rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Measuring Productivity Change of LAC Airports**

The objective of this section is to assess how airport productivity evolved in Latin America. To that end we compute annual total factor productivity change (TFPC) for LAC airports over the period 1995 to 2007. The period covered was determined by the data compiled through the questionnaires distributed for the elaboration of a World Bank report on Airports in Latin America (World Bank, 2009). We rely on the same 3-output 3-input model specification used in the calculation of technical efficiency scores of section 3 and the methodology consists in the computation of a Malmquist quantity index of TFPC based on the non-parametric DEA approach.

Figure 2 illustrates the computation of the Malmquist index in a simple one-output (y) one-input (x) setting. Assuming constant returns to scale, the best practice technologies for period _s_ and period _t_ are defined by the period _s_ and _t_ DEA frontiers, respectively. The quantities of input and output of a particular unit in periods _s_ and _t_ are defined by points  _ys_ ,<sup>_x_</sup> _s_  and  _yt_ ,<sup>_x_</sup> _t_  , respectively. Thus the distance of the unit in period _t_ relative to the period _t_ frontier is equal to _yt_ / _yd_ and the distance in period _s_ relative to the period _t_ frontier is equal to _y s_ / _yb_ . Using this information and the distances computed relative to the period _t_ frontier, _yt_ / _yc_ and _y s_ / _ya_<sup>the Malmquist index is computed as follows:</sup> The Malmquist index of TFPC presents two advantages with respect to traditional index numbers. On the one hand prices are not needed to calculate this index. On the other hand, the index can be decomposed into a measure of technical progress (TC) of the activity level taken as a whole, and another measure (TEC) that captures how each unit is catching up with respect to the technological frontier. Its main disadvantage compared with traditional index numbers is that it cannot be computed separately for each unit. Its computation relies on the estimation of sequential frontiers. Thus, panel data must be available for benchmarking purposes.<sup>12</sup>

> 12 The reader is referred to Färe _et al_ (1994) for details on the methodology, including its decomposition.

16
**Table 6. Major Variables Used in WTP Analyses**

|**Variable**|**Explanation**|**Mean**|**Std.**<br>**Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Individual mean WTP<br>(estimated)|Estimated individual mean WTP by the first-stage<br>maximum likelihood estimation|16.88|20.60|1.49|200|
|Education|universitydiploma, yes=1,no=0|0.16|0.37|0|1|
|Male|sex: male=1 female=0|0.60|0.49|0|1|
|Age|age(years)|37.57|11.39|17|78|
|Farmer|Houshold head’sprofession : farmer=1,other=0|0.44|0.50|0|1|
|Married|marital situation(married=1,other=0)|0.88|0.32|0|1|
|Houshold income|Household income(yuan)in 2006|22332.36|33749.19|500|200000|
|FamilySize|Familysize(person)|4.59|1.74|1|15|
|Income decrease|Do you expect your household income to decrease<br>in future 5years ? Yes=1,no=0|0.09|0.29|0|1|
|Donation in thepast|Donation for social charitybefore?(yes=1,no=0)|0.77|0.42|0|1|
|Important|Solid waste considered as one of the three most<br>important environmentalproblem:yes=1,no=0|0.85|0.36|0|1|
|Government<br>responsability|Do you think environmental problems should only<br>be resolved by government?yes=1,no=0|0.13|0.33|0|1|
|Serious around house|Is the trash problem around your house serious?<br>(yes=1 no=0)|0.65|0.48|0|1|
|Current cleaning<br>services|Is there regular trash cleaning activities in your<br>neighbourhood?(yes=1,no=0)|0.43|0.50|0|1|
|Not satisfied|Are you satisfied with the results of the proejct?<br>(no=1;0=yes)|0.09|0.29|0|1|
|Project implement|Do you think the project will finally be<br>implemented?(yes=1,no=0)|0.74|0.44|0|1|
|Trash site|1= respondents living close to the existing trash<br>dumpingstation;0=otherwise|0.17|0.38|0|1|
|New coverage|1= households which were not covered by the<br>existing system but will be covered by the new<br>project;0=otherwise|0.51|0.50|0|1|
|Rich|1= income equal or over 40 000 yuan per year;<br>0=otherwise|0.22|0.41|0|1|
|Midincome|1= income between 4000 and 40 000 yuan per year;<br>0=otherwise|0.54|0.50|0|1|
|Poor|1= income under or equal to 4000 yuan per year;<br>0=otherwise|0.24|0.43|0|1|

