rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1 Self-reported morbidity and healthcare utilization by age and gender (%)</u>**

||||**Suffers**<br>**from a**<br>**chronic**<br>**illness or**<br>**disability* **|**Been ill in**<br>**last 4**<br>**weeks**|**Sought**<br>**(outpatient)**<br>**medical**<br>**care in last**<br>**month****|**Hospitalized**<br>**in the last**<br>**year*****|**Has health**<br>**insurance**|
|---|---|---|---|---|---|---|---|
||_Men_|_0-15_|2.46|11.65|9.77|2.84|36.10|
|||_16-64_|12.48|4.22|8.46|2.83|33.36|
|||_65+_|55.54|16.65|39.24|9.39|69.80|
|**Albania**|_Women_|_0-15_<br>|1.68<br>|9.87<br>|8.38<br>|2.06<br>|35.87<br>|
|||_16-64_|15.95|7.84|14.97|5.60|33.42|
|||_65+_|63.72|15.82|43.83|6.81|63.95|
||**_Total_**||**_14.38_**|**_8.36_**|**_13.50_**|**_4.04_**|**_36.95_**|
||_Obs. (unw_|_eighted)_|_17,304_|_17,304_|_17,304_|_17,304_|_17,304_|
||_Men_|_0-15_|3.41|_na_|15.79|0.00|66.74|
|||_16-64_|15.78|_na_|17.76|3.23|56.43|
|||_65+_|59.87|_na_|48.16|11.12|_64.32_|
|**Bosnia and**<br>|_Women_|_0-15_|0.00|_na_|12.61|0.00|70.93|
|**Herzegovina**||_16-64_|20.30|_na_|37.46|5.47|60.25|
|||_65+_|76.02|_na_|54.95|5.59|_61.73_|
||**_Total_**||**_25.37_**|**_na_**|**_30.73_**|**_4.80_**|**_59.16_**|
||_Obs. (unw_|_eighted)_|_9331_|_9331_|_9331_|_9331_|_9331_|
||_Men_|_0-15_|5.40|4.60|6.03|_Na_|93.85|
|||_16-64_|5.50|5.90|7.91|_Na_|95.68|
|||_65+_|15.00|18.00|15.73|_Na_|95.26|
|**Montenegro**|_Women_|_0-15_|4.80|3.40|7.01|_Na_|93.01|
|||_16-64_|5.10|6.30|9.05|_Na_|95.50|
|||_65+_|23.00|23.00|19.98|_Na_|95.72|
||**_Total_**||**_6.30_**|**_6.60_**|**_8.61_**|**_Na_**|**_94.95_**|
||_Obs. (unw_|_eighted)_|_8889_|_8889_|_8889_|_8889_|_8889_|
||_Men_|_0-15_|4.17|11.78|17.37|3.96|_Na_|
|||_16-64_|15.83|9.94|12.72|2.68|_Na_|
|||_65+_|56.98|23.56|37.43|11.19|_Na_|
|**Serbia**|_Women_|_0-15_|2.88|10.26|16.09|2.57|_Na_|
|||_16-64_|20.54|15.11|21.61|4.66|_Na_|
|||_65+_|66.75|28.64|44.17|8.51|_Na_|
||**_Total_**||**_22.12_**|**_14.35_**|**_20.73_**|**_4.52_**|**_Na_**|
||_Obs. (unw_|_eighted)_|_8027_|_8027_|_8027_|_8027_|_8027_|
||_Men_|_0-15_|_na_|_na_|13.79|3.62|_Na_|
|||_16-64_|_na_|_na_|18.33|24.82|_Na_|
|||_65+_|_na_|_na_|5.49|4.83|_Na_|
|**Kosovo**|_Women_|_0-15_|<br>_na_|<br>_na_|<br>15.02|<br>3.13|<br>_Na_|
|||_16-64_|_na_|_na_|20.36|21.12|_Na_|
|||_65+_|_na_|_na_|5.72|6.01|_Na_|
||**_Total_**||**_na_**|**_na_**|**_17.85_**|**_4.82_**|**_Na_**|
||_Obs.(unw_|_eighted)_|_17917_|_17917_|_17917_|_17917_|_17917_|

* The precise definition of morbidity concepts differs somewhat across survey instruments. Table A2 in the Appendix lists the actual questions asked in survey. **Percentages refer to the past 4 weeks for all except Bosnia and Herzegovina where they refer to the past 14 months. ***Percentages refer to the past 12 months for all except Bosnia and Herzegovina where they refer to the past 14 months.

