rafmacalaba/datause-encoder-data huggingface dataset:
feasibility studies. NTDC has also been slow in commenting on interconnection permit requests (taking much longer than the 30 days specified in the regulations) and has been influenced by the Ministry in its decision making.

The combined influences of these shortcomings mean that the overall de facto regulation index is 37 percent compared to the de jure index of 60 percent, and this difference must be considered when evaluating the extent to which Pakistan followed a traditional reform approach. The de jure regulatory structure adhered fairly closely to that which would have been recommended to a country wishing to follow the traditional reform approach, but in fact the actions of the government substantially weakened the way by which regulation was applied.

57
# **Introduction and Overview**

by

## J. Michael Finger

How can we help poor people to earn more from their knowledge rather than from their sweat and muscle? This paper is about promoting the innovation, knowledge, and creative skills of poor people in poor countries, and particularly about improving the earnings of poor people from such knowledge and skills. It reviews a number of studies of the successes and problems people in developing countries have experieced in attempting to earn income from commercial applications of their knowledge.

The objective of the paper is to draw lessons from the experiences it reviews. In so doing, it aims to expand the international discourse on the role of intellectual property in development. Since the agreements reached at the Uruguay Round came into effect in 1995, the World Trade Organization (WTO) agreement on the Trade Related Aspects of Intellectual Property (TRIPS) has more or less defined the discussion of intellectual property (IP) and development. This agreement, as is explained below, is about knowledge that exists in _developed_ countries, about developing countries’ access to that knowledge, and particularly about developing countries paying for that access. This paper is about knowledge that exists or might be created in _developing_ countries.

To the extent that the international community has paid attention to knowledge in developing countries it has focused on two issues:

- The defense of “traditional knowledge” against misappropriation by industrial country interests.

- The policing of “biopiracy” on the part of industrial country interests, that is exploitation of the biodiversity that exists in developing countries to develop agricultural products, health care products, and so forth, without proper compensation to the “traditional communities” that first discovered the usefulness of such genetic material.

The experiences reviewed here call atention to a broader range of knowledge that has commercial potential in developing countries. The review will also bring out the <u>economic</u> dimension of traditional knowledge, whereas legal analysis has thus far been at the forefront. The objective is to bring into the discourse a sense of the commercial as well as the legal tasks needed to solve a developmental problem – away from “knowledge” as an isolated legal issue.

The review also calls attention to the many income-earning (rather than the incomeusing) dimensions of culture – to demonstrate that culture and commerce more often complement than conflict.

Finally the review of the economic value of traditional knowledge brings out instances in which more or less standard legal approaches have been effective. This information is useful as an antidote to the general sense of conflict between traditional knowledge and normal legal conceptions. Dilligent application, not legal innovation, is often what is needed.
out in the estimation of the gravity models of trade first estimated with United States data by Bernard et al. (2007).

The rest of this paper is organized as follows. Section II briefly compares Costa Rica’s export and GDP-per-capita growth performance during 1997-2007 with other countries and regions. Section III discusses the PROCOMER data by comparing the series with other data on Costa Rican exports. This section also describes how the firm data was cleaned and discusses key features of the resulting data set, including the lack of significant changes in the composition of exports and exporting firms across broad industrial categories. Section IV presents the microeconomic accounting frameworks used to assess the contribