rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Section1. Introduction**

Forced displacement has been surging across the world over the past decade and reached 80 million as of 2019 (e.g., UNHCR 2020). Importantly, these populations are centered in and around countries in active and protracted conflict and represent a population with some of the largest welfare deprivations in the world (e.g., UNHCR 2016; etc.). In order to meet the Sustainable Development Goals, humanitarian and development agencies will need to make much greater progress on supporting the well-being of displaced populations during conflict and after peace has been restored (e.g., World Bank 2018).

Although the forcibly displaced exhibit a wide variety of deprivations on average (e.g., UNHCR 2016), there has been limited investigation into the immediate welfare consequences of forced displacement given data scarcity. A number of reports compare postdisplacement welfare outcomes of forcibly displaced households relative to hosts (e.g., WFP 2016; World Bank 2017a; OCHA 2018; OCHA 2019b; Sharma and Wai-Poi 2019; etc.), and others investigate long-term consequences of displacement using data collected years after conflict (e.g., Ibanez and Velez 2008; Verwimp and Maystadt 2015; etc.). Furthermore, a large literature investigates violence before and after displacement in origin and destination regions (e.g., Stanley 1987; Engel and Ibanez 2007; Williams 2008; Lozano-Garcia et al. 2010; Bohra-Mishra and Massey 2011; Adhikari 2013; etc.). However, given the difficulty in collecting non-violence welfare data from displaced households leading up to, during, and immediately following displacement, it is difficult to precisely estimate how forced displacement impacts well-being and whether the impacts persist.

This paper investigates the impacts of forced displacement in the Republic of Yemen’s conflict using rare data collected during active conflict. The setting is one in which a large, forced displacement crisis began immediately after the conflict escalated in March 2015, where displacement might have been forced
than others, and deliver more mail at the same level of GDP than others. As we have seen, postal performance (in terms of letters per capita) is correlated with greater operational efficiency (measured by letters per staff, for example) and trust in the postal network (itself reflecting quality of service). At the same time, some developing countries have very large, very dense postal markets where delivery is far more straightforward.

Nonetheless, the point remains that, in most cases, even less stringent USOs would be more expensive per letter than similar obligations in the developed world, while the profitable market that might provide cross-subsidy is far smaller, because of the lower level of urban development and large corporate customers. Even in some of the richer, most population-dense, urbanized developing countries about 90 percent of routes are loss-making.<sup>9</sup> This situation would be worse in countries where only 31 percent of the population were urban (the case in low income countries). The cost and complexity of service delivery, especially in small markets, will help account for the fact that post offices frequently make significant losses in the developing world. Forty-five percent of countries with GDP per capita under $5,000 see revenues below operating expenses (let alone total costs).<sup>10</sup> UPU data suggests that 42 postal incumbents had negative net revenues in 2003, totaling nearly half a billion US dollars. In Nigeria, annual losses are above 0.05 percent of GDP.

Putting revenues and indicators of costs together suggests the scale of the problem. Low income countries will average total postal volumes lower than 10 million pieces a year. Imagine a

> 9 Traffic is measured by revenues, in this case.  British Postal Consultancy Service (2002)

> 10 Raising prices to the levels suggested by the Cohen et. al. (2003a) study would be an unworkable solution—this would doubtless reduce demand in favor of alternate providers, thereby further raising per-unit costs
wage reform. Up until 2012 there had been three minimum wage zones, A, B, and C. Zone A had the highest minimum wage and Zone C, the lowest. In 2012, the Zone B minimum wage was increased to match Zone A. That increase translated into a 3.1 percent real minimum wage increase for Zone B. Therefore, the authors of the study looked at the impact of the 2012 move to a single minimum wage for Zones A and B. The outcome, derived using the Difference‐ in‐Differences estimator, suggested that there was no negative effect on employment in Zone B, while the former Zone B workers received an immediate real increase in their wages of 2.9 percent.

The study by Maloney and Núñez Méndez (2004) also corroborated a negative correlation between employment and the minimum wage in Colombia. Based on panel data from household surveys, this study estimated that a 10 percent increase in the minimum wage during 1997‐1999 reduced employment by 1.5 percent.

Studies on Brazil suggest that minimum wage increases have had a modest negative effect on employment (Fajnzylber, 2001; Neumark et al., 2006; Lemos, 2004a). For example, in terms of elasticity, Neumark et al. (2006) found an effect of about –0.07 percent at the end of the 1990s, while Fajnzylber (2001) estimated an effect of between –0.08 and –0.05 percent in the 1980s and 1990s for workers who were earning close to the minimum wage. Lemos (2004a), in turn, found only –0.1 percent elasticity for the economy as a whole and for certain vulnerable groups. One explanation for the minimal effects on employment is that businesses were able to adjust their 8
on yield productivity, population and income growth, and climate change. These improvements may bring about reductions of between 10 and 45 percentage points in malnourishment rates for children younger than age five for the period of 2005–50.

