rafmacalaba/data-use-mentions-extended huggingface dataset:
(0.001)|(0.003)||||
|Spillover students * father’s|||||||0.001|0.001|0.003||||
|education|||||||(0.002)|(0.001)|(0.003)||||
|Treatment students *||||||||||0.001|0.001|−0.001|
|mother’s education||||||||||(0.001)|(0.001)|(0.003)|
|Spillover students * mother’s||||||||||<br>0.001|<br>0.001|<br>0.003|
|education||||||||||(0.002)|(0.001)|(0.003)|
|Constant|0.126***|0.100***|0.313***|0.115***|0.098***|0.304***|0.117***|0.150***|0.333***|0.134***|0.143***|0.393***|
||(0.016)|(0.014)|(0.030)|(0.017)|(0.019)|(0.035)|(0.019)|(0.029)|(0.040)|(0.023)|(0.024)|(0.047)|

_Source_ : Authors’ analysis from student surveys.

_Note_ : *** _p <_ 0.01,
characterized for the value added nest, for example, by the substitution elasticity _σi,V A_ . The terms _avai,r_ and _aoi,r_ are productivity parameters applied to the value added and final production nest for the block of equations (6), (7), and (8). For the experiment here we will use our estimates of MFP gaps linked to regulation – total 66.3 percent, domestic regulation 38.0 percent, and openness 20.5 percent – and apply them through the coefficient _avai,r_ . The basic macroeconomic impact of our productivity experiment is summarized in Table 8; the effects on sectoral value added shares is reported in Table 9.

Table 8: Macroeconomic Impact of Service Sector Reform

||total<br>impact|openness<br>to foreign<br>establishment|domestic<br>regulatory<br>environment|
|---|---|---|---|
|GDP, % change|21.86|5.00|16.87|
|equivalent variation|23.58|5.48|18.09|
|consumer prices|-10.02|-2.45|-7.58|
|producer input prices||||
|for commercial services|-21.27|-5.26|-16.01|

_source: Own estimates from CGE model and database._ Table 9: Comparison of Value Added Shares

||EU26|Italy 2007<br>pre-experiment|Italy<br>post-experiment|
|---|---|---|---|
|primary|0.03|0.03|0.03|
|manufacturing|0.22|0.25|0.27|
|services|0.76|0.72|0.70|

_source: Own estimates from CGE model and database._ Starting from the value added composition of the
|
|Formal total|40.3|
|Informal total|59.7|
|Wage total|45.8|
|Self-employment total|44.9|

_Source_ : Kinshasa Survey 2018.

_Note:_ Occupation types other than wage and self-employment (such as employer and family worker) are not shown.

The majority of jobs in Kinshasa are service sector jobs. With a small fraction of jobs in agriculture (5.9 percent) and manufacturing (7.8 percent), about 80 percent of jobs in Kinshasa are in the service sector (Table 4).<sup>5</sup> Kinshasa remains largely a local city—as opposed to a global city that is open to regional and global trade, markets, and investments—and it produces fewer internationally tradable goods and services (60 percent) than other world cities (Lall, Henderson, and Venables 2017).

> 4 Unless otherwise noted, this section only looks at jobs of the working-age population.

> 5 For this analysis, the service sector includes commerce; transportation, storage, and communication; financial, insurance, and real estate; public administration; and other services.

10
|62.93***|−66.62***|83.11***|27.38***|7.793***|27.33***|99.26***|
||(0.922)|(2.986)|(2.590)|(3.949)|(1.578)|(1.026)|(0.475)|(2.065)|(1.640)|
|N|6,143|6,143|6,143|6,143|6,143|6,143|6,143|6,143|6,143|
|R-2|0.175|0.430|0.234|0.213|0.593|0.566|0.124|0.397|0.133|
|Municipalities|2,048|2,048|2,048|2,048|2,048|2,048|2,048|2,048|2,048|

_Note:_ The mean income corresponds to the average household income per capita in each municipality in logarithms. Monetary values are expressed in real terms in August 2014. Pooled OLS. Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 30
**Table 3.  Share of Respondents Receiving Food Assistance**

