rafmacalaba/datause-encoder-data huggingface dataset:
protection. Section IV presents an analytical model where the government maximizes social welfare that features behavioral elements. In Section V, we modify this model to incorporate into the influential lobbying model of Grossman and Helpman. In Section VI we analyze the observed policy patterns and tools in light of our analytical predictions. Section VII concludes.

# **II. Loss Aversion in Trade Policy: An Experiment**<sup>**3**</sup>

Numerous empirical studies from the behavioral literature find that the value people assign to a loss is significantly larger than the value assigned to an equivalent gain. In particular, for small or moderate gains and losses of money, the estimated value assigned to a loss is about twice as large as a similar gain (see Kahneman, Knetsch, and Thaler (1991) for a survey of the literature). Losses are also by definition time dependent, and reflect a particular reference point. For example, a firm accustomed to a high profit level will regard a low profit level as unacceptable, while a firm accustomed to a low profit level will find maintaining that level to be satisfactory.

An implication of loss aversion and reference dependence is that the value assigned to foregone gains is lower than that assigned to losses. This is reflected in many judicial outcomes, where the weight assigned to unrealized profits is typically well below the weight assigned to incurred losses. Survey evidence also bears this out. For example, faced with excess demand for a particular car model, a company that discontinues a discount of $200 is considered fair; but, a company that raises the price by $200 is considered unfair by a majority of respondents (Kahneman, Knetsch, and Thaler, 1991). This implies that imposing a surcharge (perceived as a loss) is considered less acceptable than eliminating a discount (perceived as a gain).

Loss aversion and reference dependence also have implications for people’s perceptions of trade policy. Tharakan (1996, p. 1562) notes that “[p]erhaps the weakest point in the political economy analysis of protection has been the rather superficial way in which it has so far taken into account the public perception of trade relations.” Indeed, very few studies examine people’s sentiments on trade policy. Two exceptions are Wellisz (1982), political support function of Hillman (1989) and campaign contributions approach of Magee, Brock and Young (1989).

5
Holland, P. (1986). Statistics and Causal Inference. _Journal of the American Statistical Association_ , Vol 81: 945-60

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Spence, M. (1973), Job Market Signaling, Quarterly Journal of Economics, August l973.

Stiglitz, J. and Weiss, A. (1981) Credit Rationing in Markets with Imperfect Information The American Economic Review, Vol. 71(3): 393-410.

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- Putnam, R. D. (1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.

- Putnam, R. D. (1995a). Bowling alone: America’s declining social capital. Journal of Democracy, Vol. 1: 65-78.

- Putnam, R. D. (1995b). Tuning in, tuning out: The strange disappearance of social capital in America. Political Science and Politics, Vol. 28: 664-683.

- Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.

- Verba, S., Schlozman, K.L. and Brady, H.E. (1995). Voice and Equality: Civic Voluntarism in American Politics. Cambridge, MA: Harvard University Press.

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distributed somewhat differently across countries. Egypt sees the most new city-to-city routes (6) with Turkey under this specification.

# **VI. Conclusions**

In this study, we estimate a set of empirical models of air passenger traffic, in order to better understand the relationship between air transport policy and international traffic. We use a WTO index measure of policy commitments in both bilateral and plurilateral air services agreements, and relate these measures to ICAO data on air passenger traffic. We find that country pairs with more liberal policy commitments see greater air passenger traffic. Subsequent work at the city level indicates that, to a large degree, larger passenger numbers in more liberal agreements occur because more city pairs are served by a flight.

We use our empirical models to investigate the likely impact of two policy changes. First, we consider the impact of deepening the ACAC agreement, awarding it the same policy score as the UKSingapore bilateral agreement. Because policy within the ACAC is already fairly liberal, the deepening of the agreement has modest, but significant effects. Our estimates suggest a 30% increase in traffic between ACAC countries as a result of the policy change.

