rafmacalaba/datause-encoder-data huggingface dataset:
where � is the parent's sector, � is time, � and � are country indexes (� for the "origin" country and � for the "destination" country). As we already mentioned above, our dependent variable, �������, is the log of the value of the revenues of all subsidiaries in a particular sector �. In the regressions that follow we consider only positive values of FDI, focusing therefore only on the intensive margin of vertical FDI as a measure of the profitability of vertical integration.

�������� is a variable that captures the depth of the agreements. More precisely, it can either be a dummy equal to one if there is a PTA, the number of provisions included in the PTA, or the log of one of the two indexes constructed using the principal component analysis previously described. �������������� are captured by the variable Rule of Law from the Worldwide Governance Indicator database. ���������� represents the level of tariffs imposed by the origin country � (Germany, Japan and the US) on product �. This variable helps us to separate the impact of our PTA variable that goes beyond pure tariff liberalization.

������ is a dichotomous variable capturing the existence of a bilateral investment treaty between � and � at time �. ��� is a vector of controls for characteristics of the destination country that vary over time. It includes GDP, GDP per capita and destination country remoteness.<sup>23</sup> ��� are country-pair variables such as geographical distance, contiguity, common language, colonial relationship.

A series of fixed effects are included in the regression in order to control for potential omitted variables bias. Specifically, ���� are sector-country-time fixed effects. The inclusion of country-pair FE bears potential collinearity issues with our variables of interest since both depth and the content of PTAs do not vary much over time.<sup>24</sup> We control for country-pair characteristics by always including the set of variables � described above. ��

> 23 Remoteness is constructed following Head (2003) and Freund and Rocha (2010): Remote�� � ∑����������⁄������ where

> S is the set of all countries in the world.

> 24 In fact, our variables of interest changes from 0 to a positive number and then remains constant for the period after the signing of the agreement for all the country pairs with an agreement.

19
_Table 10: Percentage decrease in total time‐trade barriers due to the BRI_

||**Min**|**Max**|**Mean**|**Std Dev**|
|---|---|---|---|---|
|||**Wo**|**rld**||
|**% decrease in time‐trade barrier – Lower Bound**|0.0%|61.52%|1.19%|2.63%|
|**% decrease in time‐trade barrier – Upper Bound**|0.0%|65.16%|2.49%|3.66%|
|||**BRI Co**|**untries**||
|**% decrease in time‐trade barrier – Lower Bound**|0.0%|61.52%|1.74%|3.34%|
|**% decrease in time‐trade barrier – Upper Bound**|0.0%|65.16%|3.24%|4.51%|

_Note that time‐trade barrier is the product of shipping time and the value of time from Hummels and Schaur (2013) aggregated into GTAP sectors._ The last step is to calculate the total trade costs. Overall, the time barrier associated with the shipment between two cities is only a fraction of trade costs, which also contains the actual transportation cost as well as the tariffs and other monetary charges that can be applied between respective countries. In the previous section, we focused our analysis on the decrease in trade barriers that can be achieved from a reduction in shipping time and estimated associated decrease in ad‐valorem costs. We now need to account for the fact that trade costs encompassing these other elements. In particular, we assume that total trade costs are defined as follows:

# **Trade Cost = tariff + transport + time cost**

Assuming that both tariff and transportation costs are unchanged, we now compute the decrease in total trade costs that can be expected from the sheer decrease in shipping time and the value of time computed above.

We use data from the latest release of the GTAP database, version 10. One of the main advantages of the GTAP database is the ability to use data related to both tariffs and transport costs at the sectoral level. The complete database contains a total of 57 sectors. In our analysis, we focus on the 47 good sectors and do not consider the remaining 10 service sectors as the value of time and the economic determinants of trade costs for services are very different and outside of the scope of this paper. Moreover, the complete data set also contains information on other macroeconomic variables such as input‐output matrices or sectoral employment and sales, which can be very useful when using our estimates in the context of a lager general equilibrium model that needs to be calibrated.

