rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3. Data

## 3.1 The Sample

The sources of firm-level data we employed are the World Bank Enterprise Surveys (WBES) and the COVID-19 Follow-up Enterprise Surveys, both conducted by the World Bank. WBES offers an expansive array of economic data on 171,000 firms in 148 countries, and the data has been widely used in economics, finance, international business, and management research. Since the outbreak of COVID-19 pandemic, to measure the pandemic’s impact on the firms, the Followup Enterprise Surveys re-interviews those firms that had been interviewed in WBES in selected countries. The follow-up surveys contain information on sales changes, business operations, labor adjustments, access to finance, expectations about the future, and support from the government in response to COVID-19. We combine the WBES data with COVID-19 Follow-up Enterprise Surveys data. The former provides us with the pre-determined firm characteristics which are employed as independent or control variables; the latter provides seven indicators measuring firm responses during the pandemic.

By April 2021, the Follow-up Enterprise Survey has been implemented in 41 countries, and firms in 26 countries out of them have been re-contacted in the second round. The relevant WBES of these 41 countries have been posted in different years during 2016-2019. We choose to use the data of the latest survey, which means that for firms having been interviewed in two rounds, we only keep the second-round survey data and for firms that have only one round survey, we just use the data. The reason is that the COVID-19 pandemic is still ongoing, and the surveys add a few new interview questions over time, thus using the latest survey could help to examine the impact of COVID-19 more accurately. We also use the first-round survey to check the robustness and compare the results with the results of the second-round survey to explore the dynamic of firm responses during COVID-19.

We further collect country-level data from various sources. GDP per capita is from the World Development Indicators database (WDI). The stringency of government policy to contain COVID19 is from
14 decline in end-2011 in some regions—such as Singapore and Hong Kong SAR, China— partially reflecting funding difficulties.

- European banks also have significant presence in the typically longer-term risky aircraft and car leasing/shipping business that requires scarce longer-term dollar funding and higher capital. As a result, many banks have reportedly been seeking to sell these operations (appendix table A.3).

**Figure 12. Corporate Lending by European Banks**

<!-- Start of picture text --> a. Corporate Lending by European Banks, b. Net Fraction of Firms Reporting Tightening<br>Jan 2010–Mar 2012 Availability Conditions, Mar 2012<br>6<br>% year-on-year, data 30%<br>are adjusted for loan<br>4<br>sales and 25%<br>2<br>20%<br>0<br>15%<br>-2<br>10%<br>-4 5%<br>-6 0%<br>Large All SMEs Micro Small Medium Total<br>France Germany Italy Bank loans Trade credit Overdrafts<br>Portugal Spain<br>c. Net Fraction of Firms Reporting Tightening Availability Conditions, Mar 2012<br>70%<br>60%<br>Interest rates<br>50%<br>Cost other than<br>40% interest rates<br>Available size of loan<br>30%<br>or credit line<br>20% Available maturity of<br>the loan<br>10%<br>Collateral<br>requirements<br>0%<br>-10%<br>Large All SMEs Micro Small Medium Total<br>Jan 2010 Mar 2010 May 2010 Jul 2010 Sep 2010 Nov 2010 Jan 2011 Mar 2011 May 2011 Jul 2011 Sep 2011 Nov 2011 Jan 2012 Mar 2012<br><!-- End of picture text -->
# **1 Introduction**

What drives international trade? What hampers its growth? Academics and policy makers devote a lot of attention to these questions. Multiple factors have been under the lens but typically different causes have been examined separately. In this paper, we investigate the role of financial and real sector elements on trade dynamics using firm-level data from 24 countries in the Eastern Europe and Central Asia (ECA) region. We document the relative importance of adjustment at the extensive (number of exporters) and intensive (quantity of exports) margins during the period from 2002 to 2013. We then analyze the relative importance of credit as opposed to demand shocks in the firm’s decision to export and the amount of exports in a formal econometric setting.

We find that demand shocks tend to significantly affect the decision to export as well as the volumes exported. Conversely, credit constraints appear to play a more limited role. These results tend to be robust to a wide range of specifications. However, there are differences in the relative importance of these elements when we consider the type of export margin (extensive and intensive) as well as the time period (before or after the crisis). Domestic demand shocks matter across all specifications as they influence firms’ decision to export and the amount of exports. Conversely, foreign demand shocks significantly influence the intensive but not the extensive margin, and their role becomes more pronounced when we distinguish between pre- and post-crisis periods. Finally, credit constraints only play a role in the intensive margins and mainly when we do not take into account the role of the financial crisis. The firm’s subjective perception that financial constraints are a hurdle for business appears to matter more than the objective inability to secure a loan after applying. The evidence is robust to controlling for possible endogeneity.

