rafmacalaba/data-use-mentions-extended huggingface dataset:
report the change in inequality and poverty from 2019 to 2020, which can be thought of as a gross effect.

# **3.3 Estimating the observed 2020 welfare distribution**

## **(i) Household surveys**

Microdata from household surveys are available for 20 countries in 2020. Twelve of these are in Latin America, a further six in Europe and Central Asia, and two in East Asia. These countries account for 14% of the global population and 78% of the population in Latin America (Table 1). While having a household survey is clearly superior to the other data sources used in 2020, it is important to note that changes in survey design imply that these surveys are subject to greater than usual uncertainty in 2020 (for detail see Castaneda et al. 2022).

> 14 An alternative approach would be to anchor the income or consumption from household surveys to national accounts making use of other sources of data (such as tax data from fiscal authorities). For a discussion of such methods, see Piketty et al. (2018) and Lustig (2020). Note also that Lakner et al. (2022) propose a global pass-through rate of 0.85 independent of the welfare measure used. However, they also find a consumption-specific pass-through rate of 0.72. This paper adopts pass-through rates differentiated by welfare aggregate from Mahler et al. (forthcoming) who have an updated sample compared to the previous study.

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Euro Periphery|184|315|370|6|0|1|
|Emerging Latin America|61|34|32|10|13|15|
|Emerging Asia|46|37|36|29|36|43|
|New Member States|46|71|84|21|20|20|
|Candidates and Potential|46|47|51|18|22|19|
|Eastern PartnershipCountries|67|46|67|12|21|23|

Source: Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: These tables report international debt liabilities and foreign exchange reserves.

Tables 3.4a to 3.4g provide further insight into the gross external debt positions of these countries. Table 3.4a shows total cross-border loans from BIS-reporting banks. Bank loans grew very rapidly between 2002 and 2007, especially for the Baltic States. Since 2007, there has been a degree of deleveraging for many countries. Table 3.4b isolates the cross-border loans made to non-banks: these are only a small proportion of total cross-border loans for most countries. Rather, cross-border loans are typically intermediated via the domestic banking system.

In relation to foreign reserves, the Euro Periphery held very low levels of foreign reserves, in line with membership of a common currency area. While Emerging Europe holds more foreign reserves than Emerging Latin America as a ratio to GDP, the level is far below Emerging Asia. While the Eastern Partnership countries did increase reserve levels between 2002 and 2009, this was not the case for the other regions in Emerging Europe, despite the rapid growth in gross external debt liabilities.

Table 3.4c reports the stock of international debt securities issued by each country. It shows that this is not a major source of external debt financing, with the exceptions of Hungary, Lithuania and Slovenia. As is shown in Table 3.4d, it is striking that the non-banks are the 12
product is produced or sourced from (origin locations) and where it is finally sold by retailers (destination locations) typically summarizes the contribution of transport costs to overall trade costs. However, there may be other variables that can be correlated with distance which can also affect trade costs, such as the extent of competition between logistics operators, distributors or retailers (Atkin and Donaldson, 2015) or within-country “border-effects” (Borraz, Cavallo, Rigobon and Zipitria, 2016).

In this paper, we follow the price-differential approach and apply the methodology developed by Atkin and Donaldson (2015), which aims at estimating trade costs while taking into account the possibility of imperfect competition among intermediaries, controlling for spatial variation in markups. We find that intra-national trade costs in our sample of countries are between 2.5 and 14 times larger than previous estimates for the U.S. by Atkin and Donaldson (2015) using the same methodology.

This paper relates to a growing literature that attempts to estimate intra-national trade costs. Given its historically high transport costs, Africa, and in particular SubSaharan Africa, has been the focus of several papers studying trade and transport prices. Bougna et al. (2020) estimate intra-national trade costs in Rwanda using data on rural markets and find them to be 10 times higher than in the U.S. They also find that markups are higher in locations with fewer traders. Mayneris, Martin and Theophile (2020) document spatial differences in the cost of living across Ethiopian cities and find that more remote cities show significantly higher prices, less product availability, and a higher cost of living. Porteous (2019) takes a different methodological approach to Atkin and Donaldson (2015) to estimate intra-national trade costs in Sub-Saharan Africa. The paper focuses on staple cereal grains traded between 230 large regional hubs in 42 countries, and quantifies trade costs by estimating a structural model that features competitive traders (i.e., abstracting from markups), grain consumption, storage, and trade. The
20.0 min<br>15.0<br>1980s 1990s 2000s<br>(e) Since 1990 Latin American inequality has been (f) ... and eventually converged towards the highest in<br>declining. Meanwhile, Colombia‟s Gini increased... the region.<br>0.650 0.65<br>Maximum<br>0.600 Colombia 0.60 Maximum<br>Colombia<br>Colombia<br>Colombia Colombia<br>0.550<br>Median 0.55 Median<br>0.500 Average Average<br>0.50<br>0.450<br>0.45<br>0.400 Minimum Minimum<br>0.40<br>2005 2006 2007 2008 2009<br><!-- End of picture text --> Source: UNDP Human Development Indicators based on WDI data: (a) and (c). World Bank (2011): (b). WDI: (d). ECLAC database: (e) and (f). Note: The relative ranking of Colombia and Bolivia depends on the data source.

