rafmacalaba/data-use-mentions-extended huggingface dataset:
the market shares of other major providers, such as Expresso (22 percent) and Tigo (24 percent) (BuddeComm. 2020). Orange is also the first provider of 4G technologies in Senegal (Oduor 2016).

The data newly collected from providers show a much wider coverage of 3G technologies in Senegal than otherwise suggested by the Mobile Coverage Maps database. For instance, the median distance between the centroids of EAs in the 2017–18 ELEPS and the closest coverage areas of 3G technologies was 1.8 kilometers based on the Mobile Coverage Maps database (see map 1, panel b). Yet, the distance was only 0.4 kilometers based on the data provided by Orange-Sonatel for the same year (see map 1, panel c). There is thus a substantial discrepancy in mobile coverage data across the various sources of information. The analysis therefore combines the data from all the various sources of information to ensure that the 3G coverage maps are as complete as possible.

The analysis also uses the GPS coordinates of the centroids of EAs to match the locations of the households in the survey data and the fiber-optic nodes and mobile coverage maps. It would have been ideal if the exact locations of households were merged with spatial data on digital technologies. Nonetheless, the GPS coordinates of the centroids of EAs are the best source of geographical referencing data available for identifying the location of households. The same approach is adopted by Hjort and Poulsen (2019), who demonstrate that the distance between the terrestrial backbone infrastructure and sampling cluster locations still provides a reasonable measure of the accessibility to the Internet.

# **3. Estimation strategy**

To estimate the impact of fixed and mobile broadband Internet on household outcomes, the following equation is calculated: i 1D i i i , (1) where i is the outcome of interest, that is, the consumption or poverty status of household _i_ in region _r_ at year _t_ . In addition to total consumption, food and nonfood consumption are also separately ii considered
26 . |

.

-

likely to be non-linear — with the impact of a second independent decision maker being much greater than the addition of a third, for example — providing one explanation for the : insignificant results in column 2, Table 3. Similarly, the reduced dispersion of the IPC inthe DPI would also explain the differences in the results across columns 1 and 2 of Table 2.

Table 3: Differences in Coding the Index of Political Cohesiveness: Database of Political Institutions and Roubini and Sachs (1989) (OECD nations, 1975-1985

|Source ofDifference|Observations Affected|
|---|---|
|DPIcountsCDUandCSUas2parties;RS89_<br>—<br>countsthem as 1.|-FederalRepublicofGermany,<br>1983-1985|
|DPI indicates 2-party government; RS89<br>indicates a 1-partygovernment.|UnitedKingdom, 1978<br>Norway, 1975-1980<br>Sweden, 1975|
|DPI indicates aminority government; RS89<br>indicates amajority governmentofoneparty<br>|<br>oramajority coalition.|Belgium 1982-1985<br>§Denmark 1978, 1984-1985<br>Japan 1977-1980, 1984-1985<br>Norway 1985<br>Sweden 1983-1985<br>UK 1977, 1979|
|DPI indicatesaminority government, RS89<br>indicates a majoritygovernment ofone party<br>oramajority coalition. It is also possible that<br>language- based “wings” ofparties are treated<br>differently -DPI often countsthem as separate<br>parties.|Belgium, 1976-1977|
|Foreachyear,DPI records the situation as of<br>|<br>January 1; RS89 seemtouse adifferent<br>convention.|Denmark, 1981<br>Ireland, 1981<br>Norway, 1981-1982<br>Sweden, 1976, 1978-1979, 1981|
|Possiblethat language-based “wings” ofparties<br>are treateddifferently-DPI oftencountsthem<br>asmultiple parties.|Netherlands, 1978-1985<br>Belgium, 1981|
|DPI classifiesthem asparliamentary; RS89<br>classifiesaspresidential.
PHBK program costs are high. Further, according to data provided by Bank of Indonesia, 70 percent of total costs are, BI's "in-kind" costs (salary and allowance expenses of regular BI staff working on the project and rent for project office space). 45 It is therefore useful to examine program cost ratios considering total costs and "cash outlay' expenses only (all costs incurred by GTZ and BI that are in excess of regular BI staff salaries and rent for office space in BI buildings).

Even when only cash outlay costs are considered., the ratio of annual program costs to annual lending is high in 1995/96, but has declined rapidly in the last two years. In fiscal 1992/93, the program's ratio of total costs to funds lent was approximately 304 percent (or 86 percent if BI's in-kind costs are excluded from the calculation). This figure declined to approximately 94 percent by 1995/96 (or 28 percent if Bl's in-kind costs are excluded). The cost per group receiving a loan was about US$11,654 in 1992/93. (US$3,304 excluding in-kind costs) and had declined to US$1,764 (US$522 excluding in-kind costs) in 1995/96. Finally, if it is assumed that most program expenses are incurred for groups receiving their first loan, it is useful to compare annual program costs to the number of first loans received per year. The program spent an estimated US$16,856 per first loan received (USS4,729 excluding in-kind costs) in 1992/93, and about US$2,079 per first loan (US$615 excluding in-kind costs) in 1995/96 (Table 22).

