rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6737

# **Abstract**

This paper estimates two sources of benefits related to sanitation infrastructure access on early childhood health: a direct benefit a household receives when moving from open to fixed-point defecation or from unimproved sanitation to improved sanitation, and an external benefit (externality) produced by the neighborhood’s access to sanitation infrastructure. The paper uses a sample of children under 48 months in rural areas of India from the Third Round of District Level Household Survey 2007– 08 and finds evidence of positive and significant direct benefits and concave positive external effects for both improved sanitation and fixed-point defecation. There is a 47 percent reduction in diarrhea prevalence between children living in a household without access to improved sanitation in a village without coverage of improved sanitation and children living in a household with access to improved sanitation in a village with complete coverage. One-fourth of this benefit is due to the direct benefit leaving the rest to external gains. Finally, all the benefits from eliminating open defecation come from improved sanitation and not other sanitation solutions.

This paper is a product of the Sustainable Development Unit, South Asia Region; and the Water and Sanitation Program, Sustainable Development Network. 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 landres@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
# **TABLE 1 Capitalization of the stock market of Argentina, Chile, Mexico, and the Philippines, 1990-1994 (in million of U.S. dollars)**

|**Market**|1990|]1991<br>||<br>1992||<br>1993||<br>1994|
|---|---|---|---|---|---|
|Argentina|3 268|18 509|18 633|r43 967|36 864|
|Chile|13 645|27 984|29 644|44 622|68 195|
|Mexico|32 725|98 178|139 061|200 671|130 246|
|Philippines|5 927|10 197|13 794|40 327|55 519|

Source: International Finance Corporation, _Emerging stock markets factbook, 1995._ TABLE 2 **Market Concentration in the IFC General Indexes, End - 1994**

|**Market**|IFCG Index share of total<br>market capitalization|10 largest stocks' share of<br>total market capitalization|
|---|---|---|
|Argentina|50.9|41.7|
|Chile|66.1|46.4|
|Mexico|63.9|33.8|
|Philippines|54.4|44.3|

Source: Intemational Finance Corporation, _Emerging stock markets factbook, 1995._ For each country, we selected a newspaper which has a large circulation and is of particular interest to the business community.<sup>8</sup> Environmental news were collected in each of the countries over the period 1990-94 inclusively. Once these news were In the United States, the Wall Street Journal is generally the preferred source of information for conducting event-study analyses. In Argentina, environmental news were collected from the newspaper **_La_** _Nacion_ (daily circulation of approximately 250 000; ranks 3rd in Buenos Aires); in Chile, we used _El Mercurio_ (daily circulation of approximately 200 000; ranks 3rd in Santiago); in Mexico City, we used _Excelsior_ (daily circulation of 200 000; ranks 7th in Mexico City); finally, in the Philippines, news were collected from the _Manila Bulletin_ (daily circulation of 300 000; ranks 3rd in Manila). All newspapers were available from the Library of Congress for most of the period 1990-94. Information from missing issues was obtained directly from the publishers of the papers in the respective countries.

8
# Annexes

Table A1 : Analysis of expenditure on infrastructure services (2002 versus 1997) in US$

