rafmacalaba/datause-encoder-data huggingface dataset:
7 input prices and other factors. The next sections explore in more detail the underlying structure of one such model and its input requirements.

# **III. The FCC Approach**

The Federal Communications Commission (FCC) of the U.S.A.<sup>7</sup> has analyzed the use of forward-looking economic cost methodologies as the basis to determine the universal service cost, and it has concluded that the models that are being used are promising regulatory tools. The major actors in the telecommunications industry in the U.S.A. submitted their own versions of forward-looking models to the FCC for the Commission to analyze them. These models are the _Cost Proxy Model_ (CPM) of Pacific Blue and INDETEC International; the _Benchmark Cost Model 2_ (BCM2) of Sprint and US West; and the _Hatfield Model_ (Hatfield) in different versions, of AT&T and MCI. The FCC developed and adopted a model as an alternative to the models proposed by the industry players. The FCC model, known as _Hybrid Cost Proxy Model_ (or HCPM), combines appropriate principles of engineering design for different network elements with economic principles of cost minimization.<sup>8</sup> The model draws freely from engineering principles displayed in other models, thus the term 'hybrid'.

In what follows, we describe the most relevant features of the cost proxy model approach. We give an overview of the HCPM and leave the discussion on input requirements and data problems to the next section.

## **Structure of the HCPM**<sup>**9**</sup>

The 14CPM has from the beginning been designed to use sources of geocodedl' customer location data. The model can also handle Census block level data as an alternative source of publicly available data. The last version of the FCC model incorporates some minor modifications to improve the performance of the model under 7See Atkinson et al. (1997). ' See Bush et al. (1998). 9 This part follows Bush et al. (1998).
10 over that from the MICS as all other points in the stunting time series for Rwanda come from DHS); (2) survey sample size with a preference for larger surveys in order to minimize sampling error; (3) fit with the overall time series where points are dropped if they form outliers without a credible explanation; (4) preference for data points we computed from the micro-data ourselves over data points from publications.<sup>8,9</sup>

# _Complete replacement_

In some cases, we replaced points’ previous source(s) with an entirely new source. Most of the time, this occurred when we managed to obtain the microdata of a point that we had previously sourced from a publication – such as Kosovo’s 2013 skilled birth attendance rate that came from the MICS survey report in the 2018 HEFPI database and that we now computed from the microdata ourselves.

Encouragingly, in most cases, any changes in indicator values resulting from the changes in sources are small – the median change relative to the 2018 HEFPI indicator value is 3.9 percent. But sometimes, the changes are meaningful, such as for the United Kingdom’s 2003 inpatient care rate which drops by five percentage points to 8.3 percent when we exclude Eurobarometer data and exclusively rely on the General Household Survey (GHS).

> 8 For some of the points in the 2018 database which were computed as averages over multiple points, the underlying points came from the same survey. For instance, the 2018 database’s 2013 mammography rate for Belgium was computed as the mean over two rates from the European Health Interview Survey – our own microdata-based rate and the rate published by the OECD. The 2019 database uses the microdata-based rate only.

> 9 In some cases, sources changed because a source had been erroneously entered: For example, for the 2008 mammography rate for Canada, we mistakenly computed the 2018 HEFPI version’s data point as an average over a point from the 2002 Joint Canada/United States Survey of Health survey report and the 2008 point reported by OECD. This error has been corrected in the 2019 database.
to test the limited-location-choice mechanism. The main effect of global oil and gas reserves is excluded because it does not vary over countries and is hence absorbed by the time fixed effects.

It is possible that oil-and-gas-related FDI flows and political violence are the result of the discovery of one of these valuable resources. Therefore, we also control for large oil and gas field discoveries within a country using a data set obtained from the BP Statistical Review of World Energy. The variable for discovery is a dummy coded as one if a major oil or gas field was found in the country, and zero otherwise. A _major gas/oil discovery_ is a discovery of a field that contains at least 500 million barrels of oil or 79 million m<sup>3</sup> of gas (Halbouty, 2001).

We proxy oil and gas rents with a global price index of oil and gas prices ( _gasoilindex_ ). This index is constructed using data from the BP Statistical Review of World Energy. We take 2003 as the base year and subtract 100 from the index to ease the interpretation of our results. We interact this price index with battle-related deaths to test for the effect of rents on the responsiveness of resource-related FDI to political violence. The main effect of our global oil and gas price measure is excluded because it does not vary over countries and is hence absorbed by the time fixed effects.