8
and low-density settlement areas much further north of Nairobi. And it would place the center of Nairobi well outside its true central core. A higher cutoff of, say, 750 people per sq. km. (light blue areas) may be too stringent and exclude satellite cities around Nairobi that are very likely to be within the commuting zone. Obviously, other arguments about drawing boundaries can be made. In the figure we also outline in green the independent LD settlements. Some are very spatially distinct, but some follow ribbons (roads) to the north outside Nairobi, where rural areas are interspersed with urbanized settlements. In the figure everything in yellow or the Google Earth background is rural.

# **2 Defining Economic Density**

Figure 4 illustrates issues about urban density definitions.<sup>6</sup> All hypothetical cities in Figure 4 have the same total population (180) in thousands and average density (5). City 1 has no clustering. Cities 2 and 3 have the same degree of within grid square clustering, with half the grid squares with no population and half with 10 people per grid. The 10 means greater within grid square possibilities for intersecting with others (the pairwise possibilities for meetings for example, ((n-1)!). However city 2 allows for more possibilities for interactions with neighbors. Ignoring the boundaries in city 3, on average a grid squares has 40 queen neighbors, while in city 2 a grid square has 80 queen neighbors.

We now turn to two measures which reflect these differences, personal population density and De La Roca and Puga (2017) density [RPA]. For a given city area, personal population density is a weighted, rather than simple, sum of own cell population densities. So in Figure 4, that gives a value of 5 for city 1 and 10 for cities 2 and 3. The RPA measure further makes a distinction between cities 2 and 3. It does a sum of grid square measures, where each grid square measure is a distance discounted sum of your own and neighbors density out to a given radius. Each grid square measure is weighted by its population share in the city. This measure will give a higher value for city 2 than 3.

The basics of what we present is not our invention. Modi (2004) proposed the idea and term personal population density. Small and Cohen (2004) calculate, on a coarser scale,

> 6Figure 4 and the definition and decomposition of personal population density are borrowed from on-going work by Henderson, Storeygard and Weil. This is gratefully acknowledged.

10
Figure 3. Impact of ENTRE on students’ dropout rate in the experiment and the placebo Notes: The graph shows the ITT estimates of the impact of ENTRE and each of its treatment arms (named Entre 1, Entre 2 and Entre 3) on students’ dropout rate in the real experiment and in the placebo. The latter replicates the estimation using data from 2016 and 2017 (instead of 2017 and 2018), when the program did not exist.

Table 6. Placebo test of the heterogeneity in the impact of ENTRE on students’ dropout rate

