rafmacalaba/datause-encoder-data huggingface dataset:
**table A. Exogenous variables for 1**<sup>**st**</sup> **child arrival in Turkey**

|||**no 1st child**<br>**arrival**|**1st child**<br>**arrival**|**Significance**<br>**of**<br>**difference**|
|---|---|---|---|---|
|**Stability on the labour market**|_Stable full‐time employment_|0,193|0,160||
||_Stable part‐time employment_|0,008|/||
||_Stable full‐time self employment_|0,079|0,052||
||_Stable part‐time self employment_|0,040|0,054||
||_Stable unemployment_|0,045|0,014|**|
||_Stable retirement_|0,002|/||
||_Stable student_|0,192|0,008|***|
||_Stable inactivity_|0,385|0,639|***|
||_Stable military service_|0,000|/||
||_Change in activity status within the observed_<br>_three‐months period)_|0,057|0,073||
|**Partner information**|_Partner in stable employment_|0,085|0,470|***|
||_Partner not in stable employment_|0,017|0,098|***|
||_No partner_|0,898|0,432|***|
||_Partner and married_|0,101|0,568|***|
||_Partner but not married_|0,001|/||
|**Both in stable employment**||0,038|0,152|***|
|**Household wage income**|_Zero household wage income_|0,619|0,340|***|
||_Low household wage income_|0,132|0,149||
||_Medium household wage income_|0,125|0,242|***|
||_High household wage income_|0,123|0,269|***|
|**Educational attainment**|_Low education (illiterate, prim.not completed)_|0,330|0,416|***|
||_Medium education (primary and secondary)_|0,567|0,478|***|
||_High education(tertiary)_|0,103|0,106||
|**Age**|_15‐24_|0,664|0,660||
||_25‐34_|0,231|0,323|***|
||_35‐45_|0,104|0,016|***|
|*p<0,05,**p<0,01,***p<0,001|||||
|Data Base: EU-SILC LT 2006-2011, childles|s w omen aged 15-45||||

28
findings in the context of South-South migration has been confirmed by Ratha and Shaw (2007). Simulations from a general equilibrium model by van der Mansbrugghe and Roland-Holst (2009) suggest that reducing migration will not necessarily result in higher wages for native workers in receiving countries, since lower levels of migration will also lower the relative return to capital, which in turn will put downward pressure on wages.

The fiscal impact of immigration depends on the costs and contributions of the immigrant population to the welfare system of the host country. How extensive the social safety nets and welfare services are and to what extent migrants are allowed to access the domestic welfare system determines the cost on the host society, while their role as taxpayers determine their contributions to the system. The US Congressional Budget Office estimated that comprehensive immigration reform including legalization of undocumented immigrants, if undertaken, would increase federal revenues to a similar extent as the increase in federal spending on social security, healthcare and other benefits for the immigrants (CBO 2006). In the UK, already the first-generation migrants were found to do well economically and to make a net fiscal contribution (Gott & Johnston 2002). However, the limited fiscal implication of immigrants may also be due to the fact that many of them are not eligible for most benefits, and will thus need to turn to family networks instead (Papademitriou _et al._ 2009). In the developing countries the welfare benefits might not be as numerous, and neither is the tax contribution of mostly undocumented immigrants likely to be substantial, but the migrants’ value added to the production and economic growth is still a net gain for the country. Whether positive or negative, the net fiscal impacts of immigration are not likely to be large (UNDP 2009, p. 89). When it comes to the provision of health care, however, the destination countries in the South might face an increased burden. Irregular migrants in the South may have health needs that often remain unaddressed (UNDP 2009, p. 55).<sup>15</sup>

> 15 For instance, HIV prevalence is observed to rise with migration  and the situation is made worse by the fact that in every fifth destination country there are laws, regulations or policies that present obstacles to effective HIV prevention, treatment, care and support for migrants (UNAIDS 2008, 114). While people with HIV commonly return to live with family members to obtain care, some people with AIDS-related opportunistic infections migrate to obtain health care (Crush et al. 2005). This could involve cross-border movements to a country perceived to have better health care facilities.

13
<!-- Start of picture text --> Stronger legal rights, more information sharing—more credit<br>Private credit as a share of GDP<br>More<br>Less<br>LowerCountries ranked by legal rights index, quintilesHigher<br><!-- End of picture text --> <!-- Start of picture text --> Legal rights and credit information reduce risk<br>Nonperforming loans<br>More<br>Fewer<br>Lesser Greater<br>Countries ranked by legal rights for borrowers and lenders, quintiles<br>Note:Source: Relationships Doing Business are significantdatabase, IMFat 5% (2005a). level and remain significantwhen controlling for income per capita<br><!-- End of picture text -->
is somewhat improbable that these fully explain the high rate of empty backhauls – and low served kilometers – in the region (See Table 11).

