rafmacalaba/data-use-mentions-extended huggingface dataset:
et al., 2025). We extend this line by demonstrating that periods of forced displacement in weak institutional settings can reinforce the power and resources of criminal organizations linked to autocratic regimes. In this regard, we also complement the work analyzing authoritarian regimes (Besley and Kudamatsu, 2008; Acemoglu and Robinson, 2009; Acemoglu et al., 2019; Hsieh et al., 2011; Egorov and Sonin, 2023; Gonz ́alez et al., 2023; Yang, 2024 ). However, rather than focus on the effects of the regimes themselves, we investigate how forced displacement may reinforce the power of such leaders.

Finally, we contribute to the literature by combining unique data sources to study the effects of forced displacement outflows on an origin country with weak institutional development, as is typically the case with emigration of this type. In such settings, data are rarely available (Martinez 2022; Chiovelli et al. 2023), since autocratic regimes prevent their collection and such countries are often so profoundly in crisis that data collection becomes extremely challenging. For this reason, we integrate data from multiple unique sources including labor force surveys, remote-sensing variables, population censuses, and surveys of Venezuelan migrants in Colombia, as well as web-scraped digital data on electoral results, private-firm expropriations, and international economic sanctions. We can thus comprehensively assess the complexity of forced displacement and its wide-ranging effects in origin locations.

# **2 Local Context: Venezuela’s Unraveling**

Venezuela, a historically unequal society highly reliant on oil, entered an economic recession in the late 1990s due to falling oil prices and a downturn in its non-oil sector, which fueled widespread discontent and led to the election of Hugo Ch ́avez as president in 1998 (Hausmann and Rodr ́ıguez 2014). Ch ́avez’s presidency brought sweeping changes, including constitutional reforms that concentrated power in the executive branch and weakened political accountability. His administration also expropriated private firms, spurring a sharp contraction of the private sector (Panel A,
mobile phone.

## _Response Rates and Attrition_

At the end of the first round of data collection in December 2010, 68 percent of SSEPS participants completed the mobile-phone administered questionnaire. Overall response rates declined to 62 percent in January, 55 percent in February, and 52 percent in March (Table 4 and Figure 3). Both the level and trend of response rates varied substantially across town of residence (Figure 4). In Wau, for example, although response rates declined with each successive round, relative to other towns, response rates were high: 84 percent in December

> 6 The reference period for the Afrobarometer questions is one year. Because the survey was conducted on a monthly basis, a one-month reference period was of greater interest for the time series data. For this reason, in December 2010 the poverty and security questions were asked with _both_ a one-year and a one-month reference period. In subsequent months, they were asked with only a one-month reference period. The outlook questions are only asked with a one-year reference period.

10
Bruner, 2011). These business services correspond to ISIC Rev. 4 numbers 45-56 as well as 64-66, 6875 and 77-82.<sup>12</sup> Together, this grouping is referred as Digital+, as shown in Annex Table A2.

Of all bilateral trade relations that exist in the TiVA database, the analysis captures around 63 separate exporters and importers. Admittedly, by selecting TiVA over other sources of trade such as the ITPD-E, it omits a large group of developing countries. However, compared to the ITPD-E database, TiVA records more consistent bilateral trade data for the smaller group of developing countries covered. Consequently, this results in a higher number of total observations. Moreover, the share of digital services trade covered by countries sharing the same data model only increases by a small share when using the ITPD-E database, as can be seen when comparing Figure 1 with Figure A1 in the Annex.

# **3.3.1 Data models and digital services trade**

Figure 3 shows that a share of 53 percent of digital services trade takes place among country-pairs sharing a similar data model when considering the component of cross-border flows of data. This share is slightly lower when considering the component of domestic processing of data, namely 47 percent. This difference is due to the fact that, as explained, countries can be categorized into two different models across the twin components.<sup>13</sup> The rest of the digital services trade takes place between countries following different data models.

Within the portion of digital services trade covered by country-pairs that share the same data model, the vast majority of trade happens between countries that follow the conditional model. This share is around 79 percent for the component of cross-border data transfers, which increases up to 92 percent for countries following the conditional model for domestic data processing, as shown in Figure 2. A smaller fraction of trade within that portion takes place between countries sharing the open model of cross-border transfers and processing of data, with
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 5 Productivity compactness – as measured through different urban form metrics - and its subsequent economic growth, where a city’s rate of economic growth is estimated on the basis of nighttime lights data.

