rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_Import liberalization and globalization measures_**

Trade liberalization can also be measured in many different ways. The primary choice is between policies – tariff reductions, elimination of non-tariff barriers, etc -- that are thought to help globalization, and outcomes such as trade volumes that are a consequence of trade policies. Both approaches have been used in the literature. Most of the studies reviewed here used trade shares as their measure of globalization. Lundberg and Squire (2003) use the Sachs-Warner Index which, although linked to policies, has been criticized on the grounds that it captures more that trade policy. Edwards (1997) uses a variety of policy measures: average tariff; average QR (quantitative restrictions) coverage; and average black-market premium. Savvides (1998) uses a specially created measure of protection covering both tariff and non-tariff barriers compiled from UNCTAD data at the four-digit level of the Customs Cooperation Council Nomenclature. The measure is only available, however, for 1988. Finally, those studying wage-inequality within a country are often able to make use of industry-specific tariff rates and quotas.

All of the various ways of specifying variables representing trade liberalization are useful and answer interesting questions. If trade volumes are chosen then the study says something about the impact of trade volumes on inequality. And for some purposes that may be an interesting question. But, in our view, it does not say much about the impact of policy on inequality, primarily because trade volumes are not determined exclusively by policy. A wide range of factors will influence a country’s trade volume: country’s geography, technology, demand conditions in importing countries, competitors’ supply conditions, weather, and so on. Even attempts to control for these other factors will inevitably leave a residual that captures more than trade policies. We suspect that widespread reliance on trade volumes in the empirical literature reflects the relative ease of obtaining data compared with the difficulty of achieving the same for trade policies. Since we are interested primarily in how pro-openness reforms affect inequality
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grade. We find small differences. Occasionally, we see slightly larger differences, however, in all instances the effect sizes fall within the confidence interval. This sensitivity analysis increases our confidence that the EGRA data is robust to data availability biases by grade.

Figure 3.0: <!-- Start of picture text --> EGRA Coefficients<br>Grade Effects, Nationally Representative<br>-100 -50 0 50 100<br>Coefficients<br>Effects, All Grades Effects, Grade-Fixed<br>Country-Year Dummies<br><!-- End of picture text --> Overall, our inclusion of EGRA enables us to include over 48 countries with at least one data point in the last ten years. Moreover, it paves the way for inclusion of non-traditional assessments through careful choice of testing regime, indicators, language considerations, grade-level participation, and score scaling. Below we show sensitivity to this exercise comparing HLO scores when derived using EGRA versus PIRLS, an ISAT with scores produced using IRT. Since both assessments are conducted in reading at primary level, we can compare scores and expect similar results. We do this for countries that participate in both EGRA and PIRLS from 2000 onwards and take the average. This will be biased by data availability by year, for example, if Egypt participated in EGRA in 2013 and PIRLS in 2016. However, it gives us an indication of whether HLOs are reasonable when derived from EGRA. Figure 4.0 reveals that scores are roughly similar, within 5-28 points of one another. Moreover, we see that ranks are preserved: Honduras outperforms Macedonia, followed by Indonesia, then by Egypt.

7
.038|
|**CAMU**|No|2|-1.460|0.072|1.895|0.029|
||Yes|1|-2.348|0.009|2.511|0.006|
||Yes|2|-1.397|0.081|3.374|0.000|

We tested both initial equations [1] and [2] in a panel cointegration form according to the (Kao & Chiang, 2000) model. Table 4 contains results of estimated equations that adequately explain the inflation-growth nexus in the CAMU and WAMU regions. At the panel level, equation [2] better explains this nexus in CAMU and equation [1] best fits the WAMU. As the difference between the Augmented Model 1 and Augmented Model 2 is the inclusion the natural resource. As estimated equations suggest that natural resource rent significantly affect growth in both region (Appendix 5 and Appendix 6), the related thresholds could be reference points. For CAMU, Equatorial Guinea does not have data on natural resource rent. Thus, result from the augmented model 2 in CAMU cannot be considered as a reference point. This means that by considering a homogeneous cointegration relationship, the inflation threshold above which

> 2 Cointegration tests for the _Basic Model_ also conclude to the existence of a cointegration relationship.

Page | 18
States. They are employing a semi-structured open-ended protocol that covers domains that include lifehistories, social and familial relationships, and economic hardship. They plan to use a mix of traditional qualitative methods and NLP to analyze the large-N qualitative data.

