rafmacalaba/data-use-mentions-extended huggingface dataset:
This paper is structured as follows. Section 2 provides background about the related literature, the private sector prior to the pandemic in Zambia, and the government policies implemented to address COVID-19 in Zambia. Section 3 outlines the data sources that we draw on and the analysis that we conducted to produce the paper’s findings. Section 4 presents the findings and Section 5 discusses the implications, as well as areas for future research.

# **2 Background**

## **2.1 Related Literature**

This paper is related to the growing literature on the impact of COVID-19 on the private sector. Dai et al. (2021), Bartik et al. (2020), Humphries et al. (2020), Apedo-Amah et al. (2020), Adams-Prassl et al. (2020), and Fairlie (2020a, 2020b) have all documented COVID19’s impact on businesses across countries in terms of revenue loss, business closures, mass layoffs, and liquidity. In most cases, such as Karalashvili and Viganola (2021), Cirera et al. (2021), Apedo-Amah et al. (2020), and Davies et al. (2021), research has been conducted using surveys, primarily World Bank Business Pulse Surveys (BPS), as well as the COVID-19 follow-up rounds of World Bank ES. This research paints a sobering picture of the impact of COVID-19. Firms have reported a significant drop in sales and reduced access to finance, and they envision additional losses in the future due to exceptionally high uncertainties going forward. This has prompted firms to make many difficult decisions involving significant reductions to their labor force. For example, following sales declines and firm closures in North Macedonia, firms interviewed in COVID-19 follow-up surveys to the ES exhibited a net reduction in employment of 7 percent since October 2020, highlighting the persistent struggle of businesses as they continue to navigate the COVID-19 era.

Another strand of literature has used tax administrative data to estimate the impact of COVID-19 on the private
lower than those prevailing in the euro area. Based on the former data, the lower initial installment amount associated with the lower interest rate appeared attractive even amid slightly higher exchange rate volatility. However, this ignores the fact that the Swiss franc had appreciated continuously since the 1970s, albeit at a different pace and with slight interruptions: initially against the Deutsche mark, and later also against the euro. Moreover, those having recourse to this product did not consider that, being perceived by investors as a safe haven, demand for Swiss-franc-denominated assets would surge during a potential crisis (MNB, 2010). Accordingly, in certain periods the volatility of the HUF/CHF exchange rate may significantly surpass that of the HUF/EUR exchange rate. Finally, debtors ignored the risk factor implied by the fact that the domestic monetary policy has little effect on the HUF/CHF exchange rate.

As a last chapter in foreign currency lending in Hungary, already overshadowed by the looming crisis, some banks began to issue Japanese yen (JPY)-based mortgage loans in late 2007. Even before the crisis, the Japanese interest rate level had been close to zero for a protracted period, resulting in even lower initial installment amounts than those of Swiss franc loans. However, the fundamentals of the European and the Japanese economy are not closely related. Consequently, the volatility of the JPY/HUF exchange rate was considerably higher than that of the EUR/HUF or the CHF/HUF exchange rates. Although the MNB and the Hungarian Financial Supervisory Authority (HFSA) warned the banks of the risks as early as the beginning of 2008 (MNB, 2008), 11
utilization in the liner shipping sector. In particular, they consider the number of carriers, the number of bilateral links between national ports, total vessel capacity and number, the number of direct services, and the size of the largest vessel on each route. Their methodology produces a connectivity index that is a weighted average of those data, with the weights determined statistically. Hoffman and Wilmsmeier (2008) show that connectivity defined in this way is an important determinant of observed freight rates in the Caribbean.

Our approach differs from previous contributions in three main ways. First, we extend the connectivity work of Pearce (2007) and UNCTAD (2007) by embedding it in a systematic model. This approach avoids the charge that the resulting measure of connectivity is ad hoc. Second, we are interested in using connectivity as a policy tool, rather than simply a means of describing network properties, as in the applied mathematics literature. Third, we focus on the country as the level of analysis—again, this is important from a policy point of view, since many of the economic outcome variables that we would expect to be correlated with air connectivity are measured at the national, not airport, level. Trade data are an important example.

# **3 Characteristics of the Global Air Transport Network**

Before describing our measure of air connectivity and its derivation, it is useful to consider some salient characteristics of the network we are analyzing. To do this, we use data from the _Schedules Reference Service (SRS) Analyzer_ platform (www.srsanalyser.com). SRS is the leading industry database of flight information, covering passenger and cargo links among over 200 countries and territories. We use data aggregated to the country level, but SRS can also provide much more detailed information on links between individual airports; Pearce (2007) uses SRS data at the airport level, for example.