<!-- Start of picture text --> 3a. National<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>3c. IPC 2 Regions 3d. IPC 3 Regions<br>0.60 0.60<br>0.50 0.50<br>0.40 0.40<br>0.30 0.30<br>0.20 0.20<br>0.10 0.10<br>0.00 0.00<br>3d. IPC 3+ Regions 3e. IPC 4 Regions<br>0.60 0.60<br>0.50 0.50<br>0.40<br>0.40<br>0.30<br>0.30<br>0.20<br>0.20<br>0.10<br>0.10<br>0.00<br>0.00<br>Sep-2015 Oct-2015 Nov-2015 Dec-2015 Jan-2016 Feb-2016 Mar-2016 Apr-2016 May-2016 Jun-2016 Jul-2016 Aug-2016 Sep-2016 Oct-2016 Nov-2016 Dec-2016 Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jun-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018 Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br>Jan-2017 Feb-2017 Mar-2017 Apr-2017 May-2017 Jul-2017 Aug-2017 Sep-2017 Oct-2017 Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018 May-2018 Jul-2018<br><!-- End of picture text --> Notes: Authors’ calculations using the WFP mobile Vulnerability Analysis and Mapping Survey. The survey publicly reported
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Ghana’s road transport indicators are strong. By almost all measures, they are well ahead of those found among low-income peers and nearing the levels expected of a middle-income country (table 2). The length of the main (primary and secondary) network is more than adequate to achieve regional and national connectivity. The record on road network quality is quite reasonable, with 75 percent of the paved network in good or fair condition and, more impressive, 74 percent of the unpaved network in good or fair condition.

Underpinning these achievements has been a serious reform of road sector institutions that has resulted in the creation of a second generation road fund and road agency. Ghana meets almost all of the best practice guidelines for road sector institutions. The country has also adopted a fuel levy ($0.06 per liter) that, as of 2006, was commensurate with road maintenance needs (figures 4 and 5). In contrast to other African countries, Ghana allocates its road fund resources much more evenly across the different road networks—rural and urban roads receive 30 and 25 percent of the total, respectively. Overall, Ghana has allocated substantial resources to the road sector in recent years; it spends on average 1.5 percent of GDP on roads, one of the highest shares in West Africa.

**Table 2. Ghana’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-income<br>countries|**Ghana**|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km<sup>2</sup>of arable land|86.6|**158.1**|507.4|
|Unpaved road density|km/1000 km<sup>2</
employment of women in the organized sector. ASI surveys have a similar design<sup>10</sup> and stratification as the NSSO surveys, and we construct population-level estimates of employment in the organized sector at the state-industry level similar to above. We capture separate men’s and women’s employment in the organized sector based on fields in the ASI reporting plant employment by gender. We also use the 1989 ASI to check for pre-trends in women’s employment by state in the organized sector.

Unfortunately, ASI surveys do not collect the gender of the establishment owners, and so we can only examine ownership and entrepreneurship outcomes in the unorganized sector. Even within the unorganized sector, our results below emphasize household-based establishment entry rather than independent facilities. This pattern suggests that the lack of ownership data in the ASI is not a material concern as it is unlikely that we would observe heightened women’s entrepreneurship in the organized sector.

Table 1 provides descriptive statistics on our sample. The first three columns provide aggregate establishment counts, employment, and output for each year across all of India. Since we have data on the timing of political reservation implementation for all states and territories in India, we do not restrict our sample geographically. These aggregates are broken down into organized- versus unorganized-sector contributions. For the unorganized sector, we further report women-owned establishments and then household- versus non-household-based establishments for women. Columns 4 and 5 describe the growth rate of each economic activity from 1994 to 2005, and Columns 6-8 provide shares relative to the total.

As noted earlier, the unorganized sector accounts for a large share of establishment counts and employment. Moreover, these shares are very persistent, as discussed further in Ghani et al. (2013c). Women-owned establishments in the unorganized sector account for a small share of total manufacturing activity: in 2005, they represent 36% of establishments, 19% of employment, and 1% of output. Most of this activity is household based. For example, 90% of
displacements and rapid urbanization need regular updating, meaning that the manual digitization process must be replicated.

Previously, automated zone design methods have been employed to group areas into zones for a range of purposes including investigation of neighborhood effects on health and release of census data (Cockings et al. 2011; Flowerdew et al. 2007; Haynes et al. 2007). To construct the created new zones some constraints were considered such as population size and building types. However, these works were conducted in developed countries where actual household data were available, and the method relied on previously-defined sub-units or EAs. In addition, in most cases, the created zone boundaries do not match well with the logical ground natural boundary. Several studies have also employed region merging techniques to derive the census enumeration areas. For instance, Folch & Spielman (2014) showed the advantage of applying the improved max-p algorithm on growing the irregular regions from census tracts. In New Zealand, the ArcGIS/AZTool toolkit, which is a region-merging tool, was developed to design new reporting geographic units using the 2006 census data as the main input, with significant improvements over traditional methods reported (Martin and Lyndon 2009). However, all these examples used existing EAs as their base unit and no works exist that have employed region merging techniques to generate new EAs in a country where an existing product is not available.

