rafmacalaba/datause-encoder-data huggingface dataset:
been a host or a guest of someone else is an important predictor of partner choice.<sup>12</sup> Despite its correlation with some social network questions, these questions have predictive power on their own, even when census variables are introduced. While participants tend to sort along most social network variables in one-shot games, fewer variables predict pairing in the dynamic games: being relatives and having watched the partner’s store are the only significant predictors from the social networks data in the dynamic treatments.

The most notable finding is a non-result: there is no evidence that risk averse partners particularly choose each other in the dynamic games. _Not_ being risk averse has some explanatory power, but the coefficient (0.048) is considerably smaller than other variables like being a relative or having watched a partner’s store.<sup>13</sup> These data thus offer little support for Ghatak’s (1999) hypothesis that joint liability will lead to assortative matching by risk attributes.

The findings reinforce the puzzle: if pairing is done mainly on the basis of social attributes, the ability to coordinate on mixed risky-safe project choices (and thus to increase earnings) should be enhanced. It should be easier to coordinate among people who know each other well, and, with anonymity dropped, enforcement mechanisms that operate outside of the game setting may come into play. But instead we see a tendency toward safe-safe choices.

It cannot simply be that partners who know each other enjoy playing the game together and thus opt for longevity in the game over maximizing point totals: the mixed safe-risky option also guarantees longevity. One possibility is that participants judge their success relative to their close friends and relatives; in this case, they may prefer to coordinate at a low, safe level in which equal point totals are guaranteed, rather than risking the chance of imbalanced totals when trying to coordinate on risky-safe choices.

> 12 Using the same methods we regressed “Had ever been a host or guest” against social network variables as well as census variables and found (not shown) that host and guest pairs tended to be between individuals that sold the same product, were relatives or close friends, that is, people that trusted each other and knew each other well.

> 13 In general matched players do not have the same marital status, but given that being relatives is so important, it could be that parents and children play together, explaining the difference in marital status (and possibly the same religion).

24
<!-- Start of picture text --> Chart 14. Explicit Debt versus Implicit Public Pension Debt<br>in Some Major Industrial Countries, 1990<br>Canada !- d mpli:ltsocurRy debt<br>France ~.<br>Orarmny<br>Japan .<br>Unlbd Kingdomr<br>UnlWed States by puE r_ rrei * trs Ur=<br>0 50 100 150 200 250 300<br>Percentage of GOP<br>Note: The assumed discount rate is 4 percent<br>Soure: V7 de>rNoardandHard(1993); Wold Bank (1994a)<br><!-- End of picture text --> Chart 15. Share of Retirees in Adult Population of some Major Industrial Countries, 1990-2025 <!-- Start of picture text --> (Percent)<br>35-<br>31<br>30--3<br>26 25<br>24<br>25<br>10<br>5<br>U.S. Gewmany Canada Japan Italy<br>Source: U.N. population data and projections 019 225<br>No te: Popula tion 65 years o f age and o ver divided by to tal popula tion o ver 19 years of age.<br><!-- End of picture text --> 23
were partially or totally destroyed, making it impossible to live in them. 44% of the households slept in their homes, while 44% slept in a camp. 12% of the households had agricultural production as their main source of income, 37% had trade, 26% unqualified work, 17% professional work and 3% lived mostly out of transfers. Aid was received from both abroad (12%) and from within Haiti (18%).

**Table 4. Descriptive statistics**

||Mean|Std|
|---|---|---|
|Food consumption score|55.79|19.75|
|Assistance|0.44|0.50|
|Food assistance|0.32|0.47|
|Housing not damaged|0.17|0.37|
|Housing damaged but still usable|0.46|0.50|
|Housing partially destroyed|0.11|0.32|
|Housing totally destroyed|0.26|0.44|
|Sleeping in the house|0.44|0.50|
|Sleeping beside the house|0.09|0.29|
|Sleeping in the neighborhood|0.27|0.44|
|Sleeping in the commune|0.18|0.38|
|Sleeping outside the commune|0.02|0.14|
|Sleeping in a camp|0.44|0.50|
|Main income source before the earthquake|||
|Agricultural production|0.12|0.32|
|Trade|0.37|0.48|
|Unskilled work|0.26|0.44|
|Skilled work|0.17|0.38|
|Transfer|0.03|0.18|
|Other income source|0.04|0.19|
|Aid/transfers from abroad|0.12|0.32|
|Aid/transfers from Haiti|0.18|0.38|

Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. See Table 16 for a complete description of variables in directly affected and non directly affected areas.

