rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7. References**

<mark>Alder, Simon, (2014). "Chinese roads in India: The effect of transport infrastructure on economic development." Working Paper, Univ. North Carolina, Chapel Hill</mark> Allen, Treb and Costas Arkolakis, (2014) “Trade and the Topography of the Spatial Economy," The Quarterly Journal of Economics, vol. 129(3), pages 1085‐1140. 1.

Allen, Treb and Costas Arkolakis, (2019) “The Welfare Effects of Transportation Infrastructure Improvements" Working Paper.

Anderson, James E and Eric van Wincoop, (2004) “Trade Costs,” Journal of Economic Literature, 42 (3), 691–751.

Caliendo, L. and F. Parro, (2015) “Estimates of the Trade and Welfare Effects of NAFTA,” The Review of Economic Studies, 82 (1), 1–44.

Center for International Earth Science Information Network ‐ CIESIN ‐ Columbia University. 2016. ‘Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals.’ Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4SF2T42.

de Soyres, F., A. Mulabdic, S. Murray, N. Rocha, and M. Ruta, (2018) “How Much Will the Belt and Road Initiative Reduce Shipment Times and Trade Costs?,” Policy Research working paper; no. WPS 8614. Washington, D.C.: World Bank Group de Soyres, F., A. Mulabdic, S. Murray, N. Rocha, and M. Ruta, “The Belt and Road Initiative: A Structural Analysis”, forthcoming Defourny, P. (2017) ‘ESA Land Cover Climate Change Initiative (Land_Cover_cci): Land Cover Maps, v2.0.7.’ Centre
all very close to zero in magnitude, with 95 percent confidence intervals of approximately ±3 to 5 percentage points. Therefore, alleviating information and quality concerns alone does not seem to be sufficient to get more firms to use professional business services.

There is a large literature documenting the existence and importance of information frictions in limiting trade in developing countries (e.g. Jensen, 2007; Allen 2014; Jensen and Miller, 2018; Starz 2018). A key policy question is then whether simple interventions to alleviate these frictions will spur more market activity. Dillon et al. (2020) set up a yellow pages system in rural Tanzania and find that giving households information on enterprises does lead them to order more goods from outside their villages. Rudder (2020) finds that the same digital phonebook changes how firms contract with their suppliers, but does not lead to any detectable impact on firm revenues. Our work compliments this approach by testing information provision in a large urban setting and more complicated product environment, and through using ratings in addition to information. We also contribute to a recent literature examining the extent to which ratings can alleviate information and quality frictions and enhance consumer and firm welfare. Dellarocas (2003) highlights the importance of online ratings systems in building trust in situations where trade is moving from word-of-mouth reputation networks to larger scale marketplaces – as is our goal here. Reimers and Waldfogel (forthcoming) document how both professional reviews and peer star ratings boost sales of books on Amazon.com and enhance consumer welfare by allowing them to purchase better products. They note the importance of ratings for experience goods, and we view professional business services as an experience service.

# **2. The Status Quo**

We use a brand recognition exercise with firms that applied and were selected for a government business support program to document that very few business service providers have a brand that is known by small firms. We then conducted a survey of business service providers to understand the characteristics of these providers, how they
d d (m d (m d (q Which provides a simple rule to filter episodes of excessive lead time. TT = TT mmiim ) + 2 ∗σσ≈3 ∗TT mmiim ) −2 ∗TT mmqqttiiqqmm) Which provides a simple rule to filter episodes of excessive lead time.

## **Construction of the Index**

Conversely, the contribution of a region or a port to global capacity stress can be directly identified.

# **4. Findings and Applications of the Index**

The application of this analytical approach to global shipping data from 2016 to 2024 yields a global supply chain stress metric consistent with the general understanding and expert knowledge of current and past events. Globally, the GSCSI has risen consistently from early 2020 to March 2022, as expressed in terms of trade capacity (measured in TEUs, Figure 5). This indicates a considerable level of stress across the global supply chain over this period. The index went up in late 2023 in response to the Red Sea crisis.

Figure 5. ~~<u>Glo</u>~~ bal Supply Chain Stress Index (MTEU) January 2016- February 2024 (Monthly) 7
2 from pressures from interest groups, there could be considerable friction in the trade, and competition policy nexus. Thus the need for a coherent design, coordination if not integration and strict oversight, to avoid capture and distortions. This paper argues that there are good reasons to worry about the antidumping law and practice and that antidumping per se should be, if not eradicated, transferred to the jurisdiction of the competition policies agency and judged and ruled by the same economic criteria.

The plan of the paper is as follows. The next section II, discusses generic issues related to antidumping and competition policies and the background to the discussion that follows. Section III describes antidumping in the Uruguay Round. Section IV examines antidumping practice in Latin America. Section V examines competition policies in Latin America. Section VI provides conclusions regarding the integration of antidumping and competition policies.