||Quintil<br>1st|es<br>2nd|3rd|4th|5th|Total|
|---|---|---|---|---|---|---|
|**November 2002 (OPSM)**|||||||
|Household resources|||||||
|Average household expenditure|123|158|201|255|358|218|
|Average household income|97|178|237|312|559|256|
|Average expenditure on infrastructure services|21.4|28|28.2|29.2|37.9|29.1|
|Water and sewerage|4.67|5.1|5.7|5.4|6.8|5.5|
|Electricity|8.2|11.0|10.0|9.6|11.2|9.95|
|Fuels|6.5|7.3|7.9|8.1|8.9|7.8|
|Telecommunications|1.9|4.4|4.4|5.9|10.9|5.79|
|Public transport (Metropolitan Area Head of Household commuting<br>only)|12.0|17.2|20.4|18.3|17.8|17.7|
|Average household expenditure share for infrastructure (%)|16.2|15.4|13.9|12.6|11.5|13.4|
|Water and sewerage|3.5|2.8|2.8|2.3|2.1|2.6|
|Electricity|6.2|6.1|5.0|4.2|3.4|4.6|
|Fuels|5.0|4.0|3.9|3.5|2.7|3.6|
|Telecommunications|1.5|2.4|2.2|2.6|3.3|2.7|
|Public transport (Metropolitan Area Head of Household commuting|||||||
|<br>only)|9.8|10.9|10.1|7.2|5.0|8.1|
|Average household income share for infrastructure (%)|22.1|15.7|11.9|9.3|6.8|11.4|
|Water and sewerage|4.8|2.9|2.4|1.7|1.2|2.2|
|Electricity|8.5|6.2|4.2|3.1|2.0|3.9|
|Fuels|6.8|4.1|3.3|2.6|1.6|3.1|
|Telecommunications<br>Public transort (Metroolitan Area Head of Household commutin|2.0|2.5|1.9|1.9|1.9|2.3|
|p p g<br>only)|12.4|9.6|8.6|5.9|3.2|6.9|
|**May 1997 (EPH)**|||||||
|Average household expenditure|110|187|268|390|750|341|
|Average expenditure on infrastructure services|12.5|16.6|21.3|27.1|37.6|23.0|
|Water, sewerage and electricity|5.7|7.4|8.4|10.1|12.2|8.8|
|Fuels|2.7|4.7|7.1|10.1|16.9|8.4|
|Telecommunications and post|3.7|4.7|5.7|6.7|8.1|5.7|
|Public transport (Metropolitan Area households that declare journeys)|16.9|20.1|21.7|18.2|17.6|19.0|
|Average household expenditure share for infrastructure (%)|11.3|8.9|7.9|7.0|5.0|6.7|
|Water, sewerage and electricity|5.3|3.9|3.2|2.6|1.6|2.6|
|Fuels|3.4|2.5|2.1|1.8|1.1|1.7|
|Telecommunications and post|2.6|2.5|2.6|2.6|2.3|2.4|
|Public transport(Metropolitan Area households that declarejourneys)|9.1|7.7|6.2|3.7|2.0|4.3|

_Source_ : OPSM and EPH Surveys, May 1997 and November 2002 53
18 _Table 5: Results for log per capita total income_

|VARIABLE||_log pe _<br>|_r capita inc_<br>|_ome_<br>||
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**-0.062**||**-0.071**|**-0.085**|**-0.085**|
||**-2.450**||**-1.980**|**-2.860**|**-2.860**|
|SAH_POS_CH_LGE||-0.029||||
|||-0.180||||
|SAH_POS_CH_SML||0.051||||
|||0.760||||
|SAH_NEG_CH_SML||-0.096||||
|||-1.520||||
|SAH_NEG_CH_LGE||-0.191||||
|||-1.430||||
|POOR X SAH_CHANGE|||0.019|||
||||0.330|||
|HI X SAH_CHANGE||||0.096||
|||||1.480||
|URB_HI X SAH_CHANGE|||||0.101|
||||||1.340|
|RUR_HI X SAH_CHANGE|||||0.084|
||||||0.810|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|-0.072|||||
||-0.816|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.131||||
|||-0.694||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.002||||
|||0.009||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||-0.052|||
||||-1.297|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||0.011||
|||||0.195||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||0.017|
||||||0.245|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||-0.001|
||||||-0.009|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
- 38 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

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Gilbert, A (1996). The Mega-City in Latin America. United Nations University Press, USA (1996) Harari, M (2016). Cities in bad shape: Urban geometry in India. Working paper. The Wharton School, University of Pennsylvania.

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**Panel B. Numbers for 2015**

Notes: In both panels the table shows information based on sampling plans, FVA lab sample data set, and EXIM data set. The data sets are combined based on the broad product group.

To formally test the degree to which actual sampling activity adheres to the sampling plan we estimate Equation (2), which links sampling activity with planned samples by OLS. For this analysis we rely on the sampling plan data at the product group-country or group of countries-year level and the actual number of shipments sampled from the FVA lab sample data set at the same level. Table 2 presents the estimates and inference is based on Huber-White standard errors robust to heteroskedasticity. Columns (1)-(4) make use of the full sample of food of animal origin and animal feeds’ product groups while columns (5)-(8) exclude from the sample milk and milk products which deviated largely from the sampling plan in 2014 due to the toxic scare in Europe. Pet products in 2015 were also excluded as the number of planned shipments to be sampled changed dramatically due to a change in the group of countries from which shipments had to be sampled and the number of shipments to be sampled. The estimated coefficient on the planned number of import shipments to be sampled is actually negative and in some cases statistically significant when the full sample is used. However, when the two product groups are excluded, there is a positive and significant coefficient on the planned number of import shipments to be sampled in column (6) when broad product group fixed effects are included as controls. Product group fixed effects are especially important in the context of FVA samples, because they act as partial controls for cross-product variation in the expected harm from a non-compliant shipment.

