rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

<mark>Road connectivity in Cambodia is challenged by its geography, which i</mark> ncludes the low-lying central plains of the Mekong and mountains in the northeast and southwest, <mark>and its exposure to recurrent flooding, particularly in the rainy season, which is between May and September, with regional variability. Cambodia is one of the most vulnerable countries worldwide to disaster risk and climate change</mark> , ranked the 12th most disaster-prone country in the 2018 World Risk Index of 172 countries by the Institute for International Law of Peace and Armed Conflict 2018 <mark>. With</mark> 80 percent of the country within the Mekong River and Tonle Sap basins, Cambodia is especially vulnerable to floods, storms, and droughts. The infrastructure sector has seen significant damages due to disasters. For example, in 2014, the infrastructure sector bore some 86 percent of the total damages, predominantly roads and water and irrigation infrastructure <mark>(Royal</mark> Government of Cambodia 2014) with a cascading effect on local and regional economies due to disruption of infrastructure connectivity and supply chains and loss of market access for key economic goods. Looking ahead, climate change projections indicate temperature increase of 0.7°C–2.7°C by 2060 and increased intensity and frequency of extreme precipitation in the monsoon season and flooding (World Bank undated) which would only exacerbate current vulnerabilities.

The impacts of climate change are more noticeable in the rural parts of Cambodia. Higher levels of poverty compounded by poor infrastructure maintenance and limited access to all-season roads (only 5 percent of rural roads are paved) make the 79 percent of the Cambodian population living in rural areas (2016) highly vulnerable to the frequent disruptions of flooding events. The disrupted connectivity during flooding and other disasters greatly threatens the livelihood of those rural households and their access to basic services. In a context of health emergencies and pandemics, such as COVID-19, it is critical that
<!-- Start of picture text --> No transfer Social Insurance Social Safety Nets Combination of programs (incl. labor market prog.)<br>2 3 4<br>9 8<br>13 15<br>16<br>17<br>22<br>21<br>7 6 18 22 20<br>7<br>9 12<br>16<br>29<br>76 74<br>67<br>60 59<br>53<br>37<br>Africa Middle East & South Asia East Asia and Latin America Europe & World<br>North Africa Pacific & Caribbean Central Asia<br><!-- End of picture text --> Source: World Bank SP Atlas of Social Protection (ASPIRE) _– see Annex Table 3 for countries used for the calculations; country populations used as weights to expand to regions’ average_ How adequate are social protection transfers? Do they provide sufficient resources to the recipients to alleviate their poverty and avoid detrimental coping strategies? ASPIRE data can be used to compare average transfers received with the average consumption of its beneficiaries. Here also the variation across countries is large (Annex 3). Countries in Eastern Europe and Central Asia tend to provide generous support averaging 53 percent (that is, programs cover over one half of the consumption of people who receive it), while countries in Latin America seem to be calibrating their benefit systems to the scale of poverty gap and transfer on average 27 percent to the income of beneficiaries (the average poor person in these countries is about 25 percent below the poverty line). In Africa and developing Asia extreme values can be found, but for most countries with nascent systems it is very low. The lowest adequacy (less than 10 percent) regionally is South Asia and the Middle East and Northern Africa.

To sum up, most countries in the world use multiple social protection instruments, but their simple
best of our knowledge, our paper is the first to take stock of IRCs around the world based on a survey involving a large number of economies and covering several aspects of a country’s interest rate regime. Our paper also complements recent literature aimed to assess the welfare impact of IRCs at the country level (e.g. Safavian and Zia, 2018, and Alper et al., 2019, for Kenya; or Madeira, 2019, and Schmukler et al., 2018, for Chile).

The remainder of the paper proceeds as follows. The next section describes the data and how they were obtained. It also draws upon our new and comprehensive database to provide a selective overview of key aspects of IRCs collected through the survey. The third section uses the data to provide a preliminary estimation of the degree of bindingness of IRCs by calculating the gap, expressed in real terms, between legal restrictions and actual interest rates. This section also presents pairwise correlations between the degree of IRC bindingness and other dimensions of financial repression. The final section concludes.

# **II. Overview of the Survey Data**

We designed and implemented a survey to collect key features of _de jure_ IRCs on lending and deposit markets around the world. In addition to explicit regulation, governments can and often control interest rates through political pressure, moral suasion, implicit subsidies in various financing support programs, and other interventions. However, given the difficulties to track and measure _de facto_ IRCs in a cross-country setting, we took stock only of legal or _de jure_ IRCs, that is, administered caps or floors to interest rates via explicit regulation. Specifically, we focus on IRCs in lending and deposit markets, acknowledging that _de jure_ restrictions can also apply to interbank and bond markets.

