rafmacalaba/datause-encoder-data huggingface dataset:
inequality of opportunity as the share (or level) of overall inequality in a given population which exists between social groups defined by different initial circumstances (rather than within these groups). The indices are inspired by the observation that, if opportunities were equally distributed, outcomes would be orthogonal to pre-determined morally irrelevant circumstances, so that the between-type inequality share would be zero. Because not all relevant circumstances are observed, the indices provide a lower-bound estimate of inequality of opportunity.

Indices belonging to this class may differ along three dimensions: the inequality aversion parameter in the underlying inequality measure; the path of the decomposition, and the nature of the estimation procedure. We show that if we restrict our attention to path-independent decomposable inequality indices, the class collapses to a unique index, which can be estimated either parametrically or non-parametrically. The proposed parametric estimation procedure is a useful complement to the simple non-parametric decomposition both for data-efficiency reasons, and to estimate partial, circumstancespecific indices.

The second empirical tool is an opportunity-deprivation profile: the list of Roemerian _types_ (i.e. social groups that share identical circumstances) that account for the lowest-ranked p% of the population, when types are ranked by their mean advantage levels.<sup>43</sup> The profile identifies the types with the lowest-ranked opportunity sets in society. If followed over time, they would allow a practical application of Roemer’s (2006) suggestion that economic development might be measured by the rate of progress of the worst-off type.

We applied these concepts to a rich data set for six countries in Latin America, whose surveys contained information on a number of relevant pre-determined, morally irrelevant circumstances, namely: gender, race or ethnicity, birthplace, mother’s and father’s education, and father’s occupation. We calculated our unique path-independent measure of inequality of opportunity both parametrically and non-parametrically, for the distributions or earnings, household per capita income, and household per capita consumption expenditure. As expected, the non-parametric method tended to

> 43 We set p%=10%, and used consumption expenditure per capita as our indicator of economic advantage (except for Brazil, where income per capita was used instead).

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# 4. Women-headed households

Due to the prevailing patterns of male out-migration, in large part a result of population pressure and land fragmentation, there is a large and growing number of female-headed households throughout Kenya. These households play an important role in the agricultural system and may face particular constraints in assuring food security for their families.

According to data from the 1979 census, 33 percent of all rural smallholder households were headed by women with the highest percentages in Nyanza, Eastern, Western and Central Provinces. Data from Kakamega and Machakos Districts show that ever. -iese high percentages do not fully represent the extent of de facto women-hcaded households. Surveys in these areas report that 55 and 47 percent respectively of the farms were in fact managed by women (World Bank, 1989).

Female-headed households fall in the poorest category of households country-wide. Findings from the Integrated Rural Surveys showed significant differences between these households and their male counterparts: the mean annual income in male-headed households was 19 perlent greater than in femaleheaded households (ILO, 1986). Overall, women-headed households have fewer productive assets: they have less land, depend more on farming but are less involved with cash crops and cattle. Cash crops often require more labor input and a higher degree of management; similarly, improved cattle require more time spent on regular visits to veterinary officers (IIO, 1986). Female-headed households also have limited access to off-farm income-earning opportunities; this is particularly true for households headed by unmarried women since they tend to be older with lower educational attainments (ILO, 1986). As a result, these households have significantly less income than their male-headed counterparts (GOK and UNICEF, 1984).

Table 7 Percentage of Households Headed by Women by Province

||Coast|Eastern|Central|Rift|Nyanza|Western|National|
|---|---|---|---|---|---|---|---|
|1976-79|12.2|22.9|31.1|21.1|32.6|32.8|27.3|
|1979|23|37|36|29|36|36|33|
|1984a|44.4|51.3|58.4|43.1|43.8|54.8|n.a.|
|1984b|29.0|35.7|33.5|25.0|34.2|36.4|n.a|

a: never married, married in past, married with husband absent b: married, husband away, married in past Source: GOK Integrated Rural Surveys, 1976-79; 1979 Census; KCPS, 1984 (excluding North East Province). Cited in Worid Bank, 1989.

