rafmacalaba/datause-encoder-data huggingface dataset:
When the cost of operating a taxi rises, as it does in our simulations of an increase in the fuel tax rate,  , assuming no change in the demand function for taxis, some taxi operators would exit the industry and equilibrium would be re-established at higher taxi fares. But the higher fuel tax impacts a car or motorcycle passenger more severely, since the higher fuel cost is shared by fewer co-passengers. Hence, higher fuel taxes, other things constant, cause passengers to switch to taxis which then causes a higher demand for taxis at the higher fuel tax rate. This, as we will see causes more taxi operators to enter the industry while taxi fares rise. Equilibrium taxi fares are calculated by solving (13) for<sup>_f_</sup> 3<sup>.</sup> Buses are operated by a public authority and bus operations in the base case are shown to be subsidized. We treat the fare per bus trip as a policy instrument. Its value can be made positive or zero. So, unlike the case of taxis, when the fuel tax rises buses become more expensive to operate and, with fares unchanged, they become less profitable to operate. When fuel taxes rise, many trips switch to bus. By doing so they escape the impact of higher fuel taxes on their budgets, and reduce road congestion which improves welfare provided the disutility from in-bus crowding is not too severe.

Trams are also a publicly operated mode of transport and tram networks are located in the Casablanca Municipality. The expansion of tram is done by reallocating a portion of the existing road capacity for the tram network. In this situation, half of the road capacity in the relevant roads are allocated for tram expansion. The decrease in road supply is treated as a parameter for tram expansion. A tram expansion makes it easier for a commuter to switch to tram from the other modes of transport.

# **3. Calibration**

The model formulated in section 2 is empirically implemented for Greater Casablanca. Our model approximates the baseline data of 2014 displayed in Table 1, in Table 2 and in column “Base” of Table 3. The targeted share of transportation related expenditure to the average income across all modes is around 0.135.<sup>8</sup> We set the average monthly wage to match this share. We got

> 8 According to the Bureau of Labor Statistics, in the U.S. the average expenditure in 2014 was 13.5% of annual income. <u>https://www.bls.gov/opub/reports/consumer-expenditures/2014/home.htm.</u>

12
linkage is one that is unique to the ILS market and is usually only available to those few who participate in the market as broker or underwriter. Another advantage is that a secondary market in ILS trading has developed that allows insight into changes in price over time. This is unavailable in traditional markets where the philosophy has been to “write and ride” risk on an annual basis.

**Figure 1. Distribution of Perils for Index-Linked Securities since Inception**

<!-- Start of picture text --> Expected Loss Exposure (1997 - Q1 2008)<br>Japan Quake Other CAT<br>7%<br>Japan Wind 3%<br>4%<br>Euro Quake<br>1%<br>US Wind<br>44%<br>Euro Wind<br>22%<br>US Quake<br>19%<br>Exposure by Potential Limit (1997 - Q1 2008)<br>Other CAT<br>Japan Quake 2%<br>10%<br>Japan Wind<br>7% US Wind<br>Euro Quake 34%<br>1%<br>Euro Wind<br>16%<br>US Quake<br>30%<br><!-- End of picture text --> Source: Lane Financial LLC (2008).

3
# **Table 7: Impacts of RCEP with the Armington Model: Decomposed Results**

(results are percentage change from initial equilibrium, unless otherwise indicated)