||||Inter|action:|||
|---|---|---|---|---|---|---|
||(i)Pre-treatment|dropoutprobability|(ii)Gende|r(=1 if boy)|(iii)School size|(=1 if large school)|
||Placebo|Experiment|Placebo|Experiment|Placebo|Experiment|
|**_Panel (i): Pooled Model_**|||||||
|ENTRE_j_+ (ENTRE_j_) x (_Interaction j_)|0.002|-0.029**|0.002|-0.019***|-0.000|-0.020***|
||(0.005)|(0.012)|(0.006)|(0.007)|(0.000)|(0.006)|
|_ENTRE j_|-0.002|0.001|-0.002|-0.007|-0.000|-0.005|
||(0.003)|(0.006)|(0.006)|(0.007)|(0.000)|(0.006)|
|Observations|147,977|145,628|147,977|145,628|147,977|145,628|
|R-squared|0.351|0.333|0.351|0.331|0.351|0.331|
|**_Panel (ii): Treatment arms_**|||||||
|ENTRE 1_j_ +(ENTRE 1_j_) x (_Interaction j_)|-0.000|-0.032**|0.004|-0.032***|-0.000|-0.026***|
||(0.006)|(0.015)|(0.007)|(0.009)|(0.000)|(0.008)|
|ENTRE 2_j_ +(ENTRE 2_j_) x (_Interaction j_)|0.007|-0.036**|-0.006|-0.011|0.000|-0.014**|
||(0.007)|(0.014)|(0.007)|(0.008)|(0.000)|(0.007)|
|ENTRE 3_j_ +(ENTRE 3_j_) x (_Interaction j_)|-0.000|-0.020|0.007|-0.013|0.000|-0.021***|
||(0.006)|(0.015)|(0.007)|(0.009)|(0.000)|(0.007)|
|_ENTRE 1 j_|-0.001|0.000|-0.005|0.003|0.000|-0.003|
||(0.003)|(0.008)|(0.007)|(0.009)|(0.000)|(0.008)|
|_ENTRE 2 j_|-0.005|0.007|0.007|-0.011|-0.000|-0.007|
||(0.004)|(0.007)|(0.007)|(0.009)|(0.000)|(0.008)|
|_ENTRE 3 j_|-0.000|-0.005|-0.007|-0.013|-0.000|-0.004|
||(0.003)|(0.007)|(0.007)|(0.009)|(0.000)|(0.008)|
|Observations|147,977|145,628|147,977|145,628|147,977|145,628|
|R-squared|0.351|0.333|0.351|0.332|0.351|0.331|
|Controls at student level|YES|YES|YES|YES|YES|YES|
|School fixed effects|YES|YES|YES|YES|YES|YES|

Notes: 1 <mark>)</mark> Placebo: estimates usin <mark>g</mark> data from 2016 and 2017 <mark>,</mark> when the ENTRE <mark>p</mark> ro <mark>g</mark> ram did not exist. Ex <mark>p</mark> eriment: estimates usin <mark>g</mark> data from 2017 and 2018 (year of the intervention) and the preferred specification in Table 4. 2) Standard errors clustered at schoolyear level in parenthesis; 3) *** p<0.01, ** p<0.05, * p<0.1. 4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. 5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year 2017 in Placebo; = year 2018 in Experiment; 0 otherwise); _ENTRE ij_ with i=1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment 26
# 1  Before beginning the fieldwork

The objectives of the pre-field stage are to:

- Define the scope of the exercise in terms of the country-specific institutions to be analyzed;

- Understand the institutional setting governing infrastructure;

- Identify (primary and secondary) sources of data and information;

- Answer as many qualitative (and quantitative) questions as possible based on secondary sources.

The information collected in this preparatory, or pre-field, phase might in some cases be referential but in all cases will be completed and cross-checked in the course of the fieldwork, making the whole process more efficient.

One important item on the pre-field agenda is a thorough literature review to determine the extent of the information and data readily available from public sources (both primary and secondary). A starting point for the literature and data review is offered by the specialized Web sites of development institutions (World Bank, African Development Bank [AfDB], Organisation for Economic Co-operation and Development [OECD], International Monetary Fund [IMF], and donors) and of relevant institutions in the country (such as the ministry of finance, statistical offices, central bank, line ministries, parastatals, monitoring units for parastatals, and so on). This search should include, but must not be limited to:

- Recent sectoral analysis material and publications;

- Recent fiscal analysis material such as the Country Financial Accountability Assessment (CFAA), Public Financial Management (PFM) diagnostic studies, and IMF‘s Recent Economic Developments;

- Organizational charts, annual reports, budget statements, budget speeches, budget laws, Poverty Reduction Strategy Papers (PRSPs), and the Medium-Term Expenditure Framework (MTEF);

- Budget books;

- Tariff schedule publications and their indexation;

- Financial accounts of infrastructure operators and funds;

- Annual reports of infrastructure operators and funds.

The pre-fieldwork should be organized so as to:

- Produce a blueprint of the institutions involved in infrastructure provision.

- Mine existing readily and publicly available data.

- Account for country-specific circumstances (and concerns) in order to achieve better results.