Unsurprisingly, FAO (2002) concludes that the world is producing enough food to feed everyone, and even to respond to the diversity of demand. Fundamentally, the problem is that many people do not have land to cultivate, enough income to purchase food, or access to adequate safety nets. That conclusion does not mean to downplay the actual challenge of feeding the world’s (and, specifically, Asia’s) increasing population, but brings access to food and not simply the production of food to the center of the debate. Moreover, maintaining current yield growth rates still need to be sustained for decades at an annual cost that FAO estimates to be in the vicinity of US$83 billion in additional investment in agriculture across developing countries. That amount represents a financing gap of 50 percent of the current private and public investments in agriculture in the developing world, which average US$142 billion per year (FAO 2009).

In addition to lack of income to buy food and additional resources for agriculture productivity, a sustained but volatile increase in the nominal prices of food began at the start of the new century and gained strong momentum starting in 2007. The World Bank’s global Food Price Index (2005 = 100) went from 77 in January 2000 to 220 in June 2008, and, after a series of fluctuations, ended up at 223 in February 2011 (World Bank 2012b). In the case of East Asia, the United Nations Children’s Fund (UNICEF 2011) estimates that domestic prices increased by almost 90 percent between January 2007 and April 2008; domestic prices then fluctuated until November 2010, when they matched the peak in 2008 and remained at these levels through the first half of 8
Policy Research Working Paper 6071

# **Abstract**

This paper argues that state weakness is broader than implied previously in the civil war literature, and that particular types of weakness in interaction with natural resources have aggravating or mitigating consequences for the risk of civil war. While in anocracies or unstable regimes natural resources can be expected to increase the risk of civil war, we suggest that resource wealth allows weak leaders to stabilize their relationship with their inner elite circle. In particular, for regimes at risk of coup d’état, the availability of substantial resources is more likely to be channeled in ways that deter rebellion, plausibly countering the grievances generated by natural resources and rebels’ viewing of such resources as a prize for taking over the state. Data from 1946–2003 and multiple empirical operationalizations broadly support our argument. These findings are consistent with work showing that resource rents can induce stability in state - society relationships.

This paper is a product of the Economic Policy and Debt Unit, Poverty Reduction and Economic Management Network. 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://econ.worldbank. org. The author may be contacted at bodeaana@msu.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. 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
the covered portion that will introduce bias in the results obtained.

Phone surveys also commonly suffer from **nonresponse bias** whereby the pool of sampled households or individuals who are not successfully interviewed are substantially different from those interviewed. Nonresponse is a common feature of any survey including F2F surveys, but the potential for nonresponse is typically substantially higher for phone surveys. One reason nonresponse is often higher for phone surveys is the difficulties contacting respondents over the phone. Successful contact is subject to mobile network availability and quality, whether the respondent’s mobile phone is charged and operational, whether the respondent answers a call from an unknown number, etc. After successful contact, nonresponse can equally result from refusal of the respondent to participate in the interview or a breakoff mid-way through the interview. These two sources of nonresponse are also present in F2F surveys but could be more substantial in phone surveys since respondents might be less responsive to an unseen interviewer over the phone than an interviewer they meet in person. _Bias_ due to this nonresponse will be introduced if the nonresponse is nonrandom with some segments of the population being more likely to not respond. Given the additional sources of nonresponse common to phone surveys, there is also a higher potential for systematic bias due to that nonresponse.

# <mark>2.1 Methods to Counteract Bias</mark>

While coverage and nonresponse bias are real concerns with phone surveys, there are methods to attempt to reduce the bias. The methods available will vary according to the type of frame used for the survey. There are three predominant types of frames used for telephone surveys: (1) existing representative F2F surveys that collected phone numbers of respondents (for example, LSMS-ISA, DHS, HBS, etc.), (2) a list of phone numbers from a telecom provider, and (3) a list of numbers generated randomly (random digit dialing, or RDD) (Himelein et al, 2020). While there are advantages 3
In developing economies in Europe and Central Asia (ECA), the share of adults with an account at a financial institution increased from 45 percent in 2011 to 65 percent in 2017. Compared with the rest of the world, developing economies in ECA had relatively high levels of account ownership as of 2011 and experienced moderate growth over time.

Within ECA there is considerable variation across countries in the growth of account ownership (figure 1). In the euro area, 90 percent of adults already owned an account by 2011. The share increased marginally to 95 percent in 2017.

