rafmacalaba/data-use-mentions-extended huggingface dataset:
all the models, CDR analysis consistently predicted poverty rates at the _municipio_ level, though their predictive value was limited under certain experimental settings. The best models predicted total poverty levels in 2006 with an R<sup>2</sup> of 0.76, implying that approximately 76 percent of the variation in _municipio_ -level poverty rates could be explained by 2013 cell phone data, and with F1 scores of up to 0.84 for the classification models, indicating that 84 percent of _municipios_ were classified according to the correct category when three separate categories (low, medium and high poverty rates) were considered. On the other hand, the prediction rates for urban data in 2006 showed R<sup>2</sup> values of 0.69 for regression techniques and 0.73 for classification, indicating weaker predictive value. The lowest prediction rates were for rural data in 2011, with R<sup>2</sup> results of 0.46 and 0.59 for regression and classification models, respectively. Classification results were slightly better than regression results across all models due to the fact that classifying poverty rates into three classes is a simpler prediction problem than attempting to approximate real values. Experimentally, as more classes were included, predictive accuracies for classification decreased

**14**
is used) (table 3).

To solve the inconclusiveness of the results of the spill-over estimates, the authors resorted to an out-ofsample forecasting method to compare the forecasting power of various models. As shown in table 3, ceteris paribus, having a lower RMSE, the growth gap variable produces more accurate forecasts than the output gap; and the FAO Cereals sub-index produces more accurate forecasts than FAO food index. Consequently, based on the RMSE criterion, the most accurate relation would be that of the non-food price inflation regressed, among other things, on the predicted values of domestic food prices estimated from the bivariate regression of domestic food and FAO cereals sub index, and the growth gap variable. This relation indicates that a 1% increase in the domestic food price inflation is estimated to increase nonfood inflation by almost 0.6%, and that changes in domestic food price inflation explain almost 75% of changes in non-food inflation. Moreover, for the same specification, the spill-over to core inflation is also close to 0.6%.

14
75|
|Poverty rate 2013|0.23|0.16|0.23|75|
|Number of radios (HQ)|1.89|3.06|1.00|75|
|Voter Attachment to National Party - 2011|0.68|0.15|0.70|75|
|Average Score UCE - 2011|53.37|3.31|54.04|75|

_Note:_ This table presents descriptive statistics of health outcomes and characteristics of the 75 Districts included in the survey. Health outcomes are from the District League Tables produced by the Ministry of Health. The remaining district characteristics are from the 2010 Census and the Uganda Electoral Comission - details on the variables are provided in the Annex.

45
**DRAFT**

**THIS VERSION – 12/18/2020 1:06 PM**

# **COVID-19 – BUSINESS PULSE SURVEY (COV-BPS)**

## **The World Bank**

## **2020**

### **Phone interview introduction:**

Good morning/afternoon/evening.

I am calling from [insert implementing contractor], on behalf of the World Bank. This establishment was randomly selected to participate in a survey to better understand the impact of the COVID-19 pandemic on businesses in [insert country name].

The results of the survey will be used to inform government responses aiming to support businesses during the crisis. All information and opinions you provide will be anonymized. Neither your name nor the name of your establishment will be used in any document based on this survey.

### **Online interview invitation email:**

**From:** [insert implementing contractor]

**Subject** : “Help [your country] design a COVID-19 response package for companies like yours” Hello,

We are contacting you on behalf of the World Bank. This establishment was randomly selected to participate in a survey to better understand the impact of the COVID-19 pandemic on businesses in [insert country name].

The results of the survey will be used to inform government responses aiming to support businesses during the crisis. All information and opinions you provide will be anonymized. Neither your name nor the name of your establishment will be used in any document based on this survey.

