rafmacalaba/datause-encoder-data huggingface dataset:
to both of these issues in Section 4.

From a policy perspective, it is important to emphasize that the cost of titling in Tanzania are extremely high, and perhaps artificially so. Similar programs of systematic land demarcation in other settings, including the Rwanda, India (Andra Pradesh), and the Kyrgyz Republic, have achieved costs on the order of a few dollars per parcel (World Bank 2007; Ali, Deininger, and Goldstein 2011). These dramatic cost reductions have generally been achieved by abandoning cadastral surveying in favor of aerial photography and satellite imagery. But bureaucratic red tape are also a significant part of the fixed costs of surveying in Tanzania.

# **2.2 Intra-household bargaining power outcomes**

The second question we pose about the grand De Soto-inspired formalization project in Tanzania is whether it will serve to enshrine – or even exacerbate – patriarchal norms underlying customary land tenure. We begin by reviewing the existing evidence relating land tenure formalization to shifts intra-household bargaining power.

While evidence of the impact of formal joint-titling on women’s outcomes is limited, there are several studies which associate improvements in women’s property rights with other desirable outcomes such as measures of female empowerment, child health, education and women’s welfare, all of which are associated with increases in bargaining power. For example, self-reported ownership of land is positively correlated with child health status and various measures of empowerment in Nepal (Allendorf 2007) and with expenditure on ‘gendered’ goods in both China and Ghana (Wang 2011; Doss 2005). Inheritance rights, in particular, appear to matter: Peterman (2011) shows that women in rural Tanzania who enjoy improvements in inheritance rights are more likely to enter the labor market and earn higher wages. Telalagic (2012) shows that women from villages practicing matrilineal descent, whose improved inheritance rights result in a better outside-option, are less likely to utilize domestic labor as a source of bargaining power. Both Roy (2008) and Deininger, Goyal, and Nagarajan (2010) have found a positive impact stemming from India’s Hindu Succession Act, which extended inheritance rights to women, on outcomes such as female education and self-reported autonomy. Recent work by Doss, Kim, Njuki, Hillenbrand, and Miruka (2014) reveals that, in Tanzania, women who report joint-ownership of land are more involved in household decision-making.

There is also growing evidence that formal land titling itself can be advantageous to women, irrespective of their state of ownership. Using data from a Peruvian titling program with a distinct focus on joint-titling, Field (2003) demonstrated a link between title acquisition and subsequent reduction in household fertility. Galiani and Schargrodsky (2010) show that titling in Buenos Aires resulted in a reduction in household size and higher levels of child education. Evidence from Rwanda has also shown that titling programs can be successful at increasing perceived female ownership and the recording of 6
US trade barriers. In a GVC world, the GDP exposure to a trade barrier could be direct - through the country’s exports to the economy that has imposed the trade restrictive measure - or indirect - through the exports of other countries. The former can be computed looking at the German GDP directly exported to the United States, running: `icio, origin(deu) exporter(deu) importer(usa) perspective(bilateral) output(va)` . Thus, 2.8% of German GDP could be directly affected by US tariffs, while 0.9% could be affected through other countries exports to the US of German products.

If the goal is to quantify the potential exposure of German value-added to a US tariff on a specific sector, e.g. motor vehicles from Germany, a sectoral-importer perspective is the right choice: ``` .icio,origin(deu,20)imp(usa,20)output(va) Decompositionofgrossimports: Perspective:sectimp Importer:USA Origin:DEU Exporter:totalUSAimports Output:Value-Added Sectorofimport:20 Sectoroforigin:20 ```

||`Millions of $`|`% of import`|
|---|---|---|
|`Value-Added`|`17216.14`|_`6.59`_|

``` .matGDPsect=r(va) .icio,origin(deu,20) Value-Addedbyorigin/destination: Origin:DEU Output:Value-Added Sectoroforigin:20 ```

||`Millions of $`|`% of total`|
|---|---|---|
|`Value-Added`|`147493.71`|_`100.00`_|

``` .matGDPtot=r(vby) ``` ``` .di"Germanyexposureinsector20:"GDPsect[1,1]/GDPtot[1,1]*100"%" Germanyexposureinsector20:11.672456% ``` Again, the relative exposure can be easily obtained taking the ratio of the absolute exposure ($17.2 billion) with respect to the total German value-added in the motor vehicles industry ($147.5 billion). Thus, a US tariff hitting motor vehicles imports from Germany might affect around 11.7% of the value-added produced in the same sector in Germany.