> 45 Program costs considered here include all costs that Bank of Indonesi **a** nd donors attnibute to the program. These costs do not include the costs that banks incur in making program related loans. Banks' costs
<br>25.00 25.00<br>20.00 20.00<br>15.00 15.00<br>10.00 10.00<br>5.00 5.00<br>0.00 0.00<br>94 95 96 97 94 95 96 97 98 99<br>Gross firm flows Net firm flows Gross firm flows Net firm flows<br>Panel B: Employment-weighted firm flows<br>Slovenia Hungary<br>20.00 15.00<br>13.00<br>15.00<br>11.00<br>10.00 9.00<br>7.00<br>5.00 5.00<br>3.00<br>0.00<br>1.00<br>-5.00 -1.00<br>92 93 94 95 96 97 98 99 94 95 96 97 98 99<br>Gross weighted firm flows Net weighted firm flows Gross weighted firm flows Net weighted firm flows<br>Latvia Romania<br>15.00 15.00<br>13.00 13.00<br>11.00 11.00<br>9.00 9.00<br>7.00 7.00<br>5.00 5.00<br>3.00 3.00<br>1.00 1.00<br>-1.00 -1.00<br>94 95 96 97 94 95 96 97 98 99<br>Gross weighted firm flows Net weighted firm flows Gross weighted firm flows Net weighted firm flows<br><!-- End of picture text --> 17
to illustrate one other serious limitation of POVCAL data, namely, that it is overpopulated with countries from Latin America (approximately half the sample), while there are only about 50 observations (i.e., first differences in poverty) each for sub-Saharan Africa and Asia (South and East Asia combined). Eastern Europe and Central Asia contain around 40 observations (and are also somewhat overrepresented relative to their population sizes), while the Middle East and Africa contains only a handful of observations for Tunisia and Morocco only. Average changes in $1.25/day poverty over these observations are highest for East Asia (-2.2 points), as expected, followed by Eastern Europe and Central Asia (-1.6), sub-Saharan Africa (-1.5) and South Asia (- 1.2). Mean changes in food prices are positive in all regions but substantially larger in Asia and sub-Saharan Africa.

Table 1. Summary statistics for key variables

> 6 .We investigated a number of different series, including the FAO series on producer prices and an index based on GDP deflators. However, results reported in appendix S1 suggest that these two series are measured with much more error than the consumer price index series. Both series are weakly correlated with international prices, for example. It also seems conceptually less suitable to focus on producer prices, since even producers also make substantial purchases of food.

6
|p-val|0.138|0.816|0.765|0.241|0.709|

Notes: The table shows the results of regressing the variables regarding agreement with gender norm attitudes on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Post-intervention survey.

# _Parental leave usage_

Finally, we report the effects of the intervention on parental leave usage by sex in experiment 2. Table 8 shows the results on the outcome variables indicating whether the couple talked or is going to talk about how to use the parental leave (Planning), decided or will decide to use it both mother and father (Use both), use it only him(her)self (Use only me), or only his(her) partner (Use only partner), as well as a variable indicating whether the person effectively applied and became beneficiary of the parental leave after the intervention (Effective use).

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# **4 Research Design**

In this section we describe the study’s data sources, the key variables and how we construct them, and our empirical strategy. Our study relies on spatial and temporal variation of refugee settlement in Uganda from neighboring countries.

## **4.1 Parish-Years as Units of Analysis**

Our main unit of analysis is parish-years. Parishes (N = 5,133) are the smallest administrative unit above the village level; they typically encompass an average of five villages. Our study period covers five years: 2001, 2006, 2011, 2016, and 2020. Since the arrival of large numbers of South Sudanese refugees began in 2014, we have several prior years to compare periods when parishes hosted moderate numbers of refugees with the much larger numbers in 2016 and 2020.

Over the past two decades, the number of parishes in Uganda has increased from 5,238 in 2002s census to 7,241 in 2014s census to over 10,000 today (2016). This administrative fragmentation means that our units are not stable over our study period. In a major contribution, we constructed an original parish dataset to facilitate research throughout this time period.<sup>12</sup> We use shapefiles, census data, matching techniques, and manual corrections to ensure that we have the same administrative units over time, using 2001 parish boundaries as the constant unit throughout.

