rafmacalaba/datause-encoder-data huggingface dataset:
value but have different estimated values, and vice versa. A country’s estimated IEO value could presumably fall over time without its true value falling. This makes an IEO estimate potentially highly misleading. And it marks out the IEO as different from a poverty or inequality number. There may be data issues that make poverty and inequality estimates problematic, but they do not lead us to systematically underestimate what we are trying to measure by an inherently unknown amount.

To this must be added another worrying aspect of the empirical approach. The extent of inequality of opportunity reported to policy makers will be dependent on whether or not the data set collects certain types of information. So estimates across countries will either be determined by the lowest common denominator of commonly available variables, or will be simply not comparable, even as a lower bound.

The reservations above all take for granted de Barros et al.’s (2009) partitioning of influences on outcomes into illegitimate and legitimate. In the former, they include gender, race or ethnicity, birthplace, the educational attainment of the mother, the educational attainment of the father, and the main occupation of the father. All other influences necessarily fall into the group of legitimate influences. De Barros et al. (2009) explicitly highlight – with approval – the inclusion of effort and luck among these. Yet if some of the variation is due to “brute luck” in the words of Dworkin (1981b), then much of the philosophical discourse would argue for it to be a legitimate cause for intervention. Indeed, the previous section has argued that even for “option luck”, which is the result of individual choices, the extreme low end of variation in outcomes is also a legitimate cause for intervention. These considerations are swept under the carpet when inequality decompositions are presented as estimates of “inequality of opportunity.”

# **4. Children, Education and Health**

Questions about the practical usefulness of the concept of equality of opportunity become even tougher when the focus shifts from income and consumption to education and health, especially when the exercise involves children, who, even in health, end up hogging the spotlight in such exercises.

The de Barros et al. (2009) study is again a useful place to start. This is in part because of its influence, but in part because it illustrates nicely the challenges faced by anyone wanting to make the case that the equality of opportunity approach has practical usefulness. While income is the main focal variable in the IEO work, the same methodology is also used to partition inequalities in educational attainment among 15-year olds into legitimate and illegitimate sources of inequality. The same concerns about the methodology – notably the estimates being a lower bound – apply with equal force to educational attainment as to income. Conceptual and normative questions to do with the partitioning of influences into the J’s and the U’s also apply. But these also apply to the second 9
Appendix Figure A3: Reported Earnings Before Childbirth—Estimation Allowing for Treatment Effect Heterogeneity <!-- Start of picture text --> January 2012 to December 2014 October 2015 to December 2017<br>(a) Log Monthly Earnings (b) Log Monthly Earnings<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>-30 -27 -24 -21 -18 -15 -12 -9 -6 -3 -30 -27 -24 -21 -18 -15 -12 -9 -6 -3<br>Months to childbirth Months to childbirth<br>Firm size 1-50 Firm size 51+ Firm size 1-50 Firm size 51+<br>Log wage Log wage<br><!-- End of picture text --> _Note:_ Figure shows time patterns of reported earnings before giving birth, using the estimator of Sun and Abraham (2021), implemented with Stata package Sun (2021). Panel (a) shows estimates for births between January 2012 and December 2014. Panel (b) shows estimates for births between October 2015 and December 2017. Both panels show estimated _β_ parameters and 95% confidence intervals from equation (1). In both panels, the dependent variable is reported log monthly earnings. In both panels, red circles indicate smaller firms with 1 to 50 employees and gray crosses indicate larger firms with more than 50 employees. (Number of individuals in panel (a): 12,638 for firm size 1-50 and 18,321 firm size 51+. Number of individuals in panel (b): 9,903 for firm size 1-50 and 13,585 for firm size 51+.) 23
Table 4.5. Per-capita expenditure by household head's education

|||Pre-crisis|Post-cnsis|Change|Prop.change|
|---|---|---|---|---|---|
|_Illiterate._||||||
||Mean|109.903|93.196|-16.707|-0.063|
||Std.err.|8.815|6.530|8.147|0.068|
||Median|95.691|93.968|-19.966|-0.194|
||Obs.|47||||
|_Primaryeducation:_||||||
||Mean|106.613|87.963|-18.650|-0.026|
||Std.err.|3.784|3.288|3.217|0.036|
||Median|95.307|76.984|-16.382|-0.191|
||Obs.|331||||
|_Secondaryeducation._||||||
||Mean|110.027|93.051|-16.975|0.006|
||Std.err.|2.365|1.922|1.993|0.018|
||Median|91.041|75.949|-10.988|-0.135|
||Obs.|1173||||
|_Highereducation:_||||||
||Mean|182.320|145.275|-37.045|-0.049|
||Std.err.|10.751|9.152|10.321|0.045|
||Median|137.529|112.430|-21.084|-0.173|
||Obs.|215||||

The results in these tables have to be interpreted with caution. For instance, we saw in table 4 that small households suffer the most in terms of proportional change. It might be the case, however, that small households are more frequently observed in small towns where the proportional change in consumption is larger. Or that, as the same table 4 points out, smaller households have higher consumption levels, and households with higher consumption levels suffered larger drops (table 2).