# D. <u>Studying Process</u>

Empirical economics, particularly after the credibility revolution, seems almost singularly focused on outcomes. Mechanisms are generally relegated to ex-post theory, rather than careful observation of the process by which change happens. Ethnography, participant-observation, open-ended qualitative interviews, are eminently suited to filling this gap, to understanding “how”, as well “how much” (White 2008, Bamberger, Rao and Woolcock 2010). Yet, despite the increased of qualitative methods in randomized trials and experiments in adjacent disciplines such as psychology (Paluck 2010), they have not yet taken hold in economics. I will briefly review three studies that integrate serious ethnography with quantitative impact evaluations to demonstrate the added value of incorporating an element of reflexivity into causal inference and demonstrate that it can help both in improving scientific understanding and in informing policy-making.

Harkening back to Elijah Anderson’s ethnography of Philadelphia, a team of public health researchers and ethnographers collaborated on a mixed-methods study to find ways of dealing with urban “blight” in poorer areas of the city (Branas et al 2018). An ethnography of a poor “micro-neighborhood” in the city found it “visibly impacted” by drug trafficking and gun violence. Overgrown vacant lots were centers of drug dealing and violent activity and were used to store drugs and guns. Virtually all the residents interviewed in this neighborhood, in comparison with a better-off neighborhood, were very supportive of an intervention to clean up and “green” vacant lots. The team designed an intervention which cleaned vacant lots, planted new grass and trees, and installed fencing. 541 vacant lots were randomly chosen from poor neighborhoods in the city, and then randomly assigned to treatment and control groups. After a 38-month period, they found that areas
since the 1980s.

Extreme weather events result in material damages that also rose over time and were not equally distributed around the globe with EAP affected most. For instance, since the 1980s, median annual economic losses due to disasters totaled about US$18 billion in EAP and US$5 billion in ECA (Figure 5E).

# _Gradual global warming_

Analysis of countries’ vulnerability to the effects of gradual global warming and readiness to adapt to climate change is based primarily on the Notre Dame‐Global Adaptation Index (ND‐GAIN) (Figure 6). This index shows countries’ vulnerability to climate disruptions and their readiness to leverage private and public sector investment for climate action. The vulnerability indicator covers the vulnerability of six life‐ supporting sectors to climate change: food, water, health, ecosystem service, human habitat, and infrastructure. The indicator of readiness to improve resilience covers economic, governance, and social readiness. The vulnerability and readiness indicators cover 182 and 184 countries, respectively for the period 1995 to the present.

Since 1995, countries’ vulnerability to climate change, as measured by the ND Vulnerability Index, declined overall and across all regions (Figure 6B). This decrease in vulnerability reflects an increase in adaptive capacity in many countries. Yet, some countries are disproportionately more vulnerable to climate risks, such as those in SA, EAP, and SSA. These regions also are the most exposed to climate‐related risks in the form of extreme weather events.

> 11 https://www.emdat.be/

> 12 It is worth noting that part of the increase in events could be due to improved measurement and tracking of extreme weather conditions around the world over time.

24
region&end=2016&locatio ns=ZG‐ZA‐MW‐MG‐MU‐ZW‐TZ‐SZ&name_desc=true&start=1988&view=chart.</u>

> 31 Indicator TM.TAX.MANF.WM.AR.ZS in the World Bank’s open data source; derived from the World Integrated Trade Solution system, in turn based on data from United Nations Conference on Trade and Developments Trade Analysis and Information System database, and the World Trade Organization’s Integrated Data Base and Consolidated Tariff Schedules database.

> 32 The 2006 number is from the program document of P101570, paragraph 17.

39
.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.09]|[0.09]|[0.09]|[0.10]|[0.11]|
|Observations|24397|20612|21961|22266|21093|21010|23119|12281|12409|11746|10982|14361|
|Adjusted R-squared|0.04|0.03|0.04|0.03|0.03|0.02|0.01|0.04|0.04|0.04|0.04|0.03|
|Mean Male|0.25|0.18|0.15|0.14|0.14|0.14|0.12|7.83|7.86|7.87|7.75|6.97|

_Notes: * significant at 10% level, ** significant at 5% level, *** significant at 1% level._ _[1] OLS regression where Female is a dummy variable where 1 = female-led firm and 0 = male-led firm. Regression includes region of the world fixed effects. [2] Outcome Business Closed = 1 if the firm owner reported their firm was closed at the time of the survey. Outcome Time spent on paid work activities in hours per day was only collected from July 2020 survey wave (wave 3) onwards._ 36
# Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World<sup>_∗_</sup>

Marie Christine Apedo-Amah (World Bank) Besart Avdiu (World Bank) Xavier Cirera (World Bank) Marcio Cruz (World Bank) Elwyn Davies (World Bank) Arti Grover (World Bank) Leonardo Iacovone (World Bank and Hertie School)<sup>_†_</sup> Umut Kilinc (World Bank) Denis Medvedev (World Bank) Franklin Okechukwu Maduko (Central European University and World Bank) Stavros Poupakis (UCL and World Bank) Jesica Torres (World Bank) Trang Thu Tran (World Bank) Originally published in the Policy Research Working Paper Series on _October, 2020._ This version is updated on _February, 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classification: D22, L20, L25, O10 Keywords: COVID-19, Firms, Employment, Sales, Uncertainty