26
vessels that called in PR in 2019 satisfied these conditions in the LLI data. (See Panel C. in Table 1). A comparison of Panel C with Panels A and B shows that the composition of freight supply that is available on U.S.-PR routes is quite different than it is in the broader Caribbean, or even among PR routes as a whole. With the exception of just two vessels, the LLI data indicate that the JA fleet serving PR in 2019 consisted entirely of container ships and barges. Container ships are responsible for the vast majority of port calls and available DWT. The tankers, bulk, liquid and general cargo vessels that play such a large role in PR and Caribbean shipping are almost absent from the JA fleet.

While the LLI data are useful for comparing shipping in the broader Caribbean, they miss a large volume of barge traffic serving U.S.-PR routes. We redo the same calculations using publicly available data from NBIC (2024), reporting the results in Panel D of Table 1. These data better track barge traffic, resulting in a much larger share of barge traffic in Panel D than in Panel C. Although there are differences between the LLI and NBIC data, the dearth of tankers, bulk, liquid and general cargo vessels is common across the two tables.<sup>20</sup> The near absence of such vessels serving the JA market motivates our subsequent exercises involving the gravity model of trade.

# **5 Theory**

We motivate our empirical work with an adaptation of Krugman and Venables (1996). The model’s prediction for trade in final products is conventional: buyand ownership. We screen first for U.S.-flagged ships, and then check by hand to verify that the firms that own U.S.-flagged ships are located in the U.S. The JA also requires domestic crewing. Our data lack comprehensive data on crews. We assume that the ships arriving in PR that meet observable JA requirements are also U.S.-crewed. This assumption only affects our initial summary statistics; it is irrelevant to our main results.

20It is also possible to use the NBIC data to compare JA vessels with the entire fleet serving PR. This comparison returns the same lessons. We omit NBIC data on all port calls to PR for reasons of space.

14
# _Trade_

We obtained two separate trade datasets. The first is the Direction of Trade Statistics (DOTS) Database IMFDOT that includes trade information for 184 countries for the period 1950-2004, in current US$. The second dataset is the United Nations Statistics Department (UNSD) dataset COMTRADE that includes information for 207 countries for the period 1962-2004 in current US$. Sources of data feeding into the IMFDOT and into the COMTRADE datasets are different and as such, differences in annual trade values can be expected. Such differences have been observed (IMF, 1999: Table 2), although differences do not exceed 10%. We constructed separate trade variables based on both the IMF and UN datasets. We converted the trade values in these two datasets into constant 1999 US$ (for IMFDOT) and 2002 US$ (for COMTRADE). We then use annual country-level GDP data from the GGDC&CB (2005) dataset, which is expressed in 1999 (for IMFDOT) and 2002 (for COMTRADE) US$ to construct our trade variables, using 2000 as the base year. Missing trade values in particular years were ignored because our trade variables are calculated as long term averages.

The following definitions apply for the two trade variables: Let _i_ =1 and _i_ =2 be two riparian states sharing a river. Let _IMP_ 12 _t_ be import of 1 from 2 in year _t_ , [= _EXP_ 21 _t_ ]; let _EXP_ 12 _t_ be export of 1 to 2 in year _t_ , [= _IMP_ 21 _t_ ]; let _IMP_ 1 _wt_ be import of 1 from w in year _t_ ; let _IMP_ 2 _wt_ be import of 2 from _w_ in year _t_ ; let _EXP_ 1 _wt_ be export of 1 to _w_ in year _t_ ; let _EXP_ 2 _wt_ be export of 2 to _w_ in year _t_ ; let _GDP_ 1 _t_ be gross domestic product of country 1 in year _t_ ; and let _GDP_ 2 _t_ be gross domestic product of country 2 in year _t_ ; and _w_ be rest of the world (not including 1 and 2).

We first constructed two annual trade variables for each trade dataset. The first variable (TRD1) expresses total trade between 1 and 2 as a fraction of the countries’ GDP, expressing the economic importance of trade to the riparians (Sigman 2004). The second variable (TRD2) measures trade between 1 and 2 as a fraction of their trade with the rest of the world, expressing their dependence on each other (Reuveny and Kang 1996). The two trade variables that we apply to the two trade data sets are presented in equations (1) and (2).

15
Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

The sectors that emerge as more amenable to WFH tend to be the same across most countries in the PIAAC and LMPS data sets. These sectors include ICT, professional services, the public sector, and finance (Figure 7). In contrast, jobs in hotels and restaurants, agriculture, construction, and commerce are the least amenable to WFH.

