rafmacalaba/data-use-mentions-extended huggingface dataset:
Therefore, population projections made by the United Nations Population Division are used, as well as administrative information from the IESS in relation to current coverage levels per kind of benefit, number of contributors, wage levels and benefit amounts, all of which per simple age and gender.

Long-term projections are necessary for assessing how a system would react towards expected changes under given economic, political and demographical conditions. These are not predictions "to forecast future", but they allow assessing system perspectives, given certain reasonable hypotheses on longterm evolution of some determining variables. Likewise, this exercise allows assessing the impact of alternative hypotheses and/or reforms to current policies on coverage and social protection financing.

The simulated scenario in this work considers the current rules in terms of model patterns (contribution numbers, contribution rate, minimum legal age of retirement and both passive and active coverage levels). In particular, for simplicity reasons and without compromising generality, this work assumed that retirement age is 60 years of age considering 30 years of contributions. This combination of age and contribution years results in a replacement rate of 75%. Also, the current contribution rate is established to equal 8.2% as personal contribution and 1.3% as employer's contribution. In terms of system performance, it is assumed that the population percentage, by simple sex and age, contributing to social security remains stable in the long term. Finally, benefits are adjusted based on the current policy, depending on simulated inflation in the country.<sup>8</sup> Based on current parameters of the Ecuador pension system, the number of beneficiaries would shift from 340,000 in 2019 to 680,000 by 2040 and to 2.7 million by 2080 (Figure 15). On its part, the number of contributors also shows an increasing trend, though of lower volume, up to the 2060s to remain relatively stable at approximately 5 million. As a result, the system sustainability rate would present a constant decreasing trend from the
or relative factor costs to export final goods elsewhere. The original motivation for these authors was the emergence of the so-called “new trade theory,” which argued that economies of scale can explain the rise of intra-industry trade during the second half of the twentieth century. They argued that an uncomfortable fact about intra-industry trade was that it was dominated by MNEs. Be that as it may, our interest here is solely due to the contrasting predictions regarding trade flows of the two motivations for FDI: vertical FDI is more likely to result in new international trade flows, especially exports from the host country, whereas horizontal FDI seeks to overcome international trade costs by shifting production and sales into the host country. Hence, if exchange rate devaluations affect FDI, the nature of this new FDI will thus affect the extent to which exchange-rate changes affect exports in a reduced-form model.

# **III. Data**

Quarterly data on REERs, FDI and exports of goods and services come from databases maintained by the International Monetary Fund (IMF). The REER is defined by the IMF as the trade weighted real exchange rate, based on the ratio of indexes of consumer prices between pairs of trading partners.<sup>7</sup> The series cover

> 6 Krugman’s paper was widely circulated about three years prior to its publication in an edited volume in 2000. However, Blonigen (1997) predated the Krugman’s working paper.

> 7 An alternative index of real exchange rates often used in the literature is the ratio of price indexes found in the Penn World Tables, in which the United States is the benchmark. However, these data are available only in annual frequencies, not quarterly, which might be a significant disadvantage for the empirical analysis in this case. However, it is worth noting that using annual averages of the REER index and the annual data from the Penn World

5
# **Table 5. R-Squared Shapley-Owen Decomposition for Ecuador** **_Estimations Using Individual-Level Data_**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|_Outcomes:_|**Ever**<br>**employed**<br>**after**<br>**graduation **|**Percent**<br>**of time**<br>**employed**|**Wages**|
|_Percent of explained variation attributable to:_||||
|All quality determinants|35.00|37.64|32.06|
|Infrastructure|-|1.98|-|
|Costs|-|0.84|-|
|Training and curriculum|15.53|12.39|12.45|
|Faculty|9.46|15.81|10.08|
|Link with productive sector|8.78|5.83|8.09|
|Other Practices|1.23|0.80|1.44|
|Student characteristics (PCA Score)|1.71|1.42|1.52|
|Program characteristics (PCA Score)|2.27|3.65|2.49|
|HEI characteristics (PCA Score)|6.52|2.75|6.32|
|Student administrative variables (PCA Score)|19.90|27.60|22.99|
|Peer administrative variables (PCA Score)|20.01|17.48|21.04|
|Field of study fixed effects|14.59|9.47|13.59|
|R-squared|0.265|0.354|0.282|
|Obs
|World Bank region dummies|Y|Y|Y|Y|
|Year dummies|Y|Y|Y|Y|
|Sector dummies|Y|Y|Y|Y|
|World Bank income level|Y|Y|Y|Y|
|dummies|||||
|Constant|Y|Y|Y|Y|
|Observations|3,383|3,381|3,728|3,371|
|Number of firms|570|570|590|567|
|Mean of dependent variable<br>_Note_: EBITDA:Earnings before in|2.098<br>terest, taxes, depre|1.590<br>ciation, and amorti|18.84<br>zation. ROAA: Ret|6.526<br>urn on average assets.|