One of the caveats of using GTAP, however, is the geographical aggregation is less precise than our previous shipping time analysis. The data contain a total of 140 “regions” and an aggregated rest of the world. While this aggregation means that we are decreasing the geographical granularity of our results, it enables us to build trade costs estimates in a consistent way for a large number of countries and sectors.

# **b. Results**

We now present the key results of the impact of BRI related transport projects on trade costs based on the global database. Compared to the figures presented in Section 3, which related to changes in shipping 23
numbers are in different years, there are some irregular instances where the adult population exceeds the total population by approximately 15,000 people, for example, in rural areas of Ararat.

**Table 1: Population and number of voters across strata**

|Marz name|Settlement<br>type|Strata ID|Population<br>(2022 estimate<br>based on 2020<br>WorldPopdata)|Number of voters<br>(18 or older, 2023<br>election data)|Difference<br>between total and<br>adult population|
|---|---|---|---|---|---|
||||(1)|(2)|(3)=(1)-(2)|
|Aragatsotn|Urban|1|26,738|27,847|-1,109|
|Aragatsotn|Rural|2|98,949|82,622|16,327|
|Ararat|Urban|3|72,294|56,730|15,564|
|Ararat|Rural|4|136,288|150,805|-14,517|
|Armavir|Urban|5|82,953|76,205|6,748|
|Armavir|Rural|6|183,009|137,823|45,186|
|Gegharkunik|Urban|7|65,902|61,823|4,079|
|Gegharkunik|Rural|8|161,812|109,803|52,009|
|Kotayk|Urban|9|137,493|126,680|10,813|
|Kotayk|Rural|10|108,771|94,876|13,895|
|Lori|Urban|11|124,050|134,962|-10,912|
|Lori|Rural|12|87,532|76,157|11,375|
|Shirak|Urban|13|133,620|128,691|4,929|
|Shirak|Rural|14|96,856|77,832|19,024|
|Syunik|Urban|15|90,205|65,044|25,161|
|Syunik|Rural|16|44,350|33,042|11,308|
|Tavush|Urban|17|49,859|39,214|10,645|
|Tavush|Rural|18|69,943|58,907|11,036|
|Vayots Dzor|Urban|19|16,160|16,811|-651|
|Vayots Dzor|Rural|20|31,501|25,678|5,823|
|Yerevan|Urban|21|1,098,866|824,317|274,549|
|Armenia|||2,917,151|2,405,869|511,282|

_Notes_ : The 2023 election data on the number of voters or adult population and other spatial information are obtained from the CRRC-Armenia. Column (3) presents the difference between the 2022 population updated based on WorldPop data and the 2023 number of voters or adult population from the election data.

As shown in Column 2 of **Table 1** , the total number of voters or adult population is 2.4 million, which is quite close to the total population. It indicates approximately 18% of the population is children below 18 years old. However, children below 18 years of age account for around 23%-24% of Armenia’s population in other datasets. So, Armenia’s total number of adult populations from different and official data sources has been investigated to compare the aggregate numbers. **Table 2** reports the findings. The share of the adult population from the 2011 Armenia Census (population 0-17 years old) is 77%, and the adult population share based on the combination of 2022 World Bank data (population 0-14 years old) and the 2011 Armenia Census (population 15-17 years old) is 76%, indicating that election data overstates the number of adult populations by 4%-5%.

Despite these discrepancies, the PSU size measured by the number of adults or voters is not a serious concern since the total and adult populations across strata or administrative units are strongly and positively correlated with the correlation coefficient = 0.996 ( -value = 0.000). ρρ pp 8
## **Table 1. Taxes and contributions in Brazil, 2019**