A fuller explanation for low capacity utilization likely includes an inflexible market response to demand and excess capacity, both of which could be supported by an inefficient market structure and imperfect competition. Although a more flexible and efficient allocation of freight across providers (combined with alternative methods of guaranteeing reliability and service standards) could increase vehicle utilization, such solutions may not be desired by incumbent trucking companies since they would also facilitate greater competition.

**FIGURE 9:  AVERAGE HOURS OF WAIT TIME PER KILOMETER BY COUNTRY**

<!-- Start of picture text --> 0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>HON PAN GUA NIC CR ES<br>Hours per Kilometer<br><!-- End of picture text -->

**FIGURE 10:  FIRM  DISTRIBUTION OF AVERAGE TRUCK TIME USAGE (HOURS PER YEAR)**

<!-- Start of picture text --> 0 2000 4000 6000 8000<br>Average Truck Usage Time<br>.0005<br>.0004<br>.0003<br>Density<br>.0002<br>.0001<br>0<br><!-- End of picture text -->

**TABLE 11:  PERCENTAGE OF TRIPS WITH EMPTY BACKHAUL**

||Short Haul|Long Haul|
|---|---|---|
|**Costa Rica**|70.6|44.3|
|**El Salvador**|52.3|30.0|
|**Guatemala**|87.8|76.6|
|**Honduras**|40.3|41.2|
|**Nicaragua**|82.1|42.6|
|**Panama**|50.6|42.8|
|Total|**55.3**|**46.9**|

Note: Short haul is defined as less than 150 km.

15
reflected in<sup>β</sup> _i_<sup>, rather than in</sup><sup>_P_</sup> _i_<sup>. Program implementation involves program officers who have</sup> imperfect knowledge of<sup>β</sup> _i_<sup>: they perceive</sup> ω _i_ = β _i_ +η _i_ and will assign treatment if and only if ω _i_ > 0 . Assume that<sup>η</sup> _i_<sup>has mean zero and is independent ofβ</sup> _i_<sup>andε</sup> _i_<sup>. Crucially, this</sup> knowledge of program officers is unknown to the evaluator. Denote the CDF of<sup>η</sup> _i_<sup>by</sup><sup>_F_. With</sup> this assignment rule<sup>_P_</sup> _i_<sup>is exogenous (</sup><sup>_i.e._independent of</sup> ε _ij_ ).

An RCT evaluation might involve drawing a random sample from the population and assign treatment randomly within this sample. The researcher would then estimate the program’s intention to treat effect (ITE) as _E_ β _i_ . The TPE would be estimated as _E_ β _iEPi_ .

This would be incorrect since, under the assumptions made above TPE = _E_ β _iPi_ = _E_ ( β _i_ | β _i_ +η _i_ > 0) _P_ ( β _i_ +η _i_ > 0) = _E_ [(1 − _F_ ( −β _i_ )) β _i_ ] ≠ _EPEi_ β _i_ . (Note that _E_ (1 − _F_ ( −β _i_ )) = _P_ ( β _i_ +η _i_ > 0) = _EPi_ . As before, the ATET = TPE / _EPi_ .) The problem arises because in this case the RCT design does not mimic the actual assignment process. To obtain an unbiased estimate of the TPE randomization would have to take place at a higher level, that of the program officers.<sup>13</sup> The control group then consist of program officers who never “treat” and the treatment group of program officers who sometimes (but not always) treat.

