rafmacalaba/datause-encoder-data huggingface dataset:
Based on the time series on education enrollment stocks, advancement rates, repetition rates and drop-out rates an E x (E+1) skills development transition matrix (TR) is generated, where E is the number of distinct educational attainment categories. TR(e1, e2) is the probability of “advancing” from education category e1 to education category e2. In the special case when e1= e2 TR(e1, e2) refers to repetition probability.<sup>11</sup> TR(e1, E+1) is defined as the probability of successfully completing educational level e1 and leaving the education system after the successful completion. So how does the model follow educational attainment in the society? A particular education level ‘e’ can be reached in one of the following ways. First we cover the case of advancement (and repetition) within the education system.

where Mort(g,a,t) is the gender and time specific mortality probability of dying in the period when reaching age ‘a’ conditional on surviving through the previous period. Note that the model is only concerned about net flows. Beyond the age of entering primary education the net flows are always within and from (never to) the education system.

Those who finish successfully their intended level of education are transitioned out the school system according to the following equation: <mark>Δ</mark> _<mark>Skill</mark>_ <mark>(</mark> _<mark>s</mark>_ <mark>= ,2</mark> _<mark>g</mark>_ <mark>,</mark> _<mark>a</mark>_ <mark>,</mark> _<mark>e</mark>_ <mark>,</mark> _<mark>t</mark>_ <mark>) = [ 1 −</mark> _<mark>Mort</mark>_ <mark>(</mark> _<mark>g</mark>_ <mark>,</mark> _<mark>a</mark>_ <mark>− ,1</mark> _<mark>t</mark>_ <mark>− )1 ] *</mark> _<mark>Skill</mark>_ <mark>(</mark> _<mark>s</mark>_ <mark>= ,1</mark> _<mark>g</mark>_ <mark>,</mark> _<mark>a</mark>_ <mark>− ,1</mark> _<mark>e</mark>_ <mark>,</mark> _<mark>t</mark>_ <mark>− )1 *</mark> _<mark>TR</mark>_ <mark>(</mark> _<mark>e</mark>_ <mark>,</mark> _<mark>E</mark>_ <mark>+ )1 (7)</mark> The probability of dropping is the difference between 1 and the sum of the elements in a given row of the transition matrix. We have: The evolution of those who already left the education system is according to the following equation: The potential labor supply is given by those who are out of the education system, however, only the applicable labor force participation rates allow us to calculate the portion of the potential labor force that actually contributes to the human capital stock:

> 11 Note that this formulation assumes that the transition probabilities only depend on the “to” and “from” education categories, and independent of gender, age and time. The reason why we do not present a more general formulation is that we assumed an only 2 dimensional transition array in the quantitative example we present in this paper.

12
relocation program has relocated about 47,949<sup>11</sup> Venezuelans from Roraima to other Brazilian cities, where there are more opportunities for social and economic integration.

<!-- Start of picture text --> Venezuelan Migrants Venezuelan Asylum Seekers<br>96− 96−<br>91−95 91−95<br>86−90 86−90<br>81−85 81−85<br>76−80 76−80<br>71−75 71−75<br>66−70 66−70<br>61−65 61−65<br>56−60 56−60<br>51−55 51−55<br>46−50 46−50<br>41−45 41−45<br>36−40 36−40<br>31−35 31−35<br>26−30 26−30<br>21−25 21−25<br>16−20 16−20<br>11−15 11−15<br>6−10 6−10<br>0−5 0−5<br>20 15 10 5 0 5 10 15 20 20 15 10 5 0 5 10 15 20<br>Male Female Male Female<br>Migrants Asylum Seekers<br>45.03%<br>51.11% 48.89%<br>54.97%<br>Female Male Female Male<br>Migrants Asylum Seekers<br>2.06%14.02% 13.97% 14.07%<br>83.92% 71.96%<br>Married Single Married Single<br>Others Others<br>Age Group Age Group<br><!-- End of picture text --> Figure 3: Age, Gender and Civic Status of Venezuelans in Brazil Source: Author’s calculation from SISMIGRA and STIMAR.