13
3 rate generates, on average, 0.35 percent of GDP revenue. Average revenue productivity ratio for multiple VAT rate countries (0.35 percent) is slightly _lower_ thar the ratio for the single VAT rates countries (0.37). Therefore, the use of more than one rate does not help raise more revenu^- and, indeed, it seems to reduce the revenue performance of a VAT vis-a-vis countries that use single rates.

Second, the most conmmonly used single VAT rate is 10 percent, u-ed in 9 countries representing about 20 percent of the total, followed by the rate of 15 percent, which is used in 3 countries. This is consistent with the recommendations of most tax advisors to adopt the VAT at a single rate between 10 and 20 percent'.

Third, twenty five countries (constituting 50 percent of the total) have a single rate (ignoring the zero rate and/or exemption on some services, exports, etc). Seven of the remaining 25 used two rates; nine used three rates; and the remaining nine use more than three rates. **These** multiple rates offer a greater opportunity to fit tha VAT to various social and political ends. However, rate differentiation raises administrative and compliance costs which undermines the VAT revenue performance (see Tait, 1988).

Fourth, the VAT revenue and revenue productivity varies significantly across countries. The minimum revenue productivity ratio of 0.044 is found in Guinea which uses a single VAT rate of 13.6 percent. On the other end of the spectrum is Israel with the ratio of 0.653 -- fifteen times higher than in Guinea - using a single rate of 15 percent. The quality ".d efiiciency of tax administration and the size of the base, the hall.narks of the Israeli VAT system, are important factors behind this performance.

Fifth, following Cnossen (1991), the tax base for a VAT can be broadly c!assified into four categories: (i) all goods and services (G+S); (ii) goods and selected services (G+ST); (iii) goods only (G); (iv) consumer goods and capital goods (G+CG); or (v) consumer goods, selected services, and capital goods (G+ST+CG). Two thirds of the countries apply the broad base (G+S), while nine countries use goods and select services (G+ST) as the base. Evidently, there is a wide diversity in the size of the VAT base across countries, but the - neral preference towards a broad based VAT is clear.

The above facts are the averages based on the existing practice of VAT. Deviations fromn these averages and the structures of the VAT in countries that use or are planning to introduce a VAT, is a subject of potentially fruitful research. This is the course we take in this paper, focussing on th. VAT revenue performance.

# **III. Hypotheses Models and Results**

In this section we present the hypotheses and models used to test - using regression techniques - the main determinants of VAT revenue which are suggested by the studies and practice of VAT, and are consistent with the data. The recent contributions to the VAT literature (see Tait 1988, 1991;

# B/ World Bank (1991).
## Figure I

# Effect of one Additional Year of Primary Education per Person on Real GDP (%)

<!-- Start of picture text --> Percentage change in real GDP<br><!-- End of picture text --> <!-- Start of picture text --> East 4Asm<br>3% Latin<br>2%<br>1%<br>0% - South Asia<br>Africa<br>Atnas<br>-1%- . I . I<br>2.5 3 3.5 4 4.5 5<br>Average years of primary education<br><!-- End of picture text -->

# Figure 2 Effect of one Additional Year of Secondary Education per Person on Real GDP (%)

<!-- Start of picture text --> Percentage change in real GDP<br>6% -<br>Man& East Asia<br>4% Africa Latin America<br>2%<br>0% -<br>-2%<br>- 4%<br>- 6%<br>South AJ/J<br>8% -<br>0.3 0.6 0.9 1.2<br>Average years of secondary education<br><!-- End of picture text --> 23
- IPUMS. (2019). _Integrated Public Use Microdata Series, International: Version 7.2 [dataset]._ Minneapolis, MN: IPUMS: Minnesota Population Center.

- Lagakos, D. (2020). Urban-Rural Gaps in the Developing World: Does Internal Migration Offer Opportunities? _Journal of Economic Perspectives_ , _34_ (3), 174–192.

- Messina, J., & Silva, J. (2019). Twenty Years of Wage Inequality in Latin America. _Policy Research Working Paper_ (8995).