Another recent development on the calculation of shape metrics in a GIS context is the Metropolitan Form Analysis Toolbox developed by the City Form Lab (Amindarbari and Sevtsuk, 2013), an open source toolbox for ArcGIS that includes eight urban form and landuse metrics that can be used to track growth and change in cities: Size and Density, Coverage, Polycentricity, Compactness, Discontinuity, Expandability, and Land-Use Mix. Libraries such as the Metropolitan Form Analysis Toolbox and The Shape Metrics Tool have been used to analyze large samples of cities: Angel, Sheppard et al (2005) analyzed the urban extents of 3,493 cities in 1990 and 2000. Amindarbari and Sevtsuk (2013) calculate urban form and land-use metrics for Los Angeles, Singapore, Jakarta, Guangzhou and Chengdu. Other studies on urban form, that do not necessarily use the libraries mentioned above, are: Bertaud and Malpezzi (2003) calculate a range of measures of urban form for 48 major metropolitan areas around the world, and analyzed the impact of income, population, and the nature of the regulatory regime as potential determinants of urban form; Huang et al (2007) calculate seven spatial metrics of urban form for 77 metropolitan areas in Asia, US, Europe, Latin America and Australia; they found that urban agglomerations in the developing world are more compact and dense than their those in Europe or North America.

Urban planners, in the search of an integrated theory of city planning, have explored multidisciplinary approaches that integrate areas such as economics, network science, and geometry, to understand the impact of size, scale, and shape on city sustainability (Batty, 2008). This process led them to have a more
# **1 Introduction**

In the past two decades, the notable uptick in the integration of developing nations into global trade and value chains (GVCs) has sparked heightened interest among policy makers and researchers regarding the implications for labor markets. Consequently, a substantial body of literature has emerged seeking to unravel the intricate relationship between trade dynamics and localized labor market outcomes.

Standard theory about the benefits of trade assumes perfect mobility of factors across geographical regions and industries within a country. However, recent empirical evidence has shed light on the limitations of these conventional theoretical models. Topalova (2010) [9] uses data from India to indicate that regions more exposed to trade liberalization have experienced slower poverty reduction and muted consumption growth, diverging from the predictions of traditional trade frameworks. Assuming segmented labor markets, and exploiting cross-market variation in import exposure, a key study by Autor, Dorn, and Hanson (2013) [2] confirms that the ”China Shock” led to significant decline in employment and wages in more exposed U.S. regions. This sparked more studies on the repercussions of tariff changes or import competition on local labor markets (Pierre and Schott 2016 [7]; Acemoglu et al. 2016 [1]; Dix-Carneiro and Kovak 2013 [3]).

It is crucial to note that trade affects not only tradable but also non-tradable sectors within the same local labor markets; while increased import competition or market access directly impacts specific tradable sectors, there are also indirect effects on non-tradable sectors such as retail, healthcare, or hospitality in the same region. With few exceptions, current literature largely ignores estimating these indirect effects propagated through domestic production linkages within a country [10]. While a few studies have examined the indirect effects of import shocks on local labor markets, there has been limited exploration of indirect effects of exports. A central motivation for this empirical inquiry stems from the well-documented fact that as economies undergo structural transformation—that is, as they move from less agriculture to more services
emphasizing the importance of policies to support cross-border trade with Sudan (World Bank, 2014a). Since 2013, the border was re-opened leading to increased trade. However, absence of bilateral trade statistics with Sudan makes it impossible to estimate the volume.

Before 2012, exports exceeded imports considerably. Between 2008 and 2011, exports remained above 60 percent of GDP largely consisting of oil. Imports averaged 22 percent of GDP from 2008 to 2011. The oil shutdown in January 2012 led to a collapse of oil exports and, hence, a significant trade deficit. The import share of GDP increased from around 22 percent in 2011 to 39 percent in 2012 and the resulting trade deficit reached 36 percent of GDP. In absolute values, imports of goods remained stable in 2012 but GDP declined sharply (World Bank, 2014a).

South Sudan imports food and vegetables as well as transportation and machinery. In 2008, the main imports categories were food, with 26 percent of imports in the category for food stuff and 12 percent in the category for vegetables.

Nimule, on the border with Uganda, is the most important border crossing. In 2012, about 80 percent of customs revenue was collected at Nimule. Kaya, another customs station at the border with Uganda, and Juba airport each contributed a share of about 10 percent to customs revenue. Other border stations with respect to revenue generation are negligible.<sup>10</sup> The vast majority of these imports are headed to Juba, which effectively represents the only international trade node in South Sudan.