We do not include all parishes in the main specification. Given that we use a continuous measure of refugee presence, it is not _a priori_ clear whether a greater refugee presence matters beyond a certain radii. We therefore test the sensitivity of our findings to different sample radius cutoffs: parishes within 100km, 150km, 200km of any refugee settlement as well as all Ugandan parishes. Here we present results with the 150km cutoff as our main specification; SI Section S4 reports the results with the other cutoffs.

## **4.2 Main Independent Variable: Refugee Presence**

For our
|0.884|
|Number of women in the household|620|1.82|1.88|0.766|0.559|
|Number of boys (age 7-14) in the household|620|0.52|0.49|0.399|0.625|
|Number of girls (age 7-14) in the household|620|0.37|0.46|0.448|0.141|
|Number of young boys (age 0-6) in the hh|620|0.73|0.68|0.120|0.398|
|Number of young girls (age 0-6) in the hh<br>Number of rooms in house|620<br>620|0.52<br>2.13|0.56<br>1.81|0.815<br>0.017|0.473<br>0.001|

All data from 2014 baseline survey. P-values based on standard errors clustered by community. The number of observations (N) indicating child-level attrition is the difference between the number of baseline households and the number of children for whom the Denver test was completed; N for household-level attrition is the number of baseline households for whom no follow up household survey was collected; and N for mother(father)-level is the number of baseline households for whom the mother(father) could not be interviewed at follow-up.

53
follows. First, shocks of various forms affect a large share of Bangladeshi households – our survey of 2,000 households showed that more than half the sample claimed to have faced a shock over the previous year. Surprisingly, the non-poor face a larger share of these shocks compared to the poor – a closer look at this result shows that the non-poor report a significantly larger share of „asset-related‟ shocks than the poor which is consistent with the fact that the poor have fewer assets to lose. As one would expect, however, the poor are less able to cope with shocks compared to the non-poor. Importantly, the poor are

> 3 As a robustness check we also run a multinomial model where households face one of the following mutually exclusive outcomes – to not experience a shock; to experience a shock and cope with it; and to experience a shock and not be able to cope with it. This allows us to include the full sample as opposed to half the observations used in the logistic regression to look at household ability to cope. We find that the same variables that are significant in the logit model are also significant determinants of the relative probabilities of facing the above outcomes. Results are available from the authors.

12
either a manual or an electric sewing machine or both. 86% of both firms with and without workers have at least one manual sewing machine, making a shift to this less productive technology possible for most firms in the event of a power outage. About 75% of one-person firms and 88% of firms with workers have either an electric sewing machine or some other type of auxiliary electric equipment, such as an electric iron or embroidery machine, undergirding the potential productivity importance of power outages for electrified firms in this industry.

All firms in the analysis sample and most firms in the industry utilize the labor of the firm owner, who is typically a skilled garment maker who has completed apprenticeship training in the trade. About half of the firms in the sample also employ non-owner labor. To shed light on the composition of the workforce in the Ghanaian garment making industry, the analysis turns to the baseline dataset from **?** , which includes worker-level information from 752 garment making firms in Ghana.<sup>6</sup> In that sample, 630 firms employ 197 workers who have already completed an apprenticeship, 72 family workers, and 1,908 apprentices, for a workforce composition that is 91% apprentices and family workers. Although there is skill heterogeneity among apprentices (and family workers) driven by progression in the apprenticeship (among other things), for the purposes of this paper family workers and apprentices are conceptualized as unskilled labor and what are colloquially referred to as “paid workers” (who have completed an apprenticeship) as skilled labor.

Firm owners and skilled workers are generally able to utilize any of the three production technologies, and often also possess specialized skills in fashion design, embroidery, and the use of other specialized machines. Capital utilization by apprentices varies according to the capital available in their training firm and their progression in the apprenticeship, and they generally learn each production technology in order of ascending productivity. In the **?** data, over 95% of apprentices
first stage model can also be found in the census data. It is important that the variables are compared beforehand to verify that not only their definitions are in agreement, but also their distributions.<sup>9</sup> In the design of household surveys, clusters are commonly the primary sampling unit. Households within a cluster are usually not independent from one another, to allow for the clustering of households and their interrelatedness it is possible to specify equation (1) for a household as: ,– where the indexes _c_ and _h_ stand for cluster and observation, respectively – and disturbances ( _uch_ ) are assumed to have the following specification (Haslett et al., 2010): where _ηc_ and _ech_ , are assumed to be independent from each other with different data generating processes (Haslett et al., 2010).<sup>10</sup> Therefore the resulting model we wish to estimate is a linear mixed model (Van der Weide, 2014).<sup>11</sup> The literature has suggested different methods for estimating the unconditional variances of these parameters;<sup>12</sup> for the purposes of this paper focus is given to the methods presented by Elbers, Lanjouw, and Lanjouw (2003), and the adaptation of Henderson’s Method III by Huang and Hidiroglou (2003) detailed and expanded upon by Van der Weide (2014). The next section describes in detail these two approaches.