Column (1) of Table 7 shows the baseline model for the oil and gas industry. Similar to column (3) in Table 4, the results show that the effect of political violence on oil and gas FDI is comparable to the effect on total resource-related FDI flows (see columns 3-4, Table 1): battle-related deaths do not have a significant effect on oil-and-gas-related FDI. In columns (2) and (3), respectively, we add the moderator for hydrocarbon reserves and moderator for the price index. Figures 5a and 5b present the effect of these moderators, as estimated in model 3. The moderating effect of reserves on the relationship between battle-related deaths and oil and gas FDI is negative and significant, indicating that when new oil and gas reserves are discovered, MNEs active in the oil and gas sector are less willing to invest in countries marred by political violence. Nevertheless, the change in global reserves must be large for the total effect of conflict on greenfield FDI flowing to the hydrocarbon sector to be different from zero. Figure 5a shows that the effect of battle-related deaths on oil-and-gas-related FDI flows is positive if global reserves decrease by 0.5 standard deviations or more relative to the mean. However, the effect only becomes significantly greater than zero if global reserves decrease by at least 2.5 standard deviations relative to the mean. The effect of conflict on oil and gas FDI is significantly smaller than 36
# **_Expansion of telecommunications_**

The ENCOVI survey does not provide information on historical coverage trends for telecommunications at the household level. However, it is possible to trace the evolution of rural public telephones. As of 1996, GUATEL was operating some 2,000 rural public telephones, while FONDETEL added a further 1,600 between 1998/9.

Although, only about a third of the country’s 19,000 rural _towns_ have a public telephone service<sup>17</sup> , the ENCOVI reveals that 50% of rural _households_ have a public telephone in their community. This reflects the fact that the larger rural communities tend to be the first to receive a public telephone. For those living in unserved communities, the average distance to the nearest public telephone was 7.2 km (or about a 45 minute trip).

**Figure 3.3: Distance to public telephone for rural households**

<!-- 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>Within community<br>Distance to nearest public telephone (kms)<br>Outside community<br>Overall<br>1 2 3 4 5 6 7 8 9 10 >10<br>Cumulative percentage of households<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala Overall, 80% of rural households in Guatemala live within 6 km of a telephone (Figure 3.3). However, the pattern differs significantly by region (Figure 3.4). The North and Northwest of the country, together with Petén, have the worst levels of access to public telephones with average distances of 6-12 km and average journey times of around 50 minutes. By contrast, in all other regions the average distance to a public telephone is less than 5 km representing typically a half hour trip. Fewer than 10% of rural households claimed to have spent money on making a public telephone call the day before the survey<sup>18</sup> .

> 17 Information supplied by FONDETEL.

> 18 Unfortunately, the ENCOVI survey groups together expenditure on public telephone calls, faxes and postal services. The percentage reported relates to the number of people who registered non-zero expenditure in this category. Hence, it is very much an upper bound estimate for the proportion of rural households that are using public telephones. However, given the relatively scarce availability of facsimile and postal services in rural areas, it seems probable that quite a high proportion of these expenditures relate to public telephone calls.

22
# **Social Security Distortions onto the Labor Market: Estimates for Colombia**

# **José Cuesta* & Mauricio Olivera****

(*) World Bank (**) Fedesarrollo

**Key Words:** Distortions, Informality, Social Security, Social Protection, Willingness to Pay, Colombia

## **JEL Classification:** J32, J26, I38

**Acknowledgements:** This article represents only the views of the authors and not necessarily those of the Bank or its Board of Directors. Most of the work was conducted while Cuesta was working at the IADB. The authors thank Camilo Bohórquez for excellent research assistance and _Fedesarollo_ , especially N. Millán, Y. Oviedo and P. Acosta, for granting access to the survey and allow us to contribute to its expansion. The study benefited from numerous discussions and the valuable comments of R. Angulo, J.F. Arias, N. Ariza, R. Bernal, C. Pagés, S. Freije, N. González, F. Jaramillo, L. Moller, J. Nuñez, S. Levy, E. Lora, C. Lozano, R. Parot, G. Piraquive, S. Rozo and H. Zaman. Usual disclaimers apply.
absorptive capacity to learn from their neighbors as we have shown in previous heterogeneity analyses. Second, new importers will be less experienced with import procedures, often lacking the correct knowledge, manpower or internal business systems to handle the import process. They will have more barriers and costs to overcome before they start to import. Hence, in both cases, the knowledge and signals about import markets from their neighbors will not be as useful to them.

All the heterogeneity variables are included in the regression and presented in column 6 of Table 6. There is no change to the statistical significance and magnitude of the effects for each firm characteristic. This shows that each of the previously discussed effects operate independently of each other, with no two effects cancelling each other out when included simultaneously. The regression shows that learning from neighbors is most beneficial for the – base group of firms small, domestic, high productivity and experienced importers that are importing from EU markets. A one standard deviation increase in the density of neighbors is associated with a 1.3 percentage points increase in the probability of import entry. A corresponding increase in the signal from neighbors is associated with a 2.4 percentage points increase in the probability of import entry. The signal is boosted by the density of neighbors and the probability of import entry can increase by 2.3 percentage points if the interaction term increases by one standard deviation. This group of firms are precisely the type of firms that lack the resources to learn about import markets themselves (i.e., small and domestic), with the absorptive capacity to use the information (i.e., high productivity and experienced importers) and are learning about more complex markets in the EU.