The proposed regression method gives an unbiased estimator of the TPE using observational data for ( _yi_ , _Pi_ ) from a random sample of the population. The difference is that while the RCT approach compares average outcomes at the level of program officers the regression approach does so at the level of beneficiaries. The RCT approach therefore has lower statistical power.<sup>14</sup> 12
<!-- Start of picture text --> 4 12 4 a0<br>35 HH Fs<br>Kumasi and Accra i 5 3.5 y fa<br>nin imicrebbeldetrial a “=<br>=z 3 ‘ob 3 so<br>= Os =<br>a 25 “2 3g 25 +50 &<br>2585<br>a=~a 1.5 3£ =w 15 Kuiast aid Accra -a oOS<br>a4 o4 8 Wore in micrabéci Ee<br>05 Oe ai]@ 054 —— ++ 2010 BF<br>oo<br>0 + + QO 0 T i]<br>7990 1995 2000 2005. 2010 2000 2002 2004 2006 2008 2010 20i2<br>“Horizontal error bars denote time period in which prevalence or incidence was measured. Value is at midpoint of time period.<br><!-- End of picture text --> oes Modelled incidence (7%), adults 15-49, from Spectrum software Prevalence (%), females 15-24 (source: national DHS/AIS reports) seee= Prevalence (%), females 15-24 (source: nationalANC reports) ess Incidence rate (per 100 person years), adults (source: published studies, see Annex) seee= ART coverage (%)(source: UNAIDS Global Report on HIV/AIDS, 2008, 2010, 2012, 2013
38

# **Table 5**

# _Overarching Aspects—Diagnostic table_

|OverarchingAspects Variables|Top<br>Performers|Obs|At-scale programs|Obs|
|---|---|---|---|---|
|_Robust characteristics_|||||
|Targeting by years of experience|13.33%|15|12.50%|48|
|Participation has implications for status,<br>salary or promotion|87.50%|16|58.33%|48|
|_Characteristics with large coefficients_<br>Program provides other reading materials|||||
|(flashcards, word banks, reading pamphlets)|42.86%|14|20.83%|48|
|Program provides storybooks|35.71%|14|12.50%|48|
|Number of schools|148|13|6,367|29|

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
# **References**

- Abowd, J. M., Kramarz, F., & Margolis, D. N. (1999). High wage workers and high wage firms. _Econometrica_ , _67_ (2), 251–333.

- Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification properties of recent production function estimators. _Econometrica_ , _83_ (6), 2411–2451.

- Ad ̃ao, R., Carrillo, P., Costinot, A., Donaldson, D., & Pomeranz, D. (2022). Imports, exports, and earnings inequality: Measures of exposure and estimates of incidence. _The Quarterly Journal of Economics_ , _137_ (3), 1553–1614.

- Aghion, P., Bloom, N., Lucking, B., Sadun, R., & Van Reenen, J. (2021). Turbulence, firm decentralization, and growth in bad times. _American Economic Journal: Applied Economics_ , _13_ (1), 133–69.

- Almunia, M., Antr`as, P., Lopez-Rodriguez, D., & Morales, E. (2021). Venting out: Exports during a domestic slump. _American Economic Review_ , _111_ (11), 3611–62.

- Alvarez, J., Benguria, F., Engbom, N., & Moser, C. (2018). Firms and the decline in earnings inequality in Brazil. _American Economic Journal: Macroeconomics_ , _10_ (1), 149–89.

- Artu ̧c, E., Chaudhuri, S., & McLaren, J. (2010). Trade shocks and labor adjustment: A structural empirical approach. _American Economic Review_ , _100_ (3), 1008–45.

- Asquith, B., Goswami, S., Neumark, D., & Rodriguez-Lopez, A. (2019). US job flows and the China shock. _Journal of International Economics_ , _118_ , 123–137.

- Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The China syndrome: Local labor market effects of import competition in the United States. _American Economic Review_ , _103_ (6), 2121–68.

- Autor, D. H., Dorn, D., & Hanson, G. H. (2022). On the persistence of the China shock. _Brookings Papers on Economic Activity_ , _2021_ (2), 381-476.

- Autor, D. H., Dorn, D., Hanson, G. H., & Song, J. (2014). Trade adjustment: Worker-level evidence. _The Quarterly Journal of Economics_ , _129_ (4), 1799–1860.

- Bastos, P., Silva, J., & Proen ̧ca, R. (2016). Exports and job training. _Review of International Economics_ , _24_ (4), 737–756.

- Bertheau, A., Acabbi, E. M., Barcel ́o, C., Gulyas, A., Lombardi, S., & Saggio, R. (2023). The unequal consequences of job loss across countries. _American Economic Review: Insights_ , _5_ (3), 393–408.

27
group is shown in Appendix 2. Throughout the paper we follow the World Bank classification but opted to group countries in two categories: Low-income and lowermiddle-income countries and upper-middle-income and high-income countries. This grouping provides more degrees of freedom to the panel estimations.

# **4. An Estimation of the Diffusion Process of the Internet**

As noted in Jovanovic and Lach (1989), there exists strong empirical evidence to support the view that the diffusion path of both new processes and product innovations follow an S-shaped or logistic pattern. Kotler (1986) interpreted this fact as evidence in favor of the existence of four phases for technology adoption: introduction, growth, maturity, and decline.

