rafmacalaba/data-use-mentions-extended huggingface dataset:
nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situations where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations.

To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of residence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “Global Trends”, “Mid-year trends”, “Asylum trends”, to a “Statistical Yearbook”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).

Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “Refugees (including refugee-like situations)”, “Asylum seekers”, “Internally Displaced Persons”, “Returnees”, “Stateless” persons, and “Others of concern”. A situation is a pair country of origin/country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia
# **1. Introduction**

Are firms in Pakistan becoming better at what they do? Is observed GDP growth grounded on firms becoming more efficient? If so, what drives productivity improvements? These questions are relevant. As argued by Krugman (1997) “Productivity isn’t everything, but in the long-run it is almost everything”. The prosperity of countries is associated with the efficiency with which factors of production are used. Understanding productivity dynamics, and their determinants, is crucial.

In a context in which the benefits of integrating with the global economy are being questioned, measuring the impacts that trade and investment integration have on domestic productivity dynamics has relevant implications for the design of trade and investment policies. Importantly, are the gains from integration automatic and inevitable, or do some firms benefit more than others, depending on their capacity to respond to competition or adopt technologies available due to integration? The second question matters for evaluating the role of complementary policies aiming at increased productivity growth.

A growing body of the empirical literature has investigated the effect of reduced distortions in sectors producing inputs upstream on the performance of firms operating downstream either in the form of restrictions to trade or to investment. Typically, restrictions to trade – for example, in the form of tariffs – affect upstream markets producing goods inputs, while restrictions to (foreign) investment could affect both goods and services input provision. One branch of the literature has focused on the role of trade policy distortions and shows that lower tariffs on intermediate inputs increase productivity. Amiti and Konings (2007), for example, use data on Indonesia and find that, (a) lower output tariffs increase productivity by inducing tougher import competition, and (b) cheaper imported inputs also raise productivity via learning, variety, and quality effects. This channel of tariff reforms on productivity through intermediates appears to be stronger than the former channel. For India, Topalova and Khandelwal (2010) find that lower input tariffs played a larger role in boosting productivity than output tariffs
70|<br>163.30|<br>769.00|1,089.00|
|Expenditures: Army, Social|0.00|82.00|<br>360.00|<br>0.00|442.00|
|Programs & Debt Service||||||
|Total Expenditures|40.00|198.7|<br>523.3|<br>769.00|1,531.00|

The Federation and RS data do not include special funds for health care, unemployment, and pensions. The RS data do not include municipal revenues.

## **Conclusion**

Progress in developing the governments in Bosnia has been slower than was envisioned when the Dayton Agreements were penned. This is not surprising since there is little or no world experience with developing governments on the short time line presumed in the Agreements. There are positive signals in Bosnia, such as a number of government institutions becoming operative and revenues being passed to both the State and the Federation. Still, the challenge of achieving fully operational governments remains in place.

Traditional economic models of federalism, like the one on which this paper's analysis is based, suggest a government structure assuming there is an intent to achieve Pareto-efficiency for the entire country. Current attitudes in Bosnia appear consistent with the views that many people in each ethnic group see themselves as members of their group, rather than as Bosnians, and are not broadly concerned about welfare of the entire country or access to public services outside the group. The motivation for the fiscal federalism structure proposed in the Dayton Agreements is 24
through agreements with universities and pedagogical institutes ( _Institutos Superiores Pedagógicos_ , ISPED) that satisfy predetermined quality and service delivery requirements.<sup>4</sup> Data from the MINEDUC suggest that during 2010–18, the SIPROFE program delivered in-service teacher training to about 113,000 teachers (representing about 75 percent of the total teacher population). According to the MINEDUC, between 2015 and 2018, the program benefited

> 4 ISPEDs do not operate anymore. Also, universities that can participate as providers of training under the SIPROFE program have to satisfy the maximum level of accreditation (Level A). This requirement limits the territorial supply of training courses, as not many regional universities satisfy this requirement.