In figure 5. 1, we show non-parametrically the relationship between the change in log consumption (approximate in the limit to the proportional change in consumption) and initial log consumption. A vertical line at the poverty line is superimposed. Consistent with the story in table 4.2, the curve it is not flat, but negatively sloped.

In figure 5.2 to 5.4, we show this relationship after stratifying for certain characteristics. Figure 5.2 shows that, after controlling for initial log consumption, households in other towns suffer the highest drops in log consumption, compared to other country regions. From figure 5.3, it seems that household head's gender does not make a big difference in the change of log consumption after controlling for initial log consumption. However, as previously wamed, figure 5.4 suggests that, after controlling for initial log consumption, it is large households (with 4 or more members) those that suffer the larger drops in log consumption.

12
||-1.64|-0.49|**(2.10)****|-0.87|-0.74|-0.81|**(3.59)*****|
|---|---|---|---|---|---|---|---|
|**Patients Characteristics**||||||||
|Male|0.003|-0.001|0.008|-0.004|0.01|-0.095|-0.003|
||-0.16|-0.07|-1.29|-0.99|-0.56|-1.35|-0.06|
|Age|-0.001|0|0|0|0|-0.001|0.003|
||-1.11|-0.74|-0.06|-1.2|-0.06|-0.45|-1.55|
|Education|**0.061**|0.006|**0.012**|0.001|**0.032**|0.028|**0.052**|
||**(2.95)*****|-0.72|**(6.50)*****|-1.44|**(2.64)*****|-0.56|**(3.28)*****|
|Reason visit|-0.048|0.001|**-0.032**|-0.003|-0.042|-0.055|-0.059|
||-1.3|-0.05|**(3.44)*****|-0.46|-1.31|-0.64|-0.77|
|Multiple Problems|0.042|0.002|**0.021**|0.004|**0.052**|-0.014|**0.164**|
||-1.53|-0.11|**(2.89)*****|-1.18|**(1.98)****|-0.2|**(1.82)***|
|Number of days sick|-0.001|0|**0**|**0**|0.001|-0.002|0.001|
||-0.65|-1.18|**(1.67)***|**(2.58)*****|-1.62|-1.45|-0.94|
|Time to reach facility|**0**|**0**|0|**0**|0|**0**|**0.003**|
||**(2.57)****|**(2.86)*****|-0.53|**(2.11)****|-1.61|**(2.29)****|**(1.96)***|
|Visited another provider|**0.279**|**0.07**|**0.026**|0.002|**0.134**|**0.649**|**0.152**|
||**(4.97)*****|**(2.73)*****|**(3.59)*****|-0.44|**(3.59)*****|**(6.81)*****|**(2.13)****|
|Observations|1335|1335|1335|1335|1335|257|249|
|Pseudo R-squared|0.18|0.2|0.27|0.15|0.25|0.35|0.24|
|LogLikelihood|-718.02|-426.7|-246.19|-125.88|-558.15|-100.31|-130.67|

Robust z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Furthermore, there is strong evidence that patients choose the facility based on their expectation that the care they need is available. Indeed, the fact that patients think (or know) that other providers do not offer the specific service they need translates into a 13.3% higher probability that the patient has bypassed a modern provider to seek care.

Among the objective quality measures, the patients are more responsive to the ratio of skilled to unskilled. Interestingly enough, it is also the quality indicator patients are better able to gauge and is therefore closer to a subjective measure. Patients who bypassed any type of facility, and especially public facilities are more likely to visit a facility with a higher proportion of medical staff. Patients in the richest income quintile seem even more responsive to that ratio. Staff absenteeism reduces the probability that patients have bypassed another facility to come and seek care.

With respect to the price index, the less expensive the facility is relative to the average facility in the district, the more likely the patient is to have bypassed 26
Moreover, while textiles take 6.5 days to clear on average, they take only five days in South Africa and 10 days in Uganda. These findings confirm that the type of commodity is an important determinant of cargo dwell time.