_Note_ : EBITDA: <mark>Earnings before interest, taxes, depreciation, and amortization. ROAA: Return on average assets.</mark> Standard errors in parentheses. Errors are clustered by strata derived from the Coarsened Exact Matching procedure. * _p_ < 0.1, ** p < 0.05, *** _p_ < 0.01.

Table 3 reveals two interesting findings. First, it shows that SOEs that receive subsidies perform better than firms that do not. Firms that receive subsidies have no significant difference in ROAA relative to private firms. Netting out the subsidies and comparing private ROAA with SOEs’ adjusted ROAA, however, shows that SOEs that receive subsidies perform 3.8 percent worse than private firms—a large difference relative to the sample mean of comparable ROAA of 1.5 percent. This finding means that subsidies are covering up the unprofitability of SOEs by making up for their losses. Similar results are evident when looking at EBITDA to revenues and EBITDA to assets (columns 3 and 4). If we view EBITDA to revenues and EBITDA to assets as a measure of efficiency, then it is clear
**Figure 14 Fixed-line telecommunication services: Change in STRI by level of income**

<!-- Start of picture text --> Sector: Fixed-line telecom<br>BGD EGY CRI URY OMN<br>LKALKA<br>CHNCHN<br>CAN<br>IND PHL KAZ<br>MEX<br>KOR CAN<br>PAKIND CHL<br>BGD PAK VNM UKR BRA KAZ POL<br>IDNIDN PER COL ARG CHL RUSHUN NZL<br>KENKEN NGANGA ECU TUN ZAF THATHA MEX PAN TUR MYSMYSRUSHUNLTULTUPRTPRT GRCCZE KORESPESP ITAJPNFRA GBRGBRFINFIN BEL OMN DEUAUSAUT SWESWEDNKDNK NLD USA IRLIRL<br>ECU<br>DOM TUR<br>8 9 10 11<br>Avg income per capita (2008-16)<br>Note: STRI 2008-11 in blue, STRI 2016 in orange.<br>100<br>80<br>60<br>STRI 2008-11, 2016<br>40<br>20<br><!-- End of picture text -->

# <u>Mobile telecommunication services</u>

As indicated above, there have been fewer changes in the mobile telecommunications services sector, where all economies in the sample were open to foreign establishment in the mobile segment even by the end of the previous decade. Only a small number of countries had foreign equity limits. Some of those were relaxed (Canada, Korea as described above) or removed (in Malaysia and in India, subject to government approval beyond 49%) between 2008 and 2016. Hungary now allows greenfield investment. Colombia and India have relaxed the limits on the number of suppliers.<sup>19</sup>

**Figure 15 Mobile telecommunication services: Change in STRI by income group**

<!-- Start of picture text --> Sector: Mobile telecom<br>L LM UM H<br>STRI 2008-11 STRI 2016<br>50<br>40<br>30<br>20<
total number of apartments was privatized. By the end of 1994, half of the households surveyed in Moscow still lived in state-owned, municipal or enterprise housing.<sup>6</sup> The factors that influence households' privatization decisions may be broken down into four groups, each of which will be examined in turn:

- value of the property;

- demographic characteristics of the occupants;

- households' value orientations;

- the household income.

a. _The value of the property_ (here, a separate apartment or a room in a communal apartment) is determined by its market price. A very simple assumption is made: the higher the market price, the stronger the incentive to privatize. In turn, the market price of a dwelling depends on its quality and location.

Table 1.6 shows the dependence of the market price of housing on its caltegory and location. The figures are based on realty companies' data on the characteristics and cost of apartments put up for sale in April 1995.' 6 n this exaination ofthe factors inuencing housing privtization primarily data fron the tlhird wave ofthe Moscow Longitudinal Household Survey (Deebe 1994) have been used.