5
- Kosmowski, F., A. Aragaw, A. Kilian, A. Ambel, J. Ilukor, B. Yigezu, and J. Stevenson (2019). Varietal identification in household surveys: Results from three household-based methods against the benchmark of DNA fingerprinting in southern Ethiopia. _Experimental Agriculture 55_ (3), 371–85.

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- Levine, R. and D. Renelt (1992). A sensitivity analysis of cross-country growth regressions. _American Economic Review 82_ (4), 942–63.

- Lobell, D. B., G. Azzari, M. Burke, S. Gourlay, Z. Jin, T. Kilic, and S. Murray (2020). Eyes in the sky, boots on the ground: Assessing satellite- and ground-based approaches to crop yield measurement and analysis. _American Journal of Agricultural Economics 102_ (1), 202–19.

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enough observations (many countries have only a few banks). Therefore, to estimate the degree of competition, we use the available data from Bankscope for the period 1987—1996, exploring the variation across banks and over time. We do investigate, however, the stability of the competition measure over this period.

When estimating _H_ , we include several control variables at the individual bank level— specifically, the ratio of the bank’s equity to total assets, the ratio of net loans to total assets, and the logarithm of total assets (in order to control for potential size effects). We estimate the model for each banking system using OLS with fixed bank-specific effects. We also include time dummies for each banking market and exclude countries with fewer than 20 bank-year observations. As a consequence, our sample reduces to 20 countries. We estimate _H_ , and the standard deviation of _H_ , and we check whether _H_ is significantly different from 0 or 1. In all cases, statistically _H_ is significantly different from 0 (at the 1% level). Only the banking system in Netherlands has an _H_ not statistically significantly different from 1 (perfect competition) at the 1% level (Norway as well, but at the 5% level). We also perform equilibrium tests and find most countries to be in equilibrium.<sup>8</sup> Alternatively, instead of the competition measure, we use data on the banking system concentration ratio (top three banks) of CG, in which case the specification is the same as theirs.

> 8 In this model, by replacing _P_ (the total revenues to total asset ratio) by the pretax return on assets ratio and then running the same regressions, we can conduct equilibrium tests.  We found most countries to be in equilibrium, i.e., the competition measures _H_ when using the pretax return on assets ratio as dependent variable is not significantly different from 0.  Appendix Table II provides standard deviations and significance tests of the individual
Development and International Trade”, Caribbean Regional Negotiating Machinery Study, available at <u>http://www.crnm.org/studies.htm.</u> Hibbard, Judith H., Jean Stockard and Martin Tusler (2003), “Does publicizing hospital performance stimulate quality improvement efforts?”, Health Affairs; 22(2): 84-94.

Institute of Medicine (2004), “Insuring America's Health: Principles and Recommendations”.

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Kaiser Family Foundation (2004), “Employer Health Benefits 2004: Annual Survey”, Kaiser Family Foundation Health Research and Educational Trust, available at <u>http://www.kff.org/insurance/7148/index.cfm</u> .

Leslie Khoo (2003), “Trends in Foreign Patient Admission in Singapore”, Singapore Ministry of Health Information Paper 2003/01.

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|274.00|56,203,661.64|2,080,697.75|24.47|

Source: CGAP financial access report data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East Asia and the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions (for Y/N questions), or means the average value for OIC countries. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries covered by the CGAP database.

55
P-value: Video + Video x Higher Ed. = 0|0.253|0.010|0.925|
|FDR q-value: Video + Video x Higher Ed. = 0|0.179|0.061|0.436|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|827|826|826|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for high level of education, denoted as higher education completed [=1]. Higher Education Completed is an indicator variable equal to 1 if the respondent has completed education beyond high school, including technical, university, or higher studies. The dependent variables are constructed following the methodology outlined by (Kling, Liebman and Katz, 2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for gender and student status. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x Higher Ed. = 0 and Video + Video x Higher Ed. = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x Higher Ed. = 0 and Video + Video x Higher Ed. = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

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# **3. Data and Methodology**