||**Brazilian**<br>**Reais**<br>**(in millions)**|**Share of**<br>**revenues**<br>**(%)**|**GDP**<br>**(%)**|**Included**<br>**in**<br>**analysis**|
|---|---|---|---|---|
|**Total Revenue & Grants**|3,086,897|100.0|44.1||
|**Revenue**|3,086,886|100.0|44.1||
|**Tax revenue(including social contributions)**|1,748,573|56.6|25.0||
|**Direct taxes**|677,661|22.0|9.7||
|**Personal Income Tax(IRPF)**|219,386|7.1|3.1|Yes|
|**Corporate Income Tax(IRPJ, CSLL, others)**|207,532|6.7|3.0|No|
|**Income Tax(other)**|108,659|3.5|1.6|No|
|**Payroll Tax(salario educaco, others)**|24,239|0.8|0.3|No|
|**Property Tax(IPTU, ITR, ITBI, IPVA, others)**|117,845|3.8|1.7|Partial|
|**Social contributions**|811,036|26.3|11.6||
|**Social security**|529,979|17.2|7.6|Yes|
|**Other social contributions**|281,057|9.1|4.0|No|
|**Indirect taxes**|1,070,912|34.7|15.3||
|**General taxes ongoods & services**|963,607|31.2|13.8|Yes|
|_IPI_|52,440|1.7|0.7|Yes|
|_ICMS_|508,128|16.5|7.3|Yes|
|_COFINS_|237,372|7.7|3.4|Yes|
|_PIS_|51,390|1.7|0.7|Yes|
|_ISS_|71,806|2.3|1.0|Yes|
|_Other sales taxes_|42,471|1.4|0.6|No|
|**Excise taxes**|2,776|0.1|0.0|Yes|
|**Taxes on specific services**|25,514|0.8|0.4|No|
|**Taxes on use of/permission to usegoods**|35,021|1.1|0.5|No|
|**Taxes on international trade & transactions**|42,933|1.4|0.6|No|
|**Other indirect taxes**|1,061|0.0|0.0|No|
|**Nontax Revenue**|527,278|17.1|7.5|No|
|**Grants**|11|0.0|0.0|No|

Source: Brazil (2022b). IRPF stands for Imposto de Renda da Pessoa Física, IRPJ for Imposto de Renda da Pessoa Jurídica, CSLL for Contribuição Social sobre o Lucro Líquido, IPTU for Imposto sobre a Propriedade Territorial Urbana, ITR for Imposto sobre a Propriedade Territorial Rural, ITBI for Imposto sobre Transmissão de Bens Imóveis, IPVA for Imposto sobre a Propriedade de Veículos Automotores, IPI for Imposto sobre Produtos Industrializados, ICMS for Imposto sobre a Circulação de Mercadorias e Prestação de Serviços, COFINS for Contribuição para o Financiamento da Seguridade Social, PIS for Programa de Integração Social, and ISS for Imposto sobre Serviços.

# _Direct transfers and in-kind transfers_

**Direct transfers.** Following World Bank (2020), direct transfers in this study are both simulated and imputed. The transfers analyzed are:

- _Unemployment benefits_ . The insurance to unemployed is given to formal workers after their contracts are terminated with no just cause, to employees who have their contracts suspended due to professional training program sponsored by the employers, to fishmen in periods in which fishing is prohibited, and to workers rescued from forced labor. Workers are entitled to three to five monthly payments and the number of payments depends on job tenure before contract termination or

14
correlogram together with low values of i would suggest white noise process or weak correlation. A systematic pattern with a peak on the contrary, would be indicative of a correlation ρρ (kk) with the number of lags (or leads) for which the peak is found. Furthermore, it is important to look at the correlogram with some criteria of a significance level. Only a peak that satisfies such criteria could be used to conclude a significant correlation between the two variables of interest. To identify the significance of lead or lag we construct the confidence interval at 95%. For this, we used the average annual percent change in housing investment and urbanization for the respective years between 1960 and 2011. To avoid overlooking nuances due to cross-country variation across regions or income groups, we calculate cross correlation coefficients for each country based on available data between 1960 and 2011.