|||||**Arming**|**ton Struct**|**ure**||||
|---|---|---|---|---|---|---|---|---|---|
|Ben|chmark<br>**RCEP**|**FDI Services**<br>**Barriers Only**|**Cross-border**<br>**Services**<br>**Barriers Only**|**Import Trade**<br>**Facilitation**<br>**Only**|**Export Trade**<br>**Facilitation**<br>**Only**|**Import Tariffs**<br>**Only**|**RCEP**<br>**Partner**<br>**Tariffs Only**|**Import**<br>**NTMs**<br>**only**|**RCEP**<br>**Partner**<br>**NTMs Only**|
|**Scenario definition**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|
|RCEP FDI barriers (% reduction)|50|50||||||||
|Cross-border services barriers (% reduction)|50||50|||||||
|Import Trade Facilitation (% reduction for RCEP countries)|20|||20||||||
|Import Trade Facilitation (% reduction for non-RCEP countries)|5|||5||||||
|Export Trade Facilitation (% reduction)|20||||20|||||
|Export Trade Facilitation (% reduction for non-RCEP countries)|5||||5|||||
|Tariffs on Filipino Imports from RCEP (% reduction)|100|||||100||||
|Tariffs on Filipino Exports to RCEP (% reduction)|100||||||100|||
|NTMs on Filipino Imports from RCEP (% reduction)|20|||||||20||
|NTMs on Filipino Exports by RCEP partners (% reduction)|20||||||||20|
|Elasticity of Substitution Multiplier|1.24|1.24|1.24|1.24|1.24|1.24|1.24|1.24|1.24|
|FDI Elasticity Multiplier|30.00|30.00|30.00|30.00|30.00|30.00|30.00|30.00|30.00|
|**Aggregate welfare**||||||||||
|Welfare (EV as % of consumption)|1.5|0.3|0.0|0.4|0.2|-0.1|0.2|0.2|0.2|
|Welfare (EV as % of GDP)|1.2|0.2|0.0|0.3|0.1|-0.1|0.2|0.2|0.2|
|**Government budget**||||||||||
|Tariff revenue (% of GDP)|0.4|0.7|0.7|0.7|0.7|0.3|0.7|0.7|0.7|
|Tariff revenue|-47.5|0.5|0.0|0.9|1.1|-49.4|1.2|0.5|1.6|
|**Aggregate trade**||||||||||
|Real exchange rate|-0.2|0.5|0.0|0.3|-0.6|0.5|-0.5|0.2|-0.5|
|Aggregate exports|10.8|1.0|0.0|1.4|1.0|2.2|1.3|**1.1**|2.0|
|**FDI Output***|25.9|**24.4**|0.0|0.5|0.1|0.1|0.1|0.3|0.0|
|**Factor Earnings**||||||||||
|Skilled labor|1.0|0.4|0.0|0.3|0.0|0.1|-0.1|0.3|0.0|
|Unskilled labor|2.6|0.3|0.0|0.1|0.4|-0.3|1.2|-0.1|0.5|
|Capital|2.0|0.5|0.0|0.2|0.2|0.3|0.2|0.2|0.3|
|**Diagnostics****||||||||||
|Feenstra Ratio Summary|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|100.00%|
|Terms of trade|100.36%|99.98%|100.00%|99.66%|100.37%|99.60%|100.42%|99.73%|100.56%|

***** We impose an upper bound of 90 on the elasticity of substitution between FDI and domestic business services (30 times the Krugman value of σ=3); in this case, we fall short of fully matching the FDI response in the Krugman model. **The percentage change is the value indicated minus 100.

Source: Authors’ estimates 54
Table 2 presents the results of replacing earnings with the residuals. Factor 1 again captures a generational effect. A "capital intensity" factor emerges that suggests that entrepreneurs with more capital per worker tend to work longer hours, tend to be better educated and, consistent with Jovanovic's approach, have been in business longer.

Factor 3 weighs heavily on the residual, but on none of the other variables important in the previous "dualism" factor. Further, the MLE estimates suggest only 13% of the sample is found in a distinct, lower distribution that might be attributable to the disadvantaged sector of a dual labor market. The positive association of earnings in the "dualism" factor arises from the distribution of human and physical capital rather than differing returns to them.

_ib. Cluster Analysis_ The results from the cluster analysis tell a broadly similar story. The mean values for the six clusters are given in table 3, the distance between the final cluster centers in table 4, and the mean values converted back into their original range in table **5.13** Tables 6-8 provide information on a variety of firm and enterprise characteristics by cluster.