**Table** 1: Changes in Trade Orientation (percent)

||Trade reform|Openne|ss index||Trade flows||
|---|---|---|---|---|---|---|
|Country|period|Period|Change|Period|Export growth|Import growth|
|Argentina|**1989-91**|**1988-94**|**8.01**|**1990-94**|**_5.63_**|**31.62**|
|Bolivia|**1985-88**|**1984-93**|**3.56**|**1986-93**|**8.46**|**4.76**|
|Brazil|**_1990-93_**|**1989-94**|**5.19**|**1991-94**|**6.99**|**9.97**|
|Chile|**1988-91**|**1987-94
experienced<br>severe fuel shortages in Nov 2011. Fuel<br>shortages were again reported in Mar 2012.|
|Kenya<br>√|<br>**Pricing policy**: Kenya deregulated its oil industry in 1994. After<br>concerns about over-charging by oil companies had been voiced in<br>several quarters, government in Dec 2010 began setting maximum<br>retail prices by location every month for gasoline, diesel, and<br>kerosene. Price calculations are based on volumes and costs in the<br>previous three months. Government removed the VAT on LPG in<br>2005. In Apr 2011, government cut diesel and kerosene taxes by<br>20% and 30%, respectively, and in May 2011 eliminated all taxes<br>and levies on kerosene (as well as maize and wheat). Kerosene was<br>nearly 20% cheaper than diesel in Jan 2013. The price difference<br>between kerosene and diesel, and the tax difference between<br>products destined for the domestic market and those for export, are<br>two drivers of commercial malpractice.<br>**Information**: The Energy Regulatory Commission posts landed<br>costs of crude oil, gasoline, diesel, and kerosene, and maximum<br>pump prices allowed in 70 towns on its Web site every month. The<br>Petroleum Institute of East Africa also posts industry statistics and<br>price and tax data, including statistics on the results of fuel<br>marking―kerosene marker and export marker―and tracing tests.<br>The results from 2012 found that independents were more likely to<br>be in violation than integrated companies.|**Supply**: There is one refinery in Kenya,<br>which is in need of rehabilitation and not<br>capable of operating at anywhere near its<br>design capacity. During the
Policy Research Working Paper 7864

# **Abstract**

Despite record economic growth for more than a decade, poverty has remained stubbornly high in Afghanistan, especially in the regions that suffered less from conflict. This paper aims to explain this paradox by combining a model of conflict intensity at the province level over period 2007–14 with a model of consumption at the household level in 2011. Provincial data show that higher levels of conflict were positively correlated with both a larger presence of troops (international and Afghan) and larger aid flows. Household data show that the negative impact of conflict on consumption was more than offset by the positive impact of aid and troops. According to the estimates, Afghan troops contributed more to poverty reduction than international troops, possibly because they spent more locally. The paper uses the estimated models to conduct an out-of-sample validation exercise, focusing on the transition initiated in 2014. The results should be interpreted with caution, as the quantitative models cannot account for strategic shifts in the insurgency and watershed political developments. But they suggest that the reduction in the number of international troops and declining foreign aid flows led to an increase in conflict intensity and a decline in consumption per capita, matching current trends.

This paper is a product of the Office of the Chief Economist, South Asia Region and the Office of the Chief Economist, International Finance Corporation. 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 authors may be contacted at gacevedo@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. The findings
|No|
|Mean dependent var.|7.288|7.288|15.399|15.399|26.593|26.593|
|Number of countries|134|95|134|95|134|95|
|Observations|1,717|95|1,717|95|1,716|95|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Regressions are weighted by region population. Poverty data are taken from the WDI. Poverty and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1.

_Panel B: Inequality data from WDI and SWIID_

|Inequality:|Gini|– WDI|Gini –|SWIID|
|---|---|---|---|---|
|||Lon||Lon|
||Panel FE|g<br>differences|Panel FE|g<br>differences|
||(1)|(2)|(3)|(4)|
|Temperature|0.171***|0.194***|0.165***|0.255***|
||(0.023)|(0.033)|(0.037)|(0.040)|
|Precipitation|0.013**|0.022*|0.006*|0.018*|
||(0.005)|(0.013)|(0.003)|(0.010)|
|Country FE|Yes|No|Yes|No|
travel, personal care, entertainment or hobbies (this includes total time spent watching television at home, listening to music, playing an instrument, or going to movies or similar events), or socializing (including relaxing with friends or family, talking over the phone, or visiting or hosting a social event).