> _∗_ The authors gratefully acknowledge comments and feedback on the survey questionnaires as well as on earlier drafts of the paper from Caroline Freund, David Mckenzie, Francisco Campos, Bilal Zia, Nick Bloom, Mark Dutz, Ana Paula Cusolito, Jorge Rodriguez Meza, and Silvia Muzi. We also thank Santiago Reyes Ortega for his outstanding research support. We are grateful to all the colleagues and counterparts institutions that have supported the data collection (a complete list of all colleagues, collaborators and institutions that have been crucial in providing support during data collection is provided in the online appendix). We would like to especially thank Tanay Balantrapu, Joao Belivaqua Basto, and Kyungmin Lee for their help during the data collection process. We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. Financial support from USAID and World Bank for data collection is gratefully acknowledged. The views expressed in this article are solely those of the authors and do not necessarily reflect those of the
# **3.6 Correlation of crop and livestock production with urban expansion**

For each SSP scenario, we estimate grid level regressions to estimate the correlation between crop and livestock production at baseline in 2015 with projected urban expansion (both in 2040 and 2100). These correlations indicate, for each of the SSP scenarios, to what extent grids that are more likely to become more urban in the future are those that have higher (or lower) crop and livestock production today. It sheds light on whether urban expansion will occur into higher production grids, but not on whether urban expansion occurs in more grids.

_Table 4. Grid level regressions of crop production on urban expansion rates for five SSP scenarios_

|_Outcome:_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Crop production(2010, mt)|SSP 1|SSP 2|SSP 3|SSP 4|SSP 5|
|_Panel A: Urban expansion to 2040_||||||
|Urban expansion rate in 2040|67,040.29***|67,547.62***|98,514.26***|70,278.82***|50,550.85***|
||(998.5)|(1055.5)|(1357.25)|(1053.61)|(859.41)|
|Observations|832,827|832,827|832,827|832,827|832,827|
|R squared|0.0054|0.0049|0.0063|0.0053|0.0041|
|Mean crop production(2010,mt)|9,243.5|9,243.5|9,243.5|9,
buildup in cells located up to 3 to 5 km. For cells located near roads, there is still some evidence of more buildup up to 40-50 km away. For cells not near a road, the effect fades away faster. We find no evidence that densely built areas tend to be separated by a specific distance, as would be captured by a spike in the spatial correlogram. Over time, the covariogram increases, capturing the general increase in buildup.

This paper contributes to the literature on urbanization and regional development. Data on built-up area at a highly disaggregated level has recently become available (Pesaresi et al. 2016). This has encouraged a newly emerged literature that seeks to make use of this information to explore important economic realities. Bellefon et al. (2018), for instance, use buildup proportion as a way of identifying the edges of cities, arguing that partial buildup identifies sub-urban areas. We adopt a similar philosophy in the sense that we regard building concentration as an indication 3
Policy Research Working Paper 9007

# **Abstract**

There is a widespread policy view that a lack of job opportunities at home is a key reason for migration, accompanied by suggestions of the need to spend more on creating these opportunities to reduce migration. Self-employment is widespread in poor countries, and faced with a lack of existing jobs, providing more opportunities for people to start businesses is a key policy option. But empirical evidence to support this idea is slight, and economic theory offers several reasons why the self-employed may be more likely to migrate. This paper puts together panel surveys from eight countries to descriptively examine the relationship between migration and self-employment, finding that the self-employed are indeed less likely to migrate than wage workers or the unemployed. The paper then analyzes seven randomized experiments that increased self-employment, and finds that their causal impacts on migration are negative on average, but often small in magnitude.

This paper is a product of the Development Research 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 giambra@brown.edu and dmckenzie@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, 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
6|2.4|
|Georgia|6.7|8.5|11.2|11.4|
|Kazakhstan|0.3|0.3|0.3|0.3|
|Kyrgyz Republic|2.8|18.5|29.2|30.0|
|Tajikistan|9.3|37.2|40.1|29.0|
|Turkmenistan|na|0.2|0.1|0.0|
|Uzbekistan|na|7.3|9.4|3.4|

Source: World Development Indicators 5
077|-0.141|
||(0.000)***|(0.000)***|(0.034)**|(0.000)***|
|Tertiary education|0.422|0.422|0.017|-0.202|
||(0.000)***|(0.000)***|(0.696)|(0.000)***|
|Numeracy|0.023|0.015|-0.006|-0.006|
||(0.000)***|(0.117)|(0.460)|(0.489)|
|Observations|6326|6323|6328|6328|

Source: Beck (2009). The dependent variable is whether a respondent uses (i) bank, (ii) other formal, (iii) informal services or (iv) is excluded. Marginal effects are reported. Regressions are weighted and stratified.