**Figure 1. Account ownership in Europe and Central Asia varies widely by subregion**

<!-- Start of picture text --> 2011 2014 2017<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Central Central Eastern Northern Russian South Southern Turkey Western Western<br>Asia Europe & Europe Europe Federation Caucasus Europe Balkans Europe<br>the Baltics<br>share of adults with an account<br><!-- End of picture text --> _Source_ : Global Findex database. The bars represent the share of adults aged 15 and above who have an account at a financial institution or with a mobile money provider.

The pattern differs in the rest of the region. All three developing economies in Eastern Europe—Belarus, Moldova, and Ukraine—witnessed an increase in account ownership of more than 20 percentage points between 2011 and 2017 (table 1). The Baltic economies experienced an increase in account ownership from a high base level. In 2017 almost all adults in Estonia and Latvia had an account.

Financial inclusion levels are lower in Central Europe. A few Central European economies, such as Croatia and Czech Republic, had stagnant levels of account ownership
www. oecd. org/puma/sigmaweb/pubs/ENGPDF/sp 14. pdf, both from OECD's SIGMA program.

- Osbome, David and Peter Plastrik. 1997. _Banishing Bureaucracy: The Five strategies for Reinventing Government._ New York: Plume.

- PricewaterhouseCoopers Endowment for the Business of Government. 2000. _Results of the Government Leadership Survey: A 1999 Survey of Federal Executives_ at http://www.pwcglobal.com/gx/eng/ins-sol/main/index.html

- Przeworski, Adam. 1999. "On the Design of the State: A Principal-Agent Perspective " in Luiz Carlos Bresser Pereira and Peter Spink (eds.), _Reforming the State: Managerial Public Administration in Latin America._ Boulder. Colo.: Lynne Rienner.

38
can lead to the emergence of zombie firms, low aggregate investment, and debt overhang (Caballero et al., 2008; Schivardi et al., 2017; Kalemli-Ozcan et al., 2019; Xiao, 2020; Chari et al., 2021). But public programs can also reduce bankruptcies and firm failures (Carletti et al., 2020; Demmou et al., 2020; D ́ıez et al., 2021; Cros et al., 2021; Gourinchas et al., 2021).

What makes our paper unique is that we study how a sizable and sudden government credit program available to most firms has worked in practice. By analyzing the universe of bank loans and formal firms in a country, which we match with their credit history and firm-level attributes, we can obtain a systemic view. That is, rather than focusing on a single segment of firms (e.g., either SMEs or publicly listed firms), we can speak to the allocation of credit across the entire firm distribution and the program’s aggregate impacts on both banks and the government.

Having the complete set of bank transactions, firms, and credit history can be important for evaluating the behavior of the supply and demand of credit since firms and banks can substitute across lenders, borrowers, and loan types. Moreover, it provides precise estimates of the macroeconomic impacts of the loans granted by characterizing the ex-post credit exposure of firms with different risk of default. It also enables us to identify different contributors and mitigating factors of aggregate risk accumulation. Calibrating our model to these data allows for counterfactual simulations of different policies and scenarios. Our data on loan applications and approvals allow us to estimate the actual behavior of firm demand and bank supply of public guaranteed credit and what determines the equilibrium outcome. Although we find adverse selection, as the existing literature does, we show that its contribution to aggregate risk can be contained.

# **3 Data**

We use three administrative data sets from various sources in
quality [d]|% firms identifying transport as<br>major business constraint|32|27|**30**|18|

_Source_ : AICD Road Sector Database on 40 Sub-Saharan African Countries accessed June 2010.

a. Total network includes the classified and estimates of unclassified and urban networks.

b. Main network for most countries is defined as result of adding the primary and secondary networks.

c. Rural network is generally defined as the tertiary network and does not include the unclassified roads.

d. Source is World Bank–IFC Enterprise Surveys on 32 Sub-Saharan Africa countries.

Sierra Leone established a Road Fund in 1992 to ensure maintenance and rehabilitation of public roads throughout the country. Since 2009, the government has made progress improving the legislation governing the Road Fund, and separation of the Road Fund from the Sierra Leone Roads Authority is intended by 2013. The Road Fund spent about $15.3 million annually on road maintenance for the 2004– 09 period, with efforts increasing in recent years. The Road Fund‘s spending on new investment and rehabilitation averaged $0.5 million annually for the 2007–09 period. This capital spending, coupled with $8.5 million of road investments channeled through the government (during 2007–09), brings the total public capital flowing to the road network to an annual average of $9 million.