7
crisis countries had such systems in place in the 2007 and the 2011–12 survey, respectively. Also, half of the crisis countries allowed accrued though unpaid interest/principal to enter the bank income statement when loans are non-performing, but only 24 percent of non-crisis countries allowed this by 2011-12. The comparison with the 2007 survey suggests that the practice of overestimating loan interest and principal payments got worse as the crisis unfolded and as banks tried to prop up their balance sheets. Moreover, while according to the 2011–12 survey in 53 percent of crisis countries banks were allowed to upgrade the classification of a loan or advance immediately after it had been restructured, this was the case in only 30 percent of non-crisis countries. According to the 2011–12 BRSS, while 70 percent of non-crisis countries had a regulatory requirement for general provisions on loans and advances, only 25 percent of crisis countries had such provisions in place. The 2011–12 survey shows that only 30 percent of crisis countries had minimum levels of specific provisions for loans and advances set by the regulator. Finally, almost 80 percent of non-crisis countries had such requirements in place.

Fourth, regulators in crisis countries were less able to demand banks to put up more equity, to constitute greater provisions or to suspend bonus and management fee payments. Based on the 2011–12 BRSS, in 83 percent of non-crisis countries the regulator had the power to request banks to put up new equity. This was true only in 65 percent of crisis countries. Similarly, according to the 2011–12 survey, in 98 percent of non-crisis countries, the regulator could request banks to constitute provisions to cover actual or potential losses. This was true in only 90 percent of crisis countries. Finally, according to the 2007 survey, in almost 70 percent of non-crisis countries regulators could suspend banks’ decision to pay certain bonuses or management fees. This was true in only 32 and 37 percent of crisis countries, respectively.

9
, using LASSO rather than gradient boosting for the census model used to construct the benchmark village welfare measure greatly reduces the explanatory power of the geospatial model, whether it is estimated using gradient boosting or LASSO. This is because extreme gradient boosting is a tree-based method that is more robust to outliers, and generates a benchmark measure of village welfare that is far easier to predict using geospatial indicators. This suggests that the census data in this case may be susceptible to outliers that introduce noise when using linear prediction models. Overall, the results demonstrate that investing in richer and context-specific training data, such as partial registries, can greatly improve the accuracy of predictions based on geospatial data.

This paper contributes to a growing literature on using satellite imagery to predict welfare. Initially, the most common remote sensing indicator used for this kind of analysis was nighttime lights, which measure the intensity of light in specific areas. Previous studies show strong correlations between night-time lights and GDP.<sup>5</sup> However, the association between night-time lights and other measures of household welfare is weak in most contexts, suggesting the limitation of this indicator for predictions of welfare in small areas.<sup>6</sup> More recent literature has demonstrated that indicators derived from daytime imagery is better suited for predicting welfare.<sup>7</sup> Recent literature has also demonstrated that advances in machine learning and available non-traditional

> 5 Henderson et al., 2009, Pinkovskiy and Sala-I-Martin, 2016.

> 6 Mellander et al., 2013.

> 7 Jean, et al., 2016; Babenko et al., 2017; Engstrom et al., 2015; Engstrom et al., 2017; Head, A., 2017; Yeh, et al.,2020;, Chi et al (2021); Masaki et al., 2022.

4
Figure 1 illustrates the development of the average share of state ownership by bank assets. Specifically, the figure plots the average state ownership share for all countries, and separately for the groups of developing countries (and emerging markets), and high-income countries. The average state ownership share in each instance is the average of country averages for pertinent countries. During the last decades, the share of state ownership in developing countries has tended to decline, from 34.6% in 1999 to 19.4% in 2010. This decrease was especially pronounced during the years from 2007 to 2010. Average state ownership in highincome countries, instead, has increased somewhat from 7.9% in 1999 to 9.9% in 2010, with most of the increase occurring after 2007. Overall the state ownership share has been rather stable around 19%, although it declined from 17.5% in 2009 to 13.5% in 2010. These trend data suggest that government ownership of banks is likely to remain prevalent in future years. In the empirical work, as a robustness check we also consider a further breakdown of privately-owned banks into domestic and foreign banks.

Table 1 provides summary statistics for our sample of banks. The main variable of interest is the loans variable, which is the log of net loans in local currency and deflated using the national GDP deflator (see Table A2 in the Appendix for variable definitions and data sources). We consider several variables to represent the sources of bank funding: total liabilities, deposits, non-deposit liabilities, short-term funding, long-term liabilities, and equity. These variables are also constructed as the logs of amounts in local currency and deflated by the GDP deflator. To proxy for the cost of bank funding, we construct the net interest expense ratio as the log of one plus the bank‟s interest expenses over interest bearing liabilities net of the government T-Bill rate taken from the IMF International Financial Statistics database (IFS, 2012). As indices of the quality of lending, we
The modeling of _Cj_ , _Rj_ and _Ej_ is detailed in H08. Ranger et al. (2011) proposes a model version including a modeling of the household budget and of its influence on final consumption. This modeling of demand makes it possible to represent backward ripple-effects: if a sector produces less, then Eq.(4) shows that it will demand less to its suppliers, reducing their own production.