The survey is expected to take no more than 10 minutes of your time. Please follow this link to take the survey: [insert link]

### **Online interview landing page text:**

This survey is being implemented by [insert implementing contractor] on behalf of the World Bank. This establishment was randomly selected to participate in a survey to better understand the impact of the COVID-19 pandemic on businesses in [insert country name
about 8 percent of Kenyan exporters export to their main industrial market (the United Kingdom), since 58 percent of Kenyan enterprises export, this represents over 4 percent of Kenyan enterprises. The poor performance of Ethiopian exporters in regional markets probably reflects regional difficulties that have prevented Ethiopian enterprises from developing export partnerships with firms in neighboring countries (e.g., in Eritrea, Somalia and Sudan).

# **II.2 Barriers to Trade**

Many different factors affect firms’ propensities to export. Since the cost of transportation will vary between sectors, enterprises will be more likely to export in some sectors than they will be in others. Similarly, enterprise size is likely to be important—there are fixed costs associating with exporting that larger enterprises will find it easier to bear. Finally, transportation costs will vary between countries. Although exporting will generally be more difficult for enterprises in landlocked countries, it is important to note that most of the small and medium enterprises included in the investment climate surveys export to neighboring countries (including neighboring landlocked countries) rather than to more distant markets.

Another factor that might affect export performance is the existence of policy-induced barriers to trade. Export taxes are the most direct policy-induced barrier. In practice, however, export taxes—which were mostly levied on primary production in any case—have become far less important in recent years. For example, Senegal eliminated export taxes in the mid-1980s and Uganda abolished export taxes in the mid-1990s.<sup>6</sup> In addition to export taxes, tariffs and non-tariff barriers on imports might also affect exports. By increasing the cost of imports, they will divert resources towards import substituting activities, discouraging exports. In addition, if firms find it more costly to import intermediate and capital goods, this will increase production costs. Although exporters are often entitled to reimbursement of import duties under duty drawback schemes, these schemes are often complex and are administered inefficiently (Collier, 1998). Tariffs might also discourage exports, if
Table 1: List of countries

|Algeria|Djibouti|Mauritania|South Africa|
|---|---|---|---|
|Angola|Ethiopia|Morocco|Sudan|
|Benin|Gabon|Mozambique|Tanzania|
|Botswana|Gambia|Namibia|Togo|
|Burundi|Guinea-Bissau|Niger|Tunisia|
|Cameroon|Kenya|Nigeria|Uganda|
|Chad|Liberia|Rwanda|Zambia|
|Côte d’Ivoire|Madagascar|Senegal|Zimbabwe|
|Congo|Malawi|Sierra Leone||
|Democratic Republic of<br>Congo|Mali|Somalia||

Source: USGS data.

Table 2: Names of mineral commodities and their SITC codes by district, region, and country of production

|Name*|SITC<br>code|Commodity names|Price<br>data|Districts|Regions|Countries of production|
|---|---|---|---|---|---|---|
|SITC_1|120|Helium|Yes|1|1|Algeria|
|SITC_2|264|Garnet (industrial)|Yes|1|1|Kenya|
|SITC_3|273|Stones (crushed,<br>dimension), limestone,<br>gypsum, marble, and<br>silicon|Yes|23|22|Algeria, Angola, Ethiopia, Madagascar,<br>Malawi, Mauritania, Mozambique,<br>Rwanda, South Africa, Sudan, Tunisia,<br>Uganda, and Zambia|
|SITC_4|274|Sulphur|Yes|13|12|Ethiopia, Kenya, Malawi, Namibia,<br>South Africa, Zambia, and Zimbabwe|
|SITC_5|277|Diamond|Yes|27|17|Angola, Botswana, Cameroon, Liberia,<br>Namibia, Sierra Leone, South Africa,<br>Tanzania,and Zimbabwe|
|SITC_6|
(0.058)||(0.181)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N (obs.)|63981|63981|63981|63981|63981|63981|63981|63981|63981|63981|
|N (frms)|13245|13245|13245|13245|13245|13245|13245|13245|13245|13245|
|R-squared|0.109|0.11|0.107|0.11|0.108|0.113|0.108|0.113|0.109|0.11|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Export shares are measured in the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Results using TFPR based on ACF method are shown in Table A.9. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