' Using infornation supplied by reahy companies _(Nedvizhimost, 1995;Modus,_ 1995), **which** includes qualitative charcteristics and maket value estimates for 1,900 Moscow apartnents put up for sale in Apfil 1995, we estimated the value of each ofthe 2,250 apartments covered by the sociological survey (MLHS, 1994). The Decenber 1994 data on the householdl units and the April 1995 data on the housing market prices are highly comparable for purposes ofthe present analysis. With this end in view, we developed an iteration procedure and a computer program to identify all aparments in the realty companies' database that matched the MLHS, 1994 apartments by qualitative parameters: number of rooms, area of geneal and hving space, category of dwelling wall material, etc., as well as by location, which was detenmined by small
**Microcredit is concentrated in a relatively small number of leading institutions.** The top 5 MFIs in MENA represent 43 percent of total outreach, and 3 of these are from Morocco (Annex A2.4 has a country breakdown by borrowers and by loan portfolio). 70 percent of microfinance institution (MFI) branches are in Morocco.  Microcredit is not well developed in the Gulf Cooperation Council (GCC) countries, where only Bahrain and Saudi Arabia have microfinance institutions that report to the regional microfinance network (Sanabel).

**After years of overall slow growth, the rate of growth of the microcredit sector in MENA finally started to catch up with global growth rates between 2006 and 2008.** Worldwide growth rates recorded by the Micro Banking Bulletin<sup>7</sup> were 21 percent for borrowers and 35 percent for portfolio, whereas for the MENA region they were 19 percent and 30 percent respectively.  Outreach contracted in 2009 by 3 percent (78,785 borrowers), although total loan portfolio increased marginally, driven primarily by a decline in the size of the Moroccan microfinance sector. This was a consequence of reconsolidation and losses following the microfinance over-indebtedness crisis in Morocco, which is explored in more detail in Annex 5. The total number of microcredit borrowers in Morocco more than doubled from 2005 to 2007, but then fell by almost a third from 2007 to 2009.  Growth rates for individual Arab countries varied, with the highest rates in 2009 recorded in Iraq (45 percent) and Lebanon (37 percent). (see Annex tables A2.1, A2.2 for growth rates by country).

**Microcredit in MENA reaches relatively poor clients compared to other regions** (except Asia), and a reasonably high proportion of women, at 63 percent of total microcredit borrowers (although only 31% employees of Sanabel member MFIs are female). The proportion of women microcredit clients ranges from as low as 18 percent in Iraq, where female workforce participation has declined
- Tanner, J. M., Whitehouse, R. H., and Takaishi, M. (1966b). Standards from birth to maturity for height, weight, height velocity, and weight velocity: British children, 1965. ii. Archives of Disease in Childhood, 41(220):613635.

- Thapa, D. and Sijapati, B. (2004). A kingdom under siege : Nepal's Maoist insurgency, 1996 to 2003. The Printhouse ; Zed Books : Distributed in the USA exclusively by Palgrave Macmillan Kathmandu : New York, updated ed. edition.

- Uematsu, H., Shidiq, A. R., and Tiwari, S. (2016). Trends and drivers of poverty reduction in nepal : a historical perspective. Policy Research Working Paper Series 7830, The World Bank.

- Valente, C. (2014). Education and Civil Conict in Nepal. World Bank Economic Review, 28(2):354 383.

- Valente, C. (2015). Civil conict, gender-specic fetal loss, and selection: A new test of the triverswillard hypothesis. Journal of Health Economics, 39:31 50.

- Vogl, T. S. (2014). Height, skills, and labor market outcomes in mexico. Journal of Development Economics, 107:84 96.

32
21.99|Businessman|1.57|
|Primary school|14.32|Ex-security personnel|0.01|
|Literate|15.05|Other, not clear|3.56|
|Illiterate|12.47|||

Source: Author's calculation based on the INSEC's archive on the conict victims (http: //www.insec.org.np/victim/). Note: While classes 8 to 10 are dened as secondary school, 6 and 7 are lower secondary school. Nepali Congress (Democratic) and Nepali Congress are combined as one as the former was formed due to a vertical split of Nepali Congress into two in 2002. However, the parties merged into one in 2007. Similarly, Communist Party of Nepal - Marxist Leninists (CPN-ML) was reunited with the Communist Party of Nepal - Unied Marxist Leninists (CPN-UML) in 2002 but few members refused to go along the merger forming a new party with the same name. Party's sister organizations and student wings are also accounted for while assigning party aliation.