# **4. Conclusion**

Finally, Table 4 summarizes the main assumptions of the theoretical model and the findings of the statistical analysis.

|**Table 4: Theoretical Assumptions and Findings from the Statistical Analysis**|
|---|

|_Theoretical assumptions_|_Survey findings_|
|---|---|
|Warehousing-derived terminalization: terminals<br>are cheap storage units for shippers.|Verified. Small and medium retailers are likely to<br>use the port as a cheap storage facility.|
|Product characteristics and market structure are<br>the main determinants of dwell time.|Verified for market structure. Monopolies are<br>time-efficient in Kenya, Nigeria, Uganda, and<br>Zambia, but competition is time-efficient in South<br>Africa. Hence low dwell time is not necessarily<br>reflected in lower prices in the case of<br>monopolies-oligopolies (which might seek to<br>maximize profits); however, it might keep prices<br>low in competitive situations (South Africa).<br>Verified<br>for<br>product<br>category.<br>Important<br>differences are found in cargo dwell time among<br>product categories.|
|Dwell time is also about perception and<br>information. In uncertain contexts, shippers build<br>delay into their production schedule to plan for<br>the worst. Dwell time statistics are often unknown<br>or incorrect.|Verified. When the dwell time perceived as<br>―normal‖ is higher than the actual dwell time<br>(Kenya, Nigeria), shippers are likely to exert<br>pressure to shorten it; if the perceived dwell time<br>is higher than the actual dwell time (South Africa,<br>Uganda, Zambia), there might be no pressure to<br>shorten it.<br>Communicating reliable information about dwell<br>time is key to avoid ill-adapted strategies and<br>stimulate timeperformance of customs brokers.|

_Source:_ Authors based on firm surveys.

This exercise is, to our knowledge, the first of its type and does not answer all of the questions raised in this field. However, it demonstrates the crucial importance of studying private sector practices and incentives before designing any program aiming to reduce dwell time. The assumption that ―importers are the victims of long container dwell time‖ is likely to be wrong in the case of many ports in SSA, which probably explains the multiple failures of many initiatives in this area.

This kind of study should be expanded to other countries, and some issues, such as the impact of market structure on pricing strategies or the impact of information asymmetry should be investigated further.

31
annual population growth rate (from 2.9 percent in the 1970s to 1.5 percent currently) and total fertility rate (from 7 in 1975 to 2.7 in 2007).

The gender dimension of poverty turns out to be a more nuanced story. Poverty incidence appears to be slightly lower among households headed by women – femaleheaded households account for 8 percent of poor households but 12 percent of non-poor households (Table 2). However, it is important to distinguish between de facto and de jure female headed households (see Buvinic and Gupta, 1997). The economic condition of a household headed by a female where male earning members have migrated (and send remittances) can be quite different from one where the female head is the de facto main earner. Disaggregating female-headed households by marital status of the head captures this phenomenon partially. Poverty rate is just 16 percent among households headed by married women (about a third of the female-headed households in the HIES 2005 sample), compared to 48 percent among households headed by women who are divorced or separated and 37 percent when she is a widow. The regressions in section III also confirm that the association between female headedness and economic status of households depends on the marital status of women. On the whole, the evidence suggests that female headed households face considerable hardships in the absence of adult male earners.

Education is clearly associated with lower poverty (Table 3). There are two important changes between 2000 and 2005: (i) an overall improvement in education levels among household heads in 2005; and (ii) lower poverty rate in 2005 for the _same_ education level (up to secondary level). The proportion of household heads with education of secondary level or above has risen from 27 percent in 2000 to 31 percent in 7
27 by equation 6. The estimated coefficients on the industry dummy variables are interpreted as “inter-industry wage differentials” following Krueger and Summers (1987).

In the long run, the price decrease would affect the “general” component of wages. In our apparel industry application, as long as industry _j_ is female-intensive (we assumed that T&G is female-intensive), any decrease in the price of industry _j_ (T&G in our case) will affect<sup></sup> 1<sup>, which</sup> represents the gender wage gap. Of course, what time frame constitutes the “long run” is not clear at the start. Estimates of the relevant time frame for “long run” effects are rare. Robertson (2004) suggests that the Stolper-Samuelson effects begin to emerge in three to five years.

# **4.3. Main Results**

Table 5 presents the results of wage regressions for Cambodia and Sri Lanka in the short run and table 6 presents the results of wage regressions for both countries in the long run. The regression analysis was carried out using the methodology described in section 2. For our analysis, we used eight rounds of the Cambodian Socio-Economic Surveys that covered the 1996–2011 period, and the 1992–2002, 2008, 2011, and 2012 Sri Lankan Labor Force Surveys.<sup>21</sup> In both countries, working in apparel pays a premium compared to the economy average.<sup>22</sup> Column 1 of table 5 shows that the MFA-era wage premium was 39.2 percent in Cambodia and column 3 of table 5 shows that this premium was 6.7 percent in Sri Lanka. This result contrasts with table 4, which might be interpreted as demonstrating that the apparel sector is a low-wage “sweatshop” sector. In comparison to international wages, wages are indeed low in the apparel sector. But table 5 shows that relative to alternatives for similar individuals (generally younger, unmarried, less-educated females), the apparel jobs (T&G dummy) pay higher than average.<sup>23</sup>
removing the influence of the controls) are likely to adjust their firewood consumption downwards, and are also likely to have adopted biogas more frequently than other households. We have controlled for some correlates of these opportunity costs using measures of education, unemployment, and firewood collection time, but these may be inadequate.