## **3.1 Data**

To understand the impact of the COVID-19 pandemic on the performance of firms across countries, the World Bank Group, through its Business Pulse Surveys (BPS) and the Enterprise Surveys (WBES), developed an instrument to survey firms. These surveys include modules on firm characteristics (e.g., size, age, gender of the owner, debt, pre-COVID-19 sales etc.), changes since the onset of COVID-19 in employment, sales, expectations, digitalization, and public support received.<sup>6</sup> The first wave of BPS surveyed firms between May and September 2020 in 51 countries, and a follow-up wave 2 survey was implemented between October 2020 and March 2021 in 38 countries. This second round of data collection allows us to observe the evolution of business responses over time. This paper focuses on the 38 countries where both the first and the second waves of surveys have been conducted. In total, we observed 34,620 firms in wave 1; and 30,404 in wave 2, where 24,973 correspond to panel firms. Our sample comprises primarily of countries in the low and middle-income group (See Appendix B for disaggregate observations for each country included in the sample).

Firms in all sectors and size groups are surveyed. Our sample includes firms in agriculture (3.6%), manufacturing (31.6%), retail (23.2%) and other service activities (41.6%). All size groups are included in the sample: micro (firms with 0-4 employees; 33.6%), small (5-19 employees; 31.9%), medium (20-99 employees; 21.5%) and large (over 100 employees; 13.0%). For most countries, the sampling frame was based on censuses from Statistics Agencies, Ministries of Finance or Economy, or business listings from Business
**Table 3. Paasche price indices (regional averages)**

|**Region**|**Paasche Index Food**|**Paasche Index Rent**|**Overall Paasche Index**|
|---|---|---|---|
|North-Urban|0.972|0.707|0.901|
|North-Rural|0.969|0.334|0.801|
|Northeast-Urban|0.963|0.578|0.868|
|Northeast-Rural|0.959|0.252|0.792|
|_Southeast-Urban_|_1.000_|_1.000_|_1.000_|
|Southeast-Rural|0.986|0.592|0.884|
|South-Urban|0.990|1.009|0.999|
|South-Rural|0.957|0.669|0.890|
|Midwest-Urban|0.993|0.942|0.985|
|Midwest-Rural|0.997|0.671|0.933|

_Source:_ Own calculations using POF 2017/18 data. _Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations, for ease of presentation we calculated regional averages of household-specific indices and divided them by the Southeast-Urban average.

Table 4 shows the results for our food poverty line, based on our main specification as well as an alternative calculation. The food poverty line in our main specification is based on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific Paasche indices, as described above. This yields a food poverty line of R$258 in 2018 urban Southeast prices. In the alternative specification, an additional step has been taken to assign calories to food items registered in the
A detailed diagnostic research tool and a pilot of the new national QAS tool for Egyptian Kindergartens (KGs) were implemented on a nationally representative sample of schools with KGs, with overlapping data collection. Both government and data collection firm enumerators were used (and randomly assigned to districts). We compare these data sources to assess how consistent results are across the QAS tool and the diagnostic research tool, overall, and for particular items. We specifically measure exact agreement of items and Cohen’s kappa coefficient. We do not and cannot know whether the QAS tool or diagnostic research tool data, or neither, are ultimately “right or wrong,” but relatively consistent results from different measures are an important prerequisite to data quality and usability. Data were collected on the learning environment and child development outcomes. We examine what school, class, teacher, student, and enumerator characteristics predict greater consistency in logit models for each dimension of quality in Egypt’s KG QAS. We then use our findings to simulate whether a variety of different potential quality enhancement strategies would be consistently targeted.

