rafmacalaba/datause-encoder-data huggingface dataset:
38

**Notes**

1. R. Layard and J. Mincer, "Trends in Women's Work, Education, and Family Building," Journal of Labor Economics 3:1 part 2 (January 1985); T. Paul Schultz, "The Value and Allocation of Time in High Income Countries: Implications for Fertility," Population and Development Review, Vol. 12, Supplement (1986).

2. O.E. Williamson, The Economic Institutions of capitalism: Firms. Markets Relational Contracting, (New York: Free press, 1985); Y. Ben-Porath, "The F connection," Population and Develogment Review, 6:1 (March) 1-30 (1980); R. A. Pollack, "A Transaction Cost Approach to Families and Households,' Journal of Economic Literature, 22:2 (June) 1985; T. P. Schultz, "La Empresa, el empleo familiar, su dessarrollo y salarios minimos' (Firm and Family Employment: Development and Minimum Wages). Estudios de Economia, 15:1 (April) 1988, 85-125.

3. E. Boserup, Women's Role in Economic DeveloDment (London: George Allen and Unwin, 1970); R. L. Blumberg, "A General Theory of Gender Stratification," Sociological Theory 1984, pp. 23-101 (San Francisco: Jossey Bass, 1984).

4. Colin Clark expressed the view that economic progress involved a redistribution of the labor force from primary and secondary or manufacturing activity to tertiary or service industries. See The Conditions of Economic Progress, London: Macmillan, 1940. Exceptions to this pattern are noted where it was believed that the distribution of goods absorbs an unusually large share of national resources, as in some West African countries. Se P.T.
Table 3 compares the key modeling differences between the two assessments, based on the methods and assumptions utilized.

Table 3 Comparison of key modeling differences

|**Method**<br>**component**|**UN Broadband Commission**|**This Assessment**|
|---|---|---|
|**Broadband**<br>**definition**|~10 Mbps average speed|Either 30 GB/Month or 10 GB/Month|
|**Quality of**<br>**Service (QoS)**|Not modeled|50% reliability in the busiest hour of the<br>day|
|**Users**|Focuses on only unconnected users<br>(excluding underserved users)|Includes both unconnected and<br>underserved users|
|**Technologies**|Mainly 4G and satellite (but also fixed<br>fiber)|Only 4G and fixed fiber (does not include<br>satellite)|
|**Demand**<br>**approach**|Penetration up to 90%|Forecasts of unique subscribers between<br>2020-2030|
|**Universal**<br>**broadband**<br>**assumptions**|Assumes 10-20% of the hardest-to-<br>reach population use satellite|Models the full cost for terrestrial<br>infrastructure strategies|
|**Cost model**<br>**components**|Covers (i) infrastructure (capex and<br>opex), (ii) policy and regulation costs<br>and (iii) funding for ICT skills and<br>content|Covers (i) infrastructure (capex and<br>opex), (ii) network operator<br>administration (opex), (iii) spectrum<br>(capex and opex), (iv) taxation (opex) and<br>(v)aprofit margin(opex)|
|**Societal**<br>**costing**<br>**method**|Focuses on required gross investment|Calculates net private and government<br>investment, converting these estimates<br>to the societal financial cost|
|**Government**<br>**costing**<br>**method**|Focuses on required gross investment|Calculates the government subsidy<br>required after user cross-subsidization|

Generally, the UN assessment produces much smaller investment estimates than those presented in this paper. For example, the required investment to provide universal broadband across Africa is estimated to be approximately $100 billion, with countries needing to add approximately 1 billion users to 4G. This figure can be broken down based on ~$29.5 billion being spent on capex, ~$52 billion being spent on opex, ~$18 billion allocated to ICT skills and content, and the final ~$2.4 billion going to policy and regulation costs.