# _(e) Pre-earthquake conditions_

Table 5 presents summary statistics by quintile of pre-earthquake wealth. The poorest households lived mostly in the East of the directly affected area (Léogane, Gressier, Jacmel, Petit Goâve, Grand Goâve and Croix-des-Bouquets). They were mostly agricultural households: 66% of them were practicing agriculture, compared to only 5% among the wealthiest, who were mostly concentrated in Port-au-Prince or Pétionville. The poorest households lived in houses with no electricity and no toilets, and do not have access privately to water. They had no car and 15
2050. The research takes into account the projected land subsidence of the Ganges Delta as well as alternative scenarios of upstream withdrawal of freshwater.

The remainder of the paper is organized as follows: Section 2 describes the study area, the coastal zone of Bangladesh, and provides background information. Section 3 describes the modeling of climate change and other factors to determine the expected impacts on the salinity of river water in the coastal zone. Section 4 presents salinity maps, estimated isohalines (contours of equal salinity), and location-specific salinity projections for various climate-change scenarios. Section 5 concludes the paper.

# **2. Coastal Bangladesh**

The coastal area of Bangladesh (see Figure 1) covers 19 districts<sup>7</sup> facing or near the Bay of Bengal, encompassing 148 subdistricts and the Exclusive Economic Zone, accounting for 32 percent of the land area and 25.7 percent of the population of Bangladesh (BBS 2011). It has been estimated that the coastal area sustains the livelihoods of more than 37 million people.<sup>8</sup> The area’s diverse natural resources include critical ecosystems such as Sundarban mangrove forests, fisheries, shrimp farms, agriculture, and deposits of minerals and salt (GoB 2006). Export promotion sites, harbors, airports, ports, tourism, and other industries are also located in the coastal area. Yet, poverty in the coastal area is high: 14 of the 19 coastal districts have poverty rates greater than the national average. High vulnerabilities in terms of insecurity of food, income, water, health, and poverty are prominent in Bagerhat, Bhola, Noakhali, and Satkhira Districts (BBS et al. 2009; GoB 2006). Twelve of the coastal districts, comprising 51 subdistricts (covering 50 percent of the land area of the coastal zone), already face a combination of cyclone risk, salinity, and tidal water movement above critical levels.

> 7 The 19 coastal districts are Jessore, Narail, Gopalganj, Shariatpur, Chandpur, Satkhira, Khulna, Bagerhat, Pirozpur, Jhalakati, Barguna, Barisal, Patuakhali, Bhola, Lakshmipur, Noakhali, Feni, Chittagong, and Cox’s Bazar.

> 8 Initial projections indicate that population may grow to about 60 million by 2050 (World Bank 2009).

3
# I Additional quantitative results

This section includes additional quantitative results not included in the main text.

## **I.1 Additional moments for Section 4.1**

Table I.1 complements Table 5 and provides additional moments for the baseline counterfactual exercise of Section 4.1.

**Table I.1:** Baseline Counterfactual Experiments: Additional Moments

|||1988–19|96||1996–20|06|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Moments|1988|1996|Counterfactual<br>1996 (%)|1996|2006|Counterfactual<br>2006 (%)|
|Central-local spending ratio|3.63|1.85|1.83|1.85|1.05|1.06|
|Local spending|0.062|0.129|0.124|0.129|0.162|0.191|
|Central spending|0.224|0.239|0.226|0.239|0.170|0.202|
|Central-to-local transfer|0.039|0.080|0.085|0.080|0.055|0.071|

Note: Counterfactual experiments use revenue ( _e_ and _f_ ) and spending responsibility share ( _θ_ ) values from end year, and keep all other parameters at their starting year values. All spending and transfer levels are normalized by national GDP.

