rafmacalaba/datause-encoder-data huggingface dataset:
# **Estimated infrastructure costs**

There is little publicly available information on the terms and conditions of BRI projects. In order to compute the total costs associated with BRI transport infrastructure, we combine information from World Bank country teams, which draw from publicly available sources on the costs of a small subset of BRI projects, with a bottom-up approach based on the projects’ characteristics and assumptions of construction costs. Specifically, we first start by computing the length (in km) of each new rail junction, improvement of existing rails, tunnels, canals and bridges. Then we use the assumptions presented in Table 3 to quantify the cost for infrastructure projects for which we do not have country specific information, which are the large majority of cases. The cost per kilometer of improvement of existing rail is based on the expected rehabilitation and upgrade cost of the Karachi-Lahore Peshawar railway track. Assumptions on the cost of tunnels and bridges are based on Ollivier, Sondhi, and Zhou (2014).

_Table 3: Assumptions in the construction of Infrastructure Costs_

|Project type|Cost per unit<br>million of USD (per km)|
|---|---|
|new rail|12.14|
|improvement of existing rail|4.37|
|tunnel|11|
|canal|30|
|bridge|10|
|new port|case-by-case basis|
|improved port|case-by-case basis|

Based on these assumptions, Table 4 presents the total estimated costs of BRI transport infrastructure in each country.

17
<!-- Start of picture text --> 18 140<br>16<br>120<br>14<br>100<br>12<br>10 80<br>8 60<br>6<br>40<br>4<br>20<br>2<br>0 0<br>Revenue Domestic borrow ing Foreign loans<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Grants Foreign loans Net ODA<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>1980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004<br>Foreign loan<br>Domestic borrowing and revenue<br>% GDP<br><!-- End of picture text --> Source: World Development Indicators and authors’ calculation.

Note: Grants to government include unrequited, non-repayable, noncompulsory government receipts from other governments or international institutions. Foreign loans represent debt outstanding and disbursed. Net ODA disbursements equal gross ODA disbursements less principal repayments (amortization) of previous ODA loans. All variables are given as a ratio of GDP 9
# **TABLE 3: OPERATIONAL FINANCING AVAILABLE AT THE HEALTH FACILITY AND PBF PAYMENTS ASSESSED AT ENDLINE**

|Total(cost recovery, Minis|tryof health, subsidies)<br>T1(full PBF)|C1(increased financing)|p-value|
|---|---|---|---|
|Q1-2014 received|3420226|4163127|0.5281|
|Q2-2014 received|3339472|4044135|0.5143|
|Q3-2014 received|3799585|4395996|0.748|
|Q4-2014 received|3876873|3873040|0.9979|
|n|53|48|101|
|Subsidies|T1|C1|p-value|
|Q1-2014 received|1322834|1725858|0.2009|
|Q2-2014 received|1241536|1751481|0.1805|
|Q3-2014 received|1757934|1401405|0.5654|
|Q4-2014 received|1428642|1264029|0.7897|
|n|53|48|101|
||Standardized bynumb<br>|er of health workers<br>||
||T1(full PBF)|C1(increased financing)|p-value|
|Q1-2014 received|587480|559007|0.7962|
|Q2-2014 received|442595|550209|0.2212|
|Q3-2014 received|436623|471578|0.6895|
|Q4-2014 received|467895|528527|0.5869|
|n|49|43||
|Subsidies||||
|Q1-2014 received|295233|391902|0.2906|
|Q2-2014 received|242738|361116|0.1223|
|Q3-2014 received|238236|260399|0.7039|
|Q4-2014 received|212285|312874|0.2159|
|n|49|43||

36
<!-- Start of picture text --> Deposit dollarization 1995-2004<br>Balanced panel 76 countries<br>37<br>35<br>33<br>Mean<br>31<br>Median<br>29<br>27<br>25<br>1995 1998 2001 2004<br>%<br><!-- End of picture text --> Figure 2: _Trends in deposit dollarization 1995-2004_ . Data relates to balanced panel comprising all 76 countries for which data is available for all years. Source: Based on data in De Nicoló, Honohan and Ize, (2005); Levy-Yeyati (2006) “for table 1 06.xls” Has the forward march in dollarization ceased, or is this just a pause? One transitory source of a pause could be the fall in the average international value of the US dollar from the first quarter of 2002. Another exceptional event was the forced conversion of the Argentine dollar deposits. These or other short-term factors may have had an effect on dollarization ratios, as will be discussed below.