23
Halpern, R., F. Barros, B. Horta, and C. Victora. 1996. “Desenvolvimento neuropsicomotor aos 12 meses de idade em uma coorte de base populacional no Sul do Brasil: diferenciais conforme peso ao nascer e renda familiar.” _Cad. Saúde Púb_ . 12(Supl. 1): 73-78.

- Heckman, J., R. Lalonde, and J Smith. 1999. “The Economics and Econometrics of Active Labor Market Programs.” In O. Ashenfelter and D. Card., eds., _Handbook of Labor Economics_ , vol. 3A, Amsterdam: Elsevier.

- Heckman, J. and Y. Rubinstein. 2001. “The Importance of Nonccognitive Skills: Lessons from the GED Testing Program.” _American Economic Review_ 91(2): 145-49.

- Jutte, D.P., A. Burgos, F. Mendoza, C.B. Ford, L.C. Huffman. 2003. “Use of the Pediatric Symptom Checklist in a Low-Income, Mexican American Population.” _Archives of Pediatrics & Adolescent Medicine_ 157(12):1169-76.

- Lee, V.E., J. Brooks-Gunn, E. Schnur, F.R. Liaw. 1990. “Are Head Start Effects Sustained? A Longitudinal Follow-Up Comparison of Disadvantaged Children Attending Head Start, No Preschool, and Other Preschool Programs.” _Child Development_ 61(2):495-507.

- Love, J., et al. 2002. “Making a Difference in the Lives of Infants and Toddlers and Their Families: The Impacts of Early head Start.” U.S. Department of Health and Human Services.

- Lozoff, B., E. Jimenez, A.W. Wolf. 1991. “Long-Term Developmental Outcome of Infants with Iron Deficiency.” _New England Journal of Medicine_ 5(325)(10): 687-94.

- Magnuson, K.A., C.J. Ruhm, J. Waldfogel. 2004. “Does Prekindergarten Improve School Preparation and Performance?” National Bureau of Economic Research Working Paper 10452. Cambridge, MA.

- Marchman, V. A., C. Martine-Sussmann. 2002. “Concurrent Validity of Caregiver/Parent Report Measures of Language for Children Who are Learning Both English and Spanish.” _Journal of Speech, Language, and Hearing Research_ 45(5):983-97.

- Martorell, R. 1999. “The Nature of Child Malnutrition and its Long-Term Implications.” _Food and Nutrition Bulletin_ 20(3): 288-292.

- Munoz, F., C. Quilodran, P. Velasquez, C. Niedmann, A Baeza, G. Silva and M Osorio. 1989. “Acquirement of the Spanish vocabulary among rural and urban students of the 9th region.” _Revista Chilena de Pediatria_ 60(6): 354-358.

- O'Connor, D.L., R. Hall, D. Adamkin, N. Auestad, M. Castillo, W.E. Connor.2001. “Growth and Development in Preterm Infants Fed Long-Chain Polyunsaturated Fatty Acids: A Prospective, Randomized Controlled Trial.” _Pediatrics_ 108(2):359-71.

- Paxson, C., N. Schady. 2005. “Cognitive Development among Young Children in Ecuador: The Roles of Wealth, Health and Parenting.” Policy Research Working Paper No. 3605. World Bank, Washington, D.C.

- Powell, C., H. Baker-Henningham, S. Walker, J. Gernay, and S. Grantham-McGregor **.** 2004. **“** Feasibility of Integrating Early Stimulation into Primary Care for Undernourished Jamaican Children: Cluster Randomised Controlled Trial.” _British Medical Journal_ 329(7457)89: 1-4.

- Reynolds, A.J., J.A. Temple. 1998. “Extended Early Childhood Intervention and School Achievement: Age Thirteen Findings from the Chicago Longitudinal Study.” _Child Development_ 69(1):231-46.