The formulation and completion of the survey entailed a number of inter-related steps. Leveraging existing contacts at central banks and financial supervisory authorities, the initial outreach effort targeted the pertinent staff within local regulators and supervisors. However, given the sometimes-elusive nature of
year|162.88|291.68|393.95|613.07|**0.00**|
|_Institution characteristics_||||||
|HEI is public|0.40|0.49|0.25|0.44|**0.01**|
|Program has high quality accreditation|0.26|0.44|0.45|0.50|**0.00**|
|Number of programs in the HEI|4.96|3.31|6.18|4.56|**0.02**|
|_Field of study_||||||
|Health|0.04|0.19|0.08|0.27|0.17|
|Education, Arts, Humanities, and Social|0.49|0.50|0.27|0.44|**0.00**|
|Economics, Management and Accounting|0.22|0.42|0.27|0.45|0.41|
|Agronomy and Vet, Engineering and Architecture|0.24|0.43|0.38|0.49|**0.02**|
|**Panel C. Noise controls**||||||
|Number of attempts to complete the survey|9.85|3.46|9.39|3.95|0.34|
|Survey completed by phone|0.22|0.42|0.18|0.39|0.42|
|**Observations**|1|53|9|2||

# _Source_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows descriptive statistics for the 245 surveyed programs in Ecuador. The unit of observation is a program. Columns (1) and (2) refer
Yang, Yongzheng and Sanjeev Gupta. (2008) “Regional Trade Arrangements in Africa: Past Performance and the Way Forward”, African Development Review, 19(3): 399-431.

Yeats, Alexander. 1990. “On the Accuracy of Economic Observations: Do Sub-Saharan Trade Statistics Mean Anything?” _The World Bank Economic Review_ 4 (2): 135-156.

# _Appendix 1: Gravity model estimates_

Table 9 presents various estimates based on the gravity model of Behar & Manners (2010). To evaluate the change in intra- and extra-regional trade flows of SADC countries over time, the gravity model is embedded in a panel setting where the time periods are specified as 1991-95, 1996-2000 and 2001-05. Each period reflects the average of (strictly positive) annual values and reflects a value of zero if no annual flows are recorded that period. With this data, we can include dummies for the time period to depict evolution over time. We can also include dummies to indicate whether the exporter, importer or both are SADC countries. The results here are based on IMF DOTS export data. However, the specification is also estimated using data from UNComtrade (using a combination of measures reported by the exporter and importer). While there are major discrepancies in trade flows between the databases, the econometric results are generally consistent for both sets of data.

The equation is estimated using OLS and zero values are ignored. Alternative gravity model estimates based on a cross-country database for 2007 & 08 and estimated using a Poisson Pseudo Maximum Likelihood (PPML) estimator proposed by Santos Silva and Tenreyro (2006) corroborate our findings regarding the regionalization of SADC trade.

# **Table** **<u>9: Standard gravity estimates</u>**

|Dependent variable: ln<br>exports|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|ln
local politics (Danielson, 2017) and have examined the role of diasporas in funding conflict (Godwin, 2018), but little has been written about the political influence of these returnees once they are sent back home. Yet, understanding the political engagement of returnees has significant policy implications. For example, it is plausible that newly returned individuals come home with a desire to galvanize local and national politics in order to change the same conditions that initially pushed them away from their country of origin. However, it is also possible that such individuals decline to participate in politics because they are linked more strongly to the country they migrated to and may plan to try to return to in the future. As such, the political engagement of returnees has implications for migration cycles.

We focus on the case of Guatemala, which has received 18% of migrants deported from the US since 2018, second only to Mexico (Abuelafia et al., 2019). Guatemalan migrants frequently suffer extortion, kidnapping, violence, and death during their journey to the U.S. (e.g. Vogt, 2013; Heidbrink, 2020). In order to better understand the returnee experience, we collected survey data from over 1,000 Guatemalans deported from the U.S. immediately upon their return to Guatemala as well as 1 and 6 months later. We also conducted a series of interviews to better assess the results from our quantitative results.

We find evidence of two distinct, competing mechanisms linking extortion and civic action. First, we show that extortion increases respondents’ negative assessments of their personal economic conditions in Guatemala; such negative economic assessments are correlated with higher civic action. Second, we show that extortion predicts a greater likelihood of fear-induced avoidance behaviors upon return to Guatemala, and we demonstrate that higher fear of crime correlates with lower civic engagement. Overall, while these two mechanisms drive behavioral predictions of political engagement in opposite directions, we find that the mobilizing effect of perceived economic hardship is larger and more statistically robust
mostly afford housing units in precarious neighborhoods. Also, there is a stark contrast in housing rents, depending on the quality and access to services (Figure 21). Given the limited budget that can be allocated for housing, low-income households need to determine their residences by weighing the costs and benefits based on their preferences.