23
<!-- Start of picture text --> 3000000 100<br>80<br>2000000<br>60<br>1000000<br>40<br>0 20<br>01mar2019 01apr2019 01may2019 01mar2020 01apr2020 01may2020 01jun2020<br>.4<br>.2<br>.2<br>.1<br>0 0<br>-.2 -.1<br>-.4 -.2<br>01mar2019 01apr2019 01may2019 01mar2020 01apr2020 01may2020 01jun2020<br>number of travelers google serch index<br>number of travelers<br>"airport" google search index<br>growth rate: number of travelers<br>growth rate: google search for "airport"<br><!-- End of picture text --> Note: The TSA passenger throughput data cover daily arrivals for March 1-May 28, 2019 and March 1-May 28, 2020. The top panel compares raw daily passenger volumes to the Google search index for “airport”. The bottom panel shows how the daily growth rates in passenger volumes at U.S. checkpoints compare with the growth rate of Google search intensity for “airport.” Figure 3: Daily passenger volumes at U.S. TSA checkpoints and Google search data for MarchMay 2019 and 2020 We conduct a similar validation exercise for hotel and restaurant services using restaurant reservations data from OpenTable.<sup>9</sup> The dataset contains information on daily restaurant reservations at OpenTable network restaurants in seven countries (Australia, Canada, Germany, Ireland, Mexico, the United Kingdom, and the United States). The OpenTable data for 2020 are given in percentage changes relative to the same day in 2019. To make these data comparable with the Google search data, we convert the daily records into weekly averages. Figure B.1 (in Appendix B) plots daily percentage changes in Google search index for restaurant and percentage changes in actual restaurant reservations for the seven
the 2012 RR BEEPS; and finally, data on raider’s attacks was taken from complaints about raider’s activity filed with the Center “Business Against Corruption”.

Our regression analysis suggests that levels of corruption were not an important factor in investment decision, casting doubt on whether regulatory burdens are as important as uncertainty in firms’ decisionmaking. We did find evidence that uncertainty in the informal practices surrounding regulatory enforcement did matter, however. Most notably, variation in the size of bribes that firms expected to pay, as estimated by respondents, was a significant predictor of investment. The greater the variation, the less likely firms were to invest. From our prospective, this means that firms active in Russia adapted to informal practices that spring up around business-government interactions, so long as they were predictable. Similarly, we find that the greater the number of raider attacks in a region, the less likely investment. Again, this suggests that uncertainty about property rights enforcement weighed heavily on firms. Consequently, we conclude that a significant obstacle to investment decisions at the regional level in Russia stems not from the presence of informal rules _per se_ , but the lack of predictability in their application.

21
basic socioeconomic characteristics, beneficiary status and whether the household met the NAIVS eligibility criteria. Second, we conducted a survey with one male and one female member of the VVC to assess their understanding of the eligibility criteria, the process of beneficiary selection and their socioeconomic characteristics. Third, we collected village level data related to access to services, prices and the implementation of the NAIVS program. The respondent for the village survey was the Village Executive Officer or VEO, a government-appointed official in the village. Finally, we administered surveys to 10 _eligible_ households that had not received vouchers in the 2009/10 season. Five of these eligible households would receive vouchers in the 2010/11 season while the other five would not. To draw this sample, we used the VVC 10
# 2. Data & estimation

## 2.1 Data

The sample is a panel including 179 importing countries and 170 exporting countries over 1980-2005, or a notional total of 791,180 one-way trade observations, of which 321,348 have positive trade (41%).