This paper presents two contributions to this stylized fact. First, as acknowledged by Comín and Hobijn (2003), most of the previous empirical studies on the diffusion of technology do not have access to data for the initial years in which the innovation (or new product) was adopted. This translates into a lack of precision in the estimation of diffusion and, in particular, to S-shape curves that grow “too fast” during the early introductory phase. This lack of data is due to the fact that, in most cases, information on the use of a given new technology has only been collected after it has been widely adopted.<sup>11</sup> Our first contribution is to make use of a complete dataset that allows us to study the diffusion process of Internet since the first years in which it started to spread in the leading country- the U.S. This clearly overcomes the selection effect present in most of the existing studies.

The second contribution is to estimate the hypothesis that Internet diffusion follows an S- shape curve for low and high income groups of countries. There are theoretically sound reasons to believe that the diffusion process of a given technology should be significantly different for poor and rich countries (see for instance Barro and Sala-i-Martin 1997, 2004).

Figure 2 plots the actual percentage of Internet users across income groups. It is clear from this graph that, at any point in time, the degree of Internet adoption is much lower in poorer economies than in more developed ones. The use of Internet in low and lowermiddle income countries did not start until 1994, whereas in several high income countries it started already in 1990. In 2004, in high income countries, about 40 percent of the population enjoyed Internet services, while the percentage was less than 3 percent in low income countries. In other words, the diffusion curve of low income countries can be described as a right shift of the one displayed by high income countries. This stylized fact seems to support the leader-follower model of Chong and Micco (2003).

> 11 Comín and Hobijn (2003) mention the example of the telephone, which was invented by Alexander Graham Bell in 1876 but most countries did not publish official statistics on its diffusion until the early years of 1900s.

6
|AR-Maternal and Child Health and Nutrition 2(PROMIN)|Fernando Lavadenz|
|---|---|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)|Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)|Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)|Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)|Fernando Lavadenz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Municipal Development Project(02)|Miguel Mercado-Diaz|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Capital Market Development Technical Assistance Project|Margaret J. Miller|
|Vocational and Technical Education Project||
|Vocational and Technical Education Project||
|Vocational and Technical Education Project||
|Vocational and Technical Education Project||
|Urban Project||
|Urban Project||
|Urban Project||
|Urban Project||
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|New Providence Island Water Supplyand Sewerage Rehabilitation|PJ. Carlo Rietveld|
|Technical and Vocational TrainingProject(02)|Ines Girsback|
|Technical and Vocational TrainingProject(02)|Ines Girsback|
|Technical and Vocational TrainingProject(02)|Ines Girsback|
|Technical and Vocational TrainingProject(02)|Ines Girsback|
|Technical and Vocational TrainingProject(02)|Ines Girsback|
<!-- Start of picture text -->
- = 7o 5 10km<br>——<br>EN Share of economic opportunities<br>2 7 accessible to pedestrians<br>ey ( within an hour<br>MB < 10% MB > 10%<br><!-- End of picture text -->

<!-- Start of picture text --> “ = 7o 5 10km<br>——<br> EN Share of economic opportunities<br>= 7 accessible to matatu riders<br>yy within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text --> <!-- Start of picture text --> = 7o 5 10km<br>j ——<br>EN Share of economic opportunities<br>ia 7 accessible to pedestrians<br>\ within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text --> <!-- Start of picture text --> = 7o 5 10km<br>j ——<br> EN Share of economic opportunities<br>La 7 accessible to matatu riders<br>/ \ within an hour<br>\ MB < 10% MB > 10%<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 2<br>WFt<br>WA ́(t)<br>WFt,A(∞)<br>WA(t)<br>WDt(∞)<br>t =0 t<br><!-- End of picture text -->

# **3. Imperfect Factor Mobility**

The quantification of trade adjustment costs is difficult and various techniques have been used since the 1970s. I will review those shortly. But before doing that, it is useful to document briefly the prevalence of the underlying cause of TAC, factor immobility, in modern economies.

Two pieces of evidence, direct and indirect, support the presence of labor immobility.<sup>3</sup> Direct evidence on employment reallocation suggests that trade liberalization both destructs and creates of jobs _within_ industries but that it induces only limited flows of labor into expanding sectors with comparative advantage. An example is Wacziarg and Wallack (2004) who use cross-country panel data to document little inter-industry flows after liberalization. In turn, Muendler (2010) and Menezes-Filho and Muendler (2007) use the linked employer-employee Brazilian data to show that trade liberalization displaced workers from the de-protected industries but that these workers were only absorbed by the comparative advantage sectors after several years.