The paper contributes to two branches in the literature examining the roles of finance and demand shocks in international trade. The literature has mostly examined both roles separately and finds that they are important to trade
In 2011, these divisions housed 53 percent of the entire population, including 48 percent of the rural population (BBS, 2015). These divisions constitute 38 percent of the lottery applicants and 50 percent of the lottery winners. These divisions are also the two most prosperous divisions of Bangladesh. To the extent that migration improves outcomes, the results can be interpreted as a lower-bound of the impact on the entire country.

# **Sampling strategy and field protocols**

The relative scarcity of T1 and the nature of the administrative data we received guided our sampling and field protocols. We received administrative data from BMET in two separate extracts. The first extract was for the lottery losers (control group) which included information on the applicants’ names, their parents’ names, phone numbers, and the name of their unions. The second extract was the published data on lottery winners (group T1 and T2) which contained the same information except for their phone numbers. We were able to get matched phone numbers for this group from BMT only for 76 percent of group T1 and 16 percent of group T2.

To deal with this, we opted for a combination of phone and field-based tracking of respondents. In each of the sampled unions, enumerators were instructed to find all the T1 individuals. Applicants in the T2 and control groups were randomly ordered, and enumerators were instructed to follow the order in finding respondents. Enumerators would keep going down the randomized order until the number of successful interviews in that group (T2 or control) matched the number of successful interviews in the T1 group. This way, the final survey would have similar sample sizes across treatment groups within each union.

To find the respondents in a sampled union, enumerators first tried calling the applicants for whom we had phone numbers. Each applicant would be called up to five times over the course of several days. If somebody picked up the phone, we also asked if they knew the phone numbers of additional people in the treated groups. Lottery applicants
time `0‟, which is information available in household consumption surveys. Note that this approach leads to the same result as the Laspeyers measure of welfare change and is consistent with the approach proposed by Coady (2006).

# **Household expenditure on the subsidized good**

Let us start by expressing the <u>nominal</u> expenditure in period 1 of the household after the increase in price of good by: We need therefore to know the new price and the new purchased quantities.

For the change in price, we assume that this applies instantly so that: For the change in quantities, the adjustment may depend on several factors. However, for simplicity, we assume that the consumer adjusts quantities based on its preferences, and subsequently, on the elasticity of demand of the good with respect to its price. Recall that the non-compensated elasticity of demand of good with respect to its own price is Then, the new quantity at time 1 can be described as: Subsequently, the new household nominal expenditure on good k can be expressed as the change in household nominal expenditure on good _k_ is and the change in household real expenditure on good _k_ is 20
a descriptive assessment of the trade policy measures implemented in response to the COVID-19 pandemic. This paper and the accompanying database complement a growing body of policy oriented economic research studying international trade during the COVID-19 pandemic. Early in the pandemic many analysts and organizations stressed the importance of keeping borders open to allow supply chains to work efficiently in ramping up supply of critical products (e.g., Baldwin and Evenett, 2020; Bown, 2020a,b,c; Evenett, 2020; Espitia et al., 2020; Mattoo and Ruta, 2020; OECD, 2020; WTO, 2020). In practice, some countries did so; others did not. Understanding the drivers of the differences in observed trade policy responses over time and across countries and sectors is important for drawing inferences (and lessons) about the implications of the COVID-19 pandemic for international cooperation and potential rule making looking forward. The data set allows empirical analysis of a range of research questions. Some of these are discussed briefly on the penultimate section of this paper, but we do not undertake such analyses here. Instead, our main aim is to raise awareness of the data set in the hope others will be encouraged to use it.

The remainder of the paper is organized as follows. Section 2 presents the data set and provides basic summary statistics. Section 3 highlights some stylized facts that emerge from the trade policy responses to the COVID-19 pandemic. Section 4 discusses several hypotheses suggested by these stylized facts and possible research questions that could be the subject of further analysis. Section 5 concludes.

# **2. Data set**

The data collection initiative presented in this paper is confined to trade policy changes in two sectors: medical goods and medicines, and agricultural and food products. The products falling under each of

> 1 Information on trade policy changes is processed and collated by the Global Trade Alert team. The data are posted at https://www.globaltradealert.org/; https://globalgovernanceprogramme.eui.eu
. We do lose significance on the food consumption score for the IHPS sample, however.