Finally, we combine the remaining activities that do not fall under the three broad categorizations defined above as other activities. This includes, among other things, the total time spent in job search, child and elder care, non-market work, and home production, as well as time spent in education, tending to medical needs, and other civic and religious duties.<sup>11</sup>

## **3.4 Other Data**

We use GS1 Data Hub to link products with their manufacturing firms. Manufacturers purchase the right to use UPC codes from GS1. The first 6-9 digits of a UPC code corresponds to the company prefix. We follow the matching procedure of Argente et al. (2018) and are able to match almost all the products in the IRI data to their manufacturer. Other papers that use GS1 database for this purpose are Fracassi et al. (2020) and Baker et al. (2020). GS1 also provides the headquarter location of the company. In our analysis, we use this information to address the concern that inflation may be affected by the production cost of the manufacturer by omitting the upcs sold in headquarter locations where a MDP enters.

# **4 Empirical Analysis**

Causally identifying the changes in local inflation, income and shopping times for incumbent residents from entry of a new industrial plant requires a valid counterfactual that captures

> 11Job search includes all the activities related to time spent looking for new work, such as time spent in posting resumes, searching for vacancies in different job sites, and commuting to and appearing for interviews. Child care activities include the total time spent by an individual tending to, educating, and playing with
_firms find that the utilities business is just that: a business with limited upside but significant downside, because regulators limit profits and pressures exist to reduce prices further or enhance access and quality without commensurate compensation._ _When the level of prices becomes insufficient to expand systems and to provide quality service calls for reform arise. The value of private firms falls. Eventually governments may decide to buy up private firms at fairly cheap prices and try to solve the problem through nationalization. Yet, because prices are low, state firms cannot perform either without subsidy_<sup>_11_</sup> _. Thus the cycle starts again._ _Some countries, depending on the sector, have seen several cycles of privatization and nationalization, some only one and a few none. The key to end the cycle is the ability to commit to adequate prices that allow infrastructure firms to perform, whether they are privately or state owned._

# **_Establishing regulatory commitment_**

Typically governments announce some form of regulatory regime for infrastructure service providers – be they private or state-owned firms<sup>12</sup> . Firms then invest. Then it becomes clear what actual demand is like and firms operate the system to meet demand. Finally, regulators rule on the actual revenue that firms can make. At this stage regulators may de facto renege on the pre-announced pricing rule if they are under pressure to lower consumer prices or reduce profits deemed too large. Private firms know that agents of the sovereign may do so. This is a form of political risk. Firms underinvest when political risk is of significance. State-owned firms equally shy away from money-losing operations. Even in countries with strong legal systems and long regulatory traditions pressures to renege on ex ante rules can be strong. For example, in the United States in the few cases where utility regulators are actually elected by consumer-citizens pressures to curb prices have tended to undermine service provision.

To render a price regulation regime sustainable one needs to consider various mechanisms to curb ex post opportunism on
the issue of PPP factors was solved and a critical number of recent surveys was reached in 2017, MENA regional poverty rates were reported again. In the 2018 reference year, about 7 percent of MENA population were found to live below the extreme poverty line of USD1.90 2011 PPP per day, which more than tripled from the 2 percent reported in 2013.

Figure 1. Regional extreme poverty rates in the MENA region at $1.9 2011 PPP poverty line, 2010-2018 reference years <!-- Start of picture text --> 8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1%<br>0%<br>2010 2011 2012 2013 2015 2018<br>headcount poverty rate<br><!-- End of picture text --> Source: PovcalNet, May 2020.

Monitoring progress on the World Bank’s goal of boosting “shared prosperity”—defined as the income growth of the bottom 40 percent in any given country—is even more demanding in terms of data requirements. Measuring consumption/income growth across two points in time requires two comparable welfare aggregates constructed from two recent household surveys (Atamanov et al., 2016). Currently, only four countries in the MENA region meet these criteria and are included in the global shared prosperity database (2012-2017) (Figure 2).

As mentioned above, besides collecting detailed consumption data, HBSs collect information on aspects of human and social development. Thus, the usefulness of these surveys goes well beyond monitoring monetary poverty. Collecting household and individual characteristics along with locational attributes makes these surveys important for policy makers who want to design and understand the impact of public policies on socio-economic well-being. For example, the World Bank uses household and individual characteristics from HBSs to prepare profiles of the poor, or estimate the bottom 40 percent at country, regional, and global levels. These
2018); higher agricultural prices (Alix-Garcia and Saah 2010); improved employment opportunities, particularly outside subsistence farming and in wage employment; and more prevalent asset ownership (Loschmann, Bilgili and Siegel 2019). Additionally, mixed welfare effects are observed, with the impacts varying by occupation, resulting in both winners and losers (Maystadt and Verwimp 2014).

