rafmacalaba/data-use-mentions-extended huggingface dataset:
requires data from all the possible destination countries, at least the most relevant ones. In other words, emigrant stocks from a certain origin can only be measured by aggregating all migrants recorded in the censuses of all destination countries. As some important destination countries (such as Russia, South Africa, Brazil, Argentina, and Singapore) are outside the OECD, this new database ensures the coverage of essentially all emigrants from all countries in our sample. Table A2 in the appendix show that the majority of emigrants from Western Europe are in destination countries for which we have actual census data.<sup>6</sup> For most OECD countries, less than 10% of their emigrants are in countries with imputed (rather than actual) migration data. The only European country relying on imputed data for a large fraction of its emigrants is France at about 30%.

We distinguish two skill types s, denoted by s = h for college graduates (referred to as highly educated) and s = l for individuals with secondary education completed and less (referred to as less educated). The database covers the years 1990 and 2000 and the di¤erences in stocks by country of origin and destination provides the measures of the ‡ows. It focuses on individuals aged 25 and over as a proxy of the working-age labor force which is one of the main di¤erences with other migration databases (such as Ozden et al. 2010). This choice maximizes comparability between data on migration and on labor force per education attainment. Furthermore, it excludes a large number of students who emigrate temporarily to complete their education or children who migrate with their families.<sup>7</sup> The data description and some summary statistics follow.

> 6This pattern is also con...rmed in Ozden et.al. (2010) which presents global bilateral migration stocks but does not disaggregate by education levels.

> 7The dataset contains 195 source countries: 190 UN member states (after excluding North Korea)
city public transport is available, and IDPs can (in theory) commute to work (JIPS, 2019a). Tensions between IDPs and non-IDPs appear to be moderate. Our own calculations suggest that 79% of IDPs and 85% of nonIDPs agree somewhat or strongly that locals and IDPs have good relations.

The transition from a rural to an urban or peri-urban setting poses significant challenges for many IDPs. IDPs in El Fasher, and women in particular, appear to face many of the challenges and barriers to achieving sustainable livelihoods described more generally above, including endowment, institutional, and safety challenges, which often interact with the gender dimension (Bello and Baig, 2014). At the same time, coming to El Fasher also offers improvements and opportunities for some, such as the provision of public goods and services that had not been available in many places of origin, including high-quality education of children.

More generally, transitioning towards durable solutions is a highly complex, multi-actor endeavor, which also directly involves IDPs. In 2017, El-Fasher was chosen as a ‘case study’ for a concerted effort of governmental and non-governmental actors to achieve fostering local integration. The JIPS durable solution profiling exercise, which produced the survey data we analyze in this paper, was a part of this effort (JIPS, 2019a). The objective was to obtain better information on the displaced, to identify their needs and to provide a common basis for decision-making (JIPS, 2019b), as part of a five-stage process towards durable solutions.<sup>12</sup> After initial enthusiasm, the process ground to a halt due to control and authority issues (Davies and Jenner, 2020). In the meantime, the African UnionUnited Nations Hybrid Operation in Darfur (UNAMID) mission came to an end in December 2020, as planned in the peace agreement. The government is now in charge of taking over certain responsibilities currently fulfilled by NGOs, which specifically includes identifying new pathways to durable solutions.

# **2 Empirical strategy*
- a. Impacts on log household consumption were multiplied by control mean household consumption (or baseline household consumption when control mean household consumption was not reported)

- b. Impacts on per capita household consumption were multiplied by baseline mean household size (or average household size from a representative survey from the same country when mean household size was not reported)

2. Impacts on household consumption were prioritized over impacts on non-durable household consumption 3. When impacts of multiple distinct aggregations of treatment arms were reported, estimates with more disaggregated treatment arms were prioritized, and estimates with disaggregation by transfer size were prioritized 4. Estimates from the authors’ preferred specification were prioritized 5. Impacts on household consumption were annualized if reported over a different reference period

**Intent-to-treat and treatment-on-the-treated** Compliance for both UCT and TUP programs were almost always near universal, as the programs studied by papers in this analysis typically offered large transfers relative to household income and contamination was rare, but there is meaningful variation in compliance. To ensure appropriate comparison, in all cases we therefore use treatment-on-the-treated instead of intent-to-treat estimates, scaling intent-to-treat impacts on household consumption by the inverse of the impacts of assignment on participation. When transfer sizes and program costs were reported per household assigned to treatment (instead of per beneficiary), transfer sizes and program costs also needed to be scaled.