Albeit weak, our regression evidence is indicative of rough adherence to the sampling plan by FVA border agents in their sampling activity. Moreover, the fact that the coefficient on the planned number of import shipments to be sampled is larger than 1 suggests that FVA border agents are deviating from the 26
# **You Are What (and Where) You Eat: Capturing Food Away from Home in Welfare Measures**<sup>**1**</sup>

**Gabriela Farfan, Maria Eugenia Genoni, and Renos Vakis**

<u>Keywords: poverty, inequality, food consumption, welfare measurement, data collection. JEL codes: O1, C81, I31, I32.</u> 1 We are grateful to colleagues at the National Statistics Office (INEI) in Peru, and especially Nancy Hidalgo, Lucia Gaslac, and Oscar Perfecto for their countless help and advice during the data preparation and analysis. The paper benefited from comments from Gero Carletto, Dean Jolliffe, Talip Kilic, Nobuo Yoshida, members of the National Poverty Commission in Peru as well as participants of the 2014 Food Consumption Conference in Rome and the Side Event on Improving Food Consumption Measurement in National Household Surveys at the 46<sup>th</sup> Session of the UN Statistical Commission. Financial support for this research has been received from the World Bank’s KCP window as well as FAO’s funding on food consumption research. Contacts: <u>gfarfan@worldbank.org and mgenoni@worldbank.org and rvakis@worldbank.org.</u>
respect to a counterfactual scenario without gender gaps) for each of the countries in our sample. The third column shows the fraction of females who are excluded from the labor force, while columns 4 and 5 show the fraction of women excluded from employership and self-employment respectively. Column 6 shows the estimated loss in income per capita due to the three frictions, while in column 7 we can see the income loss due to the gender frictions in employership and self-employment. The dierence between these last two columns - not displayed here - would correspond to the income loss generated by the gaps in the labor force participation.<sup>8</sup> As we can see in the table, Kosovo in 2012 has the highest income loss, 28.2%, followed by Turkey with a loss of 25.3% in 2007 and 22% in 2013. Croatia is the country with the smallest income loss in both years. In column 7 we can see that Kosovo and Turkey are also the countries displaying the highest income loss due to gender frictions in entrepreneurship.<sup>9</sup> Figure 4 plots the last two columns of table 5 for the rst and the last available years.

Table 5: Income loss due to gender gaps, by country

||Year|LFP<br>friction<br>(%)|Employer's<br>friction<br>(%)|Self-employed's<br>friction<br>(%)|Total income<br>loss from<br>Gender Gaps<br>(%)|Income loss<br>due to<br>Entrepr. Gap<br>(%)|
|---|---|---|---|---|---|---|
|Albi|2008|28.14|52.29|33.83|13.24|4.3|
|ana|2012|50.16|29.64|38.73|19.87|2.95|
|Bosnia and Herzegovina|2013|38.36|48.1|13.93|16.41|4|
|Ci|2004|18.14|52.95|21.78|10.1|4.41|
|roata|2009|20.52|38.47|0|9.74|3.01|
|Kosovo|2012|68.6|83.07|75.19|28.21|8.67|
|Macedonia|2007|33.11|53.76|77.14|15.53|5.29|
||2011|34.84|49.22|71.73|15.75|4.78|
|Mt|2007|31.38|35.23|49.35|13.54|3.21|
|onenegro|2011|34.62|59.81|-|-|-|
|Sbi|2006|31.29|68.56|58.95|15.81|6.56|
|era|2010|31.84|69.04|68.38|16.2|6.89|
||2007|58.94|88.22|60.15|25.26|8.7|
|Turkey|2013|48.7|82.56|66.13|22.03|8.29|

> 8Our paper focuses on gender gaps in entrepreneurship and labor force participation. We don't have very good data on gender wage gaps and therefore we only calibrate the implied wage gap suggested by our model, without using actual data on this gap. In particular, whenever our model predicts too few self-employed women relative to the data, we impose a wage gap between men and women that pushes women towards becoming self-employed rather than workers. As it turns out, however, we only need to impose this wage gap in Croatia in 2009, and the gap is less than 3%.

> 9In our exercise we assume that countries do not have wage gaps unless we need to introduce them to match the observed fraction of female self-employed. For the set of countries studied here, only Croatia in 2009 requires the introduction of a wage gap, which is equal to about 2.5% of the wage rate that year.