The results, comparing the two tools, are relatively consistent in terms of national averages, suggesting the QAS provides valuable information for national action. On the classroom, school, and district levels, consistency is lower. Consistency varies by the dimension of quality and data collection methods used, with direct measures of child development outcomes or easily observable characteristics, such as class sizes, showing more consistency. There are relatively few relationships between consistency and school or class characteristics, which bodes well for equitable implementation. Moreover, consistency is similar with ministry employees as with professional data collectors, who were randomized across districts. This result is promising for cost-effective national scale up. However, the limited consistency of many items and dimensions means that targeted quality enhancement actions often would target different schools using the two different measures. The results underscore how QAS are best implemented as a formative system, a starting point for quality enhancement, and not as a summative system that
& Vision: How Latecomers Grow to Dominate Markets._ New York: McGraw-Hill.

- Venables, Anthony J. 2018. “Globalization and urban polarization.” _Review of International Economics_ 26 (5): 981–96.

- Woodruff, Christopher. 2018. “Addressing constraints to small and growing businesses.” Working Paper, International Growth Centre, Oxford, UK.

- World Bank Group. 2016. _World Bank Group Engagement in Situations of Fragility, Conflict, and Violence_ . Washington, DC: World Bank.

- ———. 2017. _De-risking and other challenges in the emerging market financial sector: findings from IFC’s survey on correspondent banking_ . Washington, DC: World Bank Group.

- ———. 2018a. _Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle_ . Washington, DC: World Bank Group.

- ———. 2018b. _Global Investment Competitiveness Report 2017/2018 : Foreign Investor Perspectives and Policy Implications._ Washington, DC: World Bank Group.

- ———. 2018c. “Enterprise Surveys (database).” World Bank Group, Washington, DC. <u>http://www.enterprisesurveys.org/.</u>

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|**Table 16 Comparis**|**on of reported**<br>**Uncondition**<br>|**constraints by f**<br>**al means**<br>|**ormal, sm**<br>|**all formal and**<br>|**rural firms**<br>**Survey fixed e**<br>|**ffects and**<br>|**log(L) (center**<br>|**ed on L=5**<br>|**)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||All formal|Small formal|Rural|Diff?|Small formal|Rural|Diff?|Obs (SF)|Obs (R)|
|Electricity|2.575|2.563|1.470|Smaller|2.446|1.828|Smaller|2,234|4,888|
|Telecoms|0.969|0.694|0.318|Smaller|0.602|0.316|Smaller|1,355|4,881|
|Transport|1.081|0.874|0.696|Smaller|0.864|0.725|Smaller|2,166|4,750|
|AccessLand|1.606|1.701|0.100|Smaller|1.613|0.066|Smaller|1,747|4,157|
|InadEducLabor|1.254|0.986|0.183|Smaller|0.929|0.322|Smaller|1,755|4,210|
|
,s,r_ = _α_ 0 + _α_ 1 _MedianRoadj_ + _δs_ + _θlog_ ( _dist_ ) _r_ + _νj,s,r,_ (3) where _ResidPollutionj,s,r_ is the average residual (log) pollution in the grid cell. _MedianRoadj_ is the median road size in the grid cell. _δs_ are sub-county fixed effects, as we are interested in documenting variation in pollution and firm location choices _within_ urban areas. In addition, we control for log distance to the main city in the region, _log_ ( _dist_ ) _r_ , to make sure that we do not just capture the fact that areas closer to the city center are both more polluted and more productive.<sup>30</sup> To account for spatial correlation, we use Spatial Heteroskedastic and Autocor-

> cells containing these firms, in addition to the grid cells in our sampled sub-counties. In the estimation we control for a dummy for whether the firm falls in this category. Our results are robust to dropping these firms. 29 When computing firm density, we take into account that all grid cells are not exactly 500 _m ×_ 500 _m_ . This may happen because grid cells overlapping two adjacent parishes are split at the parish level, and because parishes are not of rectangular shape. A histogram of grid cell areas can be found in Appendix Figure A18. Besides, in regressions including grid-level variables, we control for whether the grid cell has an area of less than 0.25 square _km_ (dummy), as well as for grid cell size (linear control).

> 30We do not have data on road quality. To the extent that larger roads are of higher quality and road quality reduces pollution (by reducing congestion), then our estimates of the effect of road size on pollution are a lower bound.

18