Available data managed by the Federal Police shows that the Venezuelan population in Brazil has a balanced gender distribution as Figure 3 shows, contrary to the other population movements that are observed in other parts of the world, where men form the dominant movers. Of the Venezuelan migrant population, 49% are women while 51% are men, and 45% of the asylum seekers are women while 54% are men. This Venezuelan balanced gender distribution is observed in other Latin American countries like Colombia and Peru suggesting that this gender equality may be specific to the Venezuelan migration process. Venezuelan refugees and migrants are young, and many are single parents. Of the Venezuelan migrants, 75% are below 50 years old and 50% of the refugees and migrants are between the age of 20 and 40 years old. The presence of about 20% children below the age of 20 years points to the fact that a key component of the Venezuelan migrants and asylum seekers is that there has been substantial family migration. However, about 72% of Venezuelan asylum seekers and 84% of asylum seekers above the age of 25 years report their civic status as single, signifying that most of those families consist of single parents. There is no

> 11This number represents those relocated by January 2021.

7
Table 11 displays FDI agreements contracted in 1995 by sector; these data thus signal the future sectoral distribution of FDI flows into China (assuming the contracts are implemented). The table shows that FDI in industry constitutes about two-thirds of total contracts. Real estate is the next largest sectoral FDI recipient, with about one-fifth of contracted FDI.

**Table 11: Contracted FDI Flows in China in 1995, by Sector**

$US millions

||1995<br>Value<br>Share|
|---|---|
|Total|91,282<br>100.0%|
|Agriculture|1,736<br>1.9%|
|Industry|61,648<br>67.5%|
|Construction|1,918<br>2.1%|
|Commerce|3,427<br>3.7%|
|Transportation/Communication|1,700<br>1.9%|
|Real estate|17,835<br>19.5%|
|Other|3,018<br>3.4%|

_Source: China Statistical Yearbook, 1996._

# **Observations**

In the main, China's sectoral distribution of FDI is coming in line with the rest of the world, moving toward the pattern in the developed countries. But the FDI landscape across China's sectors has been highly skewed.

In the services sector, concerted efforts are being made to attract FDI away from the real estate sector and into financial services and telecommunications--pillar sectors for mature economic development. Within the financial services sector, although recent years have seen an increase in the number of licenses granted to foreign banks to conduct foreign exchange transactions in China, these institutions' activities are limited to the domestic securities markets.

As for telecommunications, foreign firms have been banned from entering the industry. Their current activity has primarily been limited to technology licensing and the manufacturing of digital switching equipment. As China moves closer to a modern, market economy, there will be the need for foreign investment to finance the development of this vital infrastructure component.

# **IV. CONCLUSION**

This paper has analyzed the geographical and sectoral distribution that China has evidenced in FDI inflows since reform began in 1978. It shows that despite the impressive overall achievement in 16
By the start of data collection for the 2001 round, 27 months had elapsed on average since the project work ended.<sup>22</sup> How long it takes for impacts to emerge is an issue that often arises in discussions of road impacts and planning for their evaluation.

Here we are able to ascertain whether local area impacts were different in 2001 from those in 2003, after two more years had elapsed.

Focusing on the PS-based estimates, and starting with impacts by 2001, we see that across the examined indicators there is no sign of statistically significant mean impacts in the short term. The only exception is for the primary school completion rate which rose by 15 to 25 percent, according to the kernel matched and PS-weighted DD respectively. Why would better roads affect primary school completion rates? Although all communes have primary schools, secondary schools are considerably rarer. It is plausible that a road improvement now allows children to more readily reach a secondary school which will encourage both primary school completion and post primary enrollments.

The results change when we track impacts through to 2003. A number of outcome indicators now exhibit significant impacts. As a result of the road improvements, markets became newly available in close to 10 percent more project than non-project communes over the 7 years, and their frequency increased. However, despite small positive impacts on commercial establishments, none are statistically significant.