On the other hand, it is also the case that high demand for firewood arising from shocks to household utility functions would result in high firewood collection,<sup>9</sup> more labor allocated to this purpose, and, therefore, a high demand for and greater adoption of biogas to relieve the pressure on household labor supply. This would lead to our regressions under‐estimating the reduction of firewood collection due to biogas.

We use the method proposed by (Altonji, Elder, and Taber 2005), (Krauth 2011), and (Oster 2013) to bound the bias in our estimate of the impact of biogas on firewood consumption. The idea is the following. Suppose _v_ is an omitted variable in our regression model that is correlated with biogas adoption, is orthogonal to all the controls (the other right‐hand‐side variables), and whose marginal effect on firewood collection is 1. The latter two assumptions may be made without loss of generality by simply defining _v_ and choosing its units appropriately. The operational assumption that we now make is that the magnitude of the correlation of _v_ with biogas does not exceed the magnitude of the correlation of _xb_ with biogas, where _x_ is the vector of controls in our regression above and _b_ the associated coefficient parameter vector (Krauth 2011).

We note that if _v_ is uncorrelated with biogas, then the bias will be zero, and as the magnitude of the correlation of _v_ with biogas increases, so does the magnitude of the bias. Our assumption that the correlation of _v_ with biogas is no more than the correlation of _xb_ with biogas allows us to bound the bias. Using Oster’s (2013) Stata code, we find that the bound on the bias ranges from 375 kg/year in Model 1 to 412 kg/year in Model 3. This gives us a lower bound for the reduction of firewood due to biogas of 650 kg/year.

> 9 For example, the presence of a sick person who needs more firewood for heating.

10
**Table 2    Sex ratio of aborted fetuses by sex composition of existing children, southern Zhejiang Province, China, 1993.** _(N=10,782 aborted fetuses)_

|**Existing children**|**% of total abortions**|**Sex ratio of aborted fetuses***|
|---|---|---|
|None|41.9|107.9|
|**_Sons only_**|||
|<br>1|23.7|108.0|
|2+|0.8|97.6|
|**_Daughters only_**|||
|1|29.0|51.0|
|2+|1.1|53.9|
|**_Both sons & daughters_**|3.5|106.5|
|All|100.0|86.7|

**_Source_** : Das Gupta (2006), calculated from Gu and Roy (1995).

Note that the normal sex ratio of aborted fetuses is expected to be more masculine than the sex ratio at birth. This is because more males are conceived than females, and an excess of males among spontaneous abortions results in a more balanced sex ratio at birth.

8
(Menon 1993; Brown et al. 2007). Frequency estimates for activities that occur regularly but are not exceptionally salient (e.g., eating breakfast, brushing your teeth) are likely to be extrapolated based on the respondents’ assessment of the rate of occurrence. More salient activities, on the other hand, are likely to evoke a “recall-and-count” strategy, in which respondents recall each individual occurrence of the activity and aggregate them across the reference period. Neither estimation strategy is ideal, however, when activities do not follow a regular schedule and are not especially salient, such as may be the case with agricultural labor. As a result, stylized questions about the amount of time a person spends on these types of activities are likely to yield particularly erroneous estimates. This is consistent with the findings from empirical research investigating recall bias in agricultural labor statistics (Arthi et al. 2016) and time use evidence from developed countries (Juster, Ono, and Stafford 2003): stylized questions tend to yield less accurate estimates than time diaries for irregularly occurring activities, and conversely, more accurate estimates for regularly occurring activities.<sup>10</sup> As a result, stylized questions may be better suited for answering research questions about activities that tend to follow a set schedule (e.g., salaried employment), where the risk of recall bias may be considerably less.

# **3.1. SEASONALITY**

Farming is an inherently unpredictable occupation. The amount of time a person spends farming is likely to exhibit wide swings not only from one season to another but also within a season, depending on myriad factors (e.g., weather, crop choice, technology used).<sup>11</sup> Even beyond the cognitive factors discussed above, what makes measuring time use in developing countries so challenging is that so many people rely on agriculture as their primary livelihood strategy.

> 10 Similarly, Kan and Pudney (2008) compare stylized question and time diary estimates for the same individual in a British survey and find evidence of greater measurement error in stylized questions versus time diaries, though they attribute this mostly to randomness rather than systematic bias.

> 11 Seasonality bias is not limited to farming. Time spent on other activities that also follow a seasonal schedule (e.g., small businesses, construction work, migrant labor) may also exhibit seasonality bias.

11
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