33
Table 9: Import Uncertainty and Export Dynamics: Robustness Checks

||Firm Entry<br>Rate|Firm Exit<br>Rate|First yr.<br>Survival|Second yr.<br>Survival|Third yr.<br>Survival|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log import uncertainty|-0.004|-0.003|-0.036**|-0.057***|-0.072**|
||(0.006)|(0.007)|(0.014)|(0.021)|(0.036)|
|Log documents to import|-0.082**|0.081|0.052|0.281|-0.526**|
||(0.041)|(0.053)|(0.116)|(0.178)|(0.238)|
||(6)|(7)|(8)|(9)|(10)|
|Log import uncertainty|0.007|0.000|-0.045***|-0.074***|-0.090**|
||(0.006)|(0.007)|(0.014)|(0.022)|(0.038)|
|Log LPI timeliness|0.047|-0.145|-0.033|-0.530|2.018|
||(0.133)|(0.168)|(0.319)|(0.488)|(1.480)|
||(11)|(12)|(13)|(14)|(15)|
|Log import uncertainty|-0.000|0.003|-0.041***|-0.060***|-0.079**|
||(0.006)|(0.008)|(0.015)|(0.022)|(0.036)|
|Log collateral (%)|-0.034**|0.016|-0.060*|0.000|-0.040|
||(0.016)|(0.016)|(0.031)|(0.045)|(0.070)|
||(16)|(17)|(18)|(19)|(20)|
|Log import uncertainty|0.000|0.001|-0.033**|-0.037|-0.080*|
||(0.006)|(0.009)|(0.016)|(0.026)|(0.045)|
|Log no loan application (%)|-0.003|0.002|-0.023|-0.068**|-0.108**|
||(0.008)|(0.009)|(0.018)|(0.028)|(0.051)|
||(21)|(22)|(23)|(24)|(25)|
|Log import uncertainty|0.004|-0.001|-0.039***|-0.061***|-0.051|
||(0.006)|(0.008)|(0.015)|(0.022)|(0.037)|
|Log bribes (% annual sales)|-0.008|0.020*|0.012|0.003|-0.006|
||(0.009)|(0.012)|(0.027)|(0.039)|(0.065)|
||(26)|(27)|(28)|(29)|(30)|
|Log import uncertainty|-0.001|-0.010|-0.033*|-0.045*|-0.105**|
||(0.006)|(0.010)|(0.017)|(0.025)|(0.045)|
|Log licensing obstacle (%)|-0.006|-0.008|-0.031**|-0.027|-0.073*|
||(0.007)|(0.007)|(0.013)|(0.019)|(0.043)|

Notes: PPML estimates. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors clustered on exporter-importer pair in parentheses. Control variables included but not reported. Full regression tables are available upon request. The description and source of variables are provided in Table A3 in the Appendix.

