rafmacalaba/datause-encoder-data huggingface dataset:
variable representing the infrastructure, but signs of the other coefficients reinforce the former references.

# _Summary on the Estimated Elasiticities of Transport Cost per Weight_

The estimated values of the elasticities of the determinant factors of transport costs in various literatures tend to converge within the consistent ranges. The elasticities reviewed below are converted to the elasticities of transport cost per weight with respect to the various independent variables, obtained from log-linear regressions. The summary below only refers to ocean transport, except mentioned otherwise.

- _Port-to-port distance_ : around 0.14 to 0.21 for regressions on the disaggregated commodities base data. Only Limao and Venebles (2001), which uses the aggregated import charge data from matching method inclusive both ocean and land transportation, reports larger numbers: around 0.21 to 0.38. The larger numbers may reflect: (i) the higher cost land transportation; and (ii) the composition change effect that the longer distance results in comparative advantage in ocean shipping against the air, leading to higher value per weight ratio<sup>13</sup> and more expensive transport cost per weight.

- _Value per weight_ : around 0.53 to 0.63. The elasticity is less than one, implying that the _ad valorem_ transport cost decreases as the value per weight of the same commodity rises<sup>14</sup> . Within the same highly disaggregated category of commodity, transport cost takes smaller share in the sales price for the more expensive, luxurious goods.

- _Containerization ratio_ (percent change of transport cost per weight with respect to the percent point change of containerization ratio): around -0.038 to -0.081. The estimate in terms of elasticity by Hummels (2007) is -0.29 in the pooled cross-country regression, and - 0.134 if the cross-country difference is controlled.

- _Various Indicators for Port Infrastructure_ : significantly contributing to the reduction in transport costs. One point rise in Port Efficiency in the GCR index<sup>15</sup> corresponds to 4.3 percent reduction in _ad valorem_ transport cost. An increase in the number of major ports

> 13 See Harrigan (2005) for the discussion on the comparative advantages between air and ocean shipping.

> 14 The elasticity of _Ad valorem_ transport cost with respect to value per weight equals to the elasticity of transport cost per weight with respect to value per weight minus one.

> 15 The full mark of the index is 7.

15
# Figure 5 Central Bank of Hungary Real Balance Sheet and Profits

<!-- Start of picture text --> Net Credits - Base Money N Net Foreign Net Worth<br>Liabilities<br>s 1 1982 1983 1984 1985 16-.J-87 1988 1989 190<br>-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4<br>Real Profits<br>Base 3 1965S<br>180.t<br>160.<br>140.<br>120<br>100.<br>40.<br>20.<br>1982 1983 1984 1985 1986 19t7 1988 1989<br><!-- End of picture text --> 39
# outdetect set-up:

Normalization: Yeo and Johnson (2000) Z-score: (x-median)/q a=3 Outlier detection target: top and bottom (12447 observations are used)

## Incidence of outliers:

||Freq.|Percent|Share|
|---|---|---|---|
|Bottom|82|@.66|67.77|
|Top|39|@.31|32.23|
|Total|121|0.97|100.00|

Statistics for raw and trimmed pce:

||Raw<br>Trimmed|
|---|---|
|Summary<br>stats||
|Mean|897.88<br>828.38|
|Median|648.73<br>648.35|
|SD|3926.19<br>641.13|
|CV(%)|437.27<br>77.40|
|IQR|550.12<br>543.91|
|Inequality||
|Gini|@.4063<br>@.3565|
|MLD|@.2839<br>@.2076|
|Theil|@.5057<br>@.2232|
|cv2|9.5596<br>@.2995|
|A(@.125)|0.0541<br>@.0273|
|A(1)|@.2472<br>@.1875|
|A(2)|0.3776<br>@.3207|
|p90/p10|4.8185<br>4.7558|
|Poverty||
|H|@.0841<br>@.0823|
|PG|0.0186<br>@.0172|
|PG2|@.0062<br>@.0053|

