnicity, which results in a _cumulative disadvantage_ of 18.7 percentage points relative to Spanish-speaking men.

The contribution of gender and ethnicity to the cumulative impacts on ethnic minority women vary across countries. In Mexico, belonging to an ethnic minority group explains a large portion of the gap in the literacy rate between ethnic minority women and Spanish-speaking men. Even ethnic minority men in Mexico, who only face ethnicity-based disadvantage, are 13.4 percentage points less likely to be literate than Spanish-speaking men. While the disadvantage faced by ethnic minority women is larger in magnitude in Mexico compared to Bolivia (22.1 versus 18.7 percentage points), the gap between them and their male counterparts is smaller. In Peru, the relative contributions of gender and ethnicity are not as skewed (4.6 and 3.2 percentage points, respectively) and ethnic minority women face a relatively smaller but significant cumulative disadvantage, at 12.4 percentage points.

**Table 3. Literacy Regression Results - Probit**

_marginal effects, using Male Reference Language Speakers as reference group_

||Bolivia(2001)|Mexico(2010)|Peru(2007)|Sierra Leone(2004)|
|---|---|---|---|---|
|Age|-.004 ***|-.005 ***|-.002 ***|-.006 ***|
|Rural resident|-.091 ***|-.069 ***|-.113 ***|-.305 ***|
|Female|-.063 ***|-.024 ***|-.046 ***|-.213 ***|
|Minority|-.051 ***|-.134 ***|-.032 ***|-.006 **|
|Female * Minority|-.073 ***|-.063 ***|-.046 ***|.011 ***|
|Obs.|561,126|8,541,041|2,028,394|307,066|
|Note: Regressions in|clude individuals age|d 12 and above. **|*p<0.01,**p<0.05|,*p<0.10.|

In Sierra Leone, the opposite result is found, where the difference in literacy is almost exclusively across genders rather than across ethnic minority status. For example, ethnic minority men are 0.6 percentage points less likely to be literate than men in the reference group. On the other hand, women in the reference group whose mother tongue is English and/or Mende are 21.3 percentage points less likely to be literate than men in the same group. For ethnic 18
concentration as buyers and suppliers establish relationships based on accumulated quality records. Thus, there should be more variability in the degree of concentration for those goods. Accordingly, we report in Table 9 regression results of the standard deviation of concentration indices “within products” (i.e. calculated over the entire sample period for each product) on Rauch’s index of production differentiation. Note that, in so doing, we reduce the sample’s dimensionality from three (importer  product  time) to two (importer  product). We estimate the regressions with importer fixed effects.

Table 9 Regression results, volatility of concentration on product differentiation

||σ_herf<br>(1)|σ_theil<br>(2)|σ_nber<br>(3)|
|---|---|---|---|
|Reference price|0.010|0.034|0.741|
||(2.89)***|(4.90)***|(6.56)***|
|Differentiated|0.025|0.100|3.264|
||(7.57)***|(15.39)***|(20.02)***|
|Constant|0.160|0.290|0.741|
||(20.58)***|(20.80)***|(4.21)***|
|Observations|3'122|3'122|3'122|
|R-squared|0.07|0.13|0.24|
|Importer FE|yes|yes|yes|

Notes: Robust t-statistics in parentheses. Dependent variable: standard deviation of concentration index (Herfindahl, Theil or number of suppliers); sample is a cross section of SITC4 products (not all SITC4 goods are coded in Rauch’s database, hence the relatively small number of observations). Results reported use Rauch’s “liberal” classification; using his “conservative” classification yields almost identical results.

Results are as suggested by the model. The volatility of concentration indices is higher for differentiated products, and the difference between categories is very large. For instance, the standard deviation of the number of suppliers is, on average, 2.35 for homogenous goods. For differentiated goods, ceteris paribus, it rises to 5.61 (2.35 + 3.26), a 138% increase. This indeed suggests alternating periods of diversification and concentration.

The model also suggests that at the end of quality-search phases, concentration should take place on the best-performing suppliers. This is a hypothesis that is 29
Policy Research Working Paper 8020

# **Abstract**

Private savings play a pivotal role in financing development and sustaining growth. Recently, there have been many theoretical developments that underpin key determinants of savings behavior, many of which merit empirical investigation. Understanding the dynamics of the determinants of savings is crucial to inform economic policy and devise reform programs. This paper builds on earlier work examining the stability of the long-run relationship between the real interest rate, financial saving, and total saving during 1960 to 1990. The paper extends the scope of the empirical investigation of the determinants of private savings behavior in the Arab Republic of Egypt, and considers the effect of financial development. The analysis uses quarterly data covering 1991–2010, adopting a vector error correction model. The key findings attest that private savings in Egypt follow the Life Cycle Model in the long term. Controlling for population growth, the analysis finds that the real interest rate and financial development are key determinates for real private savings in the long run. The negative long-run relation between the real interest rate and private savings holds under the proposed model structure as well as for that in the earlier work. However, in the short run, inflation and exchange rate movements are key determinants for private savings decisions. Robust economic policies, inclusive of macroeconomic and monetary measures, are prerequisites for maximizing private savings and financing growth in Egypt.

This paper is a product of the Office of the Senior Vice President for 2030 Development Agenda, UN Relations and Partnerships. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at arostom@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team