In a second scenario we add Turkey to the deeper ACAC agreement, assigning the same liberal measures to Turkey-ACAC passenger flows. Due to the restrictive nature of policy commitments between Turkey and many Arab countries, this scenario generates much more growth in passenger traffic. Our estimates suggest that passenger traffic would more than double. Much of this would arise through increasing numbers of direct flights between Turkish cities and cities in the ACAC member states.

While these results may seem optimistic, there are three reasons to suggest they are not unrealistic. First, current air passenger traffic levels in the region are low, and fast growth is plausible off a small base. Second, much faster growth rates have been observed in recent years in selected regional markets. Finally, the policy changes we consider are large, with the Turkey-ACAC policies changing 23
pWram that included dght The stabilizaton strategy was based on momic fiscal and monetary policies, a revised wage and priee control agreement between the government, business and labor, and the strengthening of the balance of payments.

# Stabilization policies were designed to:

- (i) Increase the public sector primary balance, which averaged a surplus of 7% of GDP in the period 1988-91.

- (ii) Renegotiate Mexico's foreign debt to reduce the problem of excessive Mexican savings transferred abroad.

- (iii) Reduce domestic credit to the public sctor and maintain a crawling exchange rate in order to further lower inflation.

- (iv) Maintain relative prices according to demand and supply conditions, avoiding indexing and reducing inflatdonary expecations.

- (v) Obtain economic agreements among diffent ctors in order to distribute evenly the social costs of adjustment.

In November 1991, the Government sent to Congren the budget for 1992. This is the first budget in recent history which shows a public soctor suplus after including interest payments on domestic and foreign debt. For 1992, the pubec sector surplus, excluding futre privatization proceeds, is etmated at 0.8% of GDP.

3
Table 4-1. Economic Impacts by Type of Disaster (1960 – 2007)

||Damages|Estimated|Losses|Estimated T|otal Impacts|
|---|---|---|---|---|---|
||||||total impact-damage|
||total value|total value|loss-damage ratio|total value|<br>ratio|
||share across column|share across column||share across column|total impact-loss ratio|
|Climatological|84,910|40,837|0.48|72,604|0.86|
||11.4%|11.4%||10.7%|1.78|
|Geophysical|282,987|144,196|0.51|271,489|0.96|
||38.1%|40.1%||40.1%|1.88|
|Hydrological|188,360|87,994|0.47|158,678|0.84|
||25.4%|24.5%||23.4%|1.80|
|Meteorological|186,098|86,717|0.47|175,073|0.94|
||25.1%|24.1%||25.8%|2.02|
|Total|742,356|359,744|0.48|677,844|0.91<br>1.88|

Source: values of damages are based on the data of EM-DAT and Munich Re. Remark: values are in constant 2007 US$ million.

The relationships among damages, losses, and total impacts of each disaster type show some interesting features. The loss-damage ratio, dividing the value of estimated losses by the damage value, looks very similar across the different disaster types, at around 0.5. On the other hand, total impact-damage ratios, dividing the value of estimated total impacts by damage value, have noticeable differences among them: geophysical disasters have the largest ratio (0.96), followed closely by meteorological disasters (0.96), while climatological and hydrological disasters have relatively smaller values, 0.86 and 0.84, respectively. At the same time, the impact multipliers, total impact-loss ratios, have a slightly different order: meteorological disasters have the largest impact multiplier (2.02), followed by geophysical (1.88), hydrological (1.80), and climatological (1.78). Because highly aggregated SAMs for each country are used for the estimation in this study, the interpretation of these results requires some caution. Nonetheless, in general, geophysical disasters seem the most costly in absolute value, and the impacts may become large (larger total impact-damage ratio and impact multiplier) than other types of disaster. Meteorological disasters are not so straightforward: in total, their economic impacts in absolute value are about average (around 25% for damages, losses, and total impacts); however, their total 15
X