One way of detecting a change in trend is to fit nonlinear curves to the data. A linear time trend through the unbalanced sample 1990-2004 still gives an estimate of annual increase in dollarization of 0.90 percent per annum (standard error of 0.17); with fixed effects, this declines to 0.53 percent per annum (s.e. 0.06). A quadratic time trend shows a sharp slowing of the increase in dollarization (the estimated quadratic peaks at 2004— Figure 4). A cubic term in time is also significant: the estimated cubic peaks at 2001. As an alternative, fixed time effects are substituted for the time trend. These clearly show a downturn after 2001 (though the differences from year to year are not statistically significant).

4
|(8)|1.007**<br>(0.501)<br>1.029***<br>(0.286)<br>-0.564***<br>(0.0896)|20.15<br>0.021<br>0.886|yes|yes<br>yes<br>3,506|errors clustered at the||
|---|---|---|---|---|---|---|
|(7)|1.121*<br>(0.632)<br>1.043***<br>(0.240)<br>-0.565***<br>(0.149)|22.34<br>0.014<br>0.906|yes|yes<br>no<br>3,506|Robust standard||
|(6)|0.810**<br>(0.387)<br>0.397***<br>(0.0982)<br>0.120***<br>(0.0264)<br>0.156**<br>(0.0674)<br>0.408***<br>(0.0233)|19.69<br>0.058<br>0.810|yes|yes<br>yes<br>3,496|fxed efects.|_<_0.01.|
|(5)|0.835**<br>(0.370)<br>0.433***<br>(0.0988)<br>0.122***<br>(0.0246)<br>0.155**<br>(0.0688)<br>0.414***<br>(0.0241)|21.99<br>0.192<br>0.661|yes|yes<br>no<br>3,496|and country|_<_0.05; *** _p_|
|(4)|1.611***<br>(0.420)<br>0.538***<br>(0.110)<br>0.210***<br>(0.0156)<br>0.246**<br>(0.117)|20.06<br>0.027<br>0.871|yes|yes<br>yes<br>3,497|cribed above|_<_0.1; ** _p_|
|(3)|1.641***<br>(0.451)<br>0.566***<br>(0.119)<br>0.215***<br>(0.0146)<br>0.247**<br>(0.120)|22.42<br>0.090<br>0.764|yes|yes<br>no<br>3,497|cteristics des|enthesis. * _p_|
|(2)|1.534***<br>(0.362)<br>0.689***<br>(0.170)<br>0.313***<br>(0.0449)|20.39<br>0.001<br>0.969|yes|yes<br>yes<br>3,503|ividual chara|y level in par|
|(1)|1.586***<br>(0.442)<br>0.703***<br>(0.137)<br>0.321***<br>(0.0544)|22.78<br>0.001<br>0.976|yes|yes<br>no<br>3,503|d include ind|countr|
|t variable: Tax morale|ublic institutions<br>he neighbourhood<br>transport fare<br> government<br>roperty<br>in|excl. IVs<br> statistic<br> p-value|l characteristics|fxed efects<br>fxed efects<br>ons|estimates use the weights an||
|Dependen|Trust in p<br>Trust in t<br>Avoiding <br>Claiming <br>Stealing p<br>Legal orig|F-stat of <br>Hansen J <br>Hansen J|Individua|Country <br>Location <br>Observati|Notes. All||

34
**Table 2: Definition of Economic Activity**

|**Year**|**Turnover**<br>**or OPEX**|**PAYE**|**VAT**|**Duties**|**Yearly**<br>**Status**|**General**<br>**Status**|
|---|---|---|---|---|---|---|
||||**Company 1**<br>||||
|**2008**|Yes|Yes|No|Yes|Active|Active as|
|**2009**|Yes|Yes|Yes|No|Active|of 2012|
|**2010**|Yes|No|Yes|No|Active||
|**2011**|Yes|Yes|No|Yes|Active||
|**2012**|Yes|Yes|Yes|No|Active||
||||**Company 2**<br>||||
|**2008**|Yes|Yes|No|Yes|Active|Closed|
|**2009**|Yes|Yes|Yes|No|Active|in 2011|
|**2010**|Yes|No|Yes|No|Active||
|**2011**|No|No|No|No|Inactive||
|**2012**|No|No|No|No|Inactive||
||||**Company 3**||||
|**2008**|Yes|Yes|No|Yes|Active|Data|
|**2009**|Yes|Yes|Yes|No|Active|issues|
|**2010**|Yes|No|Yes|No|Active||
|**2011**|No|No|No|No|Inactive||
|**2012**|Yes|No|No|No|Active||

_<u>Note:</u>_ OPEX = operational expenditure; PAYE = pay as you earn taxes; VAT = value‐ added tax.