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<u>Table A4: Impacts of hosting refugees on the components of household consumption expenditure per adult equivalent</u>

|||Food<br>|||Education<br>||O<br>|ther non-fo<br>|od<br>|
|---|---|---|---|---|---|---|---|---|---|
||All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|
||(1)|(2)|(3)|(1)|(2)|(3)|(1)|(2)|(3)|
|Refugee Intensity|-0.163**|-0.219**|0.123|0.376|0.598|-0.565|-0.208|-0.238|-0.009|
||(0.081)|(0.086)|(0.163)|(0.460)|(0.530)|(1.001)|(0.147)|(0.175)|(0.175)|
|Constant|9.527***|9.810***|7.925***|-5.725*|-7.868**|4.292|7.721***|7.423***|8.397***|
||(0.481)|(0.521)|(0.884)|(3.062)|(3.632)|(5.149)|(0.930)|(1.112)|(1.227)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.061|0.072|0.043|0.068|0.077|0.057|0.024|0.024|0.062|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|12937|9337|3600|12937|9337|3600|12937|9337|3600|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm (first three columns) or “started log” (last six columns) of the components of total household consumption expenditure per adult equivalent. Household sample weights from ESS2 applied. The first, middle, and last three columns report results for food, education, and other non-food per adult equivalent consumption expenditures, respectively. Columns labeled (1), (2), and (3) report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 characteristics variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

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# **D Attrition**

Table A.6. Attrition between Waves

||Attrited Household [=1]<br>(1)|
|---|---|
|Household Venezuela: parents or siblings [=1]|-0.033|
||(0.024)|
|Household Venezuela: partner/spouse [=1]|-0.068<br>|
||(0.025)|
|Household Venezuela: others [=1]|0.003<br>|
||(0.034)|
|Knew of job opportunity before migrating [=1]|-0.022<br>(0.022)|
|Ever worked [=1]|-0.004<br>|
||(0.082)|
|Employed at private frm [=1]|-0.042<br>|
||(0.054)|
|Employed with Government [=1]|-0.050<br>|
||(0.060)|
|Self-employed or employer [=1]|-0.048<br>|
||(0.057)|
|Written contract [=1]|0.005|
||(0.025)|
|Gap between last job and migration (months)|-0.002<br>(0.002)|
|Years of education before migration|-0.010<br>(0.004)|
|Migrated for health reasons|0.038<br>|
||(0.034)|
|Friends/family in Colombia|-0.037|
||(0.023)|
|Had smartphone [=1]|0.007|
||(0.021)|
|Owner of dwelling in Venezuela [=1]|0.003<br>(0.031)|
|Electricity in Venezuela [=1]|-0.077<br>|
||(0.129)|
|Running water in Venezuela [=1]|0.046<br>|
||(0.032)|
|Sewage in Venezuela [=1]|-0.022|
||(0.045)|
|Female [=1]|-0.051|
||(0.023)|
|Age (years)|-0.004|
||(0.001)|
|Number of children|0.000<br>|
||(0.008)|
|Time in Colombia (months)|-0.002|
||(0.001)|
|Observations|2,200|

_Notes:_ Robust standard errors are reported in parentheses.

12
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13 implied time horizon.9<sup>1</sup> As a rough guide, cross-sectional data sets are thought to represent long run relationships whereas time series data (especially if monthly or quarterly data are used) reflect short run demand relationships. But this is not an unambiguous guide, and panel data sets (combined cross-section and time series data) further complicate interpreting the time dimension of elasticity estimates. For those demand categories with several elasticity estimates, we compared elasticity estimates for different data sets. The pattern is not clear. There is a tendency for cross-section data to produce more elastic (less inelastic) estimates, but there is no precise relationship. Consequently, our 'most likely" range of elasticities is ambiguous concerning the implied time horizon, but we expect that the upper range of our range (in absolute values) corresponds to long run as opposed to short run elasticities.