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# **_6.3 Robustness Checks_**

The greatest threat to our identification strategy is that August 2020 could be an anomalous seasonyear period, the COVID-19 pandemic notwithstanding. There could be a downward or an upward trend from previous years that is common at the national level, or that varies by province. This risk can be mitigated by including a year trend term or province-specific trend. Appendix Table 4 shows that the results are robust to the inclusion of either a national trend or provincial-specific trends.

Another concern is that our level of geographical analysis—provinces—may wash out useful variations at the smaller level of districts. Districts in Indonesia are similar to the size of commuting zones in the United States, which may be a better approximation of “local” labor markets than provinces. However, district-level identifiers are not disseminated in February rounds of the Sakernas survey. While it is feasible to estimate the COVID-19 impacts using only August rounds, this would weaken the identification strategy, removing useful seasonal variations in our estimation. In Appendix Table 5, we show that the results are robust to this switch. Nevertheless, as the seasonal variations are critical for the identification strategy, our main specification is much preferred to this alternative setup.

# **7. Conclusion**

Given the pre-existing gender differences in economic activities and social norms, among others, the COVID-19 pandemic is likely to affect women and men quite differently. The majority of the empirical findings on the pandemic’s impact in other countries suggests that female workers are disproportionately affected by the pandemic due to sectoral/occupational segregation of female workers and increased unpaid domestic care work during the pandemic. However, an opposite 39
**Table 7. Augmenting the Regressions with Measures of Inflation**

|Regression No.|1|2|3|4|
|---|---|---|---|---|
|Means of deflating|Total|Total|Total|Total|
|economic growth|CPI|CPI|CPI|CPI|
||Robust|Robust|Fixed|Fixed|
|Regressor|regressor|regressor|effects|effects|
|Economic growth (CPI)|−0.14***|−0.13***|−0.22***|−0.19***|
||(0.04)|(0.04)|(0.06)|(0.07)|
|Relative food inflation|0.04||0.12||
||(0.05)||(0.08)||
|Total inflation||0.11***||0.22**|
|||(0.05)||(0.11)|
|Number of countries|105|105|105|105|
|Number of observations|252|252|252|252|
|R-squared: overall|0.05|0.04|0.05|0.06|

_Source_ : Dependent variables are from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and food and total CPI data are from the ILO (ILO 2011).

_Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. Note that outliers are removed for all regressions. Outliers are identified based on _dfbetas_ greater than 0.20. The robust regressions are estimated using the _rreg_ command in stata with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted deflated by the total CPI. _Total inflation_ is the percent change in the food CPI between the month of the GWP survey and the month of the previous GWP survey, where the food CPI in any given month is actually the average food CPI in the previous 12 months. _Relative food inflation_ is the percentage change in the ratio of the food CPI to the nonfood CPI, where the both CPIs in any given month are actually the average CPIs in the previous 12 months. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

25
year before or after it, and all but two surveys within two years of the benchmark. For the surveys conducted in non-benchmark years, we adjust reported incomes by the Consumer Price Index of the country so that all amounts are expressed in 2002 local currency units. These amounts are then converted into international (PPP) dollars using the 2002 estimates of $PPP exchange rates provided by the World Bank. Thus, for each ventile, we calculate the average per capita amount of PPP dollars received as income (or spent in the form expenditures).<sup>15</sup> The fact that each country is divided into 20 groups of equal size (ventiles) is extremely helpful. This allows us to compare the positions of say, the third ventile of people in China with the seventh ventile of people in Nigeria etc. It also allows us to define income classes in the same way across countries. To fix the terminology, we shall call each ventile an “income class”. Income classes thus run from 1 to 20 with 20 being the highest.

Income class and country of residence pin down a person’s position in global income distribution. That position is expressed by his percentile rank in world income distribution. A person can be, say at the 72<sup>nd</sup> percentile in the world—implying that his income is higher than incomes of 72 out of each 100 people in the world. This will be referred for simplicity simply as “position” or “position in the world.” Since we divide the world into one hundred percentiles according to per capita income, the position runs from 1 (lowest) to 100 (highest). Each percentile contains in principle 1/100<sup>th</sup> of the world population included in the analysis here, _i.e_ ., approximately 57 million people.<sup>16</sup> We now move to some descriptive issues showing how the world thus “partitioned” into countries and income classes really looks.