19 Risk aversion is a dummy variable that takes on value one if the individual responds yes to the following question: You avoid taking risks with your money or resources.

29
in collecting panel data.

As the above comments suggest, NX evaluations can be data demanding as well as methodologically difficult. One might be tempted to rely instead on “short cuts” including less formal, unstructured, interviews with participants. The problem in practice is that it is quite difficult to ask counter-factual questions in interviews or focus groups; try asking someone participating in a program: “what would you be doing now if this program did not exist?” Talking to participants (and non-participants) can be a valuable complement to quantitative surveys data, but it is unlikely to provide a credible impact evaluation on its own.

> 19 On the implications of such selective survey compliance for measures of poverty and inequality and some evidence (for the US) see Korinek et al. (2006).

15
</mark>

- 1) <mark>We removed all Transco bus connections, as Transco busses were observed to be non-operational during heavy rain and flood.</mark>

- 2) <mark>We adjusted headways for each route conditional on network type as reported in section 4.1.1.</mark>

- 3) <mark>We reduced travel speeds on each route conditional on network type using the mean travel speed effects quantified in section 4.1.3: 4 km/h and 2 km/h speed reduction for Esprit de Mort and Esprit de Vie respectively.</mark>

- 4) <mark>We removed all route segments that are inundated in a 10-year flood</mark>

<mark>Building on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters</mark><sup>13</sup> in <mark>Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.</mark> <mark>The left panel of F</mark> igure 10 <mark>shows the results of travel time simulations for 8,866 daily commuter trips under dry conditions. On average, the estimated daily commute time from home to work is 43.25 minutes. The right panel of F</mark> igure 10 <mark>shows the results from hot spot analysis of the travel time estimations with blue points showing clusters of low values and red points showing clusters of high values. We observe clusters of commuters who live in the suburban neighborhoods take longer to travel to work, whereas commuters who live in areas near the center of Kinshasa city take shorter time. F</mark> igure 11 <mark>compares the results from the travel time hot spot analysis with commuter personal income hot spots and highlights areas where these plots show diverging results. The southeastern and northeastern
> For example, if _B j_ is the quality of the electricity supply from the grid, and _Gij_ is the firm’s generator, then _B j_ and _Gij_ combine to create the firm’s electricity input _Eij_ .

> Spending money on _Gij_ means the firm can generate its own electricity when there is an interruption in supply from the main electricity grid ( _B j_ ).   Electricity is then combined with labor to create output.  Although the quality of the business environment at country level cannot be directly observed, there are country-level

> proxies for _B j_ such as the degree of reliability of the electricity supply (e.g., in terms of outages).

> Corruption is another example.  In this case, _B j_ is a measure of the honesty of government bureaucrats in country _j_ , but the public input _Bij_ supplied to firm _i_

> depends not only on _B j_ but also on productivity _Aij_ : high productivity firms will attract attention from dishonest government officials looking for bribes. As a consequence, the quality of the public input supplied to high productivity firms will be lower than that supplied to low productivity firms that are ignored by the bribeseeking officials.  Thus if _B j_ is the honesty of the bureaucracy in country j, _Bij_ is the inverse of the number of inspections that a firm with productivity _Aij_ attracts (more inspections means a lower quality public input _Bij_ supplied to the firm), and _Gij_ is bribes.

Central to our analysis are the “Subjective Severity” indicators collected in the Enterprise Surveys. These are responses to questions about a feature of the business environment faced by the firm, where the question takes the form, “How much of an obstacle is X to the operation and growth of your business?”, and the respondent rates the severity on a 5-point scale of 0 (“no obstacle”) to 4 (“very severe obstacle”).

The key point about these subjective severity indicators is that these are _not_ estimates of the country-wide public input _B j_ , or
teachers are a key input in the production of education services, differences in the relative cost of teachers also influence the resources that are effectively available for education. As countries grow richer in the course of economic development the prices of most goods and services tend to rise in absolute terms, as does teachers' pay. Our simulations show that teachers earn, on average, $9,084 a year in country B compared with

> 24 For our purpose we measure demographic burden as the ratio of the population aged 6-1 1 to the total population. Because our focus here is on overall educational development, it would have been better to include in the numerator the population over a wider age range, say 6-23, so as to encompass students at al three levels of schooling. However, population structures are relatively stable, and as the accounting exercise involves comparing relative rather than absolute demographic burdens, we simply used the population data that are most readily available, i.e. those for the population aged 6-11, to compute the demographic burden.