rafmacalaba/datause-encoder-data huggingface dataset:
I POLICY **RESEARCH WORKING PAPER** 2802

# Summary findings

Estache, Manacorda, and Valletti review the stylized facts on regulatory reform in telecommunications and its effects on telecommunications development and Internet penetration in Latin America. Relying on data from the International Telecommunication Union, the Information for Development Program (InfoDev), and the World Bank for 1990-99, the authors then test econometrically the determinants of the differences in Internet penetration rates across Latin America.

The results show that effective implementation of the reform agenda in telecommunications regulation could accelerate adoption of the Internet in Latin Americaeven though it is only part of the solution (income levels, income distribution, and access to primary infrastructure are the main determinants of growth in Internet connections and use).

Regulation will work by cutting costs. Cost cutting will require that regulators in the region take a much closer look at the design of interconnection rules and at the tradeoffs that emerge from the complex issues involved. It will also require a commitment to developing analytical instruments, such as cost models, to sort out many of the problems. Appropriate cost rnodels will generate benchmarks that are much more consistent with the local issues and with the local cost of capital than international benchmarks will ever be for countries in unstable macroeconomic situations. Cost cutting will require an equally strong commitment to imposing regulatory accounting systems that reduce the information asymmetries that incumbents use to reduce the risks of entry.

All these changes will ultimately require a stronger commitment by competition agencies, since in many countries a failure to negotiate interconnection agreements will raise competition issues just as often as it will raise regulatory questions.

This paper-a product of the Governance, Regulation, and Finance Division, World Bank Institute-is part of a larger effort in the institute to increase understanding of infrastructure regulation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Gabriela Chenet-Smith, room J3-304, telephone 202473-6370, fax 202-676-9874, email address bchenet@worldbank.org. Policy Research Working Papers are also posted on theWeb athttp://econ.worldbank.org. The authors maybe contacted ataestache@worldbank.org, m.manac:orda@lse.ac.uk, or t.valletti@ic.ac.uk. March 2002. (42 pages)

> _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_ **_view_** _of the World Bank, its Executive Directors, or the countries they represent._

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<u>Table 2: Balance table.</u>

||M|ean|||
|---|---|---|---|---|
|1997 Variables|Control|Treatment|Diff|p-value|
|**Household head characteristi**|**cs**||||
|Female*|0.0571|0.0583|0.0012|0.8868|
|Age|40.5610|40.1865|-0.3745|0.4146|
|Years of schooling|2.8088|3.0293|0.2205|0.1457|
|**Income and productive activi**|**ties**||||
|Num. productive ha of land|1.7492|1.7708|0.0215|0.9048|
|log (HH monthly income PC)|4.8815|4.8130|-0.0685|0.1003|
|**HH. Characteristics and asse**|**ts**||||
|Dirt foor*|0.7271|0.6945|-0.0326|0.2831|
|Electricity*|0.6679|0.6572|-0.0106|0.8239|
|Refrigerator*|0.0463|0.0452|-0.0011|0.9262|
|Television*|0.3751|0.3359|-0.0392|0.2528|
|Draft animals*|0.3518|0.3818|0.0301|0.3222|
|**Household demographics**|||||
|Household size|6.8539|6.8566|0.0027|0.9770|
|Num children 0-7|2.1263|2.0936|-0.0327|0.5062|
|Num children 8-17|2.1323|2.1558|0.0235|0.6716|
|Num adults_>_70|0.0692|0.0706|0.0015|0.8689|
|**Localities characteristics**|||||
|Marginalization index 1995|0.6748|0.5296|-0.1452|0.1352|
|Num. observations|1,663|2,831|||

* Dummy variable

This table tests balance in observable characteristics between the PROSPERA RCT original treatment and control with our sample. Observations are restricted to those households that were found in ENCEL 2017-18 and employed in our main estimations. Balance of variables shown are calculated with the 1997 ENCASEH data. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

11
more information on the amount she received from WIF and on her project. This could then affect what type of activity she invests in, and the amount allocated to it.

