rafmacalaba/data-use-mentions-extended huggingface dataset:
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data significant.<sup>23</sup> This finding confirms the conclusion of section 2 that the direct approach is less informative about the underreporting behavior of individual firms.

Our second shot at measuring underreporting is based on the so-called indirect approach by interpreting the discrepancy between the reported sales in the survey and the sales reported to the tax office as an indicator of underreporting. This approach can be seen as a special case of the MIMIC model, where one assumes that survey sales reflect true sales.<sup>24</sup> Figure 2 shows the cumulative density functions for the reported sales to the tax office and in the survey. The distribution of sales reported in the survey first-order stochastically dominates the distribution of sales reported in the tax office. This implies that the total

**Figure 2: Cumulative Densities of (log) Sales reported in Survey and to Tax Office**

<!-- Start of picture text -->
1.0 CDF<br>0.9<br>0.8<br>0.7<br>0.6 ln ( S t )<br>0.5<br>0.4<br>ln ( S s )<br>0.3<br>0.2<br>0.1<br>3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br><!-- End of picture text -->

taxes actually paid to the tax office is less than the total tax that should have been paid, if taxes actually paid increase monotonously in sales reported to the tax office. Table 2 23 Based on tests of equal means and allowing for heteroskedasticity. 24 I.e. and in equation (11).

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<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>18.2% 18.4%<br>28.5%<br>55.5%<br>26.0%<br>53.3%<br>Planning on being vaccinated Planning on being vaccinated<br>Not planning on being vaccinated Not planning on being vaccinated<br>Unsure about being vaccinated Unsure about being vaccinated<br><!-- End of picture text --> Note: These charts are based on answers to question 3 and 34 (control group only) in Appendix B.

# <u>5.1.4 Reasons for vaccine hesitancy (based on both the phone survey and online experiment)</u>

In both the phone survey and online experiment, the main reason people stated they did not want to be (or were unsure about being) vaccinated was that they were worried about side effects from the COVID19 vaccine (see Figure 7). The same options were provided in both surveys. However, in the online experiment respondents were asked to select the main reason they were not prepared to get vaccinated, whereas in the phone survey respondents could select all that applied (see Figures 5a and 5b). Around four-fifths (78%) of respondents selected worried about side effects as a reason for hesitancy in the phone survey and around half (48%) selected this as the main reason in the online experiment.

**Figure 7 – Top three reasons for vaccine hesitancy in PNG**

<!-- Start of picture text --> a) Phone survey (late May/early June 2021) b) Online experiment (late June/July 2021)<br>.8 0.78 .5 0.48<br>.4<br>.6<br>0
either did not invest or financed their recent investment without bank credit. It further displays the share of firms with a low (1-33%), moderate (34-67%), and high (67-100%) level of bank financing for their most recent investment. The table shows that 74% of all firms are not leveraged, either because they did not invest (40%) or invested without using bank finance (34%). Those firms which did use bank finance to invest are equally divided between low debt levels (7%) moderate debt levels (8%) and high debt levels (11%). In line with our findings on the incidence of bank credit we find that leverage is substantially higher among large, audited and export orientated firms. Roughly 25% of large, audited and exporting firms have moderate to high leverage levels, while only 15% of small, non-audited or non-exporting firms do so. Again, firm age, sector of activity and ownership seem to have little effect on leverage.

The data presented above suggest that “excessive leverage” in the region may be more widespread than recent research using financial statement data for large firms suggests. Coricelli et al. (2009) examine balance sheet and income statement data for roughly 8,000 manufacturing firms from 12 countries in Emerging Europe over the period 2001-2005. Their study aims to establish the threshold of firm leverage above which external finance reduces firm productivity. Their results suggest that on average the optimal debt level for firms in the region is around 40%. They find that the share of firms with leverage exceeding this level ranges from less than 5% in Slovenia, Slovakia and Hungary to more than 15% in Bulgaria, Latvia and Russia. The BEEPS data presented above suggest that among a representative sample of firms in the region nearly 20% display excessive leverage levels (according to the Corrichelli et al. 2009 threshold).

What does this imply for the threat of **debt overhang** in the region?
# **1.5 Numbers affected**

# **Many more individuals are affected by floods to a greater or lesser degree than are**

**deemed to be killed by them** . According to EM-DAT, on average over the past three decades more than 100 million people each year have been affected by floods. This is reason enough for governments to take action towards reducing these statistics. The numbers affected has grown since 1950 from around 4 million a year to the present level, which represents more than 1% of the global population. Figure 7 shows the numbers and 10 year moving median. However, it is difficult to determine whether the number affected by events is still on an upward trajectory, has peaked or has stabilized.