**Standardization of monetary units** As a final step, all monetary values were converted to 2010 USD PPP to facilitate comparison across contexts. In addition, this conversion was done before impacts on household consumption per unit of transfer or cost were constructed;

11
2010. All households in such districts are assigned to the treatment group. The treatment definition is based on data of all oil fields in Ghana. The data gives information on the status of the oil fields (that is, whether the field is under exploration, appraisal, being drilled, or inactive). We estimate the following regression for household-level outcome variables: where hd is the outcome variable (real income and real consumption of household _h_ in district _d_ at time _t_ . Variable represents a dummy variable that takes the value of 1 YY if the household lives in a region with offshore oil and 0 otherwise; dd _Post_ is a time TT dummy for the year 2012/2013 (post oil discovery); and h a vector of household and individual covariates, including level of education, a dummy for whether the household XX has access to electricity, sex of the household head, share of household members working in agriculture, share of household members working in industry, share of household members working in services, a dummy for whether the household lives in an urban area and geographic controls,<sup>12</sup> λ denotes a district fixed effects and ε hd is the error term. The coefficient of interest is dd σ, which is the DD (difference-in-differences) estimator.

For individual-level outcome variables, we estimate the following regression: where is employment of individual _i_ in district _d_ at time period _t_ . Specifically, employment takes the value of 1 for an employment category and 0 otherwise. iid<sup>13</sup> These YY categories include manufacturing, agriculture, services, construction and retail and wholesale. We estimate all regression models by using sample weights. We control for

> 12 The geographical controls include GPS location (longitude and latitude) of a locality. For robustness, we include ecological zones such as coastal, forest, and savannah as additional geographic controls.

> 13 All employment variables are estimated using a linear probability model for ease of interpretation.

11
|0.554|
|Infrastructure|0.115***|0.118*|0.146***|
||(0.035)|(0.065)|(0.043)|
|Control Mean|0.581|0.547|0.604|
|Obs|862|357|505|
|||**Panel B: Household survey**||
|Total|0.018|0.029|0.004|
||(0.018)|(0.030)|(0.023)|
|Control Mean|0.377|0.360|0.389|
|Trust|0.022|0.032|0.020|
||(0.015)|(0.024)|(0.019)|
|Control Mean|0.287|0.280|0.291|
|Participation|0.014|0.026|-0.011|
||(0.030)|(0.052)|(0.038)|
|Control Mean|0.468|0.440|0.487|
|Obs|8413|3466|4947|

> 1 Regressions include the threshold dummy, the first and second order polynomials of the re-centered point score and their interactions with the threshold dummy and state fixed effects. For the community level analysis, robust standard errors are presented in parentheses. For the household level analysis, standard errors were clustered at the community level. *** p _<_ .01; ** p _<_ .05; * p _<_ .10

> 2 The inclusion index contained two questions related to the participation in assemblies of members without rights to communal land. In communities where there were no such individuals, these questions were skipped. To avoid working on a restricted dataset due to those missing values,
and male-owned firms. A 10% increase in tradable services employment leads to a 13.7% increase in female-owned firms in non-traded services for women compared to a statistically insignificant 1.6% increase for male-owned firms. Finally, we find that the effects are only significant for small non-tradable service firms (for firms between 1-10 workers).