26
- 46 -

Table 2.8: Principal Exports of Thailand, 1970-1985, in Current Prices

|Export<br>Commodity||Share o<br>(pe|f Expor<br>rcent)|ts a|R<br>(pe|ate of Gro<br>rcent per|wth<br>ear)|
|---|---|---|---|---|---|---|---|
||1970|<br>1975|<br>1980|1985|<br>1970-75|<br>1975-80|<br>1980-85|
|Traditional Apro-products|55.7|57.5|43.8|34.4|25.8|17.7|2.7|
|Rice|17.0|13.0|14.6|11.6|18.4|27.2|2.9|
|Rubber|15.1|7.7|9.3|7.0|9.3|28.9|1.9|
|Maize|13.3|12.7|5.5|4.0|23.7|5.1|1.1|
|Tapioca products|8.3|10.2|11.2|. 7.7|30.3|26.5|0.1|
|Sugar<br>|0.6<br>|12.7<br>|2.2<br>|3.2|127.2|-12.2|16.0|
|Tobacco leaves|1.3|1.3|1.0|0.8|23.6|19.2|2.9|
|Nontraditional Apro-product|s 7.5|11.2|12.7|18.2|35.6|27.4|15.7|
|Other CroRs. Fruits and||||||||
|Vegetable oroducts||||||||
|Mung beans|1.7|1.0|1.1|1.2|12.8|25.5|9.5|
|Sorghum|0.7|1.1|0.5|0.5|36.2|6.5|9.7|
|Fresh fruits|0.2|0.2|0.2|0.4|32.0|25.6|20.0|
|Natural orchids|0.0|0.0|0.3|0.3|n.a.|n.a.|2.9|
|Raw cotton|0.1|0.0|0.3|0.1|4.3|74.8|-9.3|
|Coffee|0.0|0.0|0.1|0.5|n.a.|n.a.|38.5|
|Kapok fiber|0.9|0.4|0.2|0.1|4.8|8.6|-2.0|
|Canned pineapple|0.4|0.8|1.1|1.7|44.5|32.9|18.1|
|Molasses|0.3|1.1|0.3|0.4|60.4|-5.3|15.8|
|Subtotal|4.3|4.6|4.0|5.1|26.4|21.3|13.0|
|Seafood Products||||||||
|Prawns|1.5|2.0|1.5|1.8|31.8|17.1|11.9|
|Fresh<br>cuttlefish|0.3|1.1|1.0|1.1|68.3|20.5|10.3|
|Fish meal|0.2|0.3|0.7|0.3|31.2|48.2|-9.0|
|Fresh fish|0.2|0.3|0.3|o.7|28.4|25.6|29.3|
|Dried cuttlefish|0.0|0.2|0.3|0.5|n.a.|29.7|22.6|
|Canned fish|0.0|0.1|0.5|2.7|n.a.|77.7|53.9|
|Canned crustaceans|0.0|0.1|0.7|1.1|n.a.|89.9|16.8|
|Subtotal|2.2|4.1|4.9|8.2|40.8|29.1|19.3|
|Animal Products||||||||
|Unworked feathers|0.1|0.0|0.1|0.2|-11.1|68.3|23.8|
|Frozen poultry|0.0|0.0|0.5|0.8|n.a.|135.8|17.5|
|Subtotal|0.1|0.0|0.6|1.0|1.1|110.8|18.5|
|Other ARro-processed||||||||
|ProductsD|0.8|2.5|2.0|3.9|58.4|29.8|12.1|
|Total Aaro-products|63.2|68.7|56.5|52.6|27.1|19.5|6.2|
|Other Manufactured Products|cl4.5|13.8|25.5|26.2|23.7|40.4|8.3|
|Other Products|22.3|17.5|18.0|21.2|19.0|25.0|11.4|
|GRAND TOTAL|100.0|100.0|100.0|100.0|25.0|24.2|7.7|

a Totals may not add up due to rounding up errors.

b Footwear, wood products, jute products, furnitures and parts and leather gloves. c Nonagro-industrial manufactured exports. Source: Table 5, Statistical Annex.
countries has shown relatively limited effects. For example, Cole et al. (2011) find that a financial literacy program for households in Indonesia has little impact on their propensity to open savings accounts. However, one important recommendation from financial literacy experts is that financial education should be delivered at “teachable moments”, when the information is most applicable to a person’s life (GAO, 2004). One of the few research studies to examine such a case is Bertrand and Morse (2011), who found that offering information and making interest rate costs more understandable right at the moment U.S. borrowers are deciding on payday loans can have large effects. Offering financial literacy training to migrants and/or their family members right before the migrant member leaves potentially offers another such moment, since this is precisely the time when migrants and family members have to decide the method of remitting, amounts and frequency of remittances, and how to manage the new and relatively large inflows of income.

This paper provides the first experimental evidence on whether financial literacy programs work to improve financial knowledge and financial management, and thereby increase savings in migrant-sending households. Our context is a pilot program on financial literacy for overseas migrant workers and their families developed as a partnership between the Government of Indonesia and the World Bank and implemented in Greater Malang area and Blitar District of East Java Province. The training program emphasized financial planning and management, savings, debt management, sending and receiving remittances, and understanding migrant insurance. One key policy question is whether such information is best delivered to the migrant worker his or her self, to someone in their remaining household, or to both. Our experiment directly tests this by means of three treatment groups: a group in which only the migrant worker receives training, a group in which the main remittance receiver or decision-maker in the remaining household receives training, and a group in which both receive training.