The generational division identified in factor 1 broadly emerges, with clusters 1-3 being largely young and capturing 53% of the sample and 4, 5 and 6 capturing older and less welleducated workers. The other divisions correspond broadly to the second factor which, as the orthogonality of the factors would predict, cuts across generations. Clusters 2 and 4 are distinctly higher in capital intensity, although the high earnings, hours worked, and education suggest that cluster 1 could also be part of an upper tier comprising 25% of the sample-- close to the 26% suggested by the MLE.

**13** In reality, the dendrograms leaned slightly more heavily to there being five factors but the six factor break, while similar, yielded an additional important division.

12
World Economic Forum (2012), _The Global Enabling Trade Report, 2012: Reducing Supply Chain Barriers_ , edited by Robert Lawrence, Margareta Drzeniek Hanouz and Sean Doherty. Available at www.weforum.org.

Worley, Chris (2009), “Documentation of the Calculation of Ownership Shares for Kenya,” mimeo. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4= <u>hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp sharing</u> Worley, Chris (2010), Documentation of Ownership Share Data for Tanzania,” mimeo. Available at: <u>https://drive.google.com/folderview?id=0B0V-t-Bs4-</u> = <u>hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp sharing</u> 38
24 <!-- Start of picture text --> Per Cent<br>5-<br>| ~~~Growth Rote\<br>2 ....<br>-1- .I'<br>IP *6<br>-10 10 30 50 70 90 110 130 150 170 190 210<br>Black Market Exchonge Rote Prmiium<br><!-- End of picture text -->

Figure 5: Effect of Black Market Exchange Rate Premium on Economic Growth Rate.

The line P denotes two standard deviations above the mean of all zero or positive black market exchange rate premia in the Pacific Basin economies, while C denotes two standard deviations above thc mean of all zero or positive black market exchange rate premia in the control group of countries. Evidently, black market exchange rate premia tended to be considerabiy higher in the control group than they did in the Pacific Basin economies.

The superior efficiency of FDI in the Pacific Basin developing market economies reflects not only less distorted financial conditions than in other parts of the developing world but also less distorted trading systems. The outward orientation of the Pacific Basin developing market economies ensures that relative prices cannot diverge too far from world market prices. Under these conditions, there are few possibilities for **FDI** to find high profits in protected markets. The favorable investment climates, however, ensure that FDI flows are readily available without the need for governments to discriminate in favor of this particular form of investment finance.<sup>1 7</sup> Hence, these economies have avoided the two major pitfalls of **FDI,** namely, low or negative productivity caused by distortions in the economy and expensive 17The Maxwell Stamp report (1991, p. 246) concludes that general economic conditions can outweigh such deficiencies as poor accounting standards: ... **f** toreign investors in Thailand do not appear to have been discouraged by these factors."
particular-year rainfall from the long-term median.<sup>5</sup> The choice of IV is consistent with existing literature (Fisher & Kandiwa, 2014; Harou, 2018; Karamba, 2013; Ragasa & Mazunda, 2018; Sibande et al., 2017). The variables also reflect the social and political capital that influences access to subsidized inputs.

Provision of irrigation infrastructure is a less endogenous in the sense that household behavior may not be integrated in the policy choice to make the investment. However, it becomes an issue if the government targets locations with specific characteristics; for instance, locations that are dry or with the poorest communities. This may dampen the impact estimates. Therefore, our modeling accounts for geospatial indicators reflecting agroecological zones. We additionally include a variable for whether the village chief receives payment for land use or sale as one of the IVs. The actions of the chief may facilitate access to irrigatable land for some individuals but may not alter household food consumption. The instrumental variables validity is checked using the IV-2SLS for panel-data models since the IV Poisson method does not produce post estimation tests. The combined effects of the interventions are assessed by including interaction terms in the models.

Models estimating program effects on food consumption scores are estimated using singleequation instrumental-variables regression via the GMM to take advantage of pooled data. Additional analysis considers food and nutrition security as measured by coping strategies using Tobit estimation. A comparison of the panel Tobit model with the pooled Tobit model using the likelihood-ratio test shows that there were panel-level effects in the relationship; hence, results based on panel data analysis are reported.