Our paper contributes to several strands of literature. First and foremost, our paper is related to the literature on structural transformation into the services sector. Eichengreen – and Gupta (2013) find that the growth of modern, tradable services finance, ICT, and business services – started at lower levels of per capita income after 1990 than in the preceding four decades, thereby benefiting developing economies relatively early in their structural transformation process. Furthermore, the growth of these services has improved educational outcomes. Oster and Steinberg 2013 show that the IT revolution in India boosted the enrollment of girls and boys, equally, in schools with English as the language of instruction. Nano et al. (2021) find that employment growth in telecommunications and financial services, boosted by liberalization in these sub-sectors, increased school enrollment rates. As a result, the increase in the skill premium was also less pronounced in India (Shastry 2012). Using data from India, Fan et al. (2021) show that even traditional, non-tradable services have contributed to productivity growth, albeit benefiting consumers at the top of the income ladder more.

Our paper also contributes to the literature on how linkages between the services and manufacturing sectors benefit overall economic growth. A substantial body of evidence across countries shows that the services “embodied” in manufactured goods have a significant impact on manufacturing productivity (Arnold et al. 2016; Arnold et al. 2011; Bas and Causa 2013; Francois and Woerz 2008). Services used as inputs in the manufacturing sector have benefited from growth in the latter too. Evidence from India shows that growth in manufacturing has accelerated growth in value added and worker productivity in services firms within the
.2|59.6|25.0|
|**Memo:**||||||||||
|BRICS (5)|27.5|51.6|22.5|9.2|14.3|8.8|18.3|37.3|13.6|
|OPEC (12)|47.1|55.9|37.7|8.5|10.4|8.4|38.6|45.5|29.4|
|WTO (50)|46.3|61.7|30.8|9.1|14.5|8.7|37.2|47.3|22.1|

Sources: UNCTAD TRAINS (applied tariffs) and WTO IDB (bound tariffs) databases.

There are a few points to note in the regional breakdown as well. First, the few Sub-Saharan African countries that have bound tariffs have done so at levels on average higher than in the other regions. But this is due primarily because of higher bindings on agricultural products. On manufactures the highest average rates are in Latin America. Also, the simple average bound tariffs on agricultural products in Africa and Latin America tend to be much higher than in Asia.

For many developing countries tariff escalation is also an issue, as other developing countries are becoming more important as markets. Escalation is measured by calculating the average tariff rates applied to three groups of products, raw materials, intermediate products and final goods, which are defined at the BEC 2-digit level (Board Economic Categories). Table 4 summarizes the information. The table shows that by and large, and for most sectors escalation 11
_Table 1: Main empirical findings from studies on ICT adoption and economic growth_

|**Growth**|**Main conclusions**|**Sample**|**Time coverage**|
|---|---|---|---|
|Dedrick et al. (2003)|Positive effect of ICT adoption on growth in the USA but<br>a mixed effects among developing countries.|Global|1986-2002|
|Draca et al. (2007)|The positive impact of ICT on USA growth was evident<br>after ICT represented a significant share of the capital.|USA,<br>Europe|1986-2006|
|Niebel (2018)|Positive relationship between ICT and GDP growth from<br>data of 59 countries from 1995 to 2010|Global|1995-2010|
|Stanley et al. (2018)|Positive impacts of adoption of mobile technologies and<br>landline on economic growth.|Global|1987-2017|
|Toader et al. (2018)|An increase of 1 percent in fixed broadband and mobile<br>phone subscriptions increases GDP per capita by 0.08<br>percent and 0.4 percent, respectively.|EU|2000-2017|
|Bertschek et al. (2015)|Telecommunications and broadband deployment have a<br>significant positive impact on economic growth and<br>productivity.|Global|1992-2015|
|Choi and Yi (2009)|Positive effects of Internet adoption and economic growth<br>based on data of 207 countries from 1991 to 2000.|Global|1991-2000|
|Maurseth (2018)|The greatest benefits of ICT adoption could lie a decade or<br>more in the past.|Global|1990-2000|
|Castaldo et al. (2018)|Positive correlation between fixed broadband and<br>economic growth in 23 OECD countries between 1996<br>and 2010.|OECD|1996-2010|
|Arvin and
43

# III. DEMAND FOR PRIVATE SECTOR PRODUCTS

The traditional assumption that commercial delivery of contraceptives serves the urban, upper classes and sets charges that are only affordable to that segment of the market has never been examined. There is very little information on the source of contraceptive services, or vhat users pay in developing countries, because most survey work has focused on utilization patterns and trends. Hov consumers get their services, vho pays for it, and the availability and effectiveness of alternative sources of services are issues that have not been addressed in any depth. Donors and developing country governments have concentrated on developing public programs, and assessing their ability to gain and retain users. The source of contraceptive services has only recently become an issue as donors and governments seek to expand available resources for family planning and health.