# V. Conclusion

Not surprisingly, because transportation is a derived demand, it tends to be inelastic. But there are exceptions, such as discretionary travel and some freight shipments. Our interest in this review is in own-price elasticities, i.e., the sensitivity of shippers or travelers to the price charged for transportation service. We exclude quality of service elasticities and, for the most part, cross-price elasticities from our review. Many demand studies investigate markets where there is competition from other modes. Even if the overall demand for transport by shippers or travelers is highly inelastic, the presence of competition generally causes the own-price elasticity of demand for a specific mode to be less inelastic than for the market as a whole. Therefore if one were interested in the overall or market price elasticities of demand for transport, we judge that they would be toward the inelastic end of the spectrum of empirical estimates we have surveyed.

The majority of studies are from developed countries. Presumably this reflects the availability of data, research resources, and domicile of those 9 This is in contrast .o estimates of cost functions which almost always state explicitly whether short run or long run interpretations are involved.
goods using U.S. import data. Given Serbia’s landlocked status, and the fact that most imports move by truck from Europe, it is difficult to estimate this value for Serbia in the same manner. We can however use the reported penalties for delay to provide a secondary estimate.<sup>20</sup> These penalties were negotiated between importing firms and freight forwarders and should therefore reflect a “market price” of an unexpected delay.<sup>21</sup> Given that the median value of an import shipment for our sample of freight forwarders is 15,000 euros (or 17,500 euros for the sub-sample reporting penalties for delays), a 150 euro penalty for a one day delay corresponds to about 1 percent (0.9 percent) of the value of the import shipment. This represents a penalty rate, so while it is the cost to the freight forwarder of delays it may overstate the true cost of delay for the importing firms.<sup>22</sup> Importing firms may also have various hedging and warehousing strategies that they employ to limit the costs of delays. Marteau _et al._ (2007) label these “induced” costs of unreliable supply chains that are borne by the importing firm. To the extent that these latter costs do not appear in the contracted penalty rate then the estimate may understate the true costs of unreliable supply chains.<sup>23</sup> Despite this clear monetary cost and despite causing disruption to freight forwarders’ operations and delivery of shipments, the impact of clearance delays on freight forwarders’ business seems to be limited. Only 26% of freight forwarders lost a client due to delays at the border post or terminals.<sup>24</sup> Of those freight forwarders that lost a client to clearance delays, 90% indicate that this situation is actually rare or happens only once a year (rather than weekly or monthly) and 70% of them experienced the most recent client loss due to delays of 1 to 2 days.<sup>25</sup>

# **V. Trade-Offs between Time Savings and Cost Savings**

Freight forwarders were asked about four hypothetical choices they or their clients might have to make in the course of their operations in order to assess if there are relevant trade-offs

> 20 An econometric study that attempts to estimate this value using Serbian import data is currently under way. 21 Note that this figure is the market value of an “unexpected” additional day in transit, while the Hummels and Schaur (2013) estimate is better understood as the estimated value of a planned additional day.

> 22 Penalty rates would likely be set higher than the actual cost of delay in order to encourage freight forwarders to minimize the frequency and length of delays

> 23 Our calculation estimates the market penalty of a realized unexpected additional delay for a single shipment.  “Induced” costs to importing firms are incurred in the expectation that such realizations are sufficiently common that they should be hedged.

> 24 These percentages are calculated out of responses by 152 freight forwarders (140 small and 12 medium/large).

> 25 These percentages are calculated out of responses by 40 freight forwarders (36 small and 4 medium/large).

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form of the transportation costs and that the freight rate contains the specific component.<sup>20</sup> They also were the first to point out that the assumption of ad-valorem transport costs is not innocuous as it underplays the effects of transportation costs and tariffs on the quality composition of exports and imports. This paper has instigated the importance of the functional form of the transportation cost for multiple aspects of trade. Feenstra and Romalis (2014) used the specific transport costs to estimate traded quality. Due to the presence of the specific component in transport costs, remote economies were predicted and found to specialize in higher quality by Lugovskyy and Skiba (2015) and in lighter varieties by Harrigan (2010) and Lashkaripour (2020).<sup>21</sup> Wong (2021) showed that specific transport costs reduce welfare and trade by more than an equal-yield ad-valorem transport costs because they are more price-distorting. We summarize the advantages and disadvantages of the ad-valorem measure as follows.