# **3 Estimating the unconditional variance of the residual parameters**

## **3.1 The ELL Methodology**

The methodology for estimating the location’s unconditional variance detailed by ELL (2002 and 2003) is presented in the discussion below. The method consists of two steps. The initial step relies on estimating a welfare model (Eq:1) using household survey data, and then obtaining generalized least square ( _GLS_ ) estimates for the model. Given the interrelatedness between households in a cluster, ordinary least
Policy Research Working Paper 8956

# **Abstract**

This paper examines whether political connections ease financial constraints faced by firms. Using firm-level data from six Central and Eastern European economies, the paper shows that politically connected firms: (i) have high levels of leverage, (ii) have low levels of profitability, (iii) are less capitalized, (iv) have low marginal productivity of capital, and (v) do not invest more than unconnected firms. Next, the paper shows that connected firms borrow more because they have easier access to credit and that political connections lead to a misallocation of capital. The results are consistent with the idea that political connections distort capital allocation and may have welfare costs.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, fdenicola@worldbank.org, ugo.panizza@graduateinstitute.ch, and richard.varghese@graduateinstitute.ch.

_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
poverty<sup>10</sup> and the fact that anthropometric indicators are heavily influenced by nonfood factors, such as health, education, family planning, and cultural norms. Bearing this in mind, we find that GDP per capita, mean household income, poverty rates, hunger rates, and anthropometric indicators are significantly correlated with the two GWP indicators, almost invariably at the onepercent level (table 2). The correlations are particularly strong for the (logarithmic) income and poverty indicators. In a very small sample—which excludes six important outliers—the correlation between the GWP indicators and the body mass index (BMI) of adult women is also very high (0.68). Table S1.1 in the appendix presents the full correlation matrix among the variables. It shows that the correlations between the GWP measure and the various benchmarks are at least as strong as the benchmark correlations for the FAO hunger measure and the World Bank poverty measure, if not stronger.

> 10 Indeed, in the context of critiquing standard poverty measures, Deaton (2010) suggested that the Gallup indicators used in this study might be more reliable than the World Bank poverty estimates. As a rough demonstration of their suitability, Deaton showed that the food security variable is highly correlated with GDP per capita.

11
files (CM), and files containing transactions between PACs and candidates (PAS). For each election cycle, aggregate contributions by each PAC to every candidate were computed from the PAS files, and then

> 9   While the three sets of models described up to this point emphasized varying degrees of pressure by farm interest groups, Paarlberg (1989) believes that they do not provide a complete understanding of U.S. agricultural policy to data.  What these theories miss, according to Paarlberg, is the government’s concern for poor economic conditions in the sector.  Such concerns motivated Roosevelt’s introduction of price support programs via the Agricultural Adjustment Act of 1933.  Those price supports are integral to U.S. agricultural policy to this day.

16
a carbon tax. It would be further important to analyze the economic impacts when both policies are implemented simultaneously. However, the lack of necessary data in the social accounting matrix (SAM), the main database for the analysis, we could not include the impacts of fossil fuel subsidy removals in this study.

The paper is organized as follows. Section 2 highlights the key features of the CGE model, and the database used for the study, followed by descriptions of scenarios considered in Section 3. The main results and their interpretations are presented in Section 4. This section also presents sensitivity analysis on selected model parameters. Finally, key conclusions and policy implications are derived in Section 5.
) team from the World Bank, and Ally.<sup>8</sup> The transit information for the operating BRT routes where provided the by DART transit agency.

For Conakry, the route map is based on maps produced for the ongoing Plan de Déplacement Urbain (PDU) de Conakry, produced by the European Union and a 2006 study produced by SYSTRA. For Bamako, the maps of the Sotrama minibus taxi network were produced and provided by the “Cellule de Préfiguration de l’Agence Urbaine de Bamako” (CPAUB) housed in the mayor of Bamako’s office. The data for Kigali were collected by IGC for a study on accessibility performed in 2016.

# **4.2 Employment Opportunities**

Data on the location of jobs were available for this study only for Kigali, Bamako and Dar es Salaam.<sup>9</sup> These data sets showed the location and number of firms at the district level, and in the case of Kigali, also the number of employees. For the other cities, an extrapolation of job locations was created by analyzing available information and creating a relative index of job-

> 7 https://www.whereismytransport.com/about/

> 8 _http://ramanihuria.org/putting-dar-es-salaam-dala-dalas-map/_

> 9 Although it exists also for Nairobi, Dakar and Kampala through travel surveys. Because we could not cover the whole sample of 11 cities with official data sources, we looked for proxies for employment data for the whole sample.

8