Poverty line: 300
**Table 5 Full Liberalization: Sensitivity Analysis for σ2**

||**Re**<br>|**al Income**<br>|**, %**<br>|**Ter**<br>|**ms of Tra**<br>|**de, %**<br>|
|---|---|---|---|---|---|---|
||σ2=6|σ2=10|σ2=14|σ2=6|σ2=10|σ2=14|
|**World total**|**0.87**|**0.88**|**0.88**|**0.00**|**0.00**|**0.00**|
|**_Low and middle income countries_**|**_1.29_**|**_1.30_**|**_1.30_**|**_-0.08_**|**_-0.08_**|**_-0.08_**|
|Bangladesh|-1.31|-1.03|-0.90|-2.33|-2.12|-2.01|
|Brazil|3.19|3.18|3.17|2.53|2.52|2.51|
|Chile|0.07|0.06|0.05|-0.08|-0.08|-0.09|
|China|0.17|0.17|0.17|0.43|0.42|0.42|
|Egypt|5.01|5.01|5.00|-0.26|-0.27|-0.27|
|India|1.24|1.24|1.23|-0.28|-0.28|-0.29|
|Indonesia|0.69|0.69|0.69|0.18|0.18|0.18|
|Pakistan|-0.33|-0.34|-0.34|-0.49|-0.49|-0.49|
|Thailand|3.49|3.49|3.48|0.10|0.09|0.08|
|Mexico|0.34|0.35|0.36|-0.55|-0.55|-0.55|
|Nigeria|6.29|6.30|6.31|-1.97|-1.96|-1.96|
|Turkey|2.20|2.21|2.21|0.76|0.76|0.77|
|Rest of Asia|4.93|4.68|4.55|-0.38|-0.63|-0.76|
|Rest of LAC|0.39|0.44|0.48|-0.22|-0.18|-0.15|
|Morocco & Tunisia|5.81|5.82|5.84|-1.78|-1.79|-1.79|
|SACU|2.24|2.25|2.26|-0.01|0.00|0.01|
|Rest of Sub Saharan Africa|-0.63|-0.55|-0.44|-2.19|-2.13|-2.05|
|Rest of the World|1.51|1.54|1.55|-0.73|-0.72|-0.71|
|**_High income countries_**|**_0.76_**|**_0.76_**|**_0.76_**|**_0.02_**|**_0.02_**|**_0.02_**|
|Australia and New Zealand|0.61|0.59|0.58|0.42|0.40|0.40|
|Rest of Europe|3.55|3.56|3.57|0.87|0.88|0.88|
|EU 27|0.95|0.96|0.95|-0.18|-0.18|-0.18|
|United States|0.13|0.13|0.13|0.03|0.03|0.03|
|Canada|0.24|0.24|0.24|-0.30|-0.30|-0.31|
|Japan|0.92|0.94|0.94|0.34|0.34|0.35|
|Korea and Taiwan, China|3.94|3.95|3.95|0.14|0.14|0.14|
|Hong Kong SAR, China and<br>Singapore|1.25|1.28|1.30|1.21|1.22|1.24|

_Source: Authors’ computation. LINKAGE Model._ While the aggregators that we use would allow incorporation of the impacts of changes in the range of products traded, our focus in this paper has been exclusively on the intensive margin of adjustment, where the quantities of currently-traded goods adjust to the changes in distortions. Our analysis allows an initial comparison of the importance of the problem of aggregation bias in global general equilibrium modeling relative to the problems associated with omission of the extensive margin of new product introduction.

A recent econometric study by Debaere and Mostashari (2010) concludes that modest liberalization by an importing country appears to have very little impact on the range of products
status), will be of interest also to those who do not share the authors'<sup>dual</sup> labor market interpretation of the results.

Data: The data used in this study are drawn from the 1974 Israel Labor Mobility Survey, conducted by the Central Bureau of Statistics and relate to married full-time working males. We estimated a series of earnings<sup>functions</sup> for the primary and secondary labor matket sectors respectively, in<sup>which the</sup> log of individual annual earnings for full-time male married workers (with wife present) is run against three sets of explanatory variables, the first two of which were included in the regression model specified in the earlier paper:

- (a) traditional human capital related variables: years of schooling (SCH), years of labor market experience (EXP, defined as age-SCH-6), EXP<sup>2</sup> , an interaction variable EXP.SCH and number of years employed at present place of work (PRESWK).