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log) expenditure on assets in past 1<br>month|
|3|Savings, investment<br>and debt index|Standardized weighted average of having or sharing a formal bank account,<br>currently saving any money, (log) amount held at bank account, (negatively<br>coded) number of formal loans received, (negatively coded) other debt,<br>(negatively coded) number of informal loans received in the past 1 month,<br>(negatively coded) (log) total amount of formal loans, (negatively coded)<br>(log) total amount of informal loans, business ownership, participation in<br>training during the past 12 months, number of trainings done in the past 12<br>months|
|4<br>_Psyc_|Business skills index<br>_hological and behavioral_|Standardized weighted average of frequency of visiting competitors,<br>frequency of asking customers about other products they would like to be<br>sold, frequency of setting sales targets, frequency of comparing targets to<br>performance, frequency of recording purchase and sales, knowledge of the<br>business register, knowledge of fees to register a business at cashier’s<br>office of the Business Register, knowledge of operating license from State<br>government, knowledge of inspections from payam authorities, knowledge<br>of taxes, knowledge of bribes (rashua), knowledge of paying an<br>intermediate person to take care of taxes, registration of company name at<br>business register, registration at cashier’s office of the Business Register,<br>obtainment of operation license from the State government, experienced<br>inspection by payam authorities, payment of formal taxes, payment of<br>bribes (rashua), payment of intermediary person to take care of taxes<br>_outcomes_|

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transit through ports remain in the container terminal much longer than customs formalities or physical transfers would need.

The main reason for this is that the container terminal is in fact a warehousing option for cargo owners and is therefore used to meet some of their temporary or long term storage needs. These needs derive from a set of constraints - such as availability of cash flow, warehousing capacity outside the port, current inventory levels or cargo time sensitivity - as well as from strategic choices on risk exposure or inventory coverage that we will refer to as inventory management decisions. For example a shipper may leave his cargo in the yard because he has got no other warehousing possibility outside the port or he may leave it there because this would delay the payment of charges and duties and reduce hence temporarily the pressure on his cash flow. For the purpose of the study we will then define a **discretionary dwell time** as the sum of all idle times between vessel arrival and exit from container yard that are strictly storage times (no clearance process or handling operation is performed).

The complexity of research on dwell time is that these three constituents of total dwell time – operational dwell time, transactional dwell time and discretionary dwell time – are actually intertwined and overlaid. Yet we can proceed to a thorough analysis of total dwell time by taking two complementary approaches:

- The independent analysis of each of the three components defined here before to identify the structural bottlenecks and improvement prospects

- The analysis of interrelationships between the three dwell time constituents using shipment level data and the identification of behavioral patterns

<!-- Start of picture text --> Vessel<br>Transfer<br>Vessel berthing to the Delivery Exit<br>Operational arrival & onto from<br>dwell time at buoy container yard or ODCY truck yard<br>discharge<br>Domiciliation<br>Establishment Payment of NVOCCs, port<br>Transactional & Pre- and Lodging authority & inspection bodies<br>dwell time shipment of Customs<
in DDI for fully liberalized non-commodity sectors following the reform, although there seems to be a decrease in manufacturing DDI when er account for Covid-19, suggesting that DDI was less resilient and may have faced some crowding out in the manufacturing sectors, but not in fully liberalized sectors. The latter is most relevant in regard to the policy analysis thus the liberalization policy may have been a more crucial factor in driving investments.

Following this, it can be concluded that despite the Covid-19 restriction, the effect of the policy reform remained important, and these results are not necessarily being impacted by economic recovery in 2021.

Table 8: Robustness Check- Possible Effects of Covid-19 Recovery on Realized, Planned FDI and DDI

||FDI||Planned F|DI|DDI||
|---|---|---|---|---|---|---|
|Manufacturing Sector (1vs0)|0.266***||0.190***||-0.124***||
||(0.0244)||(0.0225)||(0.0361)||
|100% Liberalized (1vs0)||0.0712***||0.243***||0.114***|
|||(0.0169)||(0.0169)||(0.0315)|
|Observations|168,329|122,670|141,937|104,328|168,329|151,926|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects and controlled
Source: Data from Table 1 The probability of shadow employment estimated according to the basic definition is, by design, strongly dependent on the form of contract declared by the respondent in LFS. It significantly increases if the declared employment contract is fixed term. The share of such contracts in Poland was strongly increasing in years 2003-2006 whereas in 2007-2008 it started to stabilize and then decrease. The measured average probability of shadow employment tends to follow the same path.