**Figure 20: Housing rents by locations**

<!-- Start of picture text --> (A) By precariousness (B) By precariousness and density<br>0 200 400 600 800<br>Monthly housing rent (CDF in thousands)<br>Precarious Non-precarious<br>excludes outside values<br>P low P medium P high NP low NP medium NP high<br>.015 800<br>600<br>.01<br>400<br>Kernel density<br>.005<br>200<br>Monthly housing rent (CDF in thousands) 0<br>0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on the Kinshasa Survey. _Note_ : Seven different strata as defined in the Kinshasa Survey are (a) low-density precarious areas (P low), (b) medium-density precarious areas (P medium), (c) high-density precarious areas (P high), (d) low-density nonprecarious areas (NP low), (e) medium-density non-precarious areas (NP medium), and (f) high-density nonprecarious areas (NP high). Red line indicates the median monthly consumption value (CDF 83,000).

26
the welfare aggregate following standard national income accounting practice (Deaton and Zaidi 2002).

Having constructed the welfare aggregate, there is a need to make additional adjustments to facilitate ranking of individuals or households. One of the most important adjustments is spatial and inter-temporal deflation to account for temporal and spatial difference in prices faced by the population. The second important change is related to adjustment of the welfare aggregate for household composition. This can be done by simply dividing household consumption by total household size or by taking into account economy of scale and equivalence scale.

After adjusting the welfare aggregate as described above, the second step is construction of a poverty line which is intrinsically linked to the measure of welfare. The welfare aggregate and the poverty line should be coherently measured. Households whose consumption expenditures or income fall below this line are considered poor. Defining a national official poverty line is a long empirical process led by national authorities of the country. Broadly speaking, it requires many steps including defining an average nutritional requirement or multiple requirements depending on the method chosen, costs of meeting energy requirement and the nonfood component (Ravallion 1998).

For the purpose of international comparison, international poverty lines are often used. The most widely used poverty lines are 1.25 USD at 2005 PPP and its equivalent in 2011 PPP - 1.90 USD (Ferreira et al. 2015). They were established by the World Bank as averages of the national poverty lines of the 15 poorest developing countries expressed in PPP terms to monitor global extreme poverty (Chen and Ravallion 2010). Higher international poverty lines can be used by countries to measure wellbeing if necessary. In this report international poverty lines are used based on USD in 2011 PPPs.

4
) for systematic and empirical research.

Various studies have already used the TFDD Basins at Risk database to make certain arguments about conflict and cooperation over water (Wolf, Stahl & Macomber 2003; Yoffe at al. 2004; Stahl 2005) and the application of the BWI can prove useful for ascertaining how physical phenomenon (such as droughts and floods) interact with political, economic, and social variables to explain some of these political events. An analysis of Mekong and Zambezi Treaties with regards to types of complaints and analysis of Mekong and Zambezi Treaties with regards to the water regulation mechanisms and existing institutions is provided in Tables 7 and 8, respectively. Below we begin with the Mekong and proceed to the Zambezi.

In regards to the flood events pertaining to the Mekong, the BWI predicted heavy flooding in August through October 1988, August and October 1989, and August through 21
reduced-form fixed-effect regression specification for exporting firm _i_ in year _t_ is expressed as: where the dependent variable in this specification is the firm-level DVAR estimate from Section 3, and the key variable of interest is the Connectivity Index (CI), which captures the share of domestic suppliers linked to exporting firms through HSR, reflecting the influence of HSR on firms’ DVAR through their supplier network.15 Control variables include firm-specific measures such as importweighted input tariffs and exchange rate appreciation, along with city-specific metrics such as income levels, FDI, telecommunications infrastructure, internet connectivity, passenger and goods transport, internal city transportation, industrial composition, and population density.

**Potential Domestic Suppliers and Connectivity Index** To construct the CI, we first need to identify each exporting firm’s domestic suppliers. Since data on actual domestic suppliers is unavailable— and even if it were, using such data could introduce selection bias and endogeneity issues, potentially distorting the regression analysis—we avoid these problems by constructing the CI based on each firm’s _potential domestic suppliers_ instead. This approach mitigates bias while still capturing the influence of HSR on firm DVAR through supply chain connections.16

For each exporting firm, potential domestic suppliers are identified as firms that export products with the same HS 6-digit codes as the firm’s imported inputs. For instance, if customs transaction data show that an apparel firm exports men’s shirts and imports cotton fabric under HS code “520819,” all textile firms exporting cotton fabric with the same code are considered potential domestic suppliers. The term _“potential”_ indicates that no direct transaction is observed between the textile and apparel firms, but the fabric produced by the textile firm could theoretically be used as an input for the apparel firm. This approach assumes that products with the same HS 6-digit code are substitutes.