40
TABLE 48: SUMMARY OF ESTIMATED SAVINGS FOR LGED, RHD, AND BWDB WORKS TENDERS ADMINISTERED USING ELECTRONIC PROCUREMENT FROM FY12 TO FY18 (69,240 TENDERS)

||Components|Savings<br>cro<br>|(in BDT<br>re)<br>|Savings<br>mill<br>|(in US$ ion)<br>|
|---|---|---|---|---|---|
|||Lower<br>bound|Upper<br>bound|Lower<br>bound|Upper<br>bound|
|1.|Government Ec<br>|onomic Cos<br>|t Savings<br>|||
|a.|WinningRebates(inflation adjusted)|3606.8|4004.0|424.3|471.1|
|2.|Government Adm<br>|inistrative C<br>|ost Savings<br>|||
|a.|Advertisement Cost Savings|210.7|266.4|24.8|31.3|
|b.|Tender Preparation Cost Savings|53.5|53.5|6.3|6.3|
|c.|SecurityCost Savings|11.5|11.5|1.4|1.4|
|d.|Staff-time savings|29.7|29.7|3.5|3.5|
||Total Government Savings|3912.2|4365.1|460.3|513.6|
|3.|Firm Transac|tion Cost S|avings|||
|a.|Staff-time savings|0.8|1.9|0.1|0.2|
|b.|Tender Preparation Cost Savings|-60.0|-51.1|-7.1|-6.0|
|c.|SecurityCost Savings|97.2|105.9|11.4|12.5|
||Total Private Sector Savings|38.0|56.7|4.4|6.7|

Note: lower bound estimates are produced by models employing CEM matching within years, while upper bound estimates are based on models using CEM matching within PEs.

# 9.9.2 EXTENDED SAMPLE ESTIMATE

Based on the estimates of the narrow sample, savings estimates were made for all tenders administered using the e-procurement system. The average savings by component has been mentioned in the table below.

79
Given that, with the single _S/U_ soybean exception, the variables are non-stationary, the performance of the models must be complemented by cointegration statistics (in addition to conventional statistics).

Table 2 reports parameter estimates for 1960-2012 for maize, wheat, and rice and (because of data unavailability) for 1965-2012 for soybeans and palm oil. Half of the parameter estimates are significantly different from zero at the 5 percent level while the _adjusted-R_<sup>_2_</sup> averaged 0.82 (ranging from 0.67 for palm oil to 0.90 for wheat). More important, in most cases the ADF and PP statistics confirm stationarity of the error term at the 1 percent level.

# **Discussion**

# Stock-to-Use Ratios

As expected, the _S/U_ ratio estimates are negative ranging from a high of -0.61 for maize to a low of -0.21 for rice—the only _S/U_ estimate not significantly different from zero. These estimates imply that, on average, a 10 percent decline in the _S/U_ ratio is associated with a 3 percent increase in food prices. The smaller parameter estimate for soybeans (compared to wheat and maize) may reflect not only the fact that soybean stocks can be held in bean, meal, or oil form, while only beans were used in this study, but also the generally low levels of stock-holding in that sector. From an econometric perspective, such result may also be associated with the stationarity properties of the _S/U_ ratio for soybeans—perhaps not surprisingly since it was the only variable for which stationarity was not rejected according to both ADF and PP statistics.

The _S/U_ ratio elasticity estimates for grains reported here are remarkably similar to findings reported elsewhere. For example, Bobenrieth, Wright, and Zeng (2012) estimated correlation coefficients between _S/U_ ratios and real de-trended prices for wheat, maize, and rice of -0.40, -0.50, and -0.17, respectively (compared to -0.61, -0.50, and -0.21, respectively, in the present study.) Similarly, FAO (2008, p. 6, figure 3) reported correlation coefficients between the cereals price index and various measures of _S/U_ ratios ranging from -0.47 and -0.65. These correlations led FAO (2008) to conclude not only that the low stock levels caused grain prices to spike during 2007/08, but also that prices are expected to remain elevated for some time.