When the male spouse is targeted, there is a significant positive impact on the household wage income, though there is no impact on the likelihood of the household having a wage income (see Table 3). This increase is driven by the significant positive increase in the male wage income. When the female spouse is targeted, it also has a significant positive impact on male wage income, though not wage income of the household as a whole. When both spouses are targeted, there is a significant negative interaction effect on men’s wage income, as well as on household wage income.

_Table 6: Impact on Off-farm Income_

||Bus|iness income (b<br>Ml|irr)<br>Fl|W|age income (bir|r)|
|---|---|---|---|---|---|---|
||Household|ae<br>Involved|emae<br>Involved|Household|Male|Female|
|round 3|76.321|-49.913|255.581|-4.625|98.255|29.392|
||(188.476)|(168.807)|(183.463)|(144.739)|(111.357)|(68.959)|
|FIF*round 2|365.768|228.147|222.286|-158.059|-15.834|-108.257|
||(239.300)|(231.391)|(184.740)|(110.766)|(86.415)|(81.515)|
|FIF*round 3|128.142|54.330|-71.649|279.876**|313.946***|46.971|
||(167.992)|(149.915)|(185.314)|(129.067)|(107.459)|(71.972)|
|WIF*round2|133.627|126.369|70.332|106.956|351.390|115.817|
||(488.915)|(494.487)|(256.201)|(293.143)|(231.216)|(153.292)|
|WIF*round3|794.135|745.616|974.264**|485.318|543.118*|110.809|
||(591.934)|(496.611)|(477.290)|(296.562)|(279.745)|(138.493)|
|WIF*FIF*round2|-135.500|-350.027|19.630|-112.445|-431.851|-75.048|
||(572.702)|(606.551)|(502.686)<br>-|(369.843)|(272.420)|(176.085)|
|WIF*FIF*round3|-225.808|-225.935|1,376.317**|-698.679*|-901.911**|-4.692|
||(778.345)|(661.222)|(573.448)|(387.249)|(354.416)|(180.453)|
|Observations|2,428|2,317|2,396|2,428|2,317|2,396|
|R-squared|0.057|0.072|0.025|0.079|0.073|0.060|
|Estimates: FIF2 + WIF2 + FIF_WIF2|363.900|4.489|312.200|-163.500|-96.290|-67.490|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.210|0.988|0.418|0.530|0.474|0.605|
|Estimates: FIF3 + WIF3 + FIF_WIF3|696.500|574|-473.700|66.510|-44.850|153.100|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.236|0.204|0.073*|0.840|0.809|0.301|
|Control Mean(BL)|574.800|451.700|284.900|850.400|632.500|342|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. All the income indicators are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

24
# **6.  Incorporating Biodiversity**

Drawing on a new World Bank database, we develop a multidimensional biodiversity indicator for Lao PDR to identify concentrations of diverse species and their vulnerability to encroachment and habitat loss. The database incorporates information from thousands of digital range maps (shapefiles) provided by the International Union for the Conservation of Nature (IUCN) and BirdLife International. We also incorporate ecoregion maps provided by WWF International. We use GIS overlays to compute our biodiversity indicator for each Hansen cell in our forest clearing database. Our approach follows the methodology developed in Dasgupta and Wheeler (2016), but draws on the new World Bank database to incorporate many more dimensions of vulnerability and risk. Our approach incorporates separate measures for amphibians, mammals, reptiles, birds and WWF ecoregions.

# **6.1  Species Density**

We measure species density as the species count for each 500 m Hansen cell.