# **Challenges**

Setting the fuel levy at a high enough level to fund road maintenance has been difficult for Sierra Leone. According to a United Nations Development Program (UNDP) report, the fuel-levy rate reached $0.10 per liter in 2010. Although this rate was approved in 1998, it took years to implement. The government‘s intention is to increase the rate to $0.16 per liter 2012 (UNDP 2010). This new rate will be in line with current Road Fund spending on road maintenance (figure 10), but will be as hard to attain as the previous levy increase.
. For India based on plant-level data for over 20,000 firms over 1989-2008, Mukim (2010) finds that exporting improves firm performance, though Tabrizy and Trofimenko (2010) do not (they find that self-selection of more productive firms into exporting explains the productivity differential between exporters and non-exporters). Cheaper intermediate and capital goods imports, on the other hand, may allow firms to produce existing goods using the same inputs as before, but at lower cost. They could also open up new ways of producing existing goods, and even allow entirely new goods to be made. Acharya and Keller (2009) estimate that the contribution of international technology transfer to TFP growth exceeds that of domestic R&D and that imports are a major channel for these spillovers. Topalova and Khandelwal (2011) show a causal link between increases in the number and volume of imported inputs from abroad and increases in TFP in India, while Goldberg et al. (2010b) link access to new imported inputs and new products introduced by domestic firms in India.</mark>

- **<mark>Access to knowledge through other channels</mark>**

- **_<mark>internet use</mark>_** <mark>: firms can acquire disembodied external knowledge flows from open source information such as scientific, technical and industrial journals, informal contacts and communications through networks of specialists, trade and industry associations, and other knowledge flows accessible through the internet, via e-mail or website use.</mark>

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# **_3.1 General Equilibrium Modeling_**

The study by Hertel et al. (2010) is an effort to analyze the impacts of climate change through a more careful modeling of the channels and the heterogeneity of impacts in the context of economic growth. They use disaggregated data on household economic activity (stratified by their primary source of income) within individual countries (15 developing countries) and a general equilibrium global trade model (Global Trade Analysis Project or GTAP) to explore how changes in agricultural productivity will affect poverty in poor countries. Although the extent of heterogeneity allowed by their model is limited, a key feature of their model is that allows different type of households to be affected differently by the prices of agricultural goods depending on whether they are net producers or net consumers of food.<sup>15</sup> They use three scenarios of climate change on agricultural productivity (low, medium, or high productivity) to evaluate the resulting changes by 2030 in: (i) global commodity prices, (ii) national economic welfare and (iii) the incidence on the poverty headcount rate (defined as the portion of a nation’s population living on less than $1 a day). The poverty consequences of a decline in agricultural productivity are evidenced through two channels: _changes in earnings_ and _changes in the real cost of living at the poverty line_ . The impact of a food price rise on earnings depends on the income sources for a given household group (these earnings shares are estimated from household survey data). If earnings rise faster than the cost of living for households at the poverty line in a given stratum, then the poverty headcount falls and vice versa. The responsiveness of the stratum poverty headcount to a given real income shock is determined by the density of the stratum population in the neighborhood of the poverty line, and is also estimated from the household survey data. When combined with information about the distribution of national poverty across socio-economic strata, the authors are able to
sup> A second useful database is the World Bank’s **_Doing Business_** database. This well-known database provides objective measures of business regulations in 12 areas. Some of the areas record regulations of a specific sector such as construction (which in our case seems less relevant as measures need to remain economy-wide), but most of them are country-broad regulations targeting the entire economy of setting up a business. For our categorization of TFiS measures based on the discussion in the previous section, only three areas would be relevant, namely **(a) Protecting minority investors** ; **(b) Enforcing contracts** ; and possibly **(c) Resolving insolvency** .<sup>9</sup> In large part, all three areas are related to investment measures and they can therefore be considered as mode-specific with respect to trade in services through Mode 3. In fact, the three sub-indexes measure businesses’ access to government institutions to solve investor and other contractual issues.

In particular the sub-components of the indicators of Protecting minority investors (i.e. **a** ) and Enforcing contracts (i.e. **b** ) coincide with the issues that are important for TFiS. They range from resolving issues of dispute, the use of good practices, dispute prevention and resolution, and investor-related services. Regarding this latter point, to some extent the area of Resolving insolvency (i.e. **c** ) can also be considered as relevant here. This category measures the cost and outcome of insolvency proceedings involving domestic entities as well as the strength of the legal framework applicable to judicial liquidation and

> 8 In a recently conducted survey by the World Bank among foreign direct investors, 86 percent find the legal and regulatory environment important or critically important for their investment decisions (World Bank, 2018).

> 9 Note that other areas of the Doing Business database are more sector-specific in nature such as “Dealing with construction permits