It seems that both of these trends are related to the general developments in the Polish labor market. One has to remember that in 2003 the unemployment rate in Poland was very high reaching on average 19.5%. Then it started to decrease slowly reaching 17.8% in 2005 and 13.8% in 2004. Then the pace of unemployment rate decrease accelerated even further falling to only 7.1% in 2008. It seems that the sharp increase of the number of fixed term contracts was one of the deciding factors in the gradual fall of unemployment rate in Poland between 2003 and 2005. The labor market situation was still difficult and workers were still very likely to accept fixed term jobs. At the same time, companies did not have to compete for workers with better working conditions. After 2006 when the labor market situation started to improve very dynamically, the companies started to offer them better (more stable) working conditions and workers were more reluctant to accept fixed term jobs. It is logical to assume that similar patterns of behavior exist also in shadow employment which by most of related literature is assessed as counter-cyclical.

On the other hand, the shadow employment proxy constructed without the contract variable does not have any explanatory variable. It shows that as unemployment was falling, more people with low employability started to get jobs. Additionally more jobs were also created in services and construction sectors – traditional hubs of shadow employment. Hence this proxy seems to be naturally ―pro-cyclical‖ – i.e. it increases as the labor demand grows.

It seems
equation (2) below and the second stage is equivalent to the model presented in equation (1) above.<sup>16</sup> _Foreign Bank Presencei,j,k=_ α _0 +_ β _1 Parent Bank Characteristicsi,j +_ β _2 Home-Country Regulationsj+_ β _3 Affiliate Bank Characteristicsi +_ β _4 Host-Country Factorsk+ Home-Host Proximityj,k+_ ε _i,j,k (2)_ where _Foreign Bank Presence_ i,j,k is a dummy equal to 1 if parent bank i from home-country j has any kind of operations in host-country k, and 0 if it does not have a presence in the host-country. Most variables included in equation (2) have been defined above, with the following three exceptions. First, within the parent bank characteristics, _Parent Bank Worldwide Activity_ takes into account the number of countries worldwide where each parent bank has activities (data source: Bankers’ Almanac). Second, among the regulatory variables, _Host-Country (inward) Foreign Investment Regulation_ and _Home-Country (outward) Foreign Investment Regulation_ capture government controls on inward and outward direct investment in banking, respectively. Both variables are constructed as a five year average (2002-1998) from original IMF data.<sup>17</sup> Finally, _Home-Host Proximity_ is a matrix of variables that capture the degree of economic, cultural, and institutional affinity between the home and host countries. In particular, it includes separate dummies for whether the host and home-country share (i) a common language and (ii) a

> 16 An alternative approach would be to estimate a nested logit model where the first decision level is to enter a country or not and the second is whether to operate as a branch or as a subsidiary. The problem with this approach is that we could not study the impact of affiliate characteristics on the second level decision because these factors are not observed for cases when a bank has no operations in a given host. Despite of this limitation, results
and Kim 2007). In this case, rule of thumb error depends on transaction frequency and regularity; less frequent items are likely reported with more error. Whereas recall error biases the consumption estimate downward, and telescoping creates upward bias, there is no obvious direction of bias in responses that resort to the rule of thumb instead of enumeration. We may expect this error to be especially pertinent in hypothetical consumption constructs such as questions about consumption during a usual month. Usual month consumption is an explicit attempt to abstract away from seasonal considerations in consumption; however, this type of question may pose additional cognitive demands relative to a definitive recall period in the immediate past.

- _Personal leave out error_ . Yet another source of reporting error is the inability to capture individual consumption by household members accurately if it occurs outside the purview of the survey respondent. This may be more significant for certain types of food, such as snacks or meals taken outside the home, or for personal goods such as mobile telecommunications. The degree of inaccuracy is likely to increase with the number of adult household members and with the diversity of the activities of these members outside the home (World Bank 2006).

- _Other error types_ . While the analysis in this study focuses on the four types of reporting error listed above, misreporting can also arise from other sources, such as rounding error, social desirability bias, and strategic responses. An example of the last is a respondent who understates her consumption to appear poorer because of a belief that these responses may determine the eligibility for some future social program. There may also be intentional misreporting because of respondent fatigue. So, whether

> 1 A consumption survey is a household survey that collects detailed consumption data. It has a range of labels, such as household budget survey, living standards survey, or household income, consumption, and expenditure survey. 2 See Beegle et al. (2016) for an extensive discussion of this issue in Sub-Saharan Africa.