4.5.2: Restricting the Sample to Households Interviewed during September-March. Another reason we might not be able to detect changes in food consumption outcomes from receipt of food aid under shocks may be due to the time between receipt of aid and the household interview date. While WFP distributed

> 23 Full results for the poorer household sub-sample are in Online Appendix 8.

38
<!-- Start of picture text --> Mean Median<br>Household Size 74 7.5 8.3 6.7 6.2 7.1<br>Urban(household) 31.6 28.8<br>Children share<br>Elderly share 3.2 2.7 3.7 3.3 24 3.5<br>GMD GMD Phone HFPS GMD GMD Phone HFPS<br>Note: Percentage is used for non numeric indicators.<br><!-- End of picture text -->
three years of higher secondary schooling. In addition, there is a variable reporting years of schooling available for some samples (17 out of 21 samples).

**Linking generations.** The data collection is organized at the household level,<sup>3</sup> so it is possible to link individuals who live with their parents in the same household at the time of the interview using a variable that details the relationship between each individual and the head of the household. For simplicity, I use individuals linked to their probable father and probable mother according to the procedures used by IPUMS for family interrelationships.<sup>4</sup> This method of linking generations differs from the one used in Munoz (2021a) for the main analysis, which links individuals ages 14-25 to all the older relatives living in the same household, however, as mentioned in the robustness section of Munoz (2021a), both methods give indicators of mobility that are close to being perfectly correlated.

For the main analysis, I use a sample of individuals co-residing with at least one “probable” parent, I focus on individuals with age in the range 14-25 in the main analysis where completion of primary education is the outcome of interest. However, when I explore completion of secondary education as the outcome of interest, I use individuals with age in the range 18-25. These age ranges are chosen to target individuals with an age sufficiently large so they can complete the educational level of interest if progressing in line with the official age for which the educational system is designed but young enough so they are likely to still live with their parents. The effort of maximizing the co-residence rate attempts to minimize potential truncation bias in the estimates of intergenerational mobility (see Emran, Greene, & Shilpi, 2018; Francesconi & Nicoletti, 2006). Nonetheless, Munoz and Siravegna (2021) show that the bias is small for estimates of absolute mobility that use census data. In addition, there is a low level of re-ranking
|Table A4.1: State-owned installed capacity (MW) in the reference year ................................................... 87|
|---|
|Table A4.2: State-owned transmission and distribution line data (km) in the reference year or latest year<br>with available data ...................................................................................................................................... 88|
|Table A5: Power purchase data - reference year or latest year available .................................................. 90|
|Table A6: Dispatch, sales, and T&D loss data for the reference utility in the reference year .................... 92|
|Table A7: Quality of service data ............................
>0.1<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>Normalized Management score Initial Digital Readiness Score<br>Mean management score<br>Mean digital readiness score<br><!-- End of picture text -->

**Notes:** Both panels control for size, sector, age, and country. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

In line with the extant literature on management practices (see Bloom et al., 2019, 2022; Grover and Torre, 2019), we also observe a positive relationship between management and firm size (Figure 5, panel a). That is, the selection mechanism through which more capable firms survive and grow is at work; however, informal firms exhibit a weaker sizemanagement relationship indicating that there is some degree of misallocation perpetuated due to informality, particularly by parasite firms where this relationship is the weakest. A similar pattern is also observed with respect to digital readiness (Figure 5, panel b). This suggests that the faster churning in the informal sector is not necessarily linked to productivity improvements, as also evidenced in Vietnam (Mccaig and Pavcnik, 2021).

> 18In terms of educational distribution, fully formal firms exhibit a much higher proportion of managers with a college degree compared to informal firms (Figure A6).

14
Skiba, 2004). The positive relationship between quality (and hence f.o.b. export prices) and distance that results from the endogenous quality choice of firms makes it possible to use the exporter f.o.b. unit value to help identify quality.

In particular, the heterogeneous firm trade model features a zero-cutoff-profit condition which determines the marginal exporter. As foreign demand increases, less efficient exporters enter the market, and they produce lower quality goods. This implies that quality and bilateral trade are negatively related from this supply-side equation. Combined with the positive association between trade and quality from the demand side, this makes it possible to obtain a sharper solution for quality than in the previous literature. The solution for quality is governed by cost, insurance and freight (c.i.f.) and f.o.b. prices, as well as model parameters (the elasticity of substitution, a Pareto productivity parameter, as well as a parameter governing non-homothetic demand). The fixed costs of exporting, which play an important role in the quality estimates through the supply side of the model, are captured by a general specification which depend on firm productivity and the size of the market, as well as bilateral gravity-like variables, such as language differences between the exporting and importing countries. Following Feenstra and Romalis (2014), we estimate these parameters using a gravity-type equation implied by their model (see Appendix A.1 for details). We use bilateral trade data at the HS 6-digit level in 2019.