Labor immobility is also indirectly suggested by the presence of wage differentials. Various papers have identified large inter-industry wage differentials in both developed and developing countries. The seminal work of Krueger and Summers (1989), for instance, shows that industry effects explain a large fraction of wage heterogeneity across workers. For the U.S., Gaston and Trefler (2004) find a negative correlation between these wage premiums and tariff protection. Attanasio, Goldberg and Pavcnik (2004) and Goldberg and Pavcnik (2005) also find industry-wage premium in Colombia that are, in addition, partly explained by inter-industry tariff differences. For Argentina, Galiani and Porto (2010) document a skill premium at the industry level, thus indicating that both skilled and unskilled labor may be imperfectly mobile. These premiums are also a consequence of inter-industry tariff differences. The industry-skill premiums are also prevalent in a large sample of Latin American countries, as shown by

> 3 Since this literature is very large, I review here only a few papers related factor reallocation with trade reforms.

6
## Figure: E.10.5

# HONDURAS LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> r LABOR INTENSIVE TOTAL EXPORTS I<br>25000<br>_ 20000 _<br>= 15000<br>X 10000<br>o5000<br>0<br>1970 1980 1990<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br><!-- End of picture text --> <!-- Start of picture text -->
-<br>ROW (3.<br>NA (36.74%)<br>^  ~~~~~~~~~~CA<br>CA (59.67%)  MW<br>~MEX  (0.36%)NA680)<br><!-- End of picture text -->

<!-- Start of picture text --> LABOR INTENSIV E XPORTS BY PARTNER<br>1970<br>ROW (47 NA (6.15%)<br>MEX (0 56%)<br>CA (88.58%)<br>LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>ROW (14.87%)<br>C<br>(14.94%)<br>MEX (1 50%) NA(6870%)<br><!-- End of picture text -->
32 Table 8: Contributions to total agricultural welfare and trade reduction indexes (WRIs and TRIs) from different policy instruments, developing and highincome countries, 1965-2004 (percent)

|**(a)** **WRI**|1965-71||||||1977-83||||||1989-|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Developing countries_||1972|1973|1974|1975|1976||1984|1985|1986|1987|1988|<br>2004|
|Import tax equivalent|12|15|4|3|11|13|12|12|17|18|17|13|11|
|Export tax equivalent|19|16|31|34|28|18|23|30|22|28|33|32|10|
|Import subsidy equivalent|3|4|8|7|3|2|4|2|2|1|2|2|2|
|**All (incl. domestic) measures**|**38**|**42**|**47**|**55**|**53**|**38**|**51**|**60**|**46**|**53**|**58**|**51**|**29**|
|_High-income countries_||||||||||||||
|Import tax equivalent|48|39|25|24|37|53|58|61|64|86|99|73|51|
|Export subsidies|7|6|3|1|3|3|4|4|7|11|11|8|5|
|**All (incl. domestic) measures**|**57**|**46**|**34**|**31**|**41**|**60**|**66**|**68**|**77**|**113**|**119**|**83**|**61**|

# **<u>(a) TRI</u>**

|_Developing countries_|1965-71|1972|1973|1974|1975|1976<br>1|977-83|1984|1985|1986|1987|1988|1989-<br>2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Import tax equivalent|9|13|3|2|10|11|9|10|13|14|15|11|9|
|Export tax equivalent|19|15|29|32|26|17|22|28|20|26|32|30|9|
|<br>Import subsidy equivalent|-3|-3|-8|-7|-3|-2|-4|-2|-2|-1|-2|-2|-2|
|<br>**All (incl. domestic) measures**|**25**|**22**|**20**|**28**|**37**|**25**|**27**|**39**|**32**|**38**|**45**|**39**|**15**|
|_High-income countries_||||||||||||||
|Import tax equivalent|34|29|20|17|24|35|37|40|41|60|60|51|35|
|Export subsidies|-4|-4|-2|-1|-2|-2|-3|-3|-5|-8|-8|-6|-3|
|**All (incl. domestic) measures**|**29**|**24**|**16**|**12**|**22**|**32**|**33**|**37**|**37**|**53**|**53**|**47**|**32**|

Source: Authors’ calculations based on WRIs and TRIs in Anderson and Croser (2009).