# **3. Price Differentials and Price Volatility**

The integration of spatially separated markets implies interconnectedness among them through trade or through the possibility of trade. Thus, the forces of arbitrage make the long-run price differences that remain among them reflect trade costs only.<sup>11</sup> Assessing integration involves examining carefully the evolution of prices of specific products across different cities in South Sudan, and testing their short and long-run co-movement. When doing so
**Figure 2. Food expenditure (share)**

<!-- Start of picture text --> 60<br>48<br>45<br>50 43<br>40<br>40 32<br>30<br>20<br>10<br>0<br>Bottom 20% 2nd 3rd 4th Top 20%<br><!-- End of picture text --> Source: World Bank using HIES 2018/19 for expenditure shares In response, we convert total inflation into household-specific inflation rates following Nasir et.al. (2023). The HIES 2018/19 includes information for 283 expenditure items grouped into 12 main categories based on the COICOP classification. We combine expenditures at the COICOP level with their respective inflation rates, as provided by PBS. By aggregating across COICOP categories, we estimate experienced household-specific inflation rates.<sup>9</sup> As Figure 3 shows, depending on the extent to which different COICOP categories experience differential price trends, the perceived inflation by households across the expenditure distribution will differ. For instance, in FY23, households in lower deciles of the expenditure distribution experienced inflation rates that were, on average, one percentage point higher than those experienced by the wealthiest households because of higher food price pressures. Therefore, assuming equal inflation rates across the whole expenditure distribution will potentially underestimate the projected poverty rate.

**Figure 3: Inflation Inequality across Income Deciles**

<!-- Start of picture text --> 45 Decile1 (Poorest) Decile 10 (Richest) YoY_Inflation<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Inflation<br><!-- End of picture text --> 9 Detailed descriptions of the construction of household level inflation rates for Pakistan are provided in Nasir et al. (2023)
employ the above introduced double-cost approach, adapted to capture the some of the country-specificity's as weights attached to resource and preference costs to provide a discussion of the decentralization experience in the eight countries mentioned above. These countries were selected because of their diverse decentralization experience in the road sector. Road financing and delivery remains centralized in some of these countries, whereas, in others, the functional and fiscal responsibility is decentralized.

Employing the estimated results from a cross-country panel data (taken from Humplick and Moini-Araghi, 1995) we can get the average relationship between decentralization and performance by road activity. Further, overlaying simply the country specific data obtained from the cross country sample to the general graph allows us to conduct a comparative study of proper levels of decentralization and performance for the above mentioned countries. We obtain two points for each country, one reflecting resource costs and the other preference costs; each of the points represents country specific levels of performance and decentralization. For each country, our inquiry looks into the following issues: i) what is the level of decentralization and performance? ii) what is the variation in performance, measured in terms of deviation of preference and resource costs from the estimated value and what are the factors driving cross country variations in observed levels of effort? iii) for those countries where the level of variation is high, what would be required to minimize resource and preference costs?

The expended level of effort varies from country to country depending on the weight attached to preference and resource costs in that country. The weighting procedure is driven by the objective to operate efficiently and to satisfy beneficiaries' demand. Moreover, the ability and willingness of govemments to fulfill the desired standards and objectives that are demanded from the road network is a function of the available resources, financial and institutional arrangements (whether expenditures are financed from own-source revenues or from transfers, whether these are ear-marked or not), implementation capacity, as well as fiscal and political attitudes in a given country. Actual levels of effort expended to maximize
under-report size of holdings and extent of tenancy and to over-report the crop share of tenants". Village studies report more land under tenancy than does the NSS, and show a smaller excess of reported leased-in over leased-out land (Jayaraman and Lanjouw 1998). Aside from questionable accuracy owing to reporting biases, survey data tend to overstate land inequality and need to be corrected for household size and land quality. Households with smaller holdings also tend to have fewer members, which relates in part to the lifecycle effect of subdivision upon inheritance. Smaller holdings tend to be cropped more intensively and have a higher percentage of total area under irrigation. Inequality in income potential is therefore reduced within a given region than the aggregate data suggest, while differences in agro-ecological potential mean comparisons between regions are also suspect.

Data on landlessness need to be interpreted with care. There are four main definitions: households owning no land, those who operate no land, those who neither own nor operate any land, and rural households who rely principally on wage employment. These categories typically overlap but are not identical; the differences are important with respect to control over assets and livelihood sources, and in terms of their policy implications. It is important to distinguish underlying causes of landlessness in terms of push factors (demographic pressure inducing some marginal landowners to lease out or sell their land, distress sales owing to indebtedness or other contingencies, ejection of tenants to evade provisions of tenancy acts or to allow landlords to resume self-cultivation using new technology), or pull factors (growth in non-farm employment opportunities). Some (e.g. Singh 1990) suggest that the growth in landlessness during the 1970s in agriculturally progressive states (Punjab, Haryana, Gujarat) is no cause for alarm on the grounds that it merely reflects the growing dynamism of the rural non-farm sector.