# **Figure 7. WFH index by sector of economic activity, PIAAC sample**

<!-- Start of picture text -->
1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>A=Agriculture, forestry and fishing I=Hotels and Restaurants T=Activities of households as employers F=Construction G=Commerce S=Other service activities H=Transportation and storage E=Water N=Administrative activities C=Manufacturing B=Mining and quarrying Q=Human health and social work activities R=Arts, entertainment and recreation P=Education D=Utilities O=Public administration L=Real estate activities K=Financial and insurance activities M=Professional activities J=Information and communication<br><!-- End of picture text -->

Note: The vertical axis measures the WFH index adjusted by internet access at home, in standard deviations from the mean of the PIAAC sample. A higher value indicates that jobs are more amenable to WFH.

Finally, we regress the WFH index for each data set on individual and job characteristics and confirm that even after controlling for observable characteristics, women, college graduates and salaried workers are more likely to have jobs amenable to WFH than men, lower educated, and self-employed workers (Table 3).<sup>8</sup>

> 8 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

17
# **2. Estimate deaths among women aged 15-49**

The consequent distribution of deaths by gender and age distribution is presented in Table A2.

**Table A2: Estimated EVD Deaths by Age & Gender**

|||Guinea|Liberia|Sierra Leone|
|---|---|---|---|---|
|Total Deaths **11**||1,995|3,826|3,341|
|Female deaths|<15 years old|167|360|380|
||15-44 years old|540|1,028|895|
||≥45 years old|322|490|444|
|Male deaths|<15 years old|159|373|359|
||15-44 years old|514|1,064|847|
||≥45years old|306|507|420|

Source: Total mortality by country is from WHO (2014). Distribution by age and gender is inferred by the author based on WHO Ebola Response Team (2014).

# **3. Calculate surviving children**

The age distribution of Ebola deaths among women age 15-44 is assumed to reflect the age distribution of women age 15-44, in five-year age groups. For example, in Sierra Leone, 23% of women age 15-44 are between the ages of 15 and 19, so 23% of the 895 female deaths are applied to women age 15-19.<sup>b</sup> The same process is applied for women age 45-49, using the distribution of deaths for women over age 45. These five-year age group mortality counts are then multiplied by the mean number of living children (MNLC) for women in the same age groups, from the most recent DHS for the three countries,<sup>**12**,</sup><sup>**13**,</sup><sup>**14**</sup> to calculate the total surviving children of female EVD victims age 15-49. Age 49 is the upper limit because the DHS only calculates the mean number of living children for women up to age 49.<sup>c</sup>

# **4. Subtract children over age 15**

This number is then adjusted to subtract children who are likely over the age of 15. For example, for a woman currently in the age range of 40-44, we subtract the number of living children she had when she was in the 25-29 range, since those children would now be over 15. (In fact, this is a simplification: Since this is a cross-sectional survey, the number of living children for women currently in the 25-29 range is subtracted from the number of living children currently in the 40-44 age range. If women have begun to have children later over successive cohorts, then this will underestimate the total number of orphans.) The calculations for Steps 3 and 4 are demonstrated in Table A3.

These calculations suggest that, to date, over 4,800 children under age 15 have lost a mother.

> b Obviously, EVD mortality within the 15-44 age range may follow an age distribution that is disproportionate to the population in that range (e.g., concentrated among adults age 25-29); but finer data are not available. c This will result in an underestimate of the orphans under age 15, as – in the most recent DHS – women between the ages of 35 and 49 accounted for 16% of births across the three countries, mostly concentrated in the 35-39 age range. **12** , **13** , **14**

13
Figure 1: Timeline of Study <!-- Start of picture text --> Baseline/Census<br>1027/1104 HFs (93% response rate)<br>Jan-Dec 2015<br>Partial census update [1]<br>1258 HFs<br>Oct-Nov 2016<br>Randomization listing<br>273 Markets, 1258 HFs<br>Nov 2016<br>(+90 HFs found later = 1348 HFs<br>operational at randomization) [2]<br>Treatment sample Control sample<br>186 Markets, 856 HFs (913 HFs 87 Markets, 402 HFs (435 HFs<br>operational at randomization) operational at randomization)<br>Treatment rollout [3]<br>Nov 2016 – Dec 2017<br>Endline/Census<br>Markets: 268 [4]<br>HFs: 1285/1319 (97% response rate)<br>Mar-Aug 2018<br>Treatment sample Control sample<br>Markets: 182, HFs: 883 Markets: 86, HFs: 436<br>T1<br>Markets: 89, HFs: 393<br>T2<br>Markets: 93, HFs: 490<br><!-- End of picture text --> _Notes._ [1] Due to the high turnover of facilities and delay in the implementation, we conducted a partial update of the census in markets of size 1, 2, and 3 between October and November 2016. We used this partial update of the census of 1,258 facilities located with available GPS coordinates for the randomization. [2] 90 facilities were missed or listed as temporarily or permanently closed during the randomization census. These facilities were added using a nearest-neighbor algorithm to the nearest market by endline. [3] Another partial update to the census was conducted at the end of July 2017 when the first round of inspections was completed in all counties. At this stage, only the new facilities were assigned to the markets as per randomization. [4] 268 of the randomized markets were still active at endline, or those with at least one health facility found in the market. Five markets were dropped because all the facilities permanently closed. HF = health facility.