# **5 Conclusion**

In this paper, we use data on Serbian firms to estimate the impact of information spillovers on the decision to enter new foreign sourcing markets. Our framework combines the model of importing in Antras, Fort, and Tintelnot (2017) (which suggests sharp distinctions between 20
Ownership and Corporate Control in Poland: Why State Firms Defied the Odds by Brian Pinto International Finance Corporation

# and

Sweder van Wijnbergen University of Amsterdam. LSE and CEPRThis paper expresses our personal views and does not necessarily reflect the views of the instiutions we are affiliated with. We are indebted to Sanjay Shah for excellent research assistance and to Mariusz Sumlinski for help with the data. The paper draws on a survey conducted over the years 19901992 by the first author, M. Belka and S. Krajewski. We furthernore want to thank seminar participants at Harvard University, The International Monetary Fund, The London School of Economics, New York University, Oxford University and The University of Michigan for helpful comments. Last. but not least, we gratefully acknowledge financial support from the World Bank through RPO 67825.
income and wages, covering all areas of the country. In the next section, we will delve into looking at scale effects for cities in particular.

# **4.1 The data and the sample of countries and cities**

We use the Living Standards Measurement Study data of the World Bank, where we have detailed geocoding of where families live for six countries; allowing us to map data to our spatial units: rural, LD settlements and cities. The LSMS surveys have detailed and consistent data at the household and individual levels on income, education, labor allocation, asset ownership, and dwelling characteristics. The data sets are the Tanzania Panel Household Survey (2008 and 2010), the Nigeria National Household Survey (2010 and 2012), the Uganda National Panel Survey (2009, 2010, 2011, and 2012), the Ethiopia Socioeconomic Survey (2011, 2013, and 2015), the Malawi Integrated Household Survey (2010 and 2013), and the Ghana Socioeconomic Panel Survey (2010 and 2013). Note that the dates of surveys in countries are so close together that they do not provide the opportunity to look at dynamics nor to identify urbanization effects off of movers.<sup>7</sup> These sample countries account for approximately 35% of the subcontinent’s population.

Before proceeding we note how our African countries present in terms of aspects of their urban hierarchy and what the coverage of this hierarchy is by LSMS surveys. At the country level, the six countries collectively present a regular urban hierarchy. Figure 6a shows the expected (Eeckhout, 2004) log-normal distribution of all urbanized areas (cities and settlements), although there is a right tail skew. Figure 6b ranks cities from 1 to n by size with rank 1 being largest; and plots ln population against ln rank-size, so we see that rank rises (lower order) as population declines. We see that regularity holds over much of Figure 6b, governed by an approximate Pareto distribution to the right tail in Figure 6a, although the overall slope coefficient of the log-linear fit is high at -1.20, as compared to the -1 implied by the rank-size rule and the original Zipf’s Law. The left tail in Figure 6a for smaller cities is an expected deviation in the right tail in Figure 6b from Zipf’s Law,

> 7There is an issue of the same households appearing more than once in our data, which varies from country to country. In Table 5 below for the full sample of 44,140 households, there are 23,685 unique households, meaning that 46% of the sample involves a household that is included more than once. Clustering at the local area should remove the distortion this creates. As a robustness check, we reran Tables 5-7 with just the final year sample in the year of the LSMS for each country. Results are very similar, with similar statistical significance and coefficient magnitudes.

15
<!-- Start of picture text --> 18.0<br>16.0<br>14.0 m1983 1987. 1993<br>312.0<br>E100<br>2 8.0 m1999 =m 2004<br>a<br>2 60<br>4.0<br>2.0<br>0.0<br>sT sc Non- oBc<br>1993<br>roo, 26 | 39 | 07 | 6a | 67<br><!-- End of picture text -->

<!-- Start of picture text --> 14<br>m19g3 1987-1993<br>12<br>10 m1999 =m 2004<br>a<br>g,a<br>2 6<br>g<br>4<br>;<br>0<br>1993) 65 3a<br>[200492 | a2 | tog | as | 87 |<br><!-- End of picture text -->
Unlike the GHS data, the MICS 2016/17 data however allows for the calculation of estimates of severe health deprivation according to the original definition by Gordon et al. (2013) considering additionally those children deprived if not having received any treatment when suffering from diarrhea. While the inconsistency with the GHS and DHS data in terms of rate of children not vaccinated raise concerns about data quality in the survey, still the estimated inequality between the southern and northern states, especially in the North-East and North-West regions ranging up to 42% of children under 5 not receiving diarrhea treatment whereas this share lies below 5 percent in most southern states. This seems alarming and requires more in-depth research.