# _3.1.2 Production model_

Without constraint, each sector _i_ would produce at each time step _t_ the exact level of demand _Di_ ( _t_ ). But production can be lower than demand either (i) because production capacity is insufficient; or because (ii) inventories are insufficient as a result of the inability of other sectors to produce enough (forward propagation). The production capacity of each sector depends on its stock of productive capital (e.g., factory, equipments), and on the direct damages to the sector capital (e.g., a sector that suffer from disaster damages can produce less).

The capacity and supply constraints are described by the following relationships:

- _Limitation by production capacity._ Independently of its suppliers, the production capacity _P i_<sup>_cap_</sup> of the _i_<sup>_th_</sup> sector reads:

where _Pi_<sup>_ini_</sup> is the pre-event production of this sector, assumed equal to the normal production capacity. The variable Δ _i_ is the reduction in productive capacity due to the disaster, directly because of the disaster (e.g., the volcano ash case) or through capital destructions. Like in H08, we assume that if a disaster reduces the productive capital of the industry _i_ by _x_ percent, then the production capacity of this industry is also reduced by _x_ percent: 12
particularly suitable for survival analysis as aggregation may introduce considerable bias, essentially hiding failures. A trade relationship is de...ned as a ...rm-product-destination triplet, and the duration of a trade relationship is de...ned as the time (in years) a triplet has been in existence without interruption. Our variable of interest is the survival-time of ...rm’s export relationships–the time until a trade relationship ends–across products in ...ve African countries. Then, ...rms in our sample are already surviving ...rms. They already incurred sunk entry costs. As a result, our focus is on the determinants of their ability to continuously remain on and not to enter in the market.

Ordinary Least Squares (OLS) are not suitable for duration data, essentially because survival-times are restricted to be positive, and thus, have a skewed distribution. Therefore, we model the survival of trade relationships using a Cox proportional Hazard Model (CPHM). We assume that the duration of exports of product k from ...rm i operating

> 9Since four of the ...ve African countries used in our analysis are also present in the food alert database, this may introduce an endogeneity bias. All together Ghana, Mali, Malawi and Senegal account for 2.6 percent of the food alerts, when an average exporter su¤ers around 90 alerts and an average African exporter gets rejected 12 times on average. We re-estimated the risk index dropping those four countries from the food alert database. The level of correlation between the actual and newly computed risk index is 0.9.

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**_Figure 1 ‐ Innovation rates in the main ES and innovation follow‐up survey by type of innovation_**

<!-- Start of picture text --> 80%<br>68%<br>70%<br>59%<br>60%<br>51%<br>47%<br>50% 45% 43%<br>39% 41% 41%<br>40% 35%<br>27% 27%<br>30%<br>22%21%<br>20%<br>10%<br>0%<br>Product Process Marketing R&D Methods Logistics Auxiliary<br>ES Innovation follow‐up<br>% of firms reporting innovation<br><!-- End of picture text --> _Source: Enterprise Surveys and Innovation Enterprise Surveys_

**_Figure 2 ‐ Differences in innovation rates between short and long questionnaire_**

<!-- Start of picture text --> Product Innovation Process Innovation<br>KEN UGA<br>KEN UGA NPL GHA MWI NAM<br>NPL TZA GHANGASDNMWINAM SSDZMBIND PAK SDNDRCTZANGASSD ZMBIND BGD<br>DRC<br>PAK BGD<br>0 20 40 60 80 0 20 40 60 80<br>Long questionnaire Long questionnaire<br>Marketing Innovation R&D<br>KEN<br>NAM UGA<br>SDN NGATZA MWISSDGHA INDNPL ZMB NAM<br>DRC<br>BGD UGA IND<br>PAK PAKSDNSSDGHABGDZMBKENDRC MWI<br>NGA TZA<br>NPL<br>0 20 40 60 80 100 0 20 40 60 80<br>Long questionnaire Long questionnaire<br>80 80<br>60 60<br>40 40<br>20 20<br>Short questionnaire Short questionnaire<br>0 0<br>80 80<br>60 60<br>40 40<br>20 20
reminder about the income tax filing deadline a usual communication provided by the tax authority. In contrast, those in the treatment group are informed that the tax authority observes specific transactions they engaged in, implying knowledge about (part) of their realized revenue. In that sense, we see our experimental treatment as providing taxpayers with data about the information set available to the tax authority. That allows us to estimate the average effect of treatment assignment on a range of compliance outcomes, measured using administrative data on tax filing.