By 2003, we also discover significant impacts of the road project on the services for which we have data ─ the availability of tailoring and hairdressing services. The weighted DD show that the probability of men and women’s hair dressing services being

> 22  The 2001survey was fielded about 4 months after all projects were completed. About 11% of project communes had finished their road project less than one year before.

23
**ANNEX 1: Country and year specific incidence and prevalence data, including sources**

# **Botswana**

||**Prevalence amon**<br>|**g females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.95|-|
|**1991**|-|-|2.77|-|
|**1992**|-|-|3.73|-|
|**1993**|-|-|4.65|-|
|**1994**|-|-|5.35|-|
|**1995**|-|-|5.75|-|
|**1996**|-|-|5.73|-|
|**1997**|-|-|5.50|-|
|**1998**|-|-|5.01|-|
|**1999**|-|-|4.42|-|
|**2000**|-|-|3.85|-|
|**2001**|35.1|-|3.35|-|
|**2002**|31.6|-|2.94|-|
|**2003**|32.5|-|2.56|-|
|**2004**|-|18.2|2.23|44|
|**2005**|26.1|-|1.98|64|
|**2006**|25.0|-|1.85|76|
|**2007**|24.6|-|1.74|79|
|**2008**|-|10.7|1.72|-|
|**2009**|20.8|-|1.59|90|
|**2010**|-|-|1.38|93|
|**2011**|16.1|-|1.30|>95|
|**2012**|-|-|1.25|95|

<u>ANC data sources: Botswana Second Generation HIV/AIDS Antenatal Sentinel Surveillance Technical Report 2009 and</u> 2011 <u>National DHS and AIS data sources: Botswana AIDS Impact Survey 2004 and 2008</u>

# **Burkina Faso**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|0.73|-|
|**1991**|-|-|0.70|-|
|**1992**|-|-|0.65|-|
|**1993**|-|-|0.56|-|
|**1994**|-|-|0.45|-|
|**1995**|-|-|0.35|-|
|**1996**|-|-|0.27|-|
|**1997**|-|-|0.20|-|
|**1998**|-|-|0.14|-|
|**1999**|-|-|0.10|-|
|**2000**|-|-|0.07|-|
|**2001**|-|-|0.06|-|
|**2002**|-|-|0.06|-|
|**2003**|1.9|1.3|0.06|-|
|**2004**|1.5|-|0.06|7|
|**2005**|1.4|-|0.06|19|
|**2006**|1.0|-|0.06|31|
|**2007**|1.3|-|0.06|35|
|**2008**|0.4|-|0.07|-|
|**2009**|1.3|-|0.06|44|
|**2010**|0.7|0.3|0.07|50|
|**2011**|-|-|0.06|57|
|**2012**|-|-|0.05|70|

<u>ANC data sources: Burkina Faso UNGASS Report 2008 and Burkina Faso GARPR 2012</u> <u>National DHS and AIS data sources: Burkina Faso DHS 2005 and 2010</u> 52
<!-- Start of picture text --> Quilmes Alm. Brown Avellaneda<br>.3 .16 .18<br>.16<br>.25 .14<br>.14<br>.2 .12<br>.12<br>.15 .1<br>.1<br>.1 .08 .08<br>-20 -10 0 10 20 -20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual Normalized annual<br>accumulated consumption accumulated consumption accumulated consumption<br>Flores E. Echeverria Belgrano<br>.14 .14<br>.12<br>.12 .12<br>.1<br>.1 .1<br>.08<br>.08 .08<br>.06<br>.06 .06 .04<br>-20 -10 0 10 20 -20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual Normalized annual<br>accumulated consumption accumulated consumption accumulated consumption<br>Percentage Percentage<br>Percentage Percentage<br><!-- End of picture text --> Figure 2 Region of Residence by Annual Accumulated Consumption (by 6 Largest Regions in Terms of Number of Users) _Notes:_ Scatter points correspond to local averages computed by bins with a bandwidth of 1 unit of normalized AAC. Solid lines correspond to parametric fits generated from regressions that include a constant, a treatment dummy and a linear term for normalized AAC. Dashed lines are the 95% confidence interval.