The number of leads or lags based on _cross-correlograms_ for each country is reported in Annex 2. These results suggest that the relationship between housing supply and urban growth varies by region. For example, for most countries in the EAP region, growth in housing supply is contemporaneous with urban growth. The evidence shows that other than Kiribati, which lags 5 years, almost all countries in our nine country sample within the EAP region are either contemporaneous or very close to a synchronous relationship between investment and demand. For instance, housing investment in China is contemporaneous while in the Philippines it lags demand by 2 years. This is observed by looking at Figure 4 below. The blue marks indicate the cross correlation coefficient for urbanization and growth in housing investment. In particular, the mark shown for say Lag 5 (on the X-axis) indicates the correlation between urbanization at time t and housing investment at time t-5. The red lines mark the confidence intervals for a significance level of 95%, suggesting that any correlation found between the lines would not be statistically significant. Both cross-correlograms suggest a regular pattern and therefore we cannot conclude that the relationship between these two series is just white noise. As mentioned above, the second important element of the cross-correlogram is the peak. For the Philippines we see that the peak is at lag 2 suggesting that housing investment lags demand by 2 years. We can state this with confidence given that the peak is well outside our confidence interval for a significance level of 95%. For China, the peak of the correlogram appears at lag 0 suggesting synchronism between the two variables.

12
# **_Section 5.3:  Enumeration Area Level Controls_**

At the EA level, we include a host of control variables from the 2007 and 2008 community and household-level surveys NPS surveys.<sup>9</sup> We include a measure of the percentage of the EA population that reports working in agriculture, as a proxy for district-level poverty. We also control for EA-level poverty through an aggregated factor measure of the average number of material goods within households. In addition, we include measures of ethno-linguistic fractionalization (ELF) at the EA level. Sierra Leone, like most African countries, has a heterogeneous religious and ethnic composition. In post-conflict Sierra Leone ethnicity continues to determine membership in the country’s two main political parties. The APC is associated with the Temne and Limba groups in the north and the west, while the Mende, Kissi and Sherbro groups in the south and east largely support the SLPP (Sacks and Larizza 2011, 14). Because party affiliation is largely an extension of ethnic identity in Sierra Leone, we do not have strong _a priori_ expectations about the effects of ELF at the EA level separate from political party affiliation.

Some research suggests that Sierra Leoneans who experienced violence during the war are more likely to register to vote, to attend community meetings, to participate in political and community groups, and to contribute to local public goods (Bellows and Miguel 2009, 1145). This heightened civic participation may independently increase the success of public goods provision. Therefore, we control for the war’s impact. We measure this by aggregating the household war experiences of respondents using a series 2007 NPS household survey questions that asks respondents: if they or anyone they knew was maimed or killed during the war, had a house that was destroyed, or had to flee. We include the former measure in the following models, but our results are also robust to the latter two measures.<sup>10</sup> From the 2007 community-level NPS surveys we also include several measures of EA remoteness, specifically the distance in miles from the EA center to the nearest chiefdom headquarters, the district headquarter and to Freetown, as well as to the nearest passable road.

> 9 Many questions were asked either in 2007 or 2008 causing us to pull from both survey rounds to include as many control variables as possible.

> 10 We might also expect that areas that saw the heaviest fighting would have fewer remaining health centers and a larger population with an objectively greater need for these services, leading us to observe improvements in these districts first. However, this appears not to be the case. Huge populations shifted to Freetown during the war, which caused public goods to be delivered to the capital first. The worst hit areas, such as Kono and Kailahun, had come to either rely on aid agencies or were self-sufficient (Fanthorpe 2011).

18
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## **D. CASE STUDY OF CASOP**

The Caisse de Solidarite Ouvriere et Paysanne (CASOP) is sponsored by the Zairian National Workers Union (UNTZA). The health insurance plan is only one of the many social services provided by the CASOP, which operates nationwide. The research team studied how the insurance plan works by looking at the CASOP's only polyclinic in Kinshasa, which serves all CASOP members in the city.

# 1. Terms of the Insurance Plan

The CASOP plan covers ambulatory curative cases of illness treated at the CASOP polyclinic in Kinshasa. Plan members are entitled to pay a lower fee than non-members for each day of care, which includes a nurse consultation and some basic drugs. Families of members are entitled to the same benefits as members, for no additional premiums.