How important private sector sources are or could be to contraceptive use is a difficult issue to address. for instance, the existence, effectiveness and funding of government family planning programs that heavily subsidize family planning services may siphon potential consumers avay from private sources. In this case, the extent of reliance on private sources may be more a function of dramatic price differentials than the inability of the private sector to meet the contracepting needs of couples. Vithout price and access to information, as vell as family planning source data, the achievements and potential of the private sector cannot be accurately assessed.

Nev data make it possible to assemble some information regarding vhere couples obtain contraceptives, and therefore allow some conclusions on the effective demand for private family planning services. The following sections review existing data on source of contraceptives, and, where possible, control

**SI.fps:Va1y PlUnng** Paper **('bol.P**
Estimating an average cost per day of pre-trial detention would allow us to use the total number of days of pretrial detention to estimate the total loss of income and total loss of tax revenue. The first step is to collect data on pre-trial detention and its consequences (Sarkin, 2009). We might also hypothesize that this loss is of greater effect because of the increased likelihood of expenditure by those disproportionately held in pre-trial detention – those in the ‘bottom 40’. This approach would exclude consideration of inequality-informing variables that pre-trial detention affects: the skills, training and education of the workforce to which detained persons are less exposed (Piketty, 2014). It also excludes the potential relationship to violence of the discontent that is often associated with detention (Thurston and Lebovich, 2013; Dufka, 2016).

The logic of economics, however, does not always inform political decisions about Rule of Law programming, including efforts to drive down average time spent in pre-trial detention. Political actors experience a diversity of pressures, including incentives to harm the economy of a particular territory, for example a region where perceived adversary groups reside (Toft, 2005; Carment et al., 2009). In attempting to persuade governments of the merits of economic interdependence among arguably competing domestic groups, international development actors, employing analytical approaches, should identify and sensitize key commercial stakeholders linked to policy makers.

An important part of the economic rationale for enhancing criminal justice efficacy is to build a nation’s wealth, as well as to stimulate/increase its GDP growth rate. The World Bank has quantitatively identified that equitable advancement in state justice systems also improves economic development. To drive economic development, a 1 percent increase in rule of law institutions’ value will increase intangible capital by 83 percent (Hamilton, 2006). Education and justice system investments, therefore, “...are the most important means of increasing the intangible components of total wealth” (Hamilton, 2006). For low-income countries, therefore, identifying optimally sustainable growth
# **S2 RSDP Road Dataset**

Table S1 shows the assumed travel speed information by road type and condition that the Ethiopia Road Authority used in constructing the RSDP panel dataset. The dataset provided by ERA contains the speed of the road in each year (where a speed of 0km/h is assigned to roads that had not been constructed yet). As the table shows, an increase in speed limit indicates that a road was improved except for town roads. Consequently, as our analysis relies on increased speeds to indicate improvement, our analysis focuses on roads connecting towns rather than within town roads.

|Pavement type and condition|Speed before reha-<br>bilitation<br>or<br>con-<br>struction|Speed after rehabil-<br>itation or construc-<br>tion|
|---|---|---|
|Asphalt road (expressway)||120km/h|
|Asphalt roads (Highway - as-<br>phalt concrete or surface treat-<br>ment 50km/h|70km/h||
|Federal gravel Road (high class<br>gravel roads)|35km/h|50km/h|
|Regional gravel Road (intermedi-<br>ate class gravel roads)|25km/h|45km/h|
|URRAP roads - lower district<br>level<br>roads<br>(low<br>class<br>gravel<br>roads)|20km/h|35km/h|
|Earth surfaced roads (very low<br>class public roads)|20km/h|30km/h|
|Federal gravel or regional rural<br>roads to asphalt roads|25km/h to 35km/h|70km/h|
|Town roads (asphalt)|30km/h|30km/h|
|Town roads (cobbled)|20km/h|20km/h|
|Town roads
_Source:_ Authors’ calculation based on _Statistical Yearbook of National Tax_ (NTS, 1988–2015).