While there is strong evidence that the average effect of these types of interventions is positive (see Mascagni (2018) and Castro & Scartascini (2015) for recent reviews), this is not inconsistent with negative or null impacts on some subset of taxpayers (De Neve et al., 2021). We argue that measuring this heterogeneity is crucial for authorities to better target this kind of intervention. While the return on investment of these interventions is often perceived to be extremely high given the very low marginal costs of letter or 2
et al. 2006, Ahluwalia 2000, Besley and Burgess 2004). Some authors argue that Indian manufacturing has been constrained by inadequate infrastructure and that industries that are dependent upon infrastructure have not been able to reap the maximum benefits of the liberalization’s reforms (e.g. Gupta et al. 2008, Gupta and Kumar 2010, Mitra et al. 1998).

We employ plant-level data from the years 1994, 2000, 2005, and 2007 to study the impact of highway infrastructure investments on Indian manufacturing. We study how proximity to GQ in non-nodal districts affected the organization of manufacturing activity using establishment counts, employment, and output levels, especially among newly entering plants that are making location choice decisions before or after the upgrades. This work on the organization of the manufacturing sector also considers industry-level 2
## **4.2.3 Summarizing the findings across the survey and tax administrative data**

Both data sources paint a sobering picture about how the pandemic has impacted the employment levels of formal firms. There was an initial moderate decline in employment and there is evidence to suggest that over time the situation has only worsened. Regardless, there are clearly no meaningful signs of recovery, with a non-trivial share of people who were employed in formal firms in Zambia prior to the COVID-19 lockdowns no longer having a job even almost 18 months later. As discussed at length in Section 3, these data sources are quite different, but complementary. In the case of the survey data, em- ployment refers solely to full-time employment, which may be less volatile than all types of employment captured in the tax administrative data. The fact that we see the same pattern in both the survey and tax administrative data illustrates how robust these findings are likely to be.

# **4.3 Covariates of recovery**

We conduct econometric analysis to understand what factors are associated with firms experiencing an initial decline in sales and employment as well as a recovery. Table 2 reports on the factors associated with a decline in sales and employment levels in June 2020 from pre-pandemic levels, while Table 3 reports on the factors associated with a recovery in sales and employment to pre-pandemic levels by July 2021. As discussed at length in Section 3.2, we are not claiming that there is a causal relationship between the factors that we examine and firm sales and employment. Rather, the findings that follow show which factors are most closely correlated with firms experiencing a decline and/or recovery. In the discussion that follows we make an important distinction between structural (e.g. location of firms) and firm-specific (e.g. management experience) factors as it is likely that policy makers can more easily influence the latter.

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below results from the Heckman two-step estimator that aims to unpack them.<sup>39</sup>

# **5 Cross-border Tax Effects on Inward FAI**

This section presents baseline results from the use of the FAI data just described, looks at their robustness and some extensions, draws lessons, and then explores further the differences arising from the use of data on FAI rather than FDI.

## **5.1 Baseline Results**

Table 2 reports baseline results from the estimation of equation (8) on the full sample. The first column enters separately the various tax terms other than the parent _KF_ - _METR_ , which is taken up later. The coefficients are evidently significantly different from each other, so that sufficiency of the I-METR is rejected. In terms of its underlying components, the coefficient on the rate differential term is highly significant, has the expected sign, and is large. The host country _KF_ - _METR_ enters with the expected (negative) sign but is not statistically significant; the same is true of the squared rate differential term.<sup>40</sup> Contrary to expectation, the interaction term, intended to capture possible non-linear effects, enters negatively, and with some significance.

The remaining columns of Table 2 focus more closely on effects through the rate differential, ∆ _hpt_ and the host _KF_ - _METRh_ , with corresponding adjustments to fixed effects and control variables.

Columns (2)-(4) focus on possible spillovers from statutory corporate tax rates, in all cases including host country-year and parent country fixed effects. Column (2) shows that the rate differential term ∆ _hpt_ has a positive and statistically significant relationship with FAI. And the effect is large, with an implied semi-elasticity with respect to the rate differential, _β_ ∆, of

> 39One recent application of the PPML in a related context, for example, is Damgaard, Elkjaer and Johannesen (2019), which uses the Poisson model to estimate the differential impact of gravity-type variables on
in rural-to-urban migration trends are summarized using village, household and individual data from surveys conducted by the Research Center for Rural Economy (RCRE) at China’s Ministry of Agriculture. These data have the benefit of using a consistent standard for defining a migrant over three decades. The discussion on trends is then supplemented with data from a more recent survey, the China Family Panel Survey (CFPS). Attention also focusses on how the individual characteristics of migrants and their jobs have evolved over time.

# **3.1 Measurement Issues and Data**

One important challenge for the study of migration in China involves fundamental problems of measurement. Although there is a general understanding in China that a “rural migrant” has a 8