- (b) control variables: years of residence in Israel (RESID), weeks worked in the past year (WEEKS), hours worked per week (HOURS), -

- a dummy variable relating to ethnic origin (ETHNIC, Oriental -

- 1, Western 0) and a series of dummy variables for economic sector.1°/

4
|LN full-time employees|0.127|0.030|0.0%|
|---|---|---|---|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.164|0.023|0.0%|
|Share of activities completely outsourced|0.045|0.086|60.4%|
|Share of activities partially outsourced|0.600|0.103|0.0%|
|Guateng provincial dummy|0.179|0.066|0.7%|
|Western Cape provincial dummy|-0.156|0.078|4.7%|
|sole proprietor dummy|-0.317|0.094|0.1%|
|close corporation dummy|-0.151|0.072|3.5%|
|Manufacturing|-0.191|0.089|3.1%|
|VAT|0.190|0.114|9.5%|
|PAYE|0.283|0.099|0.4%|
|Adj. R2|0.294|||
|Mean of the dependent variable|9.828|||

# **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+10% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|6.721|0.297|0.0%|
|LN full-time employees|0.128|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.165|0.023|0.0%|
|Share of activities completely outsourced|0.117|0.086|17.6%|
|Share of activities partially outsourced|0.638|0.103|0.0%|
|Guateng provincial dummy|0.180|0.066|0.7%|
|Western Cape provincial dummy|-0.153|0.078|5.0%|
|sole proprietor dummy|-0.319|0.094|0.1%|
|close corporation dummy|-0.155|0.072|3.1%|
|Manufacturing|-0.189|0.089|3.3%|
|VAT|0.194|0.114|8.8%|
|PAYE|0.280|0.099|0.5%|
|Adj. R2|0.293|||
|Mean of the dependent variable|9.766|||
|**Dependent Variable: LN of total cost of tax compliance**|Coeff.|Std. Error|Sig.|
|(Constant)|6.621|0.297|0.0%|
|LN full-time employees|0.128|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.165|0.023|0.0%|
|Share of activities completely outsourced|0.196|0.086|2.3%|
|Share of activities partially outsourced|0.681|0.103|0.0%|
|Guateng provincial dummy|0.182|0.066|0.6%|
|Western Cape provincial dummy|-0.151|0.078|5.4%|
|sole proprietor dummy|-0.321|0.094|0.1%|
|close corporation dummy|-0.158|0.072|2.8%|
|<br>Manufacturing|-0.187|0.089|3.6%|
|VAT|0.199|0.114|8.1%|
|PAYE|0.275|0.099|0.6%|
|Adj. R2|0.294|||
|Mean of the dependent variable|9.699|||

38
<!-- Start of picture text --> —Baseline scaling-up scenario<br>~Scenario with only capital inputs<br>5% , —Scenario with only labor inputs<br>—Scenario with only intermediary input<br>0%<br>5%<br>-10%<br>-15%<br>20%<br><!-- End of picture text --> <!-- Start of picture text --> 5%<br>0%<br>-5%<br>—Bakeline scaling up scenario<br>-10% |—Scekario with only capital inputs<br>—Scenario with only labor inputs<br>-15% |—Scenario with only int ry inputs<br>-20%<br>-25%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
ability of Kenyan exports to exploit alternative but lower unit value markets for frozen Nile perch (see below).

Following the lifting of the restrictions, Kenyan exports of Nile perch increased significantly through 2001 to 17,900 tonnes valued at US$48.5 million. However, in 2002 the volume of exports declined to 16,482 tonnes, largely reflecting on-going problems with the landed supply of Nile perch.

Kenyan exporters of Nile perch responded to the restrictions in the EU by exploiting alternative markets, including other industrialised countries such as Australia, USA, Japan and Hong Kong, as well as a number of middle-income countries in Latin America, Asia and the Middle East (Table 9). These exports were in the form of block frozen fish fillets that have a significantly lower price than fresh fillets (Figure 19). Exports of fresh fillets, at least initially, ceased altogether. Further, the price of frozen Nile perch fillets in these alternative markets was typically lower than that previously paid by EU buyers. As a consequence, the unit value of Nile perch exports declined through 1998 and 1999.

# **Figure 19. Unit value of Kenyan fish exports by type, 1997-2000:**

<!-- Start of picture text --> 5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>1997 1998 1999 2000 2001 2002<br>Frozen Fresh or chilled<br>US$/Tonne<br><!-- End of picture text --> _Source: CBS (2002)._ Despite the restrictions on exports to the EU, Nile perch continued to dominate fish and fishery product exports from Kenya. Thus, in 2000 for example, frozen Nile perch fillets accounted for 88 percent of total fish and fishery products exports by value and 92 percent by volume. Other significant exports include fresh, chilled and boiled crustaceans, frozen marine fish, frozen cephalopods and unsalted dried fish, although the value of each of these was less than US$1 million.