9
# **_1. Introduction_**

Farmer welfare in developing countries depends directly on the price at which they sell their produce (Jensen, 2007). Bargaining theory indicates that asymmetric information on the prevailing price between farmers and traders is detrimental to farmer welfare. Providing price information to farmers could be beneficial by increasing their bargaining power when they negotiate with traders (Jaleta and Gardebroek, 2007). They should get better prices for their produce, which, in turn, should translate into higher consumption. Press reports indicate that Filipinos farmers use mobile phones as a marketing tool and to get price information. For example, Arnold (2001) mentions that “vegetable farmers (...) were using mobile phones to market their produce in Manila.” In this paper, we explore the impact of information technologies on farmer welfare in developing countries. In particular, we assess whether the growth rate of per capita consumption is larger for farmers purchasing a mobile phone. We also discuss channels through which those impacts might materialize.

The paper makes important contributions to on-going debates concerning the impact of information technology on welfare in developing countries. First, while the literature has so far focused on the impact of information technologies on the functioning of markets, we assess their impacts on farmer welfare directly. We do so combining spatially coded data on mobile phone coverage with household panel data collected from farmers in 135 villages in some of the poorest areas of the Philippines in 2003 and 2006. Thus, we can assess if _changes_ in consumption can be attributed to mobile phone ownership, while controlling for baseline consumption, changes in household characteristics and shocks experienced by the household in the 2003-2006 period.

Second, we allow for selection on unobservables, as the decision to purchase a mobile phone is likely to be endogenous ( _i.e._ , it can be difficult to discern whether buying a mobile phone generates increased consumption or increased consumption opportunities 2
|Top manager is|13.683***|13.582***|13.572***|13.473***|13.371***|
|---|---|---|---|---|---|
|female Y:1 N:0||||||
||(0.789)|(0.783)|(0.784)|(0.756)|(0.750)|
|Age of firm (logs)||-0.062|-0.032|-0.105|0.042|
|||(0.378)|(0.385)|(0.380)|(0.385)|
|Exports (% of sales)||0.040***|0.039***|0.034**|0.030**|
|||(0.014)|(0.014)|(0.014)|(0.014)|
|Foreign ownership<br>||0.025***|0.024**|0.021**|0.024***|
|(%)||||||
|||(0.010)|(0.010)|(0.009)|(0.009)|
|Firm offers formal<br>||2.284***|2.199***|1.828***|1.992***|
|training to workers||||||
|Y:1 N:0||||||
|||(0.597)|(0.600)|(0.561)|(0.549)|
|Inadequately||-0.176|-0.109|-0.073|-0.001|
|educated workers:||||||
|Obstacle severity (0-||||||
|4 scale)||||||
|||(0.209)|(0.224)|(0.214)|(0.209)|
|Firm registered|||1.106|0.853|0.897|
|when started||||||
|operations Y:1 N:0||||||
||||(0.811)|(0.749)|(0.737)|
|Firm experienced<br>|||0.503|0.381|0.462|
|losses due to crime||||||
|Y:1 N:0||||||
||||(0.624)|(0.624)|(0.602)|
|Access to finance:|||-0.472**|-0.418**|-0.400**|
|Obstacle severity (0-<br>||||||
|4 scale)||||||
||||(0.224)|(0.210)|(0.201)|
|Tax rates: Obstacle|||0.177|0.162|0.086|
|severity (0-4 scale)||||||
||||(0.239)|(0.223)|(0.222)|
|Firm has own|||0.244|0.069|-0.063|
|website Y:1 N:0||||||
||||(0.562)|(0.551)|(0.536)|
|Fertility rate||||0.499|21.890*|
|||||(0.954)|(11.359)|
|Gender parity in||||0.075***|-0.137|
|workplace laws||||||
|||||(0.022)|(0.382)|
|Gender parity in||||20.147|611.998**|
|primary education||||||
|enrollment||||||
|||||(15.616)|(298.909)|

76
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.11. Proportion of direct flights and speed of air service in East Africa**

a. Directness of service

|. Directness of s|ervice<br>All flights|Direct flights|b. Spee|d of service|di|pia|D<br>a|estinati<br>da|on<br>n|ania|da|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Burundi<br>Ethiopia|42<br>75|31<br>54|||Burun|Ethio<br>|Keny<br>|Rwan<br>|Suda|Tanz|Ugan<br>|
|<br>Kn|193|168||Burundi||460|410|296|||512|
|ea||||||||||||
|<br>Rd|53|49||Ethiopia|460||582|620|497|557|610|
|wana<br>Sudan|33|24|n|Kenya|350|582||363|298|275|436|
|<br>Tanzania|<br>61|<br>36|Origi|Rwanda|**_16_**|620|305|||590|**_73_**|
|<br>Uganda|<br>99|<br>78||Sudan||572|330||||513|
|||||Tanzania||662|**_193_**||||661|
|||||Uganda|256|610|440|**_176_**|332|307||

Source: Bofinger 2009 .