21
**Table 7: Marginal increases (Synergies) in mean HAZ of children under 37 or 24 months**

|Adequacy<br>Coefficient<br>|Cambodia (2010)|Ethiopia (2011)|Nepal (2011)|Bolivia (2008)|Peru (2012)|Zimbabwe (2010)|Bangladesh (HK,<br>2010)|Bangladesh<br>(IFPRI, 2011)|Indonesia (RKD,<br>2010)|
|---|---|---|---|---|---|---|---|---|---|
|Food only<br>��<br>|0.220|1.000***|0.498***|0.140|0.004|‐0.031|0.055|‐1.094*|‐0.187**|
|Environment and health only<br>��<br>|0.214|0.863***|‐0.212|0.059|‐0.210|‐0.085|0.570***|‐0.824*|0.720***|
|Care only<br>��|0.092|‐0.255***|‐0.074|‐0.570***|<br>‐0.133**|‐0.158*|‐0.121*|‐0.602***|-0.219|
|Environment x Food<br>���|0.208|‐0.479|0.671*|0.166|‐0.478|‐0.028|‐0.261|2.107***|‐0.563**|
|Environment x Care<br>���|0.525|0.685*|0.951***|0.329**|0.476*|0.418|‐0.371|1.104**|‐0.136|
|Care x Food<br>���|0.172|0.708***|0.331|0.473***|‐0.090|0.332*|0.241|1.478**|0.240|
|Food x Care x Environment<br>����|‐0.557|‐1.175**|‐1.701***|‐0.573**|0.012|‐0.387|0.415|‐2.559***|0.511|
|_Observations_|_1,354_|_5,605_|_1,397_|_4,311_|_5,248_|_2,022_|_4,994_|_1,056_|_6,671_|
|_Source: Author calculations._<br>_Notes:_***p<0.01,**p<0.05,*p<0.1||||||||||
|**Table 8: Simultaneous Ade**<br>Adequacy<br>Coefficient|**quacy in 2**<br>Cambodia (2010)|**or more clu**<br>Ethiopia (2011)|**sters and th**<br>Nepal (2011)|**e total effec**<br>Bolivia (2008)|**t on mean H**<br>Peru (2012)|**AZ of child**<br>Zimbabwe (2010)|**ren under 3**<br>Bangladesh (HK,<br>2010)|**7 or 24 mon**<br>Bangladesh (IFPRI,<br>2011)|**ths**<br>Indonesia (RKD,<br>2010)|
|E x F<br>���������|0.643|1.384***|0.957***|0.365**|‐0.684**|‐0.144|0.364*|0.188|‐0.030|
|E x C<br>���������|0.832|1.293***|0.665**|‐0.183*|0.133|0.175|0.078|‐0.323|0.365|
|C x F<br>���������<br>0|.484***|1.453***|0.756***|0.043|‐0.219***|0.143|0.175*|‐0.218|‐0.166**|
|F x C x E<br>�������������<br><br>0|.875***|1.346***|0.465|0.023|‐0.419***|0.062|0.528***|‐0.391|0.365**|
|�������������||||||||||
|_Source: Author calculations._<br>_Note:_***p<0.01,**p<0.05,*p<0.1||||||||||

42
counts statistics (Pinkovskiy and Sala-i-Martin, 2016a; Clark, Pinkovskiy, and Sala-i Martin, 2017; Morris and Zhang, 2019), and to approximate economic activity at small spatial units (Nordhaus and Chen, 2015; Heger and Neumayer, 2019; Chanda and Kabiraj, 2020). A recent survey by Gibson, Olivia, and Boe-Gibson (2020) reviews more than 150 economic studies using nighttime lights and finds that they overwhelmingly use DMSP data.