# **4 Results**

# **4.1 Descriptive statistics**

Table 2 presents the descriptive statistics for the pooled sample. On average, households consumed food from 8 food groups and the maximum was 12 food groups. The consumption of 8 food groups compares well with other national-level studies (Jones et al., 2014; Matita et al., 2022). The mean FCS was 49, which indicates that food and nutritional insecurity are not severe but visible. The proportion of households with borderline and poor food security situation were 20.4 and 2.5

> 5 Long-term median rainfall data is sourced from World Bank Group, Climate Change Knowledge Portal (2023). URL: https://climateknowledgeportal.worldbank.org/.

11
S

# TABLE 1

|Program|MAJOR DIRECTL<br>Descritfoll|OANPROGR<br>|AMS OF THE<br>|U.S. GOVER<br>|NMENT, 198<br>|9<br>|Millons of**$**|
|---|---|---|---|---|---|---|---|
|<br>|p<br> <br>|New**Loans**|% Subidy|Subsidy cost|**oand**|**% wrieffs**|**L_sses**|
|Commodity Credit<br>C.orporationS<br>______________<br>|**Short**<br>tem**loans**to<br>**prou"**ofagLaIbIa<br>**commodities_**|10,746<br>(54%)|||13,695<br>**(7%)**<br>_<br>_<br>___|0.6%|76.7<br>_<br>_<br>_|
|Foreign Military<br>Sales<sup>4</sup>|PmementOf U.S.<br>**mtiy qpip m1 by**<br>forign govenmmes|4,460<br>(22%)|||20,821<br>(10%)|||
|Veterans'<br>Administration|**Purchasing**<br>of VA forclosed<br>hones by creditwory<br>indivduals|977<br>**(5%)**|16.0%|156.3|1,629<br>(1%)|||
|**Farmers' Home**<br>**Administration**<br>|Purchasing and opeating<br>**farms,**<br>**dia**<br>**assisane,**<br>**pmaving**<br>**mural**<br>**hosing,**<br>**etc.**|900<br>(4%)|8.3%|74.7|52,122<br>(25%)|1.3%|677.6|
|Export-Import<br>**Bank**<br>|**Provids**direct loans to<br>**finane**U.S.expoters|705<br>(4%)|11.6%|81.8|8,274<br>(4%)|.||
|P.L. 480**Longterm**<br>**exportcredits**<br>_|Meeting needs of developing<br>nationstrughsaleof<br> agiutual<br>commodities|739<br>(4%)|70.0%<br>**I**|517.3|12,261<br>(6%)|**I**_||

**_3_** The exact amount of subsidy in any given year depends on the difference between market prices and the support price guaranteed to the farmer.

> **4** All of **these** loans are being forgiven. The repayment record on these loans was so miserable that they have been converted into grants.
- 76 -

- Schwartz, Jordan, Luis Andres, and Georgeta Dragoiu. 2009. ―Crisis in Latin America: Infrastructure Investment, Employment and the Expectations of Stimulus.‖ Policy Research Working Paper No. 5009, World Bank, Washington, DC.

- United Nations. 2011. Final Report: United Nations Development Assistance Framework for Nepal 2008-2010. Available at <u>http://www.un.org.np/sites/default/files/report/united_nations_development_assistanc</u> - -

- <u>e_framework_2008 2010_ _evaluation_report.pdf</u>

- Ware, Glenn, Shaun Moss, Edgardo Campos, and Gregory Noone. 2007. ―Corruption in Public Procurement: A Perennial Challenge.‖ In _The Many Faces of Corruption: Tracking Vulnerabilities at the Sector Level_ , ed. Edgardo Campos and Sanjay Pradhan, 295–334. Washington, DC: World Bank.

- Wilson, Robert. 1977. ―A Bidding Model of Perfect Competition.‖ _The Review of Economic Studies_ 44: 511–18.

- Wolfstetter, Elmar. 1996. ―Auctions: An Introduction.‖ _Journal of Economic Surveys_ 10: 367–421.