> 15 As mentioned, the household surveys we use are either income- and expenditure (consumption)-based. For the simplicity of presentation we speak throughout of “income” distribution and “income” position in the world.

> 16 Unfortunately, not all percentiles are of  the exactly the same size. This is due to the “lumpiness” of the ventiles, particularly those for China and India. Thus  as one  Chinese ventile takes more than one percentile of the world income distribution, the next world percentile is accordingly reduced in size.

9
Watson hypotheses on a restricted sample of Rauch-differentiated products where he proxies search costs by the number of potential suppliers and reliability by income levels.

Evidence on trade flows from other countries largely confirmed these early findings. The determinants of export duration were explored by Nitsch (2009) using Cox regressions on a ten-year panel of German imports at the HS8 level. He found that gravity variables (distance, exporter GDP, common language, common border, etc.) influenced the duration of trade flows pretty much the same way they influenced trade volumes. Interestingly, he found that the short duration of trade flows held even when flows below 10’000 euros were excluded. Fugazza and Molina (2009) extended the exploration to a nine-year panel of HS6 bilateral trade flows between 96 countries using, as regressors, gravity variables and time required for export procedures (based on the World Bank’s _Doing Business_ surveys) as proxies for fixed costs. Besides usual findings on the effect of gravity variables and income levels, they also found that fixed costs reduced survival.<sup>2</sup> A similar exercise was carried out on Asian trade flows by Obashi (2010) with largely convergent results. In particular, the 2-to-3 year median survival seems to hold across all samples studied. Obashi also found that vertical trade relationships (involving the sale of semi-finished product) have hazard rates one-third lower than those involving the sale of final goods, and that they are less sensitive to trade costs (e.g. distance or exchange-rate fluctuations).

A smaller number of recent papers have made use of the growing availability of firm-level datasets to shed new light on the determinants of export survival. For instance, G ̈ _o_ rg et al. (2008) tested the implications of the heterogeneous-firm model of Bernard et al. (2006) on a rich panel of 2,043 Hungarian firms spanning the transition from centrally-planned to market economy (1992-2003). Their data contained firm characteristics and exports at the firm-HS6 level. They found large product turnover during the period as firms constantly rearranged their product portfolios. They also found longer survival for products located close to the firm’s core competencies and to the country’s comparative advantage. These results are consistent with those

> 2This is unintuitive: in microeconomics, the shut-down point depends on average _variable_ costs, not on fixed costs. However the fixed export costs they consider are incurred for each transaction, although they do not depend on transaction size. They are therefore not really fixed when looking at flows aggregated to the annual level.

5
<mark>India prosper and their people move up the food ladder, demand for grains has in‐ creased.” Similar arguments were advanced by noted scholars. Krugman (2008) argued that “... there’s the march of the meat‐eating Chinese—that is the growing number of people in emerging economies who are, for the first time rich enough to start eating like Westerners” (</mark> _<mark>New York Times</mark>_ <mark>editorial, April 7, 2008). Likewise, Wolf (2008) wrote: “So why have prices of food risen so strongly? ... On the demand side, strong rises in incomes per head in China, India and other emerging countries have raised demand for food, no‐ tably meat and the related animal feeds” (</mark> _<mark>Financial Times</mark>_ <mark>, April 29, 2008). In response to such concerns, the agriculture ministers of the G20 launched an interagency platform, the Agricultural Market Information System (AMIS) in order to enhance food market trans‐ parency and increase food security.</mark> But following the post‐2011 commodity price declines and, especially, the 2014 oil price collapse, concerns about whether metal reserves could meet global infrastructure needs, or whether global energy and food supplies could keep up with population and income growth, gave way to concerns about low commodity prices and suitable policies to respond to them for commodity exporting countries. Similarly, the “peak oil” hypoth‐ esis was replaced by “peak demand” discussions. Part of the reason for the overall decline in prices (and hence the reversal of views) was that persistently high prices had led to investment and innovation which eventually led to substitution among commodities, as well as increased production. The next section discusses several examples and episodes of substitution among commodities.