**Third** , the status of economic activity derived in step 2 is then merged with the RDB registration database. Of the 29,180 companies registered in the period to 2012, 58 percent can be matched with tax files, thereby indicating an activity status. The remaining 42 percent are classified as “inactive.” To confirm that the lack of activity is the result of poor communication between the RDB and the RRA, the RDB registration database is matched with the RRA’s master list of all taxpayers. The match is excellent.  Of all registered companies with the RDB, only 43 are not found on the RRA’s master list.

37
FRENCH POLYNESIA 2. Projections - NRR-1 by **2010**

|**Ywr**<br>**Pplatiln**|Di.<br>ratfo|friud|-Au<br>t<br>rate|C|CW|TFR|b<br>Ws|b<br>**Fmtm**|**in**|**1x1040**|
|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>197|66.4|190-94|**2.72**|27.4|4.0|3.19|71.3|76.4|19|23|
|199<br>226|59.7|M5-9|2.41|25.4|3.9|2.81|72.5|78.4|1S|19|
|2000<br>255|60.1|2000-04|1.95|21.9|3.9|2.48|73.8|W0.1|13|15|
|2005<br>2d1|58.1|2005-09|1.50|18.3|4.0|2.19|74.6|80.9|11|13|
|2010<br>302|52.9|2010-14|1.26|16.8|4.2|2.08|75.4|81.7|9|11|
|2015<br>322|48.0|2014-19|1.20|16.6|4.6|2.08|76.3|82.6<br>|7<br>|9<br>|
|2020<br>342|45.8|2020-24|<br>1.13|16.2|4.9|2.07|77.2|83.4|5<br>|7<br>|
|2025<br>362|45.8|2025-49|0.74|13.8|6.5|2.06|79.0|85.3|3|5|
|2050<br>435|60.4|2050-74|<br>0.24|12.3|9.9|2.06|80.7|87.3|3<br>|4<br>|
|207S<br>462|70.8|2075-99|<br>0.07|11.9|11.3|2.06<br>|81.6|88.4<br>|2<br>|3|
|2100<br>470|73.0|2100-24|0.03|11.7|11.4|2.06|82.3|89.2|2|3|
|2125<br>474<br>2150<br>476|73.9<br>74.4|2125-49|0.02|11.7|11.5|2.06|82.6|89.5|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>| gXX<e:EEEim//////Zffl5-9 555 §B<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR e0 Growth rate<br>Cowmtry Region Inca= group~ Country Region Income group Country Region Income group<br><!-- End of picture text --> 51
**Table A18:  Cingranelli Richards Human Rights Database & Political Terror Scale (HUM) Data Provider** University of Binghamton Cingranelli-Richards Human rights database (CIRI) and  University of North Carolina Political Terror Scale (PTS) **Description** United States based universities **Website** www.humanrightsdata.com **Data Source** Cingranelli-Richards Human Rights Dataset (CIRI) and the Political Terror Scale (PTS) **Type** Expert assessment **Respondents** Coding by Cingranelli-Richards and Gibney teams **Frequency** Annually since 1980 **Coverage** Global sample of countries **Public Access** Yes **Description** The Cingranelli-Richards dataset is a numerical coding on a 2 or 3 point scale of data on 13 human rights, as reported in Amnesty International Human Rights Reports and the U.S. Department of State Country Reports on Human Rights Practices.  It is produced by Professor David Cingranelli at the University of Binghamton, U.S.A. and Professor David Richards at the University of Memphis, U.S.A. and is available at www.humanrightsdata.com.  The Political Terror Scale is a numerical coding on a 5-point scale of state-sponsored domestic political terror through imprisonments, torture, disappearances and violations of the rule of law.  It is produced by Prof. Marc Gibney at the University of North Carolina and is available electronically at: http://www.politicalterrorscale.org/.  Note that the two sources are averaged and treated as a single source in Political Stability and Absence of Violence since they are based on the same underlying publications. Note also that the indicators refer to data from the previous year:  we therefore lag the data from this source by one year.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|Restrictions on domestic and foreign travel (CIRI)|X|X|X|X|X|X|X|X|X|X|
|Freedom of political participation (CIRI)|X|X|X|X|X|X|X|X|X|X|
|Imprisonments because of ethnicity race or political religious beliefs? (CIRI)|X|X|X|X|X|X|X|X|X|X|
|Freedom of Speech (CIRI)|X|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**|||||||||||
|Frequency of political killings (CIRI)|X|X|X|X|X|X|X|X|X|X|
|<br>Frequency of disappearances (CIRI)|X|X|X|X|X|X|X|X|X|X|
|Frequency of tortures (CIRI)|X|X|X|X|X|X|X|X|X|X|
|Political terror scale (PTS)|X|X|X|X|X|X|X|X|X|X|
|**Government Effectiveness**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|Independence of judiciary (CIRI)|X|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Country coverage (CIRI)**|**192**|**192**|**192**|**192**|**192**|**159**|**159**|**159**|**159**|**159**|
|**Country coverage (PTS)**|**180**|**180**|**177**|**176**|**178**|**178**|**178**|**178**|**177**|**174**|
|**Year of Publication**|**2008**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**1999**|**1997**|