- Munoz, E. (2021). Intergenerational Educational Mobility within Chile. _Available at SSRN: https://ssrn.com/abstract=3969270 or http://dx.doi.org/10.2139/ssrn.3969270_ .

- Munoz, E., & Siravegna, M. (2021). When Measure Matters: Coresidence Bias and Intergenerational Mobility Revisited. _Available at SSRN: https://ssrn.com/abstract=3969270 or http://dx.doi.org/10.2139/ssrn.3969270_ .

- Narayan, A., Van der Weide, R., Cojocaru, A., Lakner, C., Redaelli, S., Gerszon Mahler, D., . . . Thewissen, S. (2018). _Fair Progress?: Economic Mobility Across Generations Around the World_ . The World Bank.

- Neidh ̈ofer, G., Serrano, J., & Gasparini, L. (2018). Educational Inequality and Intergenerational Mobility in Latin America: A New Database. _Journal of Development Economics_ , _134_ , 329–349.

- Ravallion, M. (2016). Are the World’s Poorest being Left Behind? _Journal of Economic Growth_ , _21_ , 139–164.

- Rawls, J. (1971). _A Theory of Justice_ . Cambridge, MA: Harvard University Press.

- Solon, G. (1992). Intergenerational Income Mobility in the United States. _The American Economic Review_ , _82_ (3), 393–408.

- Torche, F. (2019). Educational Mobility in Developing Countries. _WIDER Working Paper_ (88), 1–31.

- Van der Weide, R., Ferreira de Souza, P., & Barbosa, R. (2020). Intergenerational Mobility in Education in Brazil. _mimeo_ .

- Van der Weide, R., Lakner, C., Gerszon Mahler, D., Narayan, A., & Ramasubbaiah, R. (2021). Intergenerational Mobility Around the World. _Policy Research Working Paper_ , _9707_ .

32
country increases, both, the costs of monitoring foreign clients and bank managers or loan officers in bank affiliates rise. The discussion above suggests that the distance between the bank and its customers increases the comparative advantage to international banks of extending claims locally, since local presence helps to overcome some of these informational costs. Therefore, on the basis of the costs of monitoring borrowers, distance measures should have a positive impact on the share of local claims. On the other hand, as distance measures increase, the rising costs of monitoring loan officers and affiliate managers suggest that distance might have a negative impact on the share of local claims. Thus, a priori the sign of distance measures on the share of local claims is unclear.

We control for the geographic distance between the foreign banks’ country of origin and the host country by including the log of the miles between the two countries’ capital cities. This information comes primarily from Glick and Rose (2002) and the Central Intelligence Agency (CIA) _World Factbook_ .<sup>10</sup> We also take into account that, nowadays, instant methods of communications like phones and internet connections can help bridge physical distances, making project and management oversight and access to information possible from far away places. In our regressions, we use the number of internet hosts per 1000 people in each borrowing/claim recipient country as measures of access to communications, which are like to affect monitoring and informational costs.<sup>11</sup> These data come from the World Bank _World Development Indicators_ . We expect that lending cross-border is easier to countries with better communications, allowing

> 10 When data was missing from this source, we used a software provided on the US Department of Agriculture webpage that calculates distances between more than 220 capital cities worldwide (see http://www.wcrl.ars.usda.gov/cec/java/capitals.htm). 11 In estimations that we do not report here, we also included measures of the number of phone lines in each claim recipient country. The results are not significantly different. Therefore, to save space we only report results using the number of internet hosts.

15
**Figure 1. Sectoral CO2 and non-CO2 Emissions per Value Added across Sectors (metric/million USD).**

<!-- Start of picture text --> 4,000 CO2 Emissions per VA 20,000 Non-CO2 Emissions per VA<br>High Income 15,000<br> 2,000 Lower Middle Income 10,000<br>Low<br> 5,000<br>Upper Middle<br> - -<br><!-- End of picture text --> Source: Authors’ computations using GTAP data. Utilities are removed from Figure 1 for CO2 emissions for presentational purpose, as they have nearly five times higher emissions per value added (on average) compared to transportation and communication.