- World Bank. 2005a. Project Appraisal Document: Rural Access Improvement and Decentralization Project. Report No. 31624-NP. The World Bank.

- World Bank. 2005b. _Infrastructure and the World Bank: A Progress Report_ . World Bank: Washington, DC.

- World Bank. 2007. Nepal Public Finance Management Review: Managing Public Finances for a New Nepal (No. 43384-NP).

- World Bank. 2008a. _Procurement under World Bank-Financed Projects: FY07 Annual Report_ . Operations Policy and Country Services. Washington, DC: World Bank.

- World Bank. 2009. Project Paper on Nepal Rural Access Improvement and Decentralization Project, 2009 (No. 50766-NP).

- World Bank. 2009. Enterprise Survey: 2009 Nepal. Available at

- <u>http://www.enterprisesurveys.org/ExploreEconomies/?economyid=136&year=2009</u>
In Cice = PyWylnCict + ZictS + Eict Eict = P2W2€ict + Vict InL = —(n 2)In(2m) —(n 2)In(o?) — (1 -+Yn|V| + In|A| + In|BI —(1 207)(Alnc —Z 'B'V-1B(Alnc — Z

InL = —(n 2)ln(2m) —(n 2)In(o?) (1 )In|V|-—(1 207)(Ince-—Z V~1B(Inc —Z

oz +02 a2 a2 a2 oz +a?2 og a2 a2 a2 oz +02 og
<!-- Start of picture text --> where wii?D<br>GAB<br>a; is a clinic fixed effect, 1(wRPP < 13) is an indicator of whether the clinic reported the first visit to<br>be in the first 12 weeks based on EDD, T;<br>and €;;<br><!-- End of picture text --> <!-- Start of picture text --> two measures and £ would have a coefficient of 1. However, because premature births occur before<br>EDD, we expect f to be close to but less than one. Then we can interpret the other coefficients as the<br>effect on W,;""GAB — B WijEDD<br><!-- End of picture text --> <!-- Start of picture text --> in the post period then 6, the interaction between treatment and reported having the first period<br><!-- End of picture text -->
Similar to previous studies on the impact of conflict on educational attainment, I first use data from the postconflict DHS (2006) and exploit variations in exposure to conflict by birth year cohort and district. In its simplest form, the estimating equation can be written as follows: where EDUCijt is, in turn, a dummy variable equal to one if individual _i_ in district _j_ born in year _t_ has completed primary education and zero otherwise or the number of years of education completed by this individual; δj represents district fixed effects; αt represents birth year dummies; and TOTCONFjt is the interaction between a dummy equal to one when the individual belongs to the treated cohort and the number of conflict casualties (per 1,000 inhabitants) in district _j_ during 1996– 2006. In the baseline regressions, I define the treated cohort as those aged 5 to 9 years at the beginning of the conflict in 1996, whereas the control cohort includes individuals aged 16 to 19 years at the beginning of the conflict. This choice is discussed in the preliminary analysis at the end of this section.

Under the assumption that there is no correlation between the number of district casualties and unobserved factors varying with district and birth cohort, _β_ is the causal effect of a one-unit increase in TOTCONFjt on the primary completion rate or on the number of years of education completed by exposed cohorts. A one-unit increase in TOTCONFjt roughly corresponds to one standard deviation in the district-level distribution of casualties (0.98). Another way of appraising the magnitude of _β_ is to consider a one-unit increase in TOTCONFt j<sup>as a move from the district with the least conflict to</sup> the 53rd district (out of 75) in order of conflict intensity or from the 53rd district to the 69th district in order of conflict intensity—a very large increase in conflict intensity.

There are five “developmental regions” in Nepal, which are relatively homogeneous in terms of their level of development (see figure 1). In equation _(1)_ , I implicitly restrict birth cohort effects 12
# D5. Why are Structural Stockholding Models UnsatisfactorY?