# **Table 12: Indices of Trade-Production Compatibility**<sup>**in Latin America**</sup>

# **Production**

_Exports_

||ARG|BOL|BRA|CHL|COL|CR1|ECU|GTM|HND|JAM|MEX|NIC|PAN|PER|PRY|SLV|URY|VEN|AVG|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ARG|0.50|0.34|0.49|0.35|0.36|0.39|0.37|0.46|0.34|0.32|0.43|n.a.|0.32|n.a|n.a.|n.a.|0.41|0.37|0.38|
|BOL|0.13|0.31|0.15|0.27|0.13|0.14|0.15|0.15|0.16|0.14|0.15|n.a|0.12|n.a.|n.a.|n.a.|0.11|0.12|0.15|
|BRA|0.48|0.28|0.57|0.36|0.41|0.45|0.41|0.46|0.33|0.31|0.42|n.a.|0.29|n.a.|n.a|n.a.|0.41|0.37|0.38|
|||||||||||||||||||.||
|ECU|0.28|0.31|0.19|0.16|0.29|0.31|0.46|0.23|0.35|0.23|0.38|n.a.|0.30|n.a.|n.a.|n.a.|0.18|0.44|0.28|
|G1M|0.32|0.24|0.32|0.21|0.35|0.49|0.39|0.54|0.39|0.26|0.28|n.a.|0.31|n.a.|n.a|n.a.|0.31|0.25|0.32|
|HND|0.16|0.21|0.17|0.17|0.20|0.41|0.29|0.34|0.41|0.20|0.14|n.a.|0.40|n.a.|n.a.|n.a.|0.17|0.14|0.23|
|||g|<br>I<br>|||||||||||||||||
|PAN|0.40|0.40|0.41|0.36|0.44|0.56|0.49|0.51|0.55|0.40|0.37|n.a.|0.62|n.a|n.a|n.a.|0.43|0.37|0.44|
|PER|0.37|0.36|0.35|0.38|0.33|0.39|0.36|0.41|_0.30_|0.30|0.35|_n.a._|0.28|n.a.|_n.a_|_n.a._|0.37|0.30|0.37|
|PRY|0.25|0.22|0.21|0.17|0.18|0.24|0.22|0.28|0.22|0.21|0.19|n.L|0.23|n1.|n.a|n.a|0.25|0.15|0.23|
|U|A<br>0.48|0.30|0.57|0.37|0.39|0.33|0.34|0.36|0.30|0.32|0.46|n.a|0.28|n.a.|11.a.|n.a.|0.39|_038_|0.38|
|**AVG**|032|0.26|0.30|0.25|0.30|035|0.34|0.35|030|0.25|031|n.a|0.28|na.|**n.a**|n.S.|0.28|0.29|0.29|

**Source:** Esdmaed frin data in Contrade, UN. Statistical<sup>Office; UNIDO; FAO; IECIT</sup><sup>**Metals**and Minerals</sup><sup>**datM Wotid**Bank dat</sup>
# Simulating increased competition in the ICT sector in Djibouti

We use the EDAM4 data from 2017 to explore the ICT sector in Djibouti and conduct a simulation on the potential benefits of market forces in decreasing the prices of ICT services. We present basic summary statistics on expenditures related to the sector. Next, we discuss the methodology and the tool used for simulations, before presenting the findings.

We identify as “consumer of telecom services” the households that responded having spent money during the past month<sup>15</sup> on fees on phone services (fixed or mobile), bought mobile “credit”, used a phone booth, and/or paid a monthly contract for phone (fixed or mobile) or internet services. Table 3 presents the percentage of the population that reported positive expenditures in each of the telecom items, as well as reporting spending in “any” of these categories. The relatively low penetration of telecommunication services among the Djiboutian population partly explains why only about 22 percent of the population is found to be a telecom user, with this share being driven mostly by purchase of cards of credit for mobile phone minutes and/or data. There are marked differences in usage rates across the consumption distribution. Among the poorest decile, less than 5 percent of the population declared having expenditures on telecom services. This rate increases with the level of consumption reaching 23 percent around the middle of the distribution and to about 35 percent among the top 10 percent (Table 4). Expenditures on telecom services also appear to be correlated with the level of consumption, with users in the top 10 percent of the distribution spending more than 10 times the level of expenditures of those in the bottom 10 percent. It is notable that users devote similar shares of their total expenditures to telecom services.