# **Figure 7 Number affected by flooding**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>number affected (millions) 10 year moving median<br><!-- End of picture text -->

# **1.6 Are damages – economic, financial and insured losses – increasing?**

# **On the whole, the developing nations have insurance coverage lower than 25% of**

**total losses** . In evaluating the monetary impact of flooding there are two main perspectives typically adopted: that of economic losses, which attempts to measure the impact on society of the actual damage and disruption; and that of financial, or insured losses, which covers the cost of the replacement of damaged items and associated expenditures. These types of losses can differ significantly, depending on the items included and the scale of the economic entity considered (Barthel and Neumayer 2010). For example, from Munich Re records (Kron 2011) the difference between actual and insured losses is clear. Insured losses represent between less than 1%
discusses methodology, and provides some preliminary analysis based on graphical methods and descriptive statistics. Section 4 contains our model and estimation results, and Section 5 concludes with a number of policy implications that flow from our findings, as well as suggestions for further research.

- **2 Trade Costs, Trade Facilitation, and International Production**

# **Networks**

As many authors note, vertical specialization has been at the center of international organization of production in recent decades (Helpman, 2006). Trade costs and other barriers to trade have been greatly reduced. Lower transportation costs, better and cheaper access to communication technologies, and even the reduction of tariffs have allowed firms to relocate their supply chains and production processes across multiple countries. One of the most important issues in the literature on vertical specialization is the role of trade costs within global production networks (GPNs). Yi (2003) argues that intra-GPN trade should be more sensitive to changes in trade costs, since vertical specialization causes products to move across borders many times before reaching their final consumption location. The author proves that three of the main paradigms in international trade (Ricardian, increasing returns, and Armington) are unable to mimic the nonlinearities in trade growth after WWII, without resorting to extremely high elasticities. The author develops a two-country dynamic Ricardian model of vertical specialization that is able to account for the substantial increase in trade since the 1940s.

Evidence regarding the extent and impact of trade costs within GPNs is rather limited. One interesting exception is Hanson et al. (2004), which examines the role of trade costs in U.S. multinational firms’ decision to export intermediate goods to their affiliates abroad for processing. Trade costs variables were obtained from the Feenstra (1996) dataset, based on the cost of insurance and freight, expressed as a percentage of the customs value of imports. The authors find that affiliate demand for imported 5
2010 quantities, respectively. First, Figure 5 compares the Laspeyres price indices using quantity‐ based and calorie‐based weights. The calorie‐based index is dominated by the quantity‐based index but the two distributions are quite similar. Next, Figure 6a and 6b compare the Laspeyres and Paasche indices (quantity‐based and calorie‐based weights, respectively). By definition, the Laspeyres index captures the change in prices using their consumption patterns in 2007. In contrast, the Paasche index uses consumption patterns in 2010. The difference in the two indices is striking and shows the degree to which, on average, households were able to lessen the impact of price crisis by restructuring consumption.

Table 1 shows descriptive statistics of the Laspeyres price index by province using either quantities or calories as weights. For reference, Annex Figure A1 shows commodity‐wise price changes measured by price 2010 divided by price 2007 and Annex Table A1 shows expenditure shares. It is clear that price increase is particularly large in vegetables. Table 1 point to two observations. First, the index is larger when weights are constructed from consumption quantities. This implies that households tend to consume more calories from foods that had smaller price increases at the subsequent period. Second, the means are quite similar across provinces, which indicates that, on average, price changes are uniform over the country. In the next section, we use within‐province variations of food price changes in the child‐growth regressions.

Table 2 shows food shares in value and calorie and Laspeyres index by food category. First, we observe that the consumption share of staples in calorie increased from 46 to 58 percent, while the figure in value slightly decreased from 28 to 25 percent. In contrast, the share decreased in calories in fish/meat/tofu/tempe and vegetables/fruits. Consistently, these categories experienced the largest price increase during the period. The table indicates a shift of consumption from nutritionally rich to nutritionally poor foods, pointing to an increased risk of
world (according to the size of their global assets as of December 2002).<sup>5</sup> In particular, we focus on their presence in Latin America and Eastern Europe.<sup>6,7</sup> Table 1 lists the top 100 commercial banks in the world, specifies their country of origin, and indicates the type of operations ― branch or subsidiary ― that these banks have (if any) in Latin America and Eastern Europe.<sup>8</sup> Also, to establish the degree to which each bank has international operations, we report the number of countries where each bank is present. On average, the top 100 banks have operations in more than 15 countries worldwide. Dutch banks have the highest country average, with operations in more than 38 countries. Also, a clear regional pattern emerges from this table. U.S. and Spanish banks are dominant in the Latin American region. German and Italian banks are the key players in Eastern and Central Europe.