The EU had been an important and high-value market for fresh Nile perch fillets. As a result of the restrictions, however, the unit price of fresh fillets declined in both EU and non-EU markets as Kenyan (and Tanzanian and Ugandan) exporters, in particular those that lacked the capacity to 46
adults live in urban areas, where, in addition, access to antiretroviral treatment is easier. We can thus assume that their food consumption and nutritional status will be affected negatively by the food crisis. Although there is no way, with our data, to measure the direct impact of the food crisis on the overall welfare of households, we can use the change in income, consumption, food consumption and nutritional status of children between 2007 and 2008 as proxies for that impact. The food crisis indeed reached its peak between January and June 2008 and can be considered a systemic shock.

Income, consumption and food consumption are measured on a monthly per adult equivalent basis, in meticais<sup>3</sup> . Income is the sum, over all members of the household, of labor income (wage, household business or farm), non labor income (insurance, pension, etc.), private and institutional transfers, and home produced food. Consumption is the sum of food consumption (purchased and home produced), non food items purchased, education, housing (water, electricity, etc.), health, ceremony and special events expenditures. Real income and consumption are obtained using the Mozambique Consumer Price Index (INE, 2009). As the data in 2007 and 2008 were collected across several months, the monthly values of the CPI were used to match the date of interview of each household, to ensure the inflation index used is as close as possible to what the household actually experienced. Unfortunately we were not able to use a regional CPI which, to our knowledge, does not exist in Mozambique, and thus the national index was used. The food-and-drink CPI was used to deflate the food consumption variable.

The anthropometric measures used to proxy for households’ nutritional status are the weightfor-age z-scores for children aged 1 to 10 years old, the body-mass-index and the height-for-age z- scores for children aged 5 to 19 years. These were computed using the WHO growth standard (De Onis et al., 2007).

Figure 1 shows the change in some of these measures for all households and children of the sample between 2007 and 2008. All measures of welfare show a worsening of economic conditions, except for the height-for-age measures. Mean values of the welfare and nutritional measures in 2007

> 3 100 mts = $4.32 (November 2008)

8
11 international trade relations (e.g., Balassa 1979). It relates the importance of each MENA country as a supplier of agricultural and other products to the world market (and alternatively to markets in MENA) relative to all competing exporting countries. In symbols, the indicator is computed as

# _International Trade:_

RCAj(i,w) [Xj(i,w)/ TX(i,w)) / [Xj(w,w)/TX(w,w)], (1) and

# _Intraregional Trade:_

# RCAj(i,m) = [Xj(i,m)/ TX(i,m)] / [Xj(w,m)/TX(w,m)I,

(2) where Xj(i,.) represents exports of commodity j by country i to the world (w) or MENA (m), Xj(w,.) represents exports of commodity j by the world (that is, exports of the commodity by all competing producers) to the same two markets, and TX(i,.) and TX(w,.) represent total exports of all commodities by country i and the world, respectively. Broadly speaking, RCA values greater than unity indicate comparative advantage, and RCA values less than unity indicate comparative disadvantage. 1 4

# International Trade

The exceptional comparative advantage in crude petroleum of the majority of countries in the Middle East and North Africa is apparent in the computed RCA statistics in Table 5. Although many MENA countries export substantial amounts of manufactured products, comparative 14 RCA values reflect not only fundamental factors such as natural resource endowments but also tradedistorting factors, such as foreign and domestic protection and also foreign and domestic subsidies to production and trade. Thus, for example, the RCA values computed for MENA trade in agriculture will reflect trade-distorting farm policies in the major industrial countries and the MENA countries themselves, including pricing of scarce water resources for irrigation and other agricultural uses at below their financial cost, let alone their opportunity cost, in many MENA countries (World Bank 1995b).
ondary (grade 9). We use, separately, math and Spanish results of ENLACE 9th grade of 2009–the year when the candidates applied for a scholarship–to divide our sample in terciles of the distribution of test scores. We ran two specifications within each of the terciles, with and without controls, to measure the effects of the scholarship on the probability of graduating and test scores at the end of EMS. The effects of PROBEMS on the probability of graduation, by tercile of the 9th grade math distribution, are summarized in Table 6.