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access for Chile is sufficient to make entry into NAFTA desirable. Their conclusions regarding the effects of entry in to MERCOSUR (with improved market access to partner countries) were mixed, with gains arising under some elasticity assumptions but not under others.'

# III. Conclusions

This paper has presented an index for aggregating tariff data to use in the analysis of preferential trading arrangements, and illustrated how the index can be combined with a general equilibrium model of the domestic economy that can be run on an EXCEL spreadsheet. The calculations for the case of Chile suggest that the index is simple to calculate due to its recursive structure, which allows large amounts of detailed tariff line data to be aggregated to be used with domestic production data which is only available at a much more aggregated level. It was also found that results using the tariff aggregators may differ substantially from those obtained using simple averages of tariff reductions. For example, the reductions in import prices using the index were ranged from 10-30% larger than those calculated using a simple average of tariffs, depending on the assumptions made regarding elasticity of substitution between source countries. Furthermore, there were substantial differences between the uniform tariff rate equivalent of the import price reduction and the uniform tariff rate equivalent of the average tariff rate for industries. This means that ignoring the information available in tariff line data could result in a substantial overestimate of the average tariff rate on imports when a preferential reduction is made.

The use of this tariff index data in the spreadsheet model for Chile indicated that it is possible to set up a general equilibrium model using 9 domestic sectors with a CES specification of technology and preferences that can be solved easily on an EXCEL spreadsheet. The spreadsheet model produced plausible results concerning the effects of trade liberalization on domestic goods prices and factor returns, and indicated that the welfare effects of the proposed trade agreements would have aggregate welfare consequences. In particular, the direction of welfare change depends on elasticities 24
growing literature documenting

> 2In May 2020, the Future of Business (FoB) survey was adapted from its standard bi-annual approach to run six monthly survey waves to provide timely insights on the impact of the COVID-19 pandemic on small businesses and to take the pulse of their needs and challenges.

> 3High-frequency survey data was collected in the last week of every month throughout 2020 from May 2020 to October 2020, and September 2021 at a time when traditional data collection systems were disrupted.

> 4Domestic responsibilities include household chore activities such as cooking and cleaning.

> 5School closure policies related to COVID-19 are taken from data collated by UNESCO that indicates whether a country was fully open, partially open, on an academic break or closed due to COVID-19 over time (UNESCO, 2020 and 2021). Mobility data is from Google mobility data which records the change in mobility relative to pre-COVID-19 pandemic levels (Google, 2020 and 2021).

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non -OECD than is the case with the OECD although the amount of overlap is quite large.

_Years of Education:_ Recent literature stresses the complementarities between technological progress and education. In Lucas (1988), for instance, knowledge spillovers involve interactions among smart people (see also more recently Acemoglu and Zilibotti 2001). We employ the standard measure of the quality of human capital, from the Barro and Lee (2000) data base, namely the average years of schooling at the beginning of each five year period.

_Perceived Quality of Academic Institutions_ : Low quality researchers, or even good quality researchers housed in institutions with poor incentive systems are less likely to produce patentable innovations for a given allocation of resources and would be less likely to detect possibilities for building on other individuals’ discoveries. The World Economic Forum’s <u>World Competitiveness Report</u> (2000) provides the average of the subjective rating by entrepreneurs on a 7 point scale of the perceived quality of their research institutions.

_Level of Collaboration between the Research Institutions and the Private Sector:_ The literature increasingly stresses the importance of networks and collaboration across the various elements of the NIS as essential to the efficiency of generating new ideas, and to building on existing ones. Again, the World Competitiveness Report asks entrepreneurs to rank on a seven-point scale the level of collaboration among the private sector and research institutions.

_Intellectual Property Rights (IPRs):_ IPRs, in theory, can reduce the flow of spillovers by making it more costly to create new inventions building on others’ discoveries. On the other hand, weak intellectual property protection within a country may lead firms to simply hide their discoveries preventing spillovers all together. Thus, the protection and revelation aspects of IPRs have countervailing effects with unclear ex ante impact. The measure is taken from Park (2001) who combines indexes on patents, copyrights and 17
China|2,610|28|23|
|HUN|Hungary|3,025|28|27|
|IND|India|2,993|28|25|
|IRL|Ireland|3,086|28|27|
|ISL|Iceland|1,223|28|18|

5 This harmonizes bilateral FDI from the IMF CDIS database, the OECD BMP4 database and China’s Annual Yearbook. Because aggregate and sectoral FDI positions are not directly comparable, we estimate each country’s share of aggregate FDI stock that arise from the countries that report on sectoral FDI (i.e. those in table 2).

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