Figure 1 clearly shows that transportation and communication, mining, and manufacturing are among the highest contributors to CO2 emissions per value added, while other services, finance, and trade have the lowest levels of CO2 emissions per value added. It is important to note that utilities are intentionally removed from the graph as they have nearly five times higher emissions per value added (on average) compared to transportation and communication. There is also a large dispersion within these broad sectors indicating that production structures within industries could be diverse differing among developing countries relative to the different stages of sophistication and the choice of technology. At the same time, the CO2 emissions per value added vary substantially across the different country income groups. Finally, non-CO2 emissions in terms of CO2 equivalent are multiple times higher for agriculture, mining, and utilities with a large variation across income groups. These non-CO2 emissions include methane (CH4), nitrous oxide (N2O) and the group of fluorinated gases (F-gases).

# **5. Estimation Results**

The paper estimates the impact of the share of informal workers on emissions per value added using cross country and industry data in the following regressions: OLS, OLS with control variables, industry fixed effects, country fixed effects, and industry and country fixed effects as shown in Table 1. A problem in applying OLS is that regressors can be correlated with the error term and this correlation between regressors and the errors violates an assumption necessary for the consistency of OLS. For example, 12
economies. They also discovered evidence that this export expansion was associated with sectoral inflows of patent applications and intra-firm trade within US multinational firms.

Based on this small sample, it seems that stronger IPRs, including those implemented via TRIPS, tend to increase both imports and exports among middle-income emerging economies, especially in high-technology and IPRs-sensitive goods. However, this question has rarely been studied in the context of the further elevation of standards embedded in high-protection PTAs. This is the empirical gap we hope to begin filling with this paper.<sup>3</sup> Specifically, employing the World Bank database, we ask whether legally enforceable PTAs with chapters requiring IPRs standards that exceed TRIPS expectations have some additional impact on the trade of member countries, over and above that of TRIPS. We also ask whether these effects vary by countries in different income groups (development levels) and specific clusters of industries that are highly sensitive to intellectual property protection. We pay particular attention to trade in pharmaceuticals, chemicals, and information and communication technologies, for these are the areas in which TRIPS-Plus chapters embody especially rigorous standards.

Our analysis contributes to the literature on the trade effects of PTAs. Whether such agreements increase or decrease trade, couched in terms of trade creation or trade diversion, has long been a subject of theoretical and empirical research (Romalis, 2007; Baier and Bergstrand, 2007). More recent literature suggests that PTAs generally have positive trade effects, controlling for endogeneity of selection into agreements, but the impacts are strongly heterogeneous (Baier and Bergstrand, 2009; Baier et al., 2019).

Note that such studies consider discriminatory reductions in trade barriers between members to be the policy impact of free trade agreements. Thus, they focus on bilateral or within-agreement trade effects, accounting also for trade diversion from outside. When considering IPRs, however, the logic is different, arising from the inherent spillover effects created by national IPR regimes. Specifically, the TRIPS Agreement requires countries to establish and enforce standards that are applied without discrimination, embodying both national treatment and the most-favored nation principle. Accordingly, when a country strengthens its

> 3 A recent paper by Campi and Dueñas (2019) estimated a gravity model of bilateral trade and found evidence of a positive impact five years after signing such agreements. However, this effect seemed to hold for both highintellectual property goods and low-intellectual property goods, raising some questions about the identification exercise.

4
<!-- Start of picture text --> Share of countries with equal rights to immoveable property<br>=<br>°<br>3=) ee -7<br>3 a<br>2: orenn?<br>ss<br>& wn’<br>a2 foe7vd“<br>mA<br>irs)<br>1960 1970 1980 1990 2000 2010<br>es<br><!-- End of picture text --> <!-- Start of picture text --> Share of countries with equal inheritance rights<br>wo<br>s<br>N,<br>8x j-/<br>= Hi<br>bs)3 S-J<br>5 nf<br>s rd<br>a8 ‘I<br>©<br>1960 1970 1980 1990 2000 2010<br>year<br>— ‘oss<br>and sons _—===- W e nd sbnds<br><!-- End of picture text -->