The results show positive and statistically significant effects (p-value of 0.02) of the scholarship on the probability of graduation among those candidates in the top tercile of the 9th grade math test scores distribution (i.e. those with academic _readiness_ ). The inclusion of controls does not change the results. For candidates with academic _readiness_ , receiving a scholarship increases their probability of graduation by 4 percentage points (of a mean level of 76% among the control group). We found no effects within the lowest and middle terciles of the 9th grade test scores distribution. There are also no effects of the scholarships on learning outcomes by tercile of 9th grade math or Spanish test scores.<sup>13</sup> Candidates with academic _readiness_ , those at the top tercile of the 9th grade math test scores, have the largest probability of finishing EMS (76%), as oppose to those in the bottom tercile (57%). It seems that the scholarship is only effective as long as the candidates have academic _readiness_ , otherwise the requirements of EMS are such that a scholarship is irrelevant to address this constraint. This is consistent with other school interventions that seem to build on prior success (Machin, McNally, and Meghir 2010).

In Appendix Table A5, we cross academic readiness with socio-economic status measured by the IHA. We found that the positive effects of PROBEMS on EMS graduation rates among students with academic _readiness_ hold both for poor and non-poor households, but are statistically significant only for non-poor households. The lack of statistically significant effects among poor students that show academic _readiness_ could be explained by the relatively low number of poor students in the upper tercile of the 9th grade test score distribution.

> 13Results available upon request.

21
Cobb-Douglas functions can be considered as a first order expansion in logarithms of Taylor series variables _(McFadden, 1978)._ One of the interests of this function is that all the elasticities of derived demands even cost function parameters which, under Shephard’s lemma, are also equal to cost shares. Therefore, first and second order effects are mixed in Cobb-Douglas function. This explains why, as observed above, it is advisable to use functional forms which avoid a-priori restrictions in the first and second order derivatives, i.e., flexible functional forms<sup>17</sup> .

> A year later, in 1997, a multi-product version of this paper prepared by _Jara-Díaz et al. (1997)_ is presented in the European Transport Forum. The aim of this new paper is to determine the specific marginal costs of each product, and the economies of scale and scope of Spanish port infrastructure services. The model estimated differs from the one used by _Martínez Budría (1996)_ basically in three aspects. Firstly, _Jara-Díaz et al. (1997)_ change the functional form specification and decide to apply a flexible functional form. Secondly,

> although _Martínez Budría (1996)_ acknowledged the multi-productive nature of the activity,

> in his paper, he used a product-aggregated measure, while in _Jara-Díaz et al. (1997)_ , product is defined as a five-component vector. Lastly, panel data estimation techniques used in both

> papers differ. Thus, in _Martínez Budría (1996)_ , function estimation is performed through

> least squares with dummy variables (fixed effect model), while in _Jara-Díaz et al. (1997)_ , a system of equations made up by cost function and factor-derived demand functions is estimated, using Zellner iterative technique.

The database used for cost function estimation is the same as the one used by _Martínez Budría_ , and the functional form selected for long-run cost function was the following quadratic function:

> where: _C_ = Long-run total cost, _yi_ = Output vector, _wi_ = Inputs vector, _yi_ = Mean value of

> outputs sample, _wi_ = Mean value of inputs sample, ε = Error term, α _0_ , α _i_ , β _i_ , α _ij_ , β _ij_ , δ _ij_ ,

> are parameter to be estimated. This same model was reestimated by _Jara-Díaz et al.(2002)_ ,

> 17 Furthermore, the application of flexible functional forms does away with the need to meet separability and homogeneity restrictions, since they can be considered as contrastable assumptions.

16
where the difference of the LHS increases with the expected variability of future consumption. To take into account the _riskpremium_ associated with increasing consumption variability, function OC can be introduced whereby (9) _, E[u' (C1 )]_ = _0(,E(C 1), (o,_ I w _)),_ 0c <0, **_4bi'J_** > 0 Relation (2) can thus be rewritten as (2b) _u' (Qth I_ w,) with derivatives (10) _q'Q< 0 ,q'p> 0',, R?,qY R>,0,>0_ where _pr =[tEp% c=_ and _RrQ =[_ E _l(Ri ,)]'11_ are the vectors of the expected values (as of date _e+i_ _t)_ of, respectively, commodity prices and compound gross real interest rates on assets. As discussed earlier, the sign of O', depends on the behavior of both _d Q_ and pr(.) and thus, ultimately, on the liquidity of the asset. Note, however, that O',r _(L), si' (B) >_ 0 while O', _(A)_ < 0. Finally, an increase in financial efficiency increases Q's marginal utility. It is assumed that this effect is relatively stronger for private claims, such as equity, while it is negligible for government securities which already trade at a low discount due to government guarantee (see above).

Using (2b) and positing w, = wo, rule (7) under (El)-(E4) can be written as 14