39. The discussion in the previous paragraphs makes it clear that structural estimation of the Muth model has not been very successful. There appear to be two reasons for this. First, actual commodity market stock data do not correspond very closely to the theoretical speculative stockholding concept (see also Trivedi, 1990). Stocks are also held for transactions and precautionary reasons. Moreover, stock data are frequently incomplete - it is well-known that published commodity market stock series fail to satisfy market clearing identities. Where this is the case it is likely that speculative stockholdings, other than those held on commodity exchanges, will often be particularly poorly covered.

40. The second reason for the unsatisfactory performance of the structural models relates to the fact that if futures prices are unavailable, the anticipated speculative gain is not observed. Furthermore, we have seen that IV estimation of equations in which the actual gain is substituted for the anticipated gain tends to give poor results because of the lack of good instruments for the anticipated component of the actual gain.

41. It seems likely that even in cases in which the anticipated gain is observed via the futures price that the structural approach will be relatively uninformative. This is because to the extent that stock is carried forward in periods of excess commodity supply, this will drive the expected future price down and pull the current cash price up, thereby reducing the anticipated gain. Indeed, in the extreme case of risk neutrality we have seen that whenever positive stocks are held the expected capital gain net of interest costs and depreciation is equal to zero. In that circumstance the expected price pet+llt (or the futures price ft) conveys no information additional to that in the spot price. This is not to deny that expected future market conditions are reflected in the futures price, but rather to assert that, in this case, they are equaliy reflected in the cash price. In the case of risk neutrality, therefore, the futures price only conveys information additional to 25
To reconcile our results with Filmer et al. (2005), Table 3 replicates their gender gaps, noting that these numbers do not account for differences in the entry grades of employees. We consider the 1997 crosssection in our sample and apply the same sample restrictions, to the extent that we could identify them. While our samples do not perfectly match, we obtain very similar gender and country part gaps, with large differences in salaries in favor of Part 1 men and against Part 1 women and all Part 2 employees. We then look at the subsample of staff hired after 1987, for whom we are able to identify the grade at which they were hired. The pay gaps are a bit smaller (probably because they are from younger cohorts) but still large. We then consider subgroups that are homogeneous with respect to their entry grade and these differences become much smaller and sometimes disappear altogether. In other words, our results are fully compatible with those of Filmer et al. (2005) and suggest again that differences in entry grades are a key component of differences in salaries between subgroups at the WBG.

**Table 3: Comparison to the Salary Gaps Obtained by Filmer Et Al. (2005)**

|||**Salary in 1997**|**Entry Salary**|**Tenure in 1997**|**N**|
|---|---|---|---|---|---|
|**Filmer et al.**|**Male - Part I**|100|100|11.9|1356|
|**(2005), table 1**|**Male - Part II**|95.3|91.4|12.5|857|
||**Female - Part I**|86.6|86.4|10.7|537|
||**Female - Part II**|82.5|80.6|11.2|250|
|**Replication:**|**Male - Part I**|100|N/A|11|1745|
|**Employees hired at**|**Male - Part II**|94.3|N/A|11.6|1143|
|**GE+ grades**|**Female - Part I**|84.1|N/A|10.6|752|
||**Female - Part II**|74.7|N/A|11.8|440|
|**Employees**<br>**hired**|**Male - Part I**|100|N/A|4.7|886|
|**after 1987 at GE+**|**Male - Part II**|92.5|N/A|5.3|516|
|**grades**|**Female - Part I**|88.8|N/A|4.3|405|
||**Female - Part II**|80.6|N/A|4.4|200|
|**Employees hired at**|**Male - Part I**|100|100|4.5|264|
|**GF after 1987**|**Male - Part II**|100.6|98.3|5.2|209|
||**Female - Part I**|99.1|101.5|4.6|190|
||**Female - Part II**|96.3|94.2|4.3|112|
|**Employees hired at**|**Male - Part I**|100|100|5|546|
|**GG after 1987**|**Male - Part II**|97.6|94|5.4|254|
||**Female - Part I**|93.5|94.5|3.8|157|

35