Table D.1 contains a list of prices according to payor category. Theoretically, there are four categories: individual member, individual non-member, company member, company nonmember; but there are currently no companies which are affiliated with CASOP's clinic that are not members, so in effect there are three active payor categories. Individual members are insured and pay least. Individual non-members are uninsured individuals. Company members are insured and benefit from having their companies billed the fees for their services. For all payor categories, laboratory exams and x-rays are charged separately. Drugs which are not usually kept in stock at the health center level are charged separately as well.

TabLe 0.1 Price Lfst CASOP PoLyctinic, 1989

|l<br> <br>ISERVICE|~<br>~<br> <br>I<br>MEMBE|~<br>~<br>~<br>i<br>I<br>R IMON-MEMBE|~I<br> <br>COMPANY<br>RI MEMBERI|
|---|---|---|---|
|I------<br>-------------------<br>fConsuLtation<br>**with:**|--------------<br>I<br>|-----------------<br>I<br>I||
|IMedicat Assistant|2<br>**250**<br> <br>|1<br>350<br>|1400|
|iGenerhl<br>**Physician**|I<br>350<br>|1<br>500<br>|600|
|Ispeciitized<br>**Physicien**|*****<br>450||<br>700**j**|<br>800|
||IVwith drug|1300||<br>1550|1600<br>0|
|ILaboratory:<br>**BLood**<br> <br>||<br>100<br> <br>|1<br>200 |<br> <br>|<br>200I<br>|
|ILaboratory:<br>Stools<br> <br>I<br>|I<br>100<br>I|<br> I<br>2001<br>I<br>I|<br> <br>150I<br> <br>I|
|<br> <br>INorml<br>delivery|<br>7<br>7000|<br> <br>I<br>10000||<br><br> <br>12000|
|jXray<br>(chest,<br>adult)<br>..........................................................||<br>1400|1500|I<br>**1700**||

* **This Is** the type of coanultctlon chosen by out petients **according** to the cifnic **adeinistration.**

The clinic also provides prenatal care, vaccinations, nutrition demonstrations, and minor surgery, all of which are not covered under the plan.

Nationally, the CASOP has more than 42,000 contributing members. When family members are included, the total reaches one quarter million. In Kinshasa, the total number of members (including families) was 6,691 in September ;989. Since membership is open to the entire population, this represents a tiny fraction (0.2%) of Kinshasa's total population of 3 million.

44
# **<u>Institutions</u>**

It is assumed that the physical, legal, and regulatory infrastructure and institutions support overall, including agricultural, development. We measure this influence with two variables obtained from the Freedom House – political rights and civil liberties. The measure of political rights reflects the electoral process, political pluralism and participation, and functioning of the government. The civil liberties measure includes aspects of freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights. Both measures are on a scale of 1 to 7, where 1 represents the most free and 7 the least free. If these variables matter, they are expected to be correlated with development and reflected in the development indicator. They are nevertheless introduced here explicitly because of our interest in trying to isolate the effects of institutions on agricultural productivity. Hence, the expected contribution of these variables in the present analysis is over and above that of the development variable.

# **<u>Incentives</u>**

We introduce two measures of incentives to allow for the direct effect of incentives on productivity over and above their indirect effect through resource allocation and accumulation. The measures are the terms of trade between the agricultural sector and the overall economy, obtained as the relative price (agricultural GDP deflator to total GDP deflator, lagged one period), and its variability, calculated as a moving standard deviation from the three previous periods.<sup>19</sup> Note that this measure confounds in it the various taxes or subsidies, direct or indirect, applied to the sector. The variability in agricultural prices reflects the market risk faced by agricultural producers. In addition to the sector-specific risk, there is an economy-wide market risk, that of price volatility for the economy as a whole, measured by the rate of inflation. This is calculated as the rate of change in the total GDP deflator.

> 19 In an earlier paper, Mundlak, Larson, and Butzer (1999) used the price ratio of agriculture to that of manufacturing. The reason for the change is that the alternative to agricultural resources is not limited to manufacturing, but includes also services, which may, in fact, have become more important than manufacturing, particularly in developed countries.

13