The _S/U_ ratio estimate for rice (low and not significantly different from zero) is both troubling and interesting. It most likely reflects policy distortions, including the substantial quantities of rice stocks—especially in East Asia where rice is considered a strategic commodity—that are either handled by state trading enterprises (STE) or heavily influenced by government policies (Alavi and others 2012). Indeed, Anderson and others (2009, p. 489, table 12.11) estimated that during 2000-04, rice exhibited the highest level of distortion (43 percent) compared to wheat (4 percent) and maize (3 percent), as measured by the trade restrictiveness index (TRI). The economies that contrib— 6 —
Therefore, in future cases under Article XVII:1, a panel will need to begin its analysis with an evaluation of the application of subparagraph (a). “It appears to us that these [the subparagraph (b)] types of differential treatment would be the most likely to occur in practice and, therefore, that most if not all cases under Article XVII:1 will involve an analysis of both subparagraphs (a) and (b).”<sup>31</sup> Canada argued that a panel must first find discrimination under subparagraph (a) before it can go on to make findings under subparagraph (b), and that subparagraph (b) does not create independent obligations.<sup>32</sup> Rather, “[i]n Canada's view, what subparagraph (b) does is to interpret and temper the obligation under subparagraph (a).”<sup>33</sup> While the Appellate Body did not find that a full analysis under subparagraph (a) is required before proceeding to subparagraph (b), it found that a panel must at least identify the differential treatment alleged to be discriminatory under subparagraph (a) before performing its analysis under subparagraph (b).<sup>34</sup> The Appellate Body found that an evaluation of whether discrimination is based on commercial considerations within the meaning of subparagraph (b) might depend on the type of discrimination identified under subparagraph (a).<sup>35</sup> The Appellate Body stated that “[s]ubparagraph (b) sets forth two specific conditions with which an STE must comply if allegedly discriminatory conduct falling, prima facie, within the scope of subparagraph (a) is to be found consistent with Article XVII:1.”<sup>36</sup> Thus, as suggested by Canada, subparagraph (b) can serve to “temper” or limit the scope of subparagraph (a): not all discrimination within the meaning of subparagraph (a) violates Article XVII:1.

The panel in Korea-Various Measures on Beef had said that the variables in subparagraph (b) are to be used to assess whether the national measure is discriminatory.<sup>37</sup> The Appellate Body in the present case stated that it did not decide whether a panel may find a violation of subparagraph (a) alone, without considering subparagraph (b).<sup>38</sup>

> 31   Appellate Body Report, para. 99. 32   Panel Report, para. 6.54.

> 33 _Id._ 34

> Appellate Body Report, paras. 111-112. 35

> Appellate Body Report, para. 110. 36

> _Id._

> 37

> Korea—Various Measures on Beef, para. 757.

> 38   Appellate Body Report, at para. 106, note 115.

13
Table A.2: The decision to become an exporter: before and after the crisis

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Crisis period?|0.056|0.074|0.096|0.007|0.035|0.143|0.916*|
|(1=Yes)|(0.102)|(0.148)|(0.149)|(0.115)|(0.115)|(0.703)|(0.470)|
|Subj. fnancial||-0.001<br>|-0.002<br>|||||
|constraint||(0.021)|(0.021)|||||
|Subj. fnancial||-0.015|-0.012|||||
|constraint _×_ Crisis?||(0.025)|(0.025)|||||
|Unable to obtain a||||-0.007|-0.007|-1.422*|-0.427|
|loan? (Yes=1)||||(0.033)|(0.034)|(0.757)|(0.344)|
|Unable to obtain a||||0.021|0.029|0.075|-1.085*|
|loan? (Yes=1) _×_ Crisis?||||(0.042)|(0.042)|(0.884)|(0.623)|
|Foreign shock|0.009||0.009||0.009||0.004|
|(%∆GDP)|(0.006)||(0.006)||(0.006)||(0.012)|
|Foreign shock|-0.016||-0.016||-0.016||0.019|
|_×_ Crisis?|(0.011)||(0.011)||(0.011)||(0.025)|
|Domestic shock|-0.006***||-0.006***||-0.006***||-0.007***|
|(%∆GDP)|(0.001)||(0.001)||(0.001)||(0.002)|
|Domestic shock|-0.005||-0.005||-0.006||0.008|
|_×_ Crisis?|(0.004)||(0.004)||(0.004)||(0.008)|
|Capacity|-0.000|-0.000|-0.000|-0.001|-0.000|-0.002*|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|
|Capacity _×_|0.000|0.000|0.000|0.000|0.000|-0.001|-0.003|
|Crisis?|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|
|Log(Labor|0.034***|0.040***|0.034***|0.040***|0.035***|0.003|0.004|
|Productivity)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.024)|(0.019)|
|Firm Age|0.002***<br>(0.001)|0.002***<br>(0.001)|0.002***<br>(0.001)|0.002***<br>(0.001)|0.002***<br>(0.001)|0.003**<br>(0.001)|0.003**<br>(0.001)|
|Small Firm? (Yes=1)|-0.272***|-0.272***|-0.271***|-0.271***|-0.270***|-0.387***|-0.400***|
||(0.024)|(0.024)|(0.024)|(0.024)|(0.024)|(0.075)|(0.067)|
|Foreign Ownership?|0.201***|0.197***|0.199***|0.200***|0.202***|0.123|0.120|
|(1=Yes)|(0.034)|(0.035)|(0.034)|(0.035)|(0.034)|(0.075)|(0.078)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.326|0.326|0.326|0.326|0.326|0.326|0.326|
|F stat||||||14|16|
|Kleibergen-Paap LM test||||||0.012|0.000|
|Hansen J stat||||||0.039|0.000|
|Endog. test||||||0.032|0.205|
|N|15204|15204|15204|15204|15204|15204|15204|