# **6.2  Species Vulnerability**

Species density provides critical information for assessing ecological risks, but at least two other elements are needed: (1) **_Geographic vulnerability_** , which can be proxied by **_endemicity_** : the proportion of each species’ range that lies within each cell. Species that reside in very few cells may be particularly vulnerable to habitat encroachment. For this exercise, endemicity treats all species equally at the national level, since each species has a total count of 1. Total endemicity for each cell -- the sum of its species endemicity measures -- assigns higher values to cells inhabited by species whose ranges are relatively limited. By implication, forest clearing in higher-value cells may be particularly destructive for remaining critical habitat.

20
levels; by contrast, uninsured loan take-up was uncorrelated with farmer education. This suggests that reduced take-up of the insured loan may have been due to the high cognitive cost of evaluating a complex insurance product. Most farmers were being exposed to an insurance product for the first time, and may not have understood the concept or the complicated payout schedule (which depended on the level as well as timing of rainfall). This explanation is consistent with evidence on the dampening effect of complexity on take-up of social programs in developed nations. See, for example, the discussion in Bertrand, Mullainathan, and Shafir (forthcoming) in the context of the U.S. food stamp program.<sup>6</sup> Other explanations for lower take-up of the insured loan may also apply. A rational explanation would be that farmers may simply have placed a low (in the extreme, zero) value on the insurance they were offered, perhaps because they mistrusted the insuring organization or doubted that rainfall measured at the local weather station would be highly correlated with their farm output (i.e., basis risk). Low valuation of insurance, in combination with very high price elasticity, could have caused low take-up of the credit plus insurance product. We believe this explanation cannot be sufficient because the price elasticity of credit demand would have to be extremely large to explain the full decline in take-up, although it could explain some modest portion of the decline. In addition, an explanation from the psychology literature is that offering the insured loan may have primed farmers to weight risk considerations more highly in their adoption decision. In many settings psychologists have found that “priming” by highly local and temporary influences can have large effects on decision making. For example, Bornstein (1989) and Zajonc (1968) find that mere exposure can increase affinity for certain things.

The remainder of this paper is organized as follows. In Section 2 we describe the experimental design and the survey data **.** We describe the main empirical results on the impact of the insurance on take-up in Section 3, and then in Section 4 explore a variety of determinants of take-up separately in the treatment and control groups. Section 5 assesses the evidence for a variety of explanations for lower take-up in the insured group. Section 6 concludes. Appendix A develops a simple model of technology adoption under 6 See also Duflo, Gale, Liebman, Orszag, and Saez (2005) as well as Liebman and Zeckhauser (2004).
<mark>Oladeebo, J.O. and Fajuyigbe, A.A., 2007. Technical efficiency of men and women upland rice farmers in Osun State, Nigeria.</mark> _<mark>Journal of human ecology</mark>_ <mark>,</mark> _<mark>22</mark>_ <mark>(2), pp.93-100.</mark> <mark>Saito, K.A., Mekonnen, H. and Spurling, D., 1994.</mark> _<mark>Raising the productivity of women farmers in SubSaharan Africa</mark>_ <mark>. The World Bank.</mark> <mark>Sen, A.K., 1962. An aspect of Indian agriculture.</mark> _<mark>Economic Weekly</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(4-6), pp.243-246.</mark> Vargas Hill, R. and Vigneri, M. 2009. “Mainstreaming Gender Sensitivity in Cash Crop Markets Supply Chains.” International Food Policy Research Institute, Washington, DC.

<mark>UN Women. 2015. The Cost of the Gender Gap in Agricultural Productivity in Malawi. Tanzania. and Uganda.</mark> 33
**TABLE 7: Impacts of congestion toll, gasoline tax and vehicle efficiency improvement  while** **<u>reducing the same CO2 emissions</u>**