The operations of these 100 banks capture most of the foreign bank activity in the host countries in Latin America and Eastern Europe (Table 2).<sup>9</sup> We define a foreign subsidiary as a locally incorporated bank with a foreign shareholder who owns at least 50% of the shares.<sup>10</sup>

> 5 Among the top 100 banks, we did not include banks which were owned (50 percent or more of the shares) by another large bank. For example, Bank Austria Creditanstalt AG and Credit Lyonnais SA were not included in the top 100 list because their main shareholders as of 2002 were Bayerisque Hypo-un Vereisbank AG and Credit Agricole S, both in the top 100 list. The shareholder data structure was obtained from Bankscope.

> 6 In Latin America, we examine international bank operations in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Uruguay. In Eastern Europe,
> 12. The CEPII scenario file contains 10 Doha scenarios, the base tariffs, and the preshock tariffs. The base tariffs are consistent with the GTAP tariffs at the 87-region, 57-sector level, that is, they correspond exactly to the GTAP Version 6.0 tariffs. However, when aggregated to the level of aggregation used in the model, they could differ from the CEPII aggregation if the CEPII trade weights are used because the aggregate tariffs can be quite sensitive to the trade weights. In all aggregations, the GTAP CIF-based trade weights are used to aggregate the tariffs. In the adjustment process, the GTAP weighted CEPII base tariffs are imposed over the GTAP tariffs. 13. See Malcolm (1998).

> 14. See World Bank (2005).

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poverty in a specific dimension may experience more significant changes due to concerted efforts in tackling that specific area.

The second additional explanatory variable is the sum of the headcount ratios of all other indicators in the initial survey year, excluding the indicator under consideration, ∑ ii≠cc the broader context of multidimensional poverty within which a specific indicator is embedded. This HHccii<sup>(tt). This variable captures</sup> could be important because the potential spillover effects from other indicator of poverty can influence the trajectory of poverty reduction in the indicator of interest. High levels of deprivation in other indicators may exert upward pressure on a particular indicator, either through resource constraints or through direct effects of multidimensional poverty. Conversely, in situations where overall multidimensional poverty is relatively low, improvements in a specific indicator may be more readily achieved due to a lower intensity of spillover effects.

By including these variables in our model, we consider the primary factors affecting changes in poverty levels across different indicators, allowing us to isolate the unique contribution of the structure of poverty on multidimensional poverty dynamics.

# Table 1 illustrates the results from our regression models.

In column (1) of Table 1, we present a baseline regression model that includes only the dummy variables, which accounts for approximately 29% of the observed changes in the censored headcount ratio (Adjusted = 0.29). When we add our poverty centrality index (column (2)), we get a significant improvement in the model’s explanatory power (Adjusted = 0.37). More importantly, RR<sup>2</sup> we find that more central indicators usually have larger decreases in the headcount ratio, as suggested RR<sup>2</sup> by our analysis in the previous section.

In column (3), we include the initial censored headcount ratio of the indicator c as an additional variable, while column (4) adds the sum of the headcount ratios of all other indicators, excluding the HH
_Services PTAs_ content of each agreement is coded in 64 variables, across the eight policy areas described earlier in section 1. Yet the role and relevance of the individual variables are likely to be very different; for instance, how to compare the effect of a PTA featuring a positive or a negative list approach with the effect of provisions facilitating the movement of natural persons, or the relevance of a certain type of dispute settlement? To address these issues and to set the WB-DTA data up for meaningful analysis, we propose a way of complexity reduction in this subsection.

Two main approaches have been pursued in the past. Recent works (e.g. Breinlich et al. (2021)) have deployed newly developed statistical techniques to calculate the importance of each variable or provision with respect to, say, international trade flows. These methods are based on ‘machine learning’ algorithms and do not necessitate an _ex-ante_ evaluation of which aspect of a PTA is considered more relevant for the outcome of interest. Prior to machine learning techniques, principal components analysis has sometimes been employed in studies of services trade policies, e.g. Guillin (2013).