While more recent VIIRS nighttime light data are understudied in comparison, there are some noticeable exceptions. For example, the higher frequency of these data allowed analysis of India’s demonetization in November 2016 (Beyer, Chhabra, Galdo, and Rama, 2018; Chodorow-Reich, Gopinath, Mishra, and Narayanan, 2020). They have also been used to predict GDP in metropolitan statistical areas in the United States (Chen and Nordhaus, 2019) and to analyze the impact of the recent tariff war between the United States and China on China’s economy (Chor and Li, 2021). More recently, in response to COVID-19, a growing literature uses VIIRS nighttime lights as a subnational indicator of economic activity at high frequency. Among others, the data have been used to assess the impact of COVID-19 in India (Beyer, Jain, and Sinha, 2020; Ghosh, Elvidge, Hsu, Zhizhin, and Bazilian, 2020; Beyer, FrancoBedoya, and Galdo, 2021), China (Elvidge, Ghosh, Hsu, Zhizhin, and Bazilian, 2020), and Morocco (Roberts, 2021). All these studies struggle to convert changes in VIIRS nighttime lights to changes in economic activity. Chodorow-Reich, Gopinath, Mishra, and Narayanan (2020), for example, use an elasticity estimated with annual DMPSOLS data (Henderson, Storeygard, and Weil, 2012), even though their study uses monthly VIIRS data. Most others are abstaining from the conversion and instead focus on differences between subnational entities (Beyer, Franco-Bedoya, and Galdo, 2021), thwarting quantitative economic conclusions.

So far, to the best of our knowledge, no cross-country study of the quarterly relationship between VIIRS nighttime lights and GDP exists. However, to exploit the full potential of VIIRS data for economic analysis, an elasticity to convert changes in nighttime lights into changes in economic activity is fundamental. This paper fills this important gap. In terms of the econometric framework, it is closely related to the seminal contribution of Henderson, Storeygard, and Weil (2012) and the more recent work of Hu and Yao (2021), which both estimate the annual elasticity between DMSP lights and GDP in a global sample. The elasticity estimated by the former has become the standard elasticity used to date, but its validity for higher-frequency VIIRS data is limited. In addition, identification and estimation of the elasticity depend crucially on assumptions about the signal-to-noise ratios of GDP data. Hu and Yao (2021) 4
# **I. Introduction**

The Latvian economy made great strides in recovering from the economic shock of the early transition and the adverse after-effects of the 1998 Russian financial crisis. GDP growth has been robust, averaging close to 6% p.a. over the past five years. Inflation is low. The currency is stable. Internal and external deficits are at sustainable levels. Annual FDI inflows and gross fixed capital formation, which grew at a 19% annual rate between 1996 and 2001,<sup>1</sup> are both at relatively healthy levels. Standards of living and consumption are both growing at a steady pace. And perhaps most importantly, Latvia became a member of the European Union on May 1, 2004. Nevertheless, Latvia faces serious challenges to its future growth and prosperity despite these impressive achievements and the outward appearance of macroeconomic stability and economic progress.

Most of Latvia’s growth to date has come from pent up consumer demand and efficiency gains generated by structural reforms, privatization, and reallocating resources from loss making enterprises to more profitable enterprises. Unfortunately, these are one-off gains, not inexhaustible reservoirs of growth. Latvian enterprises will be able to sustain economic growth and create high wage jobs only by becoming internationally competitive, innovating, accumulating new knowledge and technology, and finding a high value added niche in the European and global division of labor.

Looked at from this vantage point, the picture is not so rosy. Simply and starkly stated, a wide variety of recent studies suggest that the Latvian economy is not particularly competitive and, even more worrisome, they indicate that Latvia is not well positioned to gain ground in the race for global competitiveness, prosperity, and rising standards of living. All this bodes poorly for Latvia’s future competitiveness and prosperity unless leaders of Latvia’s business, government, university, and scientific communities develop and implement clear, concrete policies to address these challenges.