_Note:_ The wage and salary income taxpayer ratio precipitated in 2014 due to changes in the PIT income deduction system in 2014. The income deduction for education expenses, medical expenses, private insurance premiums, and charity contributions were shifted to tax credits, which made many marginal taxpayers tax exempt.

# Effective PIT Rate of Owner‐Operated Businesses

The effective PIT rates are estimated through micro‐simulation analysis developed by Sung (2008) using Household Income and Expenditure Survey (HIES) data, a household survey data set, collected monthly and compiled, and released by Statistics Korea every quarter year. HIES data provide monthly and quarterly income and expenditures, along with each household’s demographic characteristics, that can be used to estimate household PIT. Detailed PIT estimation methods using the HIES and summary statistics of HIES are attached in Appendixes 1 and 2.

Figure 6 illustrates the longitudinal changes of effective PIT rates both for self‐employed business income and wage and salary income for the last three decades. The two effective PIT rates have different trends. The effective PIT rate of wage and salary income increased from 1.6 percent to 4.0 percent from 1982 to 1988 and then oscillated within the range of 2.2 percent to 3.5 percent from 1989 until recently. For business income, the effective PIT rate decreased from 5.6 percent to 3.4 percent from 1982 to 1998 but turned to a continuous upward trend from 3.4 percent in 1998 to 6.3 percent in 2013 after the introduction of TIETP, except for the two dips caused by Korea’s economic crises in 2002–2003 and 2008–2009.

Figure 6 Effective PIT Rate for Business Income and Wage and Salary Income (%) <!-- Start of picture text --> 7<br>PIT for Business Income 6.3 6.2 6.3 6.2<br
<!-- Start of picture text --> a<br>2q99t00%ee, 9%e, “egetenen* tenn **ee "sees,aatetyteenery”“ 4g%o%tegt* tePYtee,ef,,0% ag<br>aN - a<br>oS a * a“ x a<br>oO “ ry “<br>2 are x a . * x “- « “« a * x A<br>- aex . * x x x * a Be * Xx a *<br>rE ° a # + * x x x<br>rd * x ty at, xe” a ae x x Re<br>n a “4 .* “ aa a<br>&a a. ay * a 4 s a . « “<br>N . 7 - “ =<br>ef¢j<br>0 20 40 60 80<br>Area<br>+ H3-CensusEB ¢ H3-CBEB (old bootstrap)<br>« ELL = H3-CBEB (old bootstrap with PSU)<br>« Direct<br>oO<br>N<br>~lo x * x x “* xx bad .<br>ouo a a eet ee a ¢ Ox x of fe<br>= o,.ost or’ Le* * oogteteoe. oXe* +,osebe, ant%fF xghto%ahah ge eet!xo SP axttataeaad ° sents<br>Xe — . . = os "tag "an x ae a . onan onyx “ gx .<br>Oo a a “ a<br>Wwe “ a<br>a ws Cotte tk a “as a<br>20 asia * * i. ae 4 “ a “aa a “ee aa * “s a we at, “ ‘<br>+pSht+ EER ER ct. chpeepee = gpot+. “HER
a range of food security outcomes that are more correlated with exposure to short-term shocks than PMT scores (Schnitzer, 2016). In the common support sample, there are no significant differences between beneficiary and non-beneficiary households, suggesting that none of these groups have been particularly affected by shocks.<sup>25</sup> With these potential limitations in mind, we offer some additional robustness tests using Propensity Score Matching (PSM).

> 24 For evidence of such a spillover effect in other cash transfer programs, see Angelucci & De Giorgi (2009). 25 Results available upon request. The food security variables include the Household Hunger Scale; three Coping strategy indices; the Food Consumption Score; and the Household Dietary Diversity Score (see McBride (2015) for an analysis and discussion of these indicators). Unfortunately, the baseline survey only includes food security variables for the Tahoua region which includes 591 households in our common support sample.

20