Despite the many potential advantages of automated EA generation, the approach has not gained significant uptake in LMIC contexts where the kinds of spatial data previously used in tools like the AZTool (such as census tracts) are unavailable or outdated. Fortunately, many new and freely available data sources have become available, which can directly inform the creation of EAs. For example, crowdsourced road and natural feature data, which can be used to define logical boundaries for EAs, are increasingly available in repositories such as Open Street Maps (OSM). Globally, OSM is ~83% complete; more than 40% of countries (including several in the developing
Health Organization, WHO. 2021. WHO Coronavirus (COVID-19) Dashboard. Available at: https://covid19.who.int/table

- Yiftachel O, Hedgcock D. 1993. Urban social sustainability: the planning of an Australian city. _Cities_ 10: 139–157.

# **Annex 1: List of countries and territories**

Afghanistan, Albania, Algeria, American Samoa*, Andorra, Angola, Anguilla*, Antigua and Barbuda, Argentina, Armenia, Aruba*, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda*, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, British Virgin Islands*, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Cayman Islands*, Central African Republic, Chad, Channel Islands*, Chile, China, Colombia, Comoros, Congo, Dem. Rep., Congo, Rep., Cook Islands*, Costa Rica, Cote d’Ivoire, Croatia, Cuba, Curacao, Cyprus, Czech Republic, Czechia*, Denmark, Djibouti, Dominica, Dominican Rep., Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Faroe Islands*, Fiji, Finland, France, French Guiana*, French Polynesia*, Gabon, The Gambia, Georgia, Germany, Ghana, Gibraltar*, Greece, Greenland*, Grenada*, Guadeloupe*, Guam*, Guatemala, Guinea, Guinea Bissau, Guyana, Haiti, Honduras, Hong Kong SAR, China*, Hungary, Iceland, India, Indonesia, Iran, Islamic Rep., Iraq, Ireland, Isle of Man*, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea, Dem. People’s Rep., Kosovo, Kuwait, Kyrgyzstan, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein
claims prior to this period. In the case of all three countries, the information we have nets intrabank positions.<sup>6</sup> Table 1 presents information on Italian banks’ foreign claims in 105 countries. Claims are zero in 9 countries (Bhutan, Botswana, Burkina Faso, Cambodia, Honduras, Niger, Papua New Guinea and Rwanda) and are 100 percent cross-border in 60 countries or 60 percent of the sample. Countries where Italian banks extend only cross-border claims include both developed and developing economies across all regions.

Italian banks hold local claims in 35 percent of the countries in our sample. These include primarily developed economies or countries in Eastern Europe and Latin America. However, among these countries, the share of local claims is never 100 percent (the largest share of local claims hovering around 90 percent is observed for countries such as Croatia, Poland, and Peru). This indicates that Italian banks do not substitute entirely local claims for cross-border lending. Even in countries where most of the banking business is conducted through the local affiliates, there is still some cross-border financing taking place.

Table 2 shows information pertaining to the overseas or foreign claims of Spanish banks. Spanish banks are not active in anyway in 14 countries in the sample —Armenia, Bangladesh, Bhutan, Botswana, Burundi, Cambodia, Jamaica, Nepal, Lesotho, Nepal, Niger, Rwanda, Sri Lanka, and Zambia—, half of which are in Africa. Spanish banks hold exclusively cross-border claims vis-à-vis 48 countries or 47 percent of the sample. Like in the case of Italian banks, the list includes developed (mostly Nordic) and developing countries across all regions. Spanish banks tend to have local operations in developed countries and in emerging economies. The

> 6 This is an important advantage of this dataset since it allows us to measure net exposure to a country. Bank level data from sources such as Bankscope do not disclose information on intragroup positions and on cross-border lending to specific hosts.

9
the world’s population. While the poverty dimension is available for 168 economies, other dimensions have lower levels of coverage, representing between 46 and 84 percent of the world’s population (see Table 5) when the +-5 year filter is included. The significant drop in the number of countries when adding dimensions beyond poverty is sometimes due to limited data access. Consequently, assessing these dimensions at the household level for certain countries is not feasible. For instance, China provides highly aggregated welfare data, consisting of only 20-bin urban/rural distributions rather than microdata files, while for other high-income countries, 400-bin data are extracted from the Luxembourg Income Study (LIS).<sup>24</sup> When we drop the +- 5 year filter and use information on full coverage from World Development Indicators the number of countries increases only slightly but the share of the population covered increases for many indicators as data for India and China can be included. More than three quarters of the World’s population is included across indicators with the exception of water where data is missing for India. In the tables below we present results including India, acknowledging that this is an underestimate of the number of vulnerable in India as we do not have information on which households are lacking water.