Studies focusing on the Global South have further documented positive impacts of hosting refugees in camps on the political views of host communities, which are linked to the effects of international aid on local public services (Zhou and Grossman 2021). The paper most closely related to this study is Davis et al. (2024), who use the 2019 Cox’s Bazar Panel Survey to document that hosting responsibilities for the displaced Rohingya people in Bangladesh are allocated in unequal fashion. Particularly, they show that refugee camps are placed in socioeconomically disadvantaged communities relative to both surrounding areas and Bangladesh as a whole.

This paper advances the literature by investigating the local development effects of hosting displaced populations in refugee camps. Our analysis leverages unique grid-level satellite data and two waves of a longitudinal study that is representative of individuals in close to the This context offers a distinctive scenario where living proximity camps. the displaced population is highly restricted, with individuals predominantly confined to and on humanitarian aid. Thus we shed on the camps completely dependent light of within with minimal interaction between the consequences hosting refugees camps displaced population and the local community in a developing country that itself relies heavily on humanitarian aid.

The rest of the paper is structured in four sections. Section II describes the displaced Rohingya population and their current situation in Bangladesh. Section III describes the empirical strategy, including the data and identification strategy. Sections IV and V discuss the results: the impacts of camps on night light density, deforestation, and individuallevel outcomes. The last section offers some concluding remarks.

7
, preCOVID-19 industry of work, and between the rural and urban sectors. These four countries represent 34 percent of the Sub-Saharan Africa population and have pre-existing face-to-face panel surveys from 2010 to 2019, which were used as a sample frame for the High-Frequency Phone Surveys (HFPS). The data allow us to track the employment of respondents who were working before the pandemic and analyze individual level indicators, such as job loss and re-employment. We analyze both immediate impacts, during the first few months of the pandemic, as well as medium-run impacts up to February/March 2021.

We find significant inequalities in the labor market impacts of the COVID-19 pandemic. During the first months of the pandemic, women were more likely to lose their job than men and job loss was concentrated among young respondents and in urban areas. Our evidence suggests that the disproportionate job loss among women was not, as one might assume, primarily driven by increased childcare demands related to

> 2 In Africa (Burkina Faso, Ghana, Kenya, Rwanda, Sierra Leone), Asia (Bangladesh, Nepal, Philippines), and Latin America (Colombia).

> 3 This indicator represents the percentage of hours lost compared to the baseline (the latest pre-crisis quarter, i.e., the 4th quarter of 2019, seasonally adjusted), adjusting for population aged 15-64 (ILO 2021).

2
Freedom to own foreign currency bank accounts

- Area 4 Freedom to trade internationally A. Taxes on international trade B. Regulatory Trade Barriers

- C. Size of the trade sector relative to expected D. Black-market exchange rate E. International capital market controls

- Area 5 Regulation of Credit, Labor and Business A. Credit market regulations B. Labor market regulations C. Business regulations

the weight equal to the inverse of its standard deviation. The other two methods weight the components based on a survey of experts in the field of economic freedom and on a survey among country experts. 20 The PCM allows for the incorporation of all information comprised in the data without imposing any specific structure on the model.

33
“Other” and then hand-matched bank names from the survey with bank names from Fitch Connect. Handmatching was necessary because the survey included spelling mistakes and in several cases the name of the bank was in local language in the survey and in English in Fitch Connect. After dropping observations with banks for which Fitch Connect does not report any financial information, we were left with a dataset of approximately 13,000 firms and more than 500 banks. While we use the matched dataset to analyze the effect of state-ownership on firms’ access to finance, we retain all the unmatched firm records to test for potential statistically significant differences in firm characteristics between matched and unmatched firms.

Note that WBES also includes data from 5 advanced European economies, but we do not use these data in our empirical analysis which focuses on emerging and developing economies. After dropping these five countries we are left with 11,840 records with a bank-firm match.

# Summary Statistics

We report summary statistics of firm characteristics in Table 2, 3, and 4. Table 2 includes the full sample of firms, while Table 3 and Table 4 report the sub-samples of firms matched to private banks, and to stateowned banks, respectively. There is a good degree of similarity among these 3 groups. The average firm size, measured by the number of workers employed by the firm, is 75 for the full sample, 81 for the subsample of firms matched to private banks, and 108 for the subsample of firms matched to state-owned banks. The average age is 18 years for the full sample, and 20 for the 2 subsamples of matched enterprises. Financing conditions faced by firms also appear to be similar across the three samples. The reported value of collateral needed for a loan, measured as percent of the loan value, is 167 for the full sample, 188 percent for firms that have a relationship with private banks, and 183 percent for firms that are financed by government-owned banks. Similarly, the proportion of loans requiring collateral is 76 percent when considering the full