57
offset by the higher costs of operating in French and Colombian cities. Grover and Maloney (2022) find that the elasticity of physical total factor productivity with respect to density is negative in Colombia, Ethiopia, and Indonesia, but positive in Chile. The bulk of the literature on agglomeration economies, however, finds evidence of strong productivity gains of urban density in both developed and developing countries (see e.g., Melo et al., 2009; Beaudry & Schiffauerova, 2009; Combes & Gobillon, 2015; Duranton, 2015; Chauvin et al., 2017; Badr et al., 2019; Jones et al., 2020; Grover et al., 2021).<sup>2</sup> At the same time, there seem to be notable differences across countries and industries (De Groot et al., 2016; Ahlfeldt & Pietrostefani, 2019).

This mixed empirical evidence on the size of net agglomeration benefits in developing economies has attracted the attention of researchers in the field of regional science and urban economics (Duranton, 2015; Glaeser and Henderson, 2017; Grover et al., 2021) and motivates the empirical investigation in this paper. By exploring how these gains vary with firm characteristics and capabilities, the tradability of the sector in which the firm operates, and the mobility within the urban center in which the firm is located, we hope to inform the debate on “sterile” agglomeration economies in the developing economies. Specifically, the paper contributes to this literature in the following ways. It uses geo-coded data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies,<sup>3</sup> from the World Bank’s formal Enterprise Surveys and a multilevel estimation technique to assess the “pure” labor productivity gains of urban density, net of negative externalities such as crime, pollution, and limited mobility. We then test to what extent these estimated agglomeration economies differ across firms operating in the tradable versus nontradable sectors and across firms of different sizes, market orientation, ownership, and management capabilities. The results suggest that the average size of agglomeration economies in the developing world is comparable to the one observed in advanced economies, but the magnitude of the benefits of density on labor productivity substantially vary across firms. In line with the Venables’ (2017a) hypothesis, returns to urban density are higher for firms operating in the tradables sector. Manufacturing, exporting, and foreign-owned firms as well as larger and more experienced firms benefit more from agglomeration externalities than firms providing services in the domestic market, domestic-owned firms, and smaller and less experienced firms.

Another contribution of this paper is its focus on urban mobility as a key agglomeration cost, which has not received sufficient attention in the agglomeration literature. Recent research by

> 2 In this regard, several papers show that the urban density effects are similar in developed and developing countries (Chauvin et al., 2017; Henderson et al. 2019).

> 3 These economies represent 70% of all developing economies and cover over 94% of the population in all developing economies.

3
|* A number of issues related to intra-household<br>inequity which highligh**t**he vulnerability of<br>children and women<br>* A range of gender-relate**d**imensions of<br>illbeing|Work by the Government-donor-NGO<br>Gender strategy<br>Working Group strongly informed by PPAs - process<br>followed in producing "Vietnam: Attacking Poverty"<br>seen as a model for work this year in producing a<br>gender strategy|
|---|---|
|The plight of unregistered urban migrants|In Ho Chi Minh City, some Districts have now changed<br>the criteria for including long-settled unregistered<br>migrants in its Hunger Eradication and Poverty<br>Reduction activities, primarily those relating to social<br>services.|
|Various issues in Ha Tinh|Directly addressed in provincial policies and HEPR<br>(including policies on commune-level fees and<br>contributions, and public investment priorities)|