The dependent variable is the aggregate one-way trade value reported in the IMF’s Direction of Trade Statistics (DOTS). It is customary in trade-volume studies to “mirror” export statistics, i.e. to disregard direct export statistics from the exporting country and instead to use import data from its partners. The reason is that customs typically monitor imports (on which duties are based) better than exports (rarely taxed). However our study purports to measure only the trade-facilitation effect of PSI, not its effect on the capacity of customs to record imports correctly. Mirrored import data would confound these two effects and would thus potentially bias results upward, generating a statistical illusion of increased volumes. In order to avoid this source of bias, we use direct export statistics, at the cost of having noisier data than if we had used mirroring.

Standard gravity regressors include GDPs in constant 1995 dollars, taken from the World Bank’s World Development Indicators; “great-circle” distances between the main industrial agglomerations of countries in the sample, taken from CEPII,<sup>9</sup> and dummy variables for common land borders, common official languages, and formal colonial ties.

The “treatment variable” is equal to one when an inspection program is in force in the importing country _j_ of a pair ( _i,j_ ) at time _t_ . It covers programs run by the largest four firms in the industry, Société Générale de Surveillance (SGS) and Cotecna Inspection SA, both based in Geneva; BIVAC International (a subsidiary of Paris-based Bureau Veritas), and Intertek, based in London. The list of programs is given in Appendix 1.

Table 1 shows descriptive statistics. For dummy variables, the mean is the
- 18 -

a complete list of this research.) Most of the work focused on Africa and was embodied in a research program undertaken in CPD. This project produced country-specific papers reviewing pay and employment policy in Sudan, Zambia, Senegal, Nigeria, and Liberia. In addition, several overview papers analyzed those countries as well as additional public sector wage and employment data for Ghana, Uganda, Kenya, Malawi and Tanzania.13 The main sources of data for this research consisted of personnel divisions of technical ministries, civil service commissions, finance and civil service ministries, and, at times, private sector surveys carried out by individual commercial enterprises. Data were thought to be best for Zambia and worst for Uganda. In general, the research stresses the diversity of employment and pay conditions in Africa. It does, however, report a number of general trends: extreme wage erosion due to infrequent sala'y adjustments and rapid price increases; increasing relative importance of non-wage benefits; increasing wage compression;and rapid expansion in government employment as a whole. The basic policy recommendation emerging from this work is that employment expansion should not be traded off against higher wages. Smaller, more productive civil services should be the objective. With regard to the public enterprise sector, Lindauer et al., argued for the separation of public enterprise and civil service salary scales.

Several other studies recently carvied out in the Bank have examined pay and employment issues. A.R. Xhan's "Liberia SAL: Wages and Employment in the Public Sector" and "Employment and Wages in Liberia, Some Preliminary Notes with Special Reference to the Public Sector" analyze growth in public sector employment wages, their impact on the wage bill, and wage compression. They present alternative instruments for the attainment of target reductions in the wage bill, including reductions of the rate of salary increase, postponements of increments and promotions, and overall reductions in employment. Other studies on this issue include Sunita Kikeri, "Compensation Systems for Public Enterprise Managers in
is also correlated with transport costs, rising transport costs are associated with rising and not declining revenue productivity contrary to the model’s prediction. Revenue productivity would still rise with local market expansion when we control for the cost of power and the cost financial services as we do Table 10B, but it would no longer be correlated with transport costs. At the same time revenue productivity is seen to decline with rising cost of power or rising cost of financial services.

Focusing on textiles, garments and the footwear industry, only one of the predictions of the model holds when we pool data across all three in estimating the baselines specification in Table 10. This is that aggregate revenue productivity rises as the local market expands, which continues to hold when we control for the cost of power and the cost of financial services in table 10c. But now revenue productivity also declines with rising transport costs just as it does with rising costs of power and rising costs of financial services.

Looking at estimates industry by industry, controlling for the costs of power and the costs of financial services makes the production data on the footwear industry (ISIC 1920) fully consistent with all three predictions of the Melitz‐ Ottaviano model. For we see in Table 10B that productivity declines with rising costs of power while it also increases with local market expansion while declining with increasing transport costs and rising costs of entry licenses. This bears sharp contrast to estimates of the basic specification in Table 10, where the only significant influence on productivity in the same industry is seen to be that of market expansion.