We conduct three rounds of follow-up surveys and find that training the family member, and training both the family member and the migrant result in increases in financial knowledge, with evidence this impact is greater when both the migrant and their family member are trained. Training results in no increase in the amount or frequency of remittances, but when offered to both the migrant and the family, leads to more financial planning and budgeting, and to more saving. The effect sizes are reasonably large: we find households in which both the migrant and their family member were trained are 19 percentage points more likely to be aware of financial terms, 12.5 percentage points more likely to have saved in a bank account, and have twice the savings out of remittances as the control group. There are smaller and less significant impacts on 3
# **Table 8. Health Shocks, Income, and Transactional Sex**

|**Panel A. Participation in Sex Sector**|(1)<br>Saw Any<br>Clients|(2)<br># of Clients|(3)<br># of Regular<br>Clients|(4)<br># of Casual<br>Clients|(5)<br>Sex Work<br>Income|
|---|---|---|---|---|---|
|Somebody in Household (other than respondent ) Sick|-0.004|-0.020|-0.011|-0.009|27.469|
||(0.017)|(0.047)|(0.026)|(0.041)|(33.777)|
|Somebody in Household Sick * Mean Daily Income|0.063|0.200|0.027|0.169|45.467|
|below Median of all CSWs|(0.025)**|(0.069)***|(0.038)|(0.063)***|(41.332)|
|Respondent Sick|-0.083|-0.170|-0.045|-0.123|-105.615|
||(0.018)***|(0.044)***|(0.023)**|(0.031)***|(22.708)***|
|Observations|12293|12293|12293|12293|12293|
|Number of women|192|192|192|192|192|
||(1)<br>Had Unprotected|(2)<br># Unprotected|(3)<br>Had Vaginal|(4)<br>Had Anal|(5)<br>Had Oral|
|**Panel B. Sexual Activities**|Sex|Sex|Sex|Sex|Sex|
|Somebody in Household (other than respondent ) Sick|0.016|0.036|-0.010|0.029|0.026|
||(0.016)|(0.058)|(0.018)|(0.017)*|(0.016)|
|Somebody in Household Sick * Mean Daily Income|0.028|0.055|0.066|0.028|0.022|
|below Median of all CSWs|(0.026)|(0.086)|(0.026)**|(0.025)|(0.024)|
|Respondent Sick|-0.003|-0.015|-0.086|-0.016|-0.012|
||(0.011)|(0.031)|(0.018)***|(0.013)|(0.012)|
|Observations|12293|12072|12293|12293|12293|
|Number of women|192|192|192|192|192|

Note: All regressions are fixed effects regressions with controls for the month and for the day of the week. Clustered standard errors at the individual level in parentheses. All regressions include controls for the round of data collection. Panels include controls for own sickness though the coefficients are not reported.

Percentile of income distribution: minimum - 162 Ksh; 25th - 520 Ksh; 50th - 777 Ksh; 75th - 1,089 Ksh; maximum - 2,340 Ksh. The exchange rate was approximately 70 Kenyan shillings to $1 US during the data collection period. * significant at 10%; ** significant at 5%; *** significant at 1%
_[Some] ... say we love those who are like ourselves._ – Aristotle 1934, p. 1155.

# **1. Introduction**

Marital sorting based on human capital has important implications for the creation, distribution, and inter‐generational transmission of welfare. While the literature finds evidence of this assortative matching – the tendency of men and women who marry to have similar human capital levels – most of this research has been conducted in rich country settings and has largely focused on sorting along educational attainment [e.g. Greenwood et al. 2014]. This is despite the growing literature emphasizing the economic importance of multiple human capital dimensions in both rich and poor contexts [e.g. Heckman et al. 2006, Liu 2013, Abay et al. 2017], as well as recent work on matching and marriage markets showing that sorting is multidimensional [e.g. Dupuy and Galichon 2014, Chiappori et al. 2018].