Successive Latvian governments have formally approved and adopted a number of policy papers including a National Concept on R&D (1998), a National Concept on Innovation (2001), the National Innovation Program (2003) and the Draft Programme Complement of the Single Programming Document for Latvia 2004-2006 (2003).<sup>2</sup> These documents clearly describe some of the most critical weaknesses and pressing challenges that Latvia

> 1 Latvia Innovation survey, chapter 3, page 17.

> 2 Priority 2 of the Single Programming Document is devoted to “Promotion of Enterprise and Innovation” and lists such objectives as creating new enterprises and increasing competitiveness of existing enterprises via transition to knowledge-intensive production and designing, producing and marketing internationally competitive products and services.

5
where civil conflicts are mostly about a public prize linked to ethnic divisions, in MENA they are mostly about a sectarian-related public prize. Future work should explore the specific mechanisms through which external military interventions of the non-neutral and non-humanitarian type affect polarization, the formation of horizontal groups contending a prize, and the measurement of overlapping polarization along multiple lines of division. The reliance on the IMI data set restricted the focus of the analysis to non-humanitarian and non-neutral external military interventions that involve the deployment of troops. Future work should look at other types of non-humanitarian and non-neutral types of interventions, including the transfer of funds and other types of support to rebel groups or to governments involved in disputes with rebels.

# **References**

Ai, C. and Norton, E.C. (2003) “Interaction terms in logit and probit models,” _Economics Letters_ 80: 123-129.

Albornoz, Facundo and Hauk, Esther. (2014) “Civil War and U.S. Foreign Influence,” _Journal of Development Economics_ 110: 64-78.

Angrist, J. and Krueger, A. (2001) “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments, _” Journal of Economic Perspectives_ 15(4 _)_ :6985 _._ Brown, Michael E. ed. (1996) _The International Dimensions of Internal Conflict_ . MIT Press, Cambridge.

Cederman, Lars-Eric and Girardin, Luc. (2007) “Beyond Fractionalization: Mapping Ethnicity onto Nationalist Insurgencies,” _American Political Science Review_ 101(1): 173-185.

Centinyan, R. (2002) “Ethnic Bargaining in the Shadow of Third Party Intervention,” _International Organization_ 56(3): 645-677.

Collier, Paul and Hoeffler, Anke. (2002) “On the Incidence of Civil War in Africa,” _Journal of Conflict Resolution_ 46(1): 13-28.

---. (2004) “Greed and Grievance in Civil War,” _Oxford Economic Papers_ 56(4): 563-95.

39
- I. Road networks in all ECA countries are upgraded to the regional mean level of quality, namely 74.52% of paved roads; and

- II. Road networks in Albania, Hungary and Romania only are upgraded as in I.

The motivation for these simulations is that raising each country’s level of road network quality to the currently prevailing regional average represents an ambitious but feasible scenario. Concretely, this means that under Simulation I, 13 out of 27 ECA countries receive an upgrade, while under Simulation II it is limited to only the three countries identified above as connected with 65% of minimum quality routes in the region.

We conduct the simulations as follows. First, we set up the policy shock by recalculating both weighted average and minimum quality measures for all inter-country routes, in exactly the same way as described in Section 3. The only difference is that country scores below the regional average are increased to that level before recalculation. Next, we calculate the resulting percentage changes in _paved_ave_ and _paved_min_ . Using our trade data and our estimated elasticities (0.79 and 0.60 respectively), we then map these policy shocks to changes in bilateral trade values. Finally, we sum estimated bilateral trade impacts across countries to give the estimated overall increase in intraregional trade.

Results in Table 12 show that the potential trade gains from an ambitious but feasible program of road upgrades are large in absolute terms. It does not seem unreasonable to consider positive impacts of the order of 50% of baseline trade, or just over US$55 billion based on total intraregional trade in 2003. These figures are based exclusively on the projected increase in intraregional trade. They do not include any flow-on effects to extra-regional trade. We therefore consider that our results lie towards the lower bound of expected total trade benefits from a road network upgrade.

- 20 -