**Table 3. Percentage of the population reporting expenditures on telecom services**

|**Telecom Service**|**% Consumed**|
|---|---|
|Fixed,mobile or cellular telephone,fax or fax machine|1.0%|
|Repair of telephonyand fax equipment|0.2%|
|Purchase of card for landline / mobile / transfer credit|15.5%|
|Telephone communication in aphonebooth(cabine)|8.6%|
|Subscription fee landline / rural telephone|1.9%|
|Other telephone and telegraph services|0.0%|
|Internet subscription fees|2.2%|
|Internet connection fee(includingcybercafe use)|0.5%|
|Telecom bundle(anytelecom service)|21.8%|

Source: own calculations using EDAM2017.

Notes: Consumption is based on reporting expenditures in the category during the last reference month.

> 15 This will clearly underestimate current users as many households may have phones but not purchased these items in the reference month when the survey was conducted. As noted above, about 75% of the population has a mobile phone, but the percent of users with recurrent payments is much lower.

11
To counter the welfare shock from the COVID-19 crisis, some households resorted to adverse coping strategies. These are captured in the HFPS using questions about whether the household used emergency savings to cover necessities, reduced the consumption of goods, or sold assets such as property. A large share of households adopted some type of negative coping strategy, including in HICs (Table A6.23, Table A6.24 and Table A6.25). In particular, the sale of assets was more pronounced among households with lower access to education and those in rural areas. While the share of households resorting to adverse measures fell over time in most instances, even the temporary adoption of such negative coping mechanisms can have longer term implications as they could represent damages to human capital (for example, if reduced consumption leads to suboptimal nutrition levels) and reductions in productive capital (in the case of selling assets) that can impact future poverty and inequality if left unmitigated.

# **6 Concluding remarks**

This paper analyzed the welfare impacts of the COVID-19 pandemic using the latest available harmonized global database of the World Bank’s high-frequency phone surveys from 80 countries—the largest known source of data on households during the pandemic. Focusing on the scarring effects of the pandemic, we show that, while there have been some improvements in employment, the labor market impacts continue to be felt over two years into the pandemic. The dynamics of labor market outcomes suggest that the initial disparities that were observed early in the COVID-19 pandemic narrowed rather slowly over time and sometimes not at all, while women and the less educated continued to report a higher incidence of work stoppages than men and better educated groups.

Another contribution of this paper is on the findings related to broader labor market impacts, beyond adjustments at the extensive margin. Analysis of job transitions throughout the pandemic show significant churning in the labor market, including significant shifts from wage employment to self-employment and from non-agriculture sectors to agriculture, likely reflecting a rise in informal and more precarious employment accompanied by lower earnings. Lower socio-economic groups, as proxied by the respondent’s level of education, were more likely to transition into these lower-quality jobs, while also being less likely to recover lost employment. This is consistent with income losses being more prevalent among households with less educated respondents, higher dependency ratios and greater incidence of labor market disruptions. Beyond labor market impacts, and likely reflecting that safety nets were insufficient to mitigate the full extent of the pandemic-induced shock, we observe heightened food insecurity and a high share of households resorting to negative coping strate28
measurements are standardized according to the International Referenced Population defined by the U.S. National Center for Health Statistics. The three commonly used indicators of malnutrition are height-for age, weight-for-age, and weight-for height. Children whose measurements fall below two standards deviations from the reference median are generally considered malnourished. Each indicator captures different aspects of malnutrition.