||**Tolling for excess**<br>**delay only**<br>**(% changes from base)**|**Equivalent e**<br>|**mission reductions**<br>|
|---|---|---|---|
|||**Gasoline tax**|**40% improvement in**<br>**car fuel efficiency**|
|Aggregatefloorspace (sq. m.)|239,090,000|239,090,000|239,090,000|
|Rent (RMB/sq.m./year)|493.39|493.39|501.24|
|Traffic speed(km/hr)|23.42|23.41|17.36|
|Auto round trip time(hrs/trip)|1.1103|1.1105|1.4978|
|After-tax cost of a car trip<br>(RMB/trip)|26.81|26.79|4.3991|
|Value of time of car users<br>(RMB/hr)|21.65|21.65|20.61|
|Totalauto person-trips perday|2,291,200 (-22.0%)|2,291,700|3,051,000|
|Total bus person-trips per day|6,896,100 (+2.5%)|6,896,000|6,700,500|
|Traffic (car equivalent<br>units/day)|2,204,100 (-15.9%)|2,204,400|2,693,600|
|Cars owned|885,300 (-20.0%)|885,450|1,153,600|
|Auto aggregate kilometers per<br>day|34,040,000 (-22.0%)|34,047,000|45,327,000|
|Car aggregate travel time<br>(hours/day)|2,543,800 (-39.6%)|2,544,900|4,569,700|
|Auto aggregate fuel cons.<br>(lit./day)|3,366,900 (-33.8%)|3,368,000|3,241,300|
|Auto CO2emissions(grams/day)<br>3<br>( 10 )<br>|11,342,000 (-36.0%)|11,346,000|11,341,000|
|Aggregate rents(RMB/year)|117,790,000,000|117,970,000,000|119,840,000,000|
|Average social welfare|7.3873|7.3873|7.4006|
|Income quintile1|_5.8985_|_5.9047_|_5.8893_|
|Income quintile2|_6.7453_|_6.7554_|_6.7410_|
|Income quintile 3|_7.3449_|_7.3940_|_7.3509_|
|Income quintile4|_7.8095_|_7.8314_|_7.8385_|
|Income quintile 5|_8.9790_|_8.3694_|_9.0212_|
|Fuel per km. of car travel<br>(liters/km.)|0.0989|0.0989|0.0715|
|Fuelperautoround trip (liters)|2.5717|2.5721|1.8592|
|Congestion toll or fuel tax<br>(RMB/km)|1.3078|1.1083|0|
|Congestion toll or fuel tax<br>(RMB/ person for a round trip by<br>car)|19.43|16.47|0|
|Aggregate daily tax revenue<br>(RMB/day)|44,510,000|37,735,000|0|

Moreover, the increase in fuel efficiency might entail huge investment higher vehicle acquisition and maintenance costs or disutility from having to own more efficient vehicles which are, on 5
“In the last completed fiscal year, what percentage of this establishment’s sales were: (a) National sales; (b) Indirect exports (sold domestically to third party that exports products); (c) Direct exports,” where the respondent’s affirmative answers to option b) or c) yield an exporter designation.

The productivity measures used in the analysis are both revenue-based, as only monetary (not physical) output and inputs are observed (World Bank, 2017). The first measure calculates TFP based on a Cobb-Douglas value added production function, where value added is proxied by the difference between the total annual sales of the establishment and total annual cost of inputs, capital is proxied by the replacement value of machinery, vehicles, and equipment, and labor is proxied by the total annual cost of labor. Factor shares in the Cobb Douglas production function are based on econometric estimation.

The second measure calculates TFP based on a gross output production function, where output is proxied by total annual sales of the establishment; capital and labor are the same as in the value-added specification, and intermediate goods is proxied by the total annual cost of inputs. Again, factor shares in the Cobb Douglas production function are based on econometric estimation.

As a third proxy for productivity, we use capacity utilization, a variable that is only available for manufacturing firms.<sup>3</sup> In particular, question F.1. asks “In the last completed fiscal year, what was this establishment’s output produced as a percentage of the maximum output possible if using all the resources available (capacity utilization)?”

# **III. Methodology**

As direct measures of cash-on-hand or cash accessible with ease are not available, we make several assumptions. In all cases, our assumptions are conservative: they serve to increase survival times. The reason for this choice is to have a lower bound on the possibility of firms resorting to bankruptcy.