# **A2. The complex nature of substitutability**

Substitutability has been a key feature of commodity markets. Theoretical discussions of substitutability go back to Hicks (1932), who wrote (pp. 124): “... a change in the relative prices of the factors of production is itself a spur to invention, and to invention of a par‐ ticular kind—directed to economizing the use of a factor which has become relatively expensive.” Hicks’ view, which was later coined the induced innovation hypothesis, has been discussed extensively, including Hayami and Ruttan 1970; Olmstead and Rhode 1993; Hanlon 2015; Newell, Jaffe, and Stavins 1999. Substitutability among commodities has been cited as the antidote to resource scarcity (Goeller and Weinberg 1978; Pei and Tilton 1999). The rest of this section first offers some examples of substitution and then discusses three episodes that have had longer‐term consequences.

Substitution can occur from a change in relative prices in the short term is alterna‐ tive materials are available (or with some lag if it entails significant costs). It can also occur in the longer term from development of new technologies and innovation (Tilton _— 34 —_
**Table 8: OLS Regressions of national Exam Scores and Circumstances**

||**Primary**<br>**12-15 y.o.**|**Preparatory**<br>**14-18 y.o.**|**Secondary**<br>**17-21 y.o.**|
|---|---|---|---|
|Female|2.1457***|2.3074***|3.7964***|
||(0.594)|(0.665)|(0.548)|
|Father’s education: primary|1.9986*|1.3525|0.0715|
||(1.186)|(1.191)|(1.005)|
|Father’s education: Preparatory/Secondary|3.0621**|0.0195|0.7825|
||(1.416)|(1.440)|(1.173)|
|Father’s education: Vocational|4.3131***|3.0546**|-0.2084|
||(1.229)|(1.326)|(1.154)|
|Father’s education: Post-secondary|6.3862***|3.7641**|3.0193**|
||(1.282)|(1.469)|(1.277)|
|Father’s education: missing|4.2317***|1.5136|0.6747|
||(1.293)|(1.257)|(1.062)|
|Mother’s education: primary|1.1897|-0.0117|-0.8078|
||(1.118)|(1.142)|(0.812)|
|Mother’s education: Preparatory/Secondary|0.2962|2.5697**|0.6547|
||(1.247)|(1.159)|(1.166)|
|Mother’s education: Vocational|2.3152**|5.9249***|3.6505***|
||(1.064)|(1.146)|(1.031)|
|Mother’s education: Post-secondary|4.6120***|9.8281***|8.0822***|
||(1.254)|(1.318)|(1.273)|
|Mother’s education: missing|-2.6107*|0.9920|-0.8473|
||(1.485)|(1.648)|(1.134)|
|Wealth quintile: second|2.0688*|-0.5497|1.2235|
||(1.212)|(1.396)|(1.051)|
|Wealth quintile: middle|3.2326***|0.1682|2.3500**|
||(1.148)|(1.329)|(1.016)|
|Wealth quintile: fourth|4.9018***|2.4809*|3.2626***|
||(1.204)|(1.358)|(1.055)|
|Wealth quintile: highest|7.7830***|6.5910***|5.5169***|
||(1.260)|(1.514)|(1.249)|
|Religion: Non-Muslim|-0.3049|-0.3096|0.5954|
||(1.568)|(1.681)|(1.052)|
|Birth Governorates|<br>Yes|<br>Yes|<br>Yes|
|Constant|72.9671***|71.7394***|62.3858***|
||(1.829)|(2.999)|(2.580)|
|Number of Obs|1497|1525|1718|
|R-squared|0.244|0.306|0.224|

_Source:_ SYPE 2009 survey.

_Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates. _* p<0.10, ** p<0.05, *** p<0.01_ The estimates of opportunity shares in the distribution of exam scores are shown in **Table 9** .

For scores at the primary completion exam, the set of circumstances used explains 24.4 percent of the overall variation in scores. This estimate varies very little with the lower alternative correction for selection, and increases to 29.1 percent with the upper-bound alternative. For scores at the preparatory completion, the uncorrected estimate is at 30.2 percent, and varies between 27.4 and 35.2 percent with the correction for selection. Finally, for scores at the secondary completion, the 25