39
small in magnitude to be of importance. Distance from the market is statistically significantly associated with lower normalized losses, but the magnitude is inconsequential.

Perhaps the most interesting results in Table 5 are those related to wealth. When using levels (columns 1-2), losses appear to be positively related to wealth. When using percentages (columns 3-4), losses are negatively related to wealth. This pattern suggests that there may be different types of loss-prone households—wealthy households that suffer large losses in levels but small losses as a percentage of total spending, and poor households that suffer small losses in levels but large losses as a percentage of total spending. In some of the analysis to follow we distinguish between households that are loss-prone in levels only, percentiages only, both, and neither, where a household is “loss-prone” if it is in the highest quartile of losses. Section S4 in the Appendix contains more details on this categorization.

# **5 What explains the observed purchasing patterns?**

We now turn to hypotheses about what might lead households to engage in financially inefficient purchasing by foregoing bulk discounts. We consider a number of possibilities: binding liquidity constraints, utility from frequent shopping, transport costs, storage costs, ignorance of bulk discounts, purchasing in small quantities as a form of self- or other-control, avoidance of social taxation, and coordination costs within the household. These hypotheses arose from three sources: discussions with individuals and focus groups in Tanzania (described in Section 3), our own hypothesizing based on the literature or our knowledge of the context, and suggestions from colleagues or seminar participants.

Our analysis uses observational data, so it is not possible to say that a mechanism is relevant or irrelevant for all households. Rather, we assess which mechanisms do or do not seem to have a substantial impact on the losses that we observe. Our findings provide an important check on certain hypotheses, and should be especially useful as a guide to steer future research.

Because it is important for assessing
_The Optimality of Being Efficient_ _4_ Treasury auctions, where resale is especially important. Bidding behavior is significantly affected by resale. Agastya and Daripa (1998) also focus on Treasury auctions, emphasizing the interaction of the futures market, the auction, and resale. Haile (1998), using a reduced-form representation of the resale market, characterizes equilibrium bidding behavior in standard single-good auctions. Haile (1997) examines resale in a setting where bidders acquire additional information after the auction. Haile (1996) empirically tests the model using U.S. Forest Service timber data. Horstmann and LaCasse (1997) consider resale by the seller to a potentially different set of bidders. Our paper is most closely related to several recent studies of optimal auctions with multiple goods. For example, Krishna and Perry (1997) find conditions under which the Vickrey auction is optimal among efficient mechanisms. Armstrong (1997) is also interested in when an optimal auction is efficient. He shows that with two goods and two types, the optimal auction is always efficient (this result, however, does not generalize to three types). Avery and Hendershott (1998) analyze optimal bundling in a multiple objects setting.

Our paper is organized as follows. In section 2, we establish the seller's general incentive to misassign goods and we identify settings where the optimal auction is efficient. In section 3, we solve two variations on the optimal auction, which recognize the possibility of resale. Section 4 proves that perfect resale destroys the seller's incentive to misassign goods. Section _5_ establishes tiit, with perfect resale, any misassignment of goods results in strictly lower seller revenues than the best efficient assignment. In section 6, we show that the Vickrey auction is not distorted by the possibility of resale.

# **2 THE INCENTIVE TO MISASSIGN THE GOOD**

There are two ways an optimal auction can fail to be efficient: (1) the seller can withhold some quantity; and (2) the seller can award quantity to
A country starts to impose the reference lockdown when the infected population reaches 2.6%. This rate is based on the COVID-19 prevalence at the time of the first lockdown in the Italian municipality of Vo, the site of the first COVID-19-related death detected in Italy (Lavezzo et al., 2020). In our calibration, the countries start to impose the reference lockdown policy between week 9 and 13, with an average start date at week 11.