The dependent variable is the _Prob_ ( _Exportit >_ 0). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

22
firm-level data from the World Bank Enterprise Survey and combine it with PM 2.5 data estimated by van Donkelaar et al. (2016) (based on NASA MODIS and MIRS satellite data). We perform the analysis at the global level and then focus on how these effects are different for African firms.

When compared to the rest of the world, African firms experience different effects in terms of pollution. While a global analysis shows that the association between pollution and labor productivity is positive in the beginning and negative as pollution rises (quadratic), African firms experience a different story. PM 2.5 levels are negatively associated with labor productivity for these firms at low levels of pollution. Between approximately 8 μ and 25 μ , the effects turn positive, showing that as pollution and growth rise, the positive effect from growth /mm<sup>3</sup> /mm<sup>3</sup> offsets the negative effects of pollution. After a not so high level of pollution (an annual average equal to 25 μ ) its effects on employment and labor productivity growth are negative again. The fact that African cities are growing fast but PM 2.5 levels have not yet reached the high /mm<sup>3</sup> levels experienced in places like China and India, poses an opportunity to plan ahead and avoid the mistake of not accounting for the negative effects associated with fast and unplanned growth. PM 2.5 has a negative effect on firm performance at levels that are not as high as the ones that these countries are currently experiencing. Pollution management is crucial to tackle the problem before it gets more serious. African cities could benefit from the examples we see now in cities that have experienced fast growth.

Looking at business environment and the effects on firm performance, we find that the only variable that affects African firms differently is capacity agglomeration. The effect of capacity agglomeration on labor productivity growth is stronger for firms in this region. An increase of 0.12 percentage points in capacity agglomeration results in an increase of almost 2 percentage points in labor productivity growth. There is no evidence of sorting of firms in big cities and most BE environment variables appear to have similar effects, regardless of the city size. This suggests that the stronger effect of capacity agglomeration for African firms is not clouded by sorting of firms or city size.

These results suggest that African firms face a few different challenges compared to firms in other regions. Moreover, addressing these problems could be key in pushing economic growth 24
**Figure 5.** Levels of CAP funds received by different member states are drastically different.

<!-- Start of picture text --> Total CAP Payments (2008 - 2013)<br>8E+10<br>7E+10<br>6E+10<br>5E+10<br>4E+10<br>3E+10<br>2E+10<br>1E+10<br>0<br>Purchasing Power Standard<br><!-- End of picture text --> _Source: DG AGRI (2017) Clearance Audit Trail System (CATS) database provided by the European Commission_ Figure 5 above shows how the allocation of total CAP payments differs across the EU member states. The EU determines how much CAP funds each of the member states receives. Nevertheless, member states have certain flexibility in allocating the CAP funds between the program’s pillars. This creates heterogeneity as to how the funds are spent, between both pillars, within countries, as showed in Figure 6 below.

**Figure 6** . There is clear heterogeneity of CAP funds allocation choices made by different countries.

<!-- Start of picture text --> Composition share of CAP payment by country (2008-2013)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LFA Agrienvironment Investment aid Other Pillar 2 payments Decoupled payments Coupled payments<br><!-- End of picture text --> _Source: DG AGRI (2017) Clearance Audit Trail System (CATS) database provided by the European Commission_ _Allocation of CAP funds based on regional characteristics_ To explore the characteristics of regions where the CAP funds tend to reach, we create four different clusters of regions at the NUTS1/NUTS2 levels based on the average holding size and the number of employees per holding. For each of these variables we create categories to group the existing regions. Table 4 below simplifies the clusters by characteristics, in which regions are grouped. Table 10