12
# **Annex of descriptive statistics**

**Table A1. Descriptive statistics: all, employed, and non-employed**

||||**All**||||**Employe**|**d**|||**Non**|**-employe**|**d**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|Obs|Mean|Std. Dev.|Min|Max<br>Obs|Mean|Std. Dev.|Min|Max|Obs|Mean|Std. Dev.|Min<br>Max|
|**Log of total monthly labor income (in**<br>**th. of old Manats)**|1896|5.9355|0.5171|2.3026|8.8537<br>1896|5.9355|0.5171|2.3026|8.8537|0||||
|<br>**Women**|4972|0.4670|0.4990|0|1<br>1896|0.3470|0.4762|0|1|3076|0.5410|0.4984|0<br>1|
|**Log of weekly hours of work**|1896|3.6412|0.3126|0.6931|4.4308<br>1896|3.6412|0.3126|0.6931|4.4308|0||||
|**Primary education or below (including**<br>**vocational education)**|4972|0.0933|0.2909|0|1<br>1896|0.0601|0.2378|0|1|3076|0|0|0<br>1|
|<br>**General education**|4972|0.1480|0.3552|0|1<br>1896|0.0723|0.2590|0|1|3076|0.1947|0.3961|0<br>1|
|**Secondary general education**|4972|0.5706|0.4950|0|1<br>1896|0.6113|0.4876|0|1|3076|0.5455|0.4980|0<br>1|
|**Secondary specialized education**|4972|0.0796|0.2708|0|1<br>1896|0.1060|0.3079|0|1|3076|0.0634|0.2437|0<br>1|
|**University and Master program**|4972|0.1084|0.3109|0|1<br>1896|0.1503|0.3575|0|1|3076|0.0826|0.2753|0<br>1|
|**Actual work experience**<sup>**1**</sup>|4972|4.5270|4.6817|-1|23<br>1896|6.2043|4.7842|-0.0192|22.2578|3076|3.4932|4.3039|-1<br>23|
|**Teenagers (15-19 years)**|4972|0.3435|0.4749|0|1<br>1896|0.1667|0.3728|0|1|3076|0.4525|0.4978|0<br>1|
|**Young adults (20-24 years)**|4972|0.3449|0.4754|0|1<br>1896|0.3708|0.4831|0|1|3076|0.3290|0.4699|0<br>1|
|**Young old (25-29 years)**|4972|0.3115|0.4632|0|1<br>1896|0.4626|0.4987|0|1|3076|0.2185|0.4133|0<br>1|
|**Women engaged**|4972|0.0346|0.1828|0|1<br>1896|0.0306|0.1723|0|1|3076|0.0371|0.1889|0<br>1|
|**Men engaged**|4972|0.0282|0.1654|0|1<br>1896|0.0538|0.2257|0|1|3076|0.0124|0.1105|0<br>1|
|**Women married**|4972|0.1036|0.3047|0|1<br>1896|0.1129|0.3165|0|1|3076|0.0979|0.2972|0<br>1|
|**Men married**|4972|0.0833|0.2763|0|1<br>1896|0.1614|0.3680|0|1|3076|0.0351|0.1841|0<br>1|
|**Women divorced or widowed**|4972|0.0074|0.0860|0|1<br>1896|0.0100|0.0996|0|1|3076|0.0059|0.0763|0<br>1|
|**Men divorced or widowed**|4972|0.0024|0.0491|0|1<br>1896|0.0042|0.0648|0|1|3076|0.0013|0.0360|0<br>1|
|**Men with children**|4972|0.0672|0.2504|0|1<br>1896|0.1297|0.3361|0|1|3076|0.0286|0.1667|0<br>1|
|**Women with children**|4972|0.0917|0.2887|0|1<br>1896|0.1065|0.3086|0|1|3076|0.0826|0.2753|0<br>1|
|**Fatherless**|4972|0.1372|0.3441|0|1<br>1896|0.1693|0.3751|0|1|3076|0.1174|0.3219|0<br>1|
|**Motherless**|4972|0.0354|0.1848|0|1<br>1896|0.0454|0.2081|0|1|3076|0.0293|0.1686|0<br>1|
|**Household size: 1 member**|4972|0.0193|0.1376|0|1<br>1896|0.0332|0.1793|0|1|3076|0.0107|0.1030|0<br>1|
|**2 members**|4972|23.0185|14.0182|1|81<br>1896|22.6978|14.7750|1|81|3076|23.2162|13.5293|1<br>81|