Stunting, or low height for age, is considered an indicator of chronic malnutrition. Height for age measures linear growth and therefore a low score is indicative of a cumulative growth deficit. Stunting is often the result of inadequate feeding practices over a long period and/or repeated illness. It is likely to persist even after these conditions are eliminated. Wasting, or low weight-for-height, measures body mass in relation to body length and is generally considered to reflect acute malnutrition. As an indicator, wasting is likely to vary over short periods of time due to food availability and disease prevalence. Underweight, or low weight for age, is a composite of height for age and weight for height. It is generally considered a general indicator of malnutrition, since a child that is underweight could be stunted or wasted, or both stunted and wasted.

Table 1 compares the proportion of children malnourished, according to the definitions discussed above, in Ethiopia and other low-income Sub-Saharan countries. The data used to calculate the indicator averages for low income Sub-Saharan countries include all countries for which DHS data for children under five years of age was available over the past decade.<sup>4</sup> If more than one survey was available for a given country, only data from the most recent survey was included. Compared to other low-income Sub-Saharan countries, Ethiopia has the second highest rate of stunting and underweight. The proportion of children stunted is 50 percent higher than the average for those countries. In general, children in rural areas are more likely to be stunted than children in urban areas. The difference between the proportion of children underweight is also significantly higher. However, comparisons between the proportion of children underweight and wasted needs to be qualified, as different surveys took place in different years. Restricting the sample to surveys that took place between 1999 and 2001, however, does not significantly affect the regional average for these indicators. It is also

> 4 We restricted the sample to surveys carried out since 1991. Some DHS datasets only collected children’s measurement data for children under 3 years of age. Since the Ethiopia dataset includes these measurements for all children under five, we chose to make the comparison with similar datasets. Alternatively we could have chosen to restrict the samples to children under 3 years of age only, then all data would be comparable.

6
Cross-country evidence shows that more concentrated banking systems are less likely to suffer systemic fragility; at the same time, competition also contributes to greater stability (Beck, Demirguc-Kunt and Levine, 2006; Schaeck, Cihak and Wolfe, 2006). When analyzing the channels through which concentration might be positively associated with banking system stability, Beck et al. find tentative evidence that more concentrated banking systems allow better possibilities for banks to diversify risk. On the other hand, they do not find any evidence that it is easier for bank supervisors to monitor more concentrated banking systems or that stability results from market power and the consequent increase in franchise values of banks in more concentrated banking systems. In summary, higher concentration levels do not necessarily imply less competition, but might affect bank stability through other channels. Considering bank-level data, researchers have arrived at different conclusions. On the one hand, Boyd, de Nicoló and Jalal (2006) find banks are closer to insolvency, i.e. more likely to fail, in countries with more concentrated banking systems. On the other hand, Schaeck, Cihak and Wolfe (2006) and Schaeck and Cihak (2007) show that banks have higher capital ratios in more competitive environments.

Summarizing, there is no clear indication that competition is detrimental per se for bank stability or that a more concentrated banking system necessarily implies less competition. Similarly, cross-country studies have shown little effect of market structure on net interest margins, but rather highlighted the importance of a contestable and open banking market without restrictive regulatory policies (Demirguc-Kunt, Laeven and Levine, 2004). Evidence for Uganda has shown that macroeconomic policies and deficiencies in the contractual framework are the most important factors explaining high spreads and margins (Beck and Hesse, 2009). While small scale can also explain high spreads, market structure indicators are not significantly associated with bank efficiency. Overall, while larger banks might result in greater overall efficiency, the resulting higher concentration is not necessarily associated with increased or reduced efficiency. The effect of market structure on access to financial services, on the other hand, points to a potentially negative effect of a more concentrated banking system on access to loans by small firms (Beck, Demirguc-Kunt and Maksimovic, 2004) and lower geographic outreach (Beck, Demirguc-Kunt and Martinez Peria, 2008).

33