A different approach to complexity reduction is to rely to some extent on expert judgement. Rather than letting an algorithm run through all possible combinations of provisions and identifying those with more explanatory power, selection and grouping of PTA provisions is guided by an _a priori_ assessment of the significance of provisions, or combination of provisions, in relation to a certain outcome. Although this approach is more subjective, it has a range of advantages: it leverages the experience of researchers with services trade policies, it is more transparent compared to a black box machine learning algorithm, and—in an attempt to mitigate the inherent subjectivity—it can be set up for placebo tests such that no significant results should be obtained where there ought not to be any effect.

24
analysis of household income, expenditure, poverty, and children’s schooling outcomes. It also describes households’ occupational mobility over time. Section 4 presents the trends in assets, net worth, and indebtedness to examine whether microcredit borrowers are trapped in debt. Section 5 presents the impacts of microcredit on household and individual outcomes based on household FE estimates. Section 6 presents the same impacts using alternate estimation techniques. Finally, Section 7 concludes.

# **2. The Long Panel Survey and Its Data Characteristics**

The survey data used in this study were derived from a long panel survey over 20 years. The first survey round, conducted in 1991/92, studied the role of microfinance in the economic and social upliftment of the poor. Carried out jointly by the World Bank and the Bangladesh Institute of Development Studies (BIDS), the survey covered 1,769 households randomly selected from 87 villages in 29 _upazilas_ (rural subdistricts) of Bangladesh.<sup>5</sup> A second survey round, conducted in 1998/99, could not retrace 131 of the 1,769 households from the 1991/92 survey, leaving 1,638 available households, with an attrition rate of 7.4 percent. The 1998/99 survey also included new households from old villages and newly included villages; in total, 2,599 households were surveyed, of which 2,226 were from old villages and 373 from new ones. Among the 2,226 households in old villages, 279 were newly sampled ones and 1,947 were from the old panel surveyed in 1991/92. There were more panel households surveyed in 1998/99 (1,947) than in 1991/92 (1,638) because some from the old panel left after the first survey to form new households.

The households were surveyed a third time in 2010/11 with the help of the Institute of Microfinance (InM). This third survey round tried to revisit all of the households (
ceptions such as Ukraine and Russian Federation (and to a smaller degree Armenia), where the adverse economic conditions in recent years, and military conflict in the case of Ukraine, are reflected in the sharply lower expectations with respect to future socio-economic mobility. In other words, there is a considerable degree of stability in the region with respect to the beliefs about equality of opportunity; similarly, even though data from the 2010 round of the survey were collected in the aftermath of the financial crisis, which may have altered individuals beliefs about mobility (in the empirical analysis I will account for this explicitly), these beliefs also appear to be reasonably stable rather than transitory.

Having these inter-temporal patterns in mind, the estimates from the empirical specification in (1) are presented in Table 4. The results are presented for the pooled sample of countries, and also, separately, for the European Union member states and Non-EU countries. To some degree this distinction between EU and non-EU countries is arbitrary. The results are reported for these country groupings for the following reasons: (i) because this is a standard division of countries in publications based on LiTS survey data, inclusively in the publications by EBRD that rely on LiTS data; (ii) given the focus on institutional aspects, the distinction is meaningful analytically in the sense that in EU countries formal institutions may be stronger, and similarly in non-EU countries reliance on informal institutions (such as connections, for instance) may be more prevalent. I will return to this below when I discuss the use of connections.

Column (1) of table 4 presents the estimates of regressions for the pooled LiTS sample. Both hypotheses appear to be rejected by the data. First, when there is inequality of access to opportunities, connections matter ( _δ_ 2 + _δ_ 3 _<_ 0), meaning that expectations of future mobility are lower in the group that views connections to be vital but does not have any connections, in comparison with the group that similarly views connections to
asked to sign a contract in front of a witness from the chamber, and were told that their reputation with the chamber would be affected by any non-payment. The signing of the agreement took place at the chamber itself before the chamber’s lawyer who also undertook the legal registration work. Second, they were told that the ability of KCG Equity to make future investments in other businesses in the community would depend on their performance and repayments. Third, it was emphasized that keeping several years of revenue-sharing records would provide the necessary information to make a persuasive case to banks or other financial institutions for subsequent financing needs.

We conducted awareness programs for groups of candidate firms identified and invited by the chamber, and explained the details of the micro-equity contract. Interest in this financial product 16