The length of the reference lockdown policy is based on Flaxman et al. (2020) that estimates the impacts of non-pharmaceutical interventions in 11 European countries during the first months of the pandemic.<sup>15</sup> We drop the four countries that only imposed mild or no lockdown policies (Denmark, Norway, Sweden, and Switzerland) and work with the remaining seven countries.<sup>16</sup> We compute the lockdown length for each country based on the difference between the reported lockdown date and the end of the sample period in Flaxman et al. (2020). The lockdown policies range between 43 to 54 days, with an average of 7 weeks, which we use as the length of the reference lockdown. The strength of the lockdown was inferred from the GDP decline in the first two quarters of the year 2020. As explained later in this section, we calibrate _θ_ = 1, which implies that a lockdown policy of _μ_ ̄ reduces aggregate ̄ labor supply and GDP by _μ_ . Therefore, an _x_ -day lockdown reduces the two-quarter GDP by _xμ/_ ̄ 180, from which one can infer _μ_ ̄, conditional on the length of the lockdown calibrated ̄ above and the observed decline in GDP. For example, the inferred _μ_ in Germany with a 6 _._ 68 percent decline in GDP and a 43-day lockdown is 0.0668*180/43 = 0.2796. We repeat the calculation for all seven countries and find that the strength of the lockdown policy to be between 28
unavailable for a small subset of exporting ...rms, which are therefore excluded from the ...nal sample used in the empirical analysis.

Importing countries. We have supplemented the ...rm data with information on each importing country, namely its real GDP (measured at PPP), GDP per worker, and the distance between its most populated city and Lisbon (measured in Kms). The source of the information on distance is CEPII. The remaining variables come from the World Development Indicators (WDI) of the World Bank. Whenever WDI data were reported missing, we have used instead information from the CIA factbook. These data are available for 199 destination markets, including the Portuguese-speaking countries listed in Table 1.

Portuguese emigrants. We have further added information on the number of Portuguese emigrants in each importing country. These data come from the Global Migrant Origin Database (GMOD) from the the University of Sussex’s Development Research Centre on Migration, Globalization and Poverty. The stocks refer to the year 2000, and are available for 193 of the 199 countries mentioned above. The full GMOD data set consists of a 226x226 matrix of origin-destination stocks. The data are obtained by disaggregating the information on migrant stocks in each destination country as given in the 2000 round of its population census.

The ...nal data set used in the estimation gathers information on 14,782 exporting ...rms and 193 destination markets. Table A1 provides summary statistics on these data.<sup>8</sup>

# 5 Networks and trade at the country level

Table 2 reports descriptive statistics on the distribution of exporters across destinations. As can be seen, on average each ...rm exported to 3.4 countries. However, the mean hides signi...cant ...rm heterogeneity. More than one-half of all exporters sell to only one destination (54.2%), but they tend to be relatively small exporters, accounting for only 6.8%
Resources Assessment data of the FAO.<sup>7</sup> For example, a sq km of forest in India stores 4,100 tons of CO2. In Brazil, that figure is 12,100 tons; in Indonesia it is 13,800 tons.

The CO2 equivalent of excess forest can be priced at the GCI to get an excess forestry benefit for a country. Of course, the specific model used to determine the predicted area of forests needs to be worked out. A potential first pass is in Annex 1 which computes the CO2 stored by the ‘excess’ forest in 2019 controlling for a country’s income level, population density, temperature and precipitation, land area, agricultural land, and forest endowment in 1990. Table 3 highlights the top 10 countries that could be credited for forestry based on our first approximation estimates.<sup>8</sup> China leads the way. Its initial forest coverage was around 1.6 million sq km in 1990. Given its zChina to have around 1.8 million sq km of forest in 2019. Its actual forest cover in 2019, however, is around 2.2 million sq km. Hence it overperformed compared to the global average, making it eligible for USD 0.36 billion as credit using a GCI of USD 0.3 per ton.

The key finding is that measures that protect forest cover, over and beyond a country’s development, natural endowment and demography, have only a limited impact in offsetting carbon emissions. The change in the stock of emissions since 1990 is mostly the result of rapidly increasing production and consumption of carbon.

> 7 http://countrystat.org/home.aspx?c=FOR.

> 8 Countries could also be credited at the GIC rate on a flow basis for the carbon absorbed annually by their “excess” forests. This would create an additional incentive to maintain, or even expand, forest cover. These calculations can be done using the methods
For the measure of the GVC participation of a sector in a country, we need a data set on international input-output tables (IOTs). The Asian Development Bank’s Multi-regional input-output tables (MRIOT) version published in 2019 covers 61 individual economies-and the rest of world as one group for a total of 62--and 35 sectors. It is the most appropriate dataset because it has the broadest coverage of countries (especially for developing Asia)