**Stylized Fact 14.** _The advantages of the ad-valorem measure of transport costs are: (i) it is in line with many trade models (ii) it is available for many countries and years; and (iii) it is intuitive and comparable with tariffs._

_At the same time, the functional form of the transportation cost contains a significant specific (per unit) component. This fact is important for estimating the traded quality and welfare gains from trade._ The data on the per-unit measure of transport costs are relatively sparse. Earlier papers on the specific freight rates explored the determinants of the per-weight freight rates. Mainly because in the BTI dataset—the pioneering dataset for measuring specific freight rates—other units were not reported. Lashkaripour (2020) made a point that consumers buy units, not kilos of manufactured goods, and thus the per-unit measure is more relevant from the perspective of trade models. The per-unit freight rates are available only in a handful of datasets: e.g., the US Imports by Census and

> 20They estimated the price elasticity of freight rate to be 0.6. The elasticity consistent with the ad-valorem functional form would have to be equal to one.

> 21Theoretically, remote in this case refers to the higher transportation costs to the destination markets. Due to the lack of transportation costs for many countries, the measure of remoteness is often based on the average distance or tradeweighted average distance. Lugovskyy and Skiba (2015) constructed the MQC (Multilateral Quality Compensation) index that combines the trade-weighted importer’s preferences for quality and the composition of transportation costs at the exporter-HS6 level and discusses their distribution (see their Fig 1).

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**Table 3: Growth of Export Prices and Volumes**

<u>(in percent)</u>

||**1994**<br>Price|**-1999**<br> <br>Volume|**200**<br>Price|**0-2010***<br> <br>Volume|**199**<br>Price|**4-2010***<br>Volume|
|---|---|---|---|---|---|---|
|**Traditional**|0.7|10.6|13.7|5.3|9.1|7.2|
|**Non-Traditional**|-0.5|12.2|2.4|10.6|1.4|11.1|

* data as at August 2010 Source: BCRP. Own elaboration

**Figure 5: Prices and Volumes of Traditional Exports 1993-2009** (1994=100)

<!-- Start of picture text --> 400<br>Price<br>350<br>Volume<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-07 Jan-09<br><!-- End of picture text --> By contrast, the growth of revenues from non-traditional exports stems mostly from volumes. This positive behavior is likely a result in large part of the success of micro reforms and the overall sustained improvement in the business climate since the 1990s. As proof of this, note that while between 1994 and 1999 the prices fell by an average of 0.5%, volumes grew by 12.2%, and later, when prices increased by 2.4% on average between 2000 and 2010, volumes continued to grow by 10.6% (see Figure 6). This prevalence of the volume effect in non-traditional products is confirmed by the information presented in Table 3, which reveals that in the period 1994-2010, volumes grew by 11.1% while prices only rose by 1.4%. By contrast, during the same period, in the case of traditional exports, prices have contributed somewhat more than volumes to the growth in export values (9.1% and 7.2%, respectively).

11
<!-- Start of picture text --> $1,600<br>$1,400<br>$1,200<br>$1,000<br>$600<br>S400 6<br>$200<br>s- . . .*. . . . . .<br>O, _, ,, ,, C , , C ,<br>Z m1_ T T, ~2 T El a,<br>IMalawi SSA |<br><!-- End of picture text -->

Figure 6: MPTC/MTL - Revenue per Main Line (In US$) Source: International Telecommnunication Union (2001).

Although, as noted above, MPTC did not keep audited accounts, it did report some data to the International Telecommunication Union. Since this data is derived from unaudited accounts, it should be treated with considerable caution. Revenue per mainline, which is the direct reflection of tariff and traffic volume, fluctuated significantly (see Figure 6). Revenue per main line declined from US$1,400 in 1992 to US$386 in 1995 with the depreciation of the Kwacha, which declined by 325 percent over this period. On average, Malawi's revenue per main line was about 80 percent of the average for Sub-Saharan Africa. This suggests that relatively low tariffs resulted in low revenue per main line, aggravating MPTC's poor performance. However, revenue per main line increased significantly after commercialization, increasing to US$877 in 1998, and almost caught up with the SSA average, which had been falling over time. The rebound in revenue after commercialization was due to traffic and tariff increases and the pegging of international tariffs to US$.

16
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