# Likely influence through the embryonic Poverty Reduction Strategy

As part of the planning initiatives taking place this year, a Government committee under the leadership of MoLISA has been established to draft a poverty reduction strategy to the year 2010. Though the mandate of the drafting committee remains quite narrowly focussed on targeted interventions, the committee is receptive to suggestions from the PWG that this narrower strategy be placed within a broader framework of analysis. The PWG will therefore be helping Government efforts in two ways. First influencing the narrower poverty reduction strategy, bringing the experience of the PPAs and other studies into the proposals of that strategy. The second way in which the PWG will be influencing Government strategic planning will be to help the drafting committee of the Poverty Reduction Strategy to relate the narrower, targeted program to broader initiatives to reduce poverty. This work responds to the Government request made at the CG meeting, which asked the PWG to work on a _comprehensive_ poverty reduction strategy. Work on this has already begun. In a recent meeting of the PWG, members have looked at important sectoral, structural and macro issues which influence the creation of opportunities, the promotion of equity and the reduction of vulnerability for the poor.

In July, a three-day retreat was arranged for members of the PWG, other Government agencies, donors and representatives of civil society to brainstorm on the main elements of poverty reduction strategy for Vietnam. There was energetic discussion and debate on important policy measures needed to take forward a comprehensive poverty reduction strategy. Sensitive issues such as corruption, ethnic inequality, domestic violence, the status of urban migrants were all discussed openly. Findings from the PPA studies have informed this debate and many feel that they have played an important role in allowing participants to feel more comfortable discussing these issues. The committee drafting the Poverty Reduction Strategy is using the proceedings from the workshop as background material.

# **Wider Impacts**

# **_Strengthening relationships_**

A very positive aspect of the work in 1999 was the opportunity it offered for agencies 21
<!-- Start of picture text --> 10<br>=<br>a<br>5<br>£<br>E<br>E O-prcceec rece necc nese necc ence ence escent nec necenec tees<br>cS<br>3<br>Ss<br>S<br><<br>5<br>0 20 40 ., 60 80 100<br>Percentile<br>— 2021 (nowcast, linear GIC)<br>—— 2021 (nowecast, convex GIC)<br><!-- End of picture text --> <!-- Start of picture text --> ay .<br>Poverty line<br>. 15<br>BT |<br>\\<br>05 \<br>—_——<br>0 l<br>019Daily consumption#5 © per10.capita (201115USD PPP) 20<br>[—] 2016 (last survey)<br>[_] 2021 (nowcast, linear GIC)<br>[] 2021 (nowcast, convex GIC)<br><!-- End of picture text -->
kernel, specifically, Gaussian for this step but report results from another kernel method, Epanechnikov, to test the robustness of the results.<sup>17</sup> Finally, we take the difference in earnings between the matched participants and nonparticipants, and sum over all the differences to obtain the ∆ _e_ as in equation (2).

# **5. Results and discussion**

# **5.1 The determinants of participation in the non-farm sector**

Before focusing on comparing outcomes of participation in the non-farm sector, it is interesting to identify the main determinants of participation into non-farm self-employment and wage employment.

> The econometric analysis confirms that _education_ is a significant determinant of participation in the non-farm sector, in particular in non-farm wage employment.  For both men and women, higher levels of education are positively associated with higher probability of participating in the non-farm sector (Figure 1).  We find that having some education is associated with a greater chance of participating in self-employment for men, while only post-primary education matters for women’s participation. Education is even more strongly correlated with participation in wage employment for both men and women. This correlation increases with education level with the strongest effect coming from having secondary or further education (Table 10 and Table 11). When we look at the two types of self-employment (columns 3 and 4 in Table 10 and in Table 11), we see that education is more strongly correlated with participating in self-employment in an enterprise with assets. This is consistent with the idea that such activities are more complex and often specific skills. For women, education does not even enter as a determinant of participation in non-farm self-employment without assets, indicating that these activities are probably low-level occupations.

> We also find that lack of _household wealth_ is not an impediment to entering non-farm wage employment (column 2 in Table 10 and in Table 11).  There is no difference in the probability of non-farm wage employment between men and women from wealthy households on the one hand – defined as those with average land and livestock holding of the top 20% in land and livestock holding – and the men and women from poorer households on the other hand.  Wealth, as defined here, also matters little in the likelihood of self-employment among women (column 1 in Table 11).  It does matter among men, but only consistently for enterprises which have assets, which offers partial support to the presence of credit constraints in the economy (column 1, 3 and 4 in Table 10).  It seems that individuals from richer households can afford to bear the risk involved in self-employment, thanks to the capital available from the family.

> 17 We have used the Stata program developed by Sianesi (2001). In order to check the robustness of the results, we also used the programs created by Becker and Ichino (2002), the results (not shown) were similar.

12