> 3 The capacity utilization measure is taken as a proxy for labor productivity as the latter should rise when the same labor is using more of the available fixed capital.

6
narrowed significantly along with shrinking inequality in income distribution, a rise in purchasing power, and increase in access to healthcare and other basic services among the poor. Burgard (2002) also measures inequality of opportunities with a special focus on racial inequalities in child stunting in Brazil and South Africa. The paper finds that racial differences and household socioeconomic status are strongly correlated with stunting.

Singh (2011) uses inequality of opportunity indices to measure inequalities in malnutrition and immunization for children in India, and finds significant regional disparities. In another study, Pathak and Singh (2011) use bivariate analysis, poor-rich ratio and concentration indices to evaluate the trends for malnutrition. Mohan and Pathak (2009) use a similar methodological approach to measure the inequalities in access to maternal care services and child immunization. Both studies find significant disparities in these outcomes between the poor and rich in India.

Limwattananon et al. (2010) in Thailand and Axelson et al. (2012) in Vietnam use concentration indices to examine the inequities in maternal health and early child health outcomes. Both find significant inequalities between the poor and non-poor. In a study in Bangladesh, Anwar et al. (2008) employs multivariate logistics regression to examine inequities in the use of maternal healthcare services and finds that significant inequalities due to asset ownership, area of residence, and parental education.

There have been a few studies on inequality of opportunity in Egypt. Barros et al. (2012) find that Egypt is one of the least inequitable countries in terms of skilled birth attendant and measles immunization. It ranks 50<sup>th</sup> in a sample of 54 Millennium Development Goals (MDGs) Countdown countries based on the concentration index of selected indicators. Wagstaff (2003), analyzing a 1995/6 Egypt DHS, states in a cross-country analysis that Egypt has high concentration indices (i.e., high inequality) in under-five mortality rate and infant mortality rate compared to other countries. Another study by Boutayeb and Helmert (2011) analyzed disparities in maternal care between rich and poor women, as well as differences between women living in urban and rural regions in Egypt.

5
where _wi_ is the budget share allocated to good “i”, in our case being food, ε _ij_ is the price elasticity of good “i” with respect to price change “j”. Therefore, the critical elements to estimate the compensating variation for the poor given a change in relative prices are: (a) the share of food consumption in total household budget of the poor, (b) the change in relative prices, and (c) the elasticity of substitution between food and non-food items for households below the poverty line. The CV for the old poor plus the extra cost accounted by the new poor will give the change in poverty deficit (PD) brought about by the increase in the price of food.

The three parameters required for our computation vary across countries and between households within the same country. Calculating the PD for a large number of countries implies collecting --and in some cases estimating-- this household-level information, which is a gigantic undertaking. In this paper we simplified the task by computing the PD for each country under three different scenarios capturing plausible values of the three parameters yielding three scenarios: a central one, and a lower and upper bounds scenarios, respectively. The values of the parameters are discussed in the next section.

# **B. Stylized facts and data issues**

Figure 1 shows how the food budget shares and the price elasticity of demand change with the income level in a sample of about 100 countries.<sup>5</sup> . As income increases, the food budget share declines from around 70 percent to 10 percent. The price elasticity of food demand is non-linear, decreasing at low income levels, and then increasing, with a range from -.4 to -.1.