Controlling for the cost of power and financial services also removes the association between the cost of entry licensing and revenue productivity in the textile industry as reported in Table 10. At the same time productivity declines with rising cost of power while increasing with local market expansion just as it does in the footwear industry and, indeed, in most of the 11 industries covered by our data.

The contrast between the results of Table 10B and Table 10 is similar when it
expenditures below the poverty line. It is important to note that poorer households are only out-of-pocket in the short-term; this analysis does not include the economic benefits of health and education services which households will enjoy in the longer-term and which will help reduce poverty.

Figure 10: Poverty headcount before and after fiscal interventions <!-- Start of picture text --> 25<br>19.8<br>20<br>17.7 17.8<br>15<br>10<br>Market income Disposable income Consumable income<br>Poverty headcount (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data. Note: Per CEQ technical recommendations, in-kind health and education transfers are not included when estimating the impact on poverty.

18
The Quarterly Journal of Economics</mark>_ <mark>112, no. 3 (1997): 729-758.</mark> <mark>Besley, Timothy, and Torsten Persson. "JEEA-FBBVA lecture 2017: The dynamics of environmental politics and values."</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>17, no. 4 (2019): 993-1024.</mark> <mark>Bhandary, Rishikesh Ram, and Kelly Sims Gallagher. "What drives Pakistan’s coal-fired power plant construction boom? Understanding the China-Pakistan Economic Corridor’s energy portfolio."</mark> _<mark>World Development Perspectives</mark>_ <mark>25 (2022): 100396.</mark> <mark>Biardeau, Léopold T., Lucas W. Davis, Paul Gertler, and Catherine Wolfram. "Heat exposure and global air conditioning."</mark> _<mark>Nature Sustainability</mark>_ <mark>3, no. 1 (2020): 25-28.</mark> <mark>Bistline, John. Household Environmental Kuznets Vurves: Evidence from Passenger Transport Emissions. EPRI Working Paper 2022.</mark> <mark>Bruckner, Benedikt, Klaus Hubacek, Yuli Shan, Honglin Zhong, and Kuishuang Feng. "Impacts of poverty alleviation on national and global carbon emissions."</mark> _<mark>Nature Sustainability</mark>_ <mark>(2022): 1- 10.</mark> 16
induced by tradable job losses. Therefore, the instrumented total job losses is intended to capture all types of job losses driven by the initial rounds of tradable job losses.

Finally, note that all regressions in this paper are weighted by each county’s number of households, and standard errors are clustered at the state level.

# **3.2 Data**

The primary source of our data is the Census Bureau. We use employment data in March 2007 and March 2010 from the County Business Pattern (CBP) data set, because these dates represent the lowest and highest points of the US aggregate unemployment rate during the Great Recession. This data comes with flags representing employment ranges, which we replace with average employment values. We follow Mian and Sufi (2014)’s classification of the tradable sector based on global trade data: a 4-digit NAICS industry is defined as tradable if it has imports plus exports equal to at least $10,000 per worker, or if total exports plus imports exceed $500M. Table 3.2 shows that tradable jobs on average account for 14.5% of a country employment. During the Great Recession tradable jobs suffered devastatingly: their employment shrank by about 19%. The average Bartik instrument takes the value of 0.0271. This means that demand-driven tradable job losses account for about 2.71% of total 2007 employment.

We use data from the United States Department of Health and Human Services (US DHHS) Centers for Disease Control and Prevention (CDC) WONDER online database to construct our dependent variables. The dependent variable is the log difference in the number of deaths between 2008-2010 and 2005-2007 periods. While single-year numbers are more straightforward, there are not enough observations to yield meaningful analyses at the disaggregated level (by types of mortality and age groups). Thus, we resort to 3-year totals, which we extract from the CDC WONDER database (2005-2007 instead of 2007, 2008-2010 instead of 2010). The idea is to 10