An advantage of this approach—matching upstream and downstream firms based on product codes—is that it eliminates the need for firm-level matching by names or reliance on industry IO tables, which
endogenous, in that it is an increasing function of past sales due to learning on the part of consumers (up to a maximum point, which is not predictable _a priori_ ). However, the initial investment required to penetrate a new market, stimulate demand, and learn the market’s potential size will suffer the same appropriability problem: imitators can free ride, leading to underinvestment by entrepreneurs. Both the uncertainty and endogeneity of foreign demand would justify subsidizing entry into new markets.

Based on a similar argument of free-riding on market-cultivating expenditures, originally advanced by Bhagwati (1968), Mayer (1984) constructs a model of foreign-market cultivation which assumes actual consumption experiences are required to learn about a commodity’s qualities. The model indicates that subsidization of infant-exporters is a first-best policy. Another extension relates to foreign standards (Granslandt and Markusen 2000). When attempting to export a good to a foreign market, the first entrant will have to make the initial investments in product and process redesign to meet foreign product-safety standards. However, market failures will arise if redesigns are non-excludable, as free-riding will reduce returns of the first entrant.

While interesting, these models have not been subjected to systematic empirical testing. This is likely due in part to a lack of disaggregated worldwide production data, combined with no obvious method of testing for the presence of these market failures. As described in section 3, we will use disaggregated export data, which unlike domestic production data, is widely available at a highly disaggregated level. In addition, we suggest the following framework to evaluate the importance of market failures in the discovery process.

7
>10 0<br>8<br>-10<br>6<br>4 -20<br>2<br>0 -30<br>Credit Growth: SCBs 3mma Export Growth 3mma<br>Apr-13 Aug-13 Dec-13 Apr-14 Aug-14 Dec-14 Apr-15 Aug-15 Dec-15 Apr-16 Aug-16 Dec-16 Apr-17 Aug-17 Dec-17 Apr-18 Apr-13 Sep-13 Feb-14 Jul-14 Dec-14 May-15 Oct-15 Mar-16 Aug-16 Jan-17 Jun-17 Nov-17 Apr-18<br><!-- End of picture text --> Note: Credit data is from the Reserve Bank of India; Exports data is from Ministry of Commerce and Industry. 3mma refers to three months moving average.

> 33 Due to the GST-related uncertainties, producers destocked their existing inventories, while exports declined, and gold imports nearly doubled, as buyers front-loaded their purchases. Once the initial uncertainties abated, economic activity recovered, and new orders, including in manufacturing, reportedly picked up.

> 34 In our analysis we see the transient impact of demonetization on financial, real estate and professional services, and on construction, but not so much on other sectors of the economy. On the uses side, deceleration was more evident in an already slowing rate of investment; and in an escalated level of import of gold (possibly due to capital flight).

> 35 Outstanding credit by only Scheduled Commercial Banks; we do not consider credit by non-scheduled banks or other financial corporations. The same applies to the discussion of bank credit under section 4.3.

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Policy Research Working Paper 10152

# **Abstract**

As COVID-19 vaccines have become more widely available in Sub-Saharan Africa, vaccination campaigns in the region have struggled to pick up pace and trail the rest of the world. This paper presents new evidence on vaccine hesitancy, uptake, last-mile delivery barriers, and potential strategies to reach those who remain unvaccinated. The data come from high-frequency phone surveys in five countries in East and West Africa (Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania). The surveys were conducted by countries’ national statistical agencies, have national scope, are cross-country comparable, and draw their samples from nationally representative sampling frames. The findings show that across the study countries, a majority is willing to get vaccinated. Still, vaccine hesitancy is non-negligible among those pending vaccination. Concerns about side effects of the vaccine are the primary reason for hesitancy. At the same time, many who are willing to get vaccinated are deterred by a lack of easy access to vaccines at the local level. Radio broadcasts have widespread reach and medical professionals have good rapport among the unvaccinated population. Furthermore, social ties and perceptions as well as intrahousehold power relations matter for vaccine take-up. Based on the findings, the paper elaborates policy options to boost vaccination campaigns in Sub-Saharan Africa.

This paper is a product of the Development Data Group, Development Economics. 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 pwollburg@worldbank.org.

_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.