**Figure 1 Food budget share and price elasticity of demand across countries**

<!-- Start of picture text --> 80 -.05<br>70 -.10<br>60<br>-.15<br>50<br>-.20<br>40<br>-.25<br>30<br>-.30<br>20<br>10 -.35<br>0 -.40<br>6 7 8 9 10 11 12 6 7 8 9 10 11 12<br>(log) GDP per capita (log) GDP per capita<br>price elasticity<br>food budget share<br><!-- End of picture text --> Our estimations consider a range for the food budget shares from 50 percent to 70 percent, with a mid point of 60 percent. Recall that Figure 1 shows country averages, and hence the poor in each country will have food budget shares higher than the national

> 5 Data source for food budget shares and price elasticities Seale et.al. (2003).
29 Table 5: Annual NACs for rice, wheat and all farm products, by country group, 1972 to 2008 (1 + NRA/100)

|(a) Rice|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|**2005**|**2006**|**2007**|**2008**|
|**World exporters**|0.87|0.62|0.56|0.73|0.88|1.11|1.31|1.52|1.69|1.58|1.05|0.97|1.05|0.75|
|<br>**World importers**|1.37|1.03|0.75|1.07|1.17|1.45|1.61|1.62|1.64|1.49|1.51|1.47|1.52|1.07|
|High-income countries|1.29|0.95|0.77|1.07|1.26|1.70|2.09|2.82|3.17|2.85|2.27|1.89|1.70|1.61|
|<br>Developing countries|1.11|0.83|0.64|0.91|1.03|1.24|1.37|1.28|1.29|1.15|1.27|1.24|1.34|0.81|
|Asia|1.15|0.84|0.58|0.89|1.02|1.26|1.42|1.35|1.46|1.25|1.30|1.22|1.28|0.74|
|Africa|1.10|0.84|0.66|0.99|1.06|1.21|1.17|1.16|1.29|1.12|1.06|1.15|1.36|0.79|
|Latin America|1.05|0.81|0.75|0.82|0.96|1.27|1.65|1.34|0.90|0.96|1.44|1.35|1.44|0.94|
|(b) Wheat|||||||||||||||
||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|**2005**|**2006**|**2007**|**2008**|
|**World exporters**|1.14|0.88|0.83|0.97|0.98|1.17|1.19|1.71|1.70|1.41|0.90|0.95|0.90|0.86|
|<br>**World importers**|1.09|0.73|0.76|0.96|0.94|1.29|1.46|1.74|2.04|1.79|1.50|1.43|1.14|0.97|
|High-income countries|1.11|0.82|0.80|0.93|0.94|1.47|1.71|2.17|2.51|2.13|1.86|1.70|1.19|1.16|
|<br>Developing countries|1.10|0.72|0.74|1.01|0.95|1.06|1.09|1.22|1.33|1.20|1.07|1.00|0.98|0.80|
|Asia|1.35|0.80|0.89|1.21|1.01|1.20|1.20|1.28|1.42|1.46|1.08|1.00|0.92|0.59|
|Africa|0.99|0.73|0.64|0.87|0.84|0.92|0.91|1.20|1.38|1.11|1.13|0.99|1.10|0.92|
|Latin America|1.02|0.63|0.72|0.96|1.07|1.14|1.27|1.20|1.16|1.09|1.01|1.02|0.93|0.84|
|(c) All covered farm|produ|cts|||||||||||||
||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|||||
|**World exporters**|0.94|0.83|0.80|0.87|0.80|0.86|0.99|1.16|1.15|1.04|||||
|<br>**World importers**|1.49|1.30|1.22|1.40|1.58|1.73|1.89|2.05|2.22|1.88|||||
|High-income countries|1.46|1.34|1.26|1.40|1.62|1.81|1.96|2.25|2.44|2.04|||||
|<br>Developing countries|1.02|0.88|0.86|0.95|0.93|0.99|1.09|1.10|1.11|1.05|||||
|Asia|1.30|1.04|0.98|1.09|1.16|1.29|1.45|1.46|1.42|1.36|||||
|Africa|0.90|0.80|0.78|0.83|0.80|0.80|0.85|0.90|0.97|0.91|||||
|Latin America|1.01|0.92|0.92|1.08|1.06|1.10|1.28|1.16|1.03|0.99|||||

a Unweighted average of national NACs. Source: Authors’ calculations based on updated NRA estimates from Anderson and Valenzuela (2008).