42
15 Figurel: HISTOGRAMS FOR INDIVIDUAL PRICE CHANGES FOR SELECTED QUARTERS

# _UNWEIGHTED DISTRIBUTION_

<!-- Start of picture text --> WEIGHTED DISTRIBUTION<br><!-- End of picture text --> <!-- Start of picture text --> 16 Sep-89 40 T<br>141 35 4<br>121 30<br>1 0 25+<br>1 201<br>6<br>. t 15<br>2 - 5E -W-<br>50 1 20 0<br>6 6o 6 6 0 0 0o co 6 66 6666 Eo<br>LE 4 i8 Dec-89 _+ 01E1ln _G l1m111<br>14 I5<br>12 o CO O S N 0 CO 0 gO 20 ° °. 0. ) N 0 C' 0<br>-- 10<br>2 a<br>CO O S - C UO ') 0 LO c') CO CD 0<br>N) CD 0 5 N CD 0) 0 0 0~~~~~~c 0 C') 0 0 0 0 OC 0 - EOC 0<br>60 040 N 26 0 0 0<br>35- 60T Se-956 Sep-95 40<br>50 30 t ; 5~~~~~~~~~~~0_<br>40 25<br>;30 20<br>1 ~~~~~~~~~~~~~15<br>a g204. E2{<br>o - LIL10o 1 0 1-_ -- C° - _ N -. I15| 8 N - L _ _ O _ _ _ _ O O O O_ _ _O _ _<br>1 00 ~~~~~~0 I) 0 0 0 0 0 0 0 0<br>co) C') N~ r Lo 0 0 8 0 0, 0 0 0 0.C<br>35 Sep-96 60<br>30 50<br>25 40<br>>120 30<br>0 ~~~~~~~~~~~~~20<br>I0*10 1<br>5.-<br>0 -a+---0 r--<br>co CO -NmC ' co Nm CO co C) 0) CO') 0 C 0 8 _D0<br>flC')C) 0C 0 20 0<br>0 0 9 9 0 0 0 0 0~~~~~~~~~~~~~~~~~~~~c 0 0 0 0<br><!-- End of picture text -->

_source: author's calculations using GUS data_
# _Electricity:_

Panel (d) shows the trends in EGC per 1,000 workers (in megawatts). Egypt exceeds developing countries in this measure, but has fallen far behind World. The gap between Egypt and World has even widened in the recent period as growth in EGC has stagnated in Egypt since the 1990s. The quality indicator of electricity, power loss (in percentage of total output), displayed in panel (e), improved in the 1990s, but it reversed to the 1980s level in the most recent period.<sup>20</sup> Although a decline in electricity quality seems to be a worldwide trend, the electricity sector in Egypt shows some signs of weakening in both indicators of quality and quantity.

# **C. Infrastructure Indices by Sector**

Some empirical literature that studies the impact of infrastructure on economic performance uses a single infrastructure sector (i.e. the number of main lines for telecommunications) as a proxy for infrastructure.<sup>21</sup> Others build aggregate indices of infrastructure quantity and quality that capture the stock of different types of infrastructure assets and the quality of services in different infrastructure sectors separately (i.e. quantity and quality indices for telecommunications).<sup>22</sup> In this paper, however, instead of focusing on a single sector or building synthetic indices capturing either quantity or quality aspect of infrastructure assets, we construct indices by major infrastructure sectors that simultaneously capture both quantity and quality features of infrastructure. In order to build the sectoral indices, we combine the information of quantity and quality indicators for the following three sectors, respectively: transport, telecommunications, and electricity.<sup>23</sup>

> 20 The measure of power loss is transformed in a way such that an increase in value indicates an improvement in quality.

> 21 Easterly (2001) and Loayza, et al (2005).

> 22 In a series of works by Calderon (2008) and Calderon and Serven (2004, 2008), they constructed synthetic indices of infrastructure quantity and quality, which consist of quantity and quality indicators from the three infrastructure sectors: roads, telecommunications, and power.

> 23 In order to construct the indices, we used time series data for infrastructure stock and quality indicators in Egypt. When we closely checked the original data, we encountered some issues. That is, some indicators had missing observations, and others fluctuated in an unreasonable way over time. In order to solve these problems, we first interpolated the missing observations for the length of roads and the paved road ratio to

20