The aggregate data also fail to offer insights into the operation of land markets. Land transfers come about through subdivision (legal and customary), gifts (usually within families, see Rosenzweig and Wolpin 1985
(0.136)|(0.262)|(0.148)|(0.275)|
||[0.002]|[0.132]|[0.001]|[0.107]|
||-0.138|0.031|-0.111|-0.017|
|Mask manufacturing exposure_d ×_Aug_t_|(0.197)|(0.311)|(0.221)|(0.341)|
||[0.485]|[0.920]|[0.617]|[0.960]|
|Estimation method|OLS|OLS|OLS|OLS|
|Firm-by-product FE|||||
|Destination sub-District FE|X|X|X|X|
|Month FE|X|X|X|X|
|Residualized shocks||X||X|
|Controls _×_ Month FE||X|||
|# observations|506|506|506|506|
|# clusters (sub-Districts)|86|86|86|86|

# **G Number of paracetamol receipts as a proxy for COVID-19 infections**

In Section 3.1.2, we used number of purchases of paracetamol, a commonly recommended fever medicine, as a proxy for COVID-19 infections, absent data on subA33
willingness to pay from household surveys and exempting poor land owners would allow to eliminate informality without jeopardizing the registry’s ability to sustain itself from fee revenue (Ali _et al._ 2021).

These examples illustrate that fee reductions before launching titling initiatives are feasible and may be needed if overstaffing of land agencies or complex procedures make implementation of progressive laws unaffordable (Hunt 2004). However, a more frequent response was for donors to subsidize first registration, mostly in rural areas, a move justified with reference to the fixed cost of creating functioning registries. This approach faces two challenges: On the one hand, potential economic benefits from documenting land rights will be higher (due to higher land values) and cost of service delivery lower (due to parcel density) in urban than in rural areas. A focus on rural areas only thus requires an economic justification, especially as in many African settings, demand for documented land rights in urban areas is high: in Dar es Salaam, most slumdwellers paid into a savings scheme to finance title acquisition (Ali et al. 2016) with willingness to pay of US$200 for title instead of much cheaper substitutes of low quality (Manara 2022),<sup>5</sup> but supply constraints impeded an effective response (Manara & Regan 2021). Informal residents’ willingness to pay for title was also high in urban Democratic Republic of Congo (Balan et al. 2020). High demand for documented land rights is reported from Zambia (Ali et al. 2020a), Ghana (Ehwi et al. 2021), and Nigeria (Goodfellow & Owen 2020). In fact, efficient urban titling programs could be self-financing if, instead of charging high upfront fees that credit constrained households may be unable to afford, a small fraction of the associated increases in land values is captured _ex post_ via land taxes (Hawley _et al._ 2018).

A second challenge is that, even if a registry is successfully established, failure to reduce the
Vietnam. We discuss the estimation method and provide the empirical results, respectively, in Sections 4 and 5. We conclude in Section 6.

# **<mark>2. Data sets</mark>**

The main data set used in this study is the most recent Vietnam Household Living Standards Surveys (VHLSS) from 2010 to 2016. The VHLSSs have been conducted every two years by the General Statistics Office of Vietnam (GSO) with technical support from the World Bank in Vietnam since 2002. The latest VHLSS was conducted in 2016. In this study, we use the 2010-2016 VHLSS instead of those carried out before 2010, since the more recent surveys contain more information on employment consisting of monthly wages and formal employment.

The 2009 Population and Housing Census is used as the sampling frame. Of 10,896 communes, 3,133 were chosen as the primary sampling units. A village was randomly selected from each commune, and around 15 households were selected randomly from the village. Each VHLSS covers around 46,000 households selected from 3,063 areas of the master sample frame, and is divided into 2 types: (1) The sample for the income survey includes 36,756 households and collects information to assess non-monetary living standards at the national, regional and provincial/city level; (2) The sample for the income-expenditure survey includes 9,189 households and collects sufficient information for further assessment and analysis of monetary living standards at the national and regional level.

In this study, we used the full sample of the VHLSS to increase the number of children born in January and February. The total number of households and household members sampled in VHLSS are as follows:

- VHLSS 2010: 46,995 households with 185,696 household members.

- VHLSS 2012: 46,996 households with 182,042 household members.

- VHLSS 2014: 46,335 households with 178,267 household members.

- VHLSS 2016: 46,380 households with 175,340 household members.