# **4.2 Results**

We estimate the quality and quality-adjusted prices of Africa’s imports of digital goods, using detailed data on international trade flows and tariffs, and applying the Feenstra and Romalis (2014) methodology outlined above. We find that African nations tend to import relatively low quality, low price digital goods, as would be expected given their relatively low income per capita. Once quality differences are accounted for, digital goods in AfCFTA are sourced at
places. We find no evidence that built-up cells are located at more or less regular distances from each other. The same is true for cells located along a road – where distance better represents travel cost and time using road transport. Why this is the case is unclear: after all, any look at a map or a Google Earth picture indicates that towns are seldom located next to each other. But in practice, small differences in terrain and obstacles to travel seem to offer enough disruption to blur the distance regularity predicted by theory. This is true even in Ghana where the terrain is fairly uniform, compared to other parts of the world, and lacks major rivers other than the Volta.

A related strand of literature has sought to characterized the size distribution of cities, taking Zipf’s Law as starting point (Gabaix 1999, Arshad et al. 2018). While some evidence has been offered that city sizes follow an exponential distribution, more recent evidence has cast doubt on the idea, arguing that the distribution of city sizes diverges from Zipf’s Law when more complete data are used at a higher degree of disaggregation. We contribute to this literature by showing that at the very smallest level – i.e., partial buildup of a small geographical cell, Zipf’s Law certainly does not apply: the log of the buildup proportion of cells is far from forming a linear relationship with the log of the rank of that buildup proportion. We see this as extending earlier work to the lowest level of geographical disaggregation.

We start in section 2 by explaining how the data are constructed. A brief conceptual framework is introduced in Section 3. Empirical results are presented in Section 4.

# **2 Data**

Our main variable of interest _yit_ measures the proportion of a cell _i_ in year _t_ that is covered by buildings, as inferred from a high-definition satellite image of Ghana. This variable comes from the Global Human Settlement Layer (GHSL) data that have been put together by the European
indicating that 95 percent of prices were within 80–120 percent of the national average price.

Table A2.6: Shares of total household expenditure on LPG for all households and for purchasing households in India (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.0|0.2|0.0|6.9|8.0|7.0|
|**2**|0.1|0.7|0.2|6.3|7.0|6.5|
|**3**|0.4|1.4|0.5|6.0|6.9|6.4|
|**4**|0.9|2.6|1.4|5.2|5.9|5.5|
|**5**|2.0|2.9|2.6|4.0|3.8|3.9|
|**All**|0.5|2.5|1.1|4.8|4.3|4.5|

_Source:_ Authors’ calculations.

# **Indonesia (SUSENAS, January Panel Module 2005)**

The data are taken from the consumption module of the National Socio-Economic Survey (SUSENAS) administered by Badan Pusat Statistik-BPS Statistics Indonesia. The fieldwork for the survey was carried out between January and March 2005
# Inequality under COVID-19: Taking Stock of High-Frequency Data for East Asia and the Pacific

_Lydia Y. Kim, Maria Ana Lugo, Andrew D. Mason, Ikuko Uochi_<sup>_1_</sup> _World Bank_ Keywords : COVID-19 pandemic, inequality, poverty, East Asia and the Pacific JEL classification : D30, I31, O15, J6.

> 1 Lydia Kim, Maria Ana Lugo, and Ikuko Uochi are from Poverty and Equity Global Practice, for East Asia and the Pacific; Andrew Mason is from the Chief Economist Office for East Asia and the Pacific. Corresponding authors: <u>mlugo1@worldbank.org</u> and <u>ykim14@worldbank.org. Rinku Murgai (Practice Manager, POV EAP) and Hassan Zaman (EFI Director for EAP) provided overall</u> guidance on this project. We received invaluable comments from Rabia Ali, Tanida Arayavechkit, Kristen Himelein, Aaditya Mattoo, Ambar Narayan, Emilie Perge, Ririn Salwa Purnamasari, Matthew Wai-Poi, and Judy Yang. We thank the country survey teams for providing us access to the datasets and providing feedback on the analysis.
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