We present novel evidence of positive assortative matching along cognitive and socio‐emotional skills, time and risk preferences, and education using unique data from rural Mozambique, Côte d’Ivoire, and Malawi. In each country, the observed sorting patterns are robust to accounting for simultaneous sorting along the multiple, potentially correlated human capital dimensions. While assortative matching could in part be caused by frictions constraining the search for dissimilar partners, we find strong spousal similarity both overall and within villages. Moreover, against the notion that spouses grow alike over time, we find that sorting is equally strong when we restrict our analysis to younger couples. Taken together, the evidence thus suggests that assortative matching is the outcome of a marriage market equilibrium shaped by spouse preferences. To the best of our knowledge, we are the first to provide systematic evidence of assortative matching on multiple human capital dimensions in Africa.

# **2. Data**

# **2.1. Data Sources**

We exploit three data sources. First, we use data from a survey conducted in April and June 2016 across 150 randomly selected rural communities in the Tsangano, Angónia, Macanga, and 2
<mark>economic characteristics. Our main anthropometric outcome of interest most likely to be impacted by parents’ exposure to refugee shock in the long-run is the Height for Age Z score (HAZ). Together with stunting defined as a HAZ score below -2 SD, the WHO considers this indicator as a marker of a deficient nutritional environment in early childhood and a predictor of future well-being in adulthood. More specifically, stunting is associated with poor birth outcomes, lower labor market participation, lower earnings, higher incidence of infectious diseases and lower cognitive ability (Case and Paxson 2008, 2010; Bhalotra and Rawlings 2011; Leroy and Frongillo 2019). The HAZ or Standard Deviation (SD) score reflects the cumulative linear growth of the child and is defined as follows:</mark> <mark>where H</mark> ��<sup><mark>is the height of the child</mark></sup> <mark>i in the age or sex group a ; m</mark> �� <mark>is the median of children’s height for individuals of the same age/sex group in the WHO reference population and σ</mark> �� <mark>is the standard deviation of children’s height of the same age/sex group in the reference population. Z-scores below - 6SD and above 6SD are considered by the WHO as biologically implausible values (0.5% or 49 children out of 10,233 in our sample are presenting such abnormal anthropometric measures and are therefore removed from our analysis). An under five years old is defined as severely stunted if the HAZ score is lower than -3 SD. In our sample, 29.8% of children are stunted and 9.8% are severely stunted.</mark> <mark>DHS surveys also include information on labor market participation, wealth, years of education, land/house ownership, and decision-making on health care of mothers and fathers. These are variables that can be used to test for alternative predictors of children’s stunting. They can also be used to test for mechanisms and channels of transmission of the refugee shock on less than five years old children’s HAZ or the probability of stunting. Table A3 in the annex provides all definitions of the variables used in the paper.</mark> <mark>For our main intergenerational analysis, we use the latest 2015/2016 DHS survey of Tanzania collected between August 2015 and February 2016 with migration history of mothers and fathers and information on the exact years they moved to their current area of residence. The data contain unique GPS information of each household cluster, allowing us to accurately assess the intensity of refugees’ influx by relying on the distance between households and each of the 13 historical refugee camps. In total, our analysis relies on a sample of 13,266 women ages 15-49 and 10,223 children ages 0-5 living in 608 clusters across the entire country. The sub-sample used for the analysis includes those households who never migrated (90.1%) or migrated before or during the refugee crisis (9.9%). For these households,</mark> 12
# **Table 3:  Heterogeneous Effects of MFI Coverage on moneylender Interest Rate**