**Table 4: Number of countries with survey data and dimensions of vulnerability in 2019**

||All|Income|Education|Social<br>protection|Financial<br>inclusion|Water|Electricity|
|---|---|---|---|---|---|---|---|
|_Region_||||||||
|East Asia and the Pacific|26|21|15|13|9|14|13|
|Europe and Central Asia|31|30|25|18|26|24|24|
|Latin America and the Caribbean|31|25
C1. Hence, it is safe to assume that omitting observations to avoid contamination bias does not introduce a selection bias. In this paper, we focus on targeting and short-term income impacts of the WWS program, and for this we compare assigned workers in T1 and C1.<sup>16</sup> The WWS Household Survey collected aggregate household income information and in-

> 14 We are grateful to Mihails Hazans for his advice on sampling.

> 15We keep only those assigned workers in T1 group who have participated in WWS for at least one month. This led to further dropping of 12 assigned individuals from group T1.

> 16Assigned individuals in group T2 and C2 were surveyed to study the medium/longer-term employability impacts of the WWS program.

8
during the conflict) and returnees (i.e. those who left and returned) perceive each other in a way that would lead to greater social cohesion after conflict. Divisions that contributed to conflict, such as clan, ethnic, regional or class lines, can still be present after conflict and can overlap with return patterns if returnees or stayees are concentrated in a particular group. In this case, out-migration of particular groups can reify and reinforce conflict-era cleavages, and then reintroduce those cleavages through the process of refugee return.

The process of large scale emigration during the conflict can also lead to the creation of new divisions in society (e.g. stayees versus returnees). These divisions can either replace or get mixed with more traditional identity categories. There is evidence of these types of divisions in many countries including Bosnia and Herzegovina, Burundi, El Salvador, and South Sudan (Schwartz 2019). In this case, out-migration and return can affect social cohesion by creating new divisions (i.e. stayees versus returnees) within previously coherent social groups.

The impact of repatriation is not confined to identity issues only, there could be broader economic concerns (Ruiz and Vargas-Silva 2021). This is particularly the case in communities that have limited natural resources. The influx of a large number of returnees can lead to more competition for those resources (e.g. land, water) and affect the level of social cohesion in the community.

To explore the impact of refugee return on social cohesion we use data collected in Burundi in 2015. This country experienced a major conflict during the 1993-2005 period, in which close to 5% of the population was killed and around 10% was displaced to other countries, mainly to neighbouring Tanzania (Ruiz and Vargas-Silva 2015; Ruiz and Vargas-Silva 2016). The
(panel A) provides estimates of household-level returns to education, _R_<sup>_pn_</sup> and _R_<sup>_pf_</sup> , in rural China and tests the null hypothesis that _R_<sup>_pn_</sup> = _R_<sup>_pf_</sup> using data from two rounds (1995 and 2002) of the Chinese Household Income Project (CHIP) survey. The standard errors are clustered at the primary sampling unit (county). The estimates based on the 5-year average income of a household in CHIP 2002 data in the last two columns of Table 4 show that the null hypothesis cannot be rejected with a P-value equal to 0.58.<sup>37</sup> The evidence from the 1995 data (three year average income) also delivers a similar conclusion: the null hypothesis cannot be rejected with a P-value of 0.33.<sup>38</sup> The conclusion that

> 34To see this clearly, consider the polar case where schooling is provided only by the government free of charge and there is no private schools (or private tutoring). In this case, the scope for parental financial investment to improve a child’s educational attainment is effectively nonexistent, making _θ_ 2 ≈ 0. 35The available estimates on Mincerian labor market returns to education at the individual level in China show low returns in the early years after the reform, but there is evidence of increasing returns in the later years, as one would expect with the deepening of the labor market. The evidence also suggests higher labor market returns in nonfarm occupations (DeBrauw and Rozelle (2008)).

> 36Note that the estimates of the effects of father’s education on household permanent income using CHIP data do not suffer from truncation bias, unlike the estimates of intergenerational persistence; whether some of the children were nonresident at the time of the survey is not relevant for this analysis.

> 37The 5 year income data cover from 1998 to 2002 in
elections held in pre-democratic times.

We then use panel data for eleven Latin American countries, covering the late XIX century to the present, to show that higher legislative malapportionment makes democratic

> 2 In this paper we use the term dictatorship to refer to any non-democratic regime.

> 3 This argument is closely related to the models developed in Acemoglu and Johnson (2006, 2008) where the elite manipulates institutions to increase their political power after transition to democracy.

> 4 We chose to limit the analysis to Latin America for several reasons. First, a long time series of data on legislative malapportionment is available for these countries. Second, the history of Latin American countries is characterized by many episodes of transition from dictatorship to democracy (and vice versa), providing variation for our empirical analysis. Finally, the relatively high degree of historical and institutional homogeneity in this sample of countries allows us to better isolate the relationship between legislative malapportionment and other political and economic outcomes.

2
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