Ethiopian Airlines and Kenya Airways have been gaining a larger share outside of East Africa in markets in West and Central Africa (table 2.13). They are using fifth-freedom provisions to take over routes discontinued following the collapse of major carriers in West Africa. One effect of the two carriers’ expansion is a slow rise in east-west traffic.

**Table 2.12. Grading of the level of the implementation of the Yamoussoukro Decision**

|**Community**|**General status of YD implementation**|**Status of air services liberalization**|**Overall**<br>**implementation**<br>**score**|
|---|---|---|---|
|AMU|No implementation.|No liberalization within the AMU initiated, but need<br>is recognized.|1|
|Banjul Accord Group|Principles of the YD agreed upon in a<br>multilateral air services agreement.|Up to fifth freedom granted, tariffs are free, and<br>capacity/frequency is open.|4|
|Economic and<br>Monetary Community<br>of Central Africa|Principles of the YD agreed upon in an<br>air transport program. Some minor<br>restrictions remain.|Up to fifth freedom granted, tariffs are free, and<br>capacity/frequency is open. Maximum two carriers<br>per state may take part.|5|
|Common Market for<br>Eastern and<br>Southern Afric|Full liberalization agreed upon (―legal<br>Notice No. 2‖), but application and<br>implementation remain pending until a<br>joint competition authority is<br>established.|Pending. Operators will be able to serve any<br>destination (all freedoms), and tariffs and<br>capacity/frequency will be free.|3|
|East African<br>Community|EAC council issued a directive to<br>amend bilaterals among the EAC states<br>to conform with the YD.|Air services are not liberalized, as the amendments<br>of bilaterals remain pending.|3|
|Southern African<br>Development<br>Community|No steps taken toward implementation,<br>although the civil aviation policy<br>includes gradual liberalization of air<br>services within the SADC.|No liberalization has been initiated.|2|
|West African<br>Economic and<br>Monetary Union|The YD is fully implemented.|All freedoms, including cabotage, granted. Tariffs<br>have been liberalized.|5|

31
**Table 8 Pooled OLS Method with Alternative Indicators for Investment Climate**

||Rule of<br>Law|LPI|Infrastructure|Property|Property(IEF)|Power<br>Outage|
|---|---|---|---|---|---|---|
|RealGDP(PPP)|0.993|0.870|0.978|0.994|1.017|1.009|
||(0.031)***|(0.037)***|(0.030)***|(0.034)***|(0.032)***|(0.037)***|
|log(TTRI)|-0.309|-0.330|-0.397|-0.411|-0.358|-0.297|
||(0.091)***|(0.092)***|(0.084)***|(0.096)***|(0.097)***|(0.110)***|
|Log(Area)|-0.087|-0.072|-0.079|-0.091|-0.095|-0.095|
||(0.038)**|(0.034)**|(0.040)**|(0.041)**|(0.039)**|(0.033)***|
|Remoteness|-0.419|-0.482|-0.504|-0.533|-0.593|-0.364|
||(0.158)***|(0.159)***|(0.163)***|(0.175)***|(0.166)***|(0.169)**|
|Export Growtht,t-1|0.106|0.131|0.134|0.121|0.104|0.104|
||(0.046)**|(0.040)***|(0.048)***|(0.047)**|(0.048)**|(0.043)**|
|log(MATTRI)|-0.164|-0.218|-0.526|-0.536|-0.766|-0.049|
||(0.043)***|(0.049)***|(0.170)***|(0.167)***|(0.348)**|(0.068)|
|log(MATTRI)*Indicator|0.067|0.121|0.299|0.058|0.157|-0.093|
||(0.039)*|(0.056)**|(0.117)**|(0.025)**|(0.085)*|(0.052)*|
|Indicator|0.292|0.376|0.599|0.114|0.505|-0.279|
||(0.054)***|(0.107)***|(0.195)***|(0.039)***|(0.110)***|(0.071)***|
|Constant|3.910|7.399|4.321|4.457|3.187|3.343|
||(1.578)**|(1.438)***|(1.591)***|(1.833)**|(1.674)*|(1.857)*|
|Observations|205|198|193|189|202|110|
|R-squared|0.954|0.959|0.954|0.954|0.950|0.960|

* Pooled OLS results. Robust standard errors clustered by country are in parentheses. All regressions control for year fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

18