||**2nd Q**|**uartile**|**3rd Quartile**|**Trea**<br>**3rd**|**tment**<br>**Quarti**|**le**|**4th Qu**|**artile**|
|---|---|---|---|---|---|---|---|---|
||OLS|KV|OLS<br>KV|OLS|KV|KV*|OLS|KV|
||(1)|(2)|(3)<br>(4)|(5)|(6)|(7)|(8)|(9)|
|**Dummy for High MFI Coverage**|**3.114**|**6.474**|**2.936**<br>**-5.845**|**2.368**|**19.784**|**5.886**|**9.935**|**32.112**|
||**(1.45)**|**(1.00)**|**(1.61)**<br>**(0.56)**|**(1.68)***|**(0.86)**|**(0.79)**|**(4.89)*****|**(3.85)*****|
|No.ofObservations|400|400||595|595|595|532|532|
|**Control Group**|**1st qu**|**artile**|**1st quartile**|**1st and**|**2nd qua**|**rtiles**|**1st and 2nd**|**quartiles**|
|**Zero Stage: Heteroskedastic Probit**|||||||||
|LR test of heteroskedasticity|||||||||
|χ2||19.13|6.70||3.25|41.69||39.47|
|p-value||0.00|0.04||0.20|0.00||0.00|
|**First Stage of IV Regression**|||||||||
|Angrist-Pischke F Statistic||38.46|38.09||8.29|81.65||69.44|
|P-value||0.00|0.00||0.00|0.00||0.00|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute t statistics in parentheses

33
The Panel had reserved on its analysis of subparagraph (a), subject to its determination under subparagraph (b). Under these circumstances, the Appellate Body found that the Panel’s inquiry under subparagraph (b) was made in the context of an inquiry into alleged discriminatory practices, and therefore the Panel did not misapply these two provisions by focusing its analysis on subparagraph (b).<sup>39</sup> That is, given that the Panel found no non-commercial sales within the meaning of subparagraph (b), and the U.S. did not establish an independent claim of discrimination under subparagraph (a), there was no error.

The logic advanced by the Appellate Body is not ineluctable. The “shall be understood” language could equally be understood to _deem_ failure to “make purchases or sales solely in accordance with commercial considerations” to constitute “discrimination” under subparagraph (a), reversing the order of analysis. The U.S. alleged pursuant to Article XVII:1 that Canada engaged in discrimination _by virtue of its alleged noncommercial_ sales.<sup>40</sup> Indeed, the U.S. argued that “on the facts of this case, the alleged discriminatory sales behaviour by the CWB is a necessary result of the CWB not making sales solely in accordance with commercial considerations.”<sup>41</sup> Furthermore, all discrimination in the sense of subparagraph (a) (in the WTO sense) would be expected to involve decisions based on non-commercial considerations. Discrimination in the WTO sense involves government (policy) measures that distinguish between goods in a way that a private person, absent the policy, would not.

Discrimination in this context is the use of impermissible considerations in choosing suppliers or customers:<sup>42</sup> commercial considerations are always permissible under WTO law. Moreover, in practice it will frequently be difficult, if not impossible, to objectively determine whether or not a specific transaction is based on commercial considerations, as pricing and allocation decisions tend to be multidimensional and may reflect factors/strategies that are not known or observable to outsiders.

Thus, it seems logically impossible for subparagraph (b) to narrow the scope of subparagraph (a). Although the Appellate Body suggests that measures that comply with

> 39   Appellate Body Report, paras. 119-123. 40   Appellate Body Report, para. 118.

> 41

> 42

> Panel Report, para. 6.58.

> _See_ Panel Report, para. 6.88.

14
program effect as the difference between the potential outcome (y1i) for children in a treated village (Ti = 1) in the presence of the treatment and the potential outcome (y0i) for children in a treated villages in the absence of the treatment.

However, since we do not observe the potential outcome for children in a treated village in the absence of the treatment, (y0i ), we use children in control villages (Ti = 0) as the counterfactual. We assume that the potential outcome for children in a treated village in the absence of the treatment would be the same as the potential outcome for children in the absence of the treatment in control villages, or Therefore, the AIT is given by Given that we have both a baseline and a follow up surveys, we use a difference-in-differences (DID) specification to estimate the program impact.

Where yi is the outcome of interest measured at the child level, Ti is the treatment indicator, _Xi_ is a vector of child characteristics (e.g. gender, age), and F indicates the follow up survey. The impact of the program is given by β2. The analysis then compares age cohorts rather than changes in individuals over a panel.

Our identification strategy could be weakened if control communities are indirectly affected by the program. For example, there could be cross over in which households in control villages have their children attend school in treatment villages so that they gain access to the program. Also households in the program villages could chose to foster in children from villages 11