Figure23: State Level Health Deprivation <!-- Start of picture text --> Health Deprivation - by State - MICS<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17. Note: Individuals with missing age were dropped from the analysis.

As discussed in the data section, data on certain deprivations are only collected at the household level. Hence, any variation in deprivation levels across age groups is limited to varying shares of younger or older children living in more or less deprived households. This section therefore only represents the estimates of water, sanitation, shelter and information for children in general.

Figure 24: State Level Health Deprivation (Immunization and Diarrhea Treatment a) b) c) 35
<!-- Start of picture text --> Distribution of labor productivity<br>4 } \\<br>i 2—— 2014 :i<br>5\ — 2019 i<br>: \ 2021 :<br>0 ee: _———_—_—<br>5 10 15<br>: Log labor productivity :<br>i kernel = epanechnikov, bandwidth = 0.1245 H<br><!-- End of picture text -->
lower the CDS spread, of firms located in countries with stronger pre-PEPP fiscal responses to the COVID-19 crisis. To test this, we include the Fiscal response/GDP variable, which is the total amount of pandemic-related economic stimulus spending in a firm’s country of residence announced up to March 17, 2020 divided by the country’s GDP. Fiscal response/GDP has a mean of 0.044. These data are obtained from Hale et al. (2020).

# **2.2 Methodology**

Using an event study methodology, we estimate specifications of the following general form: i 1 i 2 i 3A A A A i 4 i i + ii ii ii ii ii ii PP = ββi FF + ββ XX + ββ AAi AAi + ββ FF ∗FFi AAF iiAAiir AA/G(3) PP ii ii ααwhere i is a valuation change variable, such as the excess stock return or the change in the + εε ii CDS premium, for firm PP _i_ in industry _j_ and country _k_ on the event day of March 19, 2020. i is a ii set of firm financing variables (Investment grade, Non-investment grade, and Bonds/assets) that FF reasonably imply greater benefits for a firm from the PEPP. Thus, the estimated coefficients 1 are expected to be positive and negative, if i stands for the excess stock return and the change ββ ii in the CDS premium, respectively. i is a set of additional firm-level variables (at a minimum, PP ii Leverage, Log of assets, ROA, and Book-to-market) that potentially affect the benefits of the XX PEPP for the firm. A A A A i indicates whether a firm is in an industry that is ii affected relatively strongly by the COVID-19 crisis. A priori, is it not clear whether firms in AAi AAi more affected industries stand to gain more or less from the PEPP. Hence, the estimated coefficient 3 could be of either sign in, say, an excess stock return regression. i ∗ ii ββ i is a set of interactions of firm-level financing variables and the fiscal FF ii FFiresponse variable for country AAF iiAAiir AA/G PP _k_ . A strong fiscal response could enlarge the scope for a firm to form: 16
Schwartz (1986), using data for 1976-81, found that dumping margins of individual firms declined following the initiation of an investigation. However, they did not attempt to test whether investigations increased overall import prices for the affected industry. Nor have previous studies compared the impact of investigations with the duties which may (or may not) follow. To properly measure these effects separately would require a tiime series on the affected product.3 Finally, there have been no attempts to measure the different impact of investigations conditional on their outcomes. Do investigations which terminate in a duty have a greater effect than others? Prussa (1988) and Staiger and Wolak (1990) suggest that investigations which do not culminate in duties may actually lead to higher price increases than duties. Since petitions for investigations may be withdrawn, collusive agreements between domestic and foreign firms may lead to hig:-er prices even if no duties are imposed.

This paper addresses these empirical questions using a dataset which combines cross section and time series data to create a panel for 41 different product groups over 1981-1986. LFV cases were brought against half the products, creating a control group for comparison. The time series nature of the dataset also makes it possible to separately estimate the effects of investigations and duties. The results suggest )hat for a number of sectors, import prices increase by as much as 10 percent dui:.g an _nvestigation--suggesting that investigating an exporter for unfair pricing behavior may I ave as powerful -in effect on import prices as the duty itself.

3 One interesting approach to this problem is taken by Hartigan et al (1989) who use a time series of firm stock prices to measure the impact of antidumping investigations and duties on the reported stock value of domestic firms involved in anti-dumping or countervailing duty complaints. However, the impact of anti-dumping investigations and duties cannot be separated in their analysis, since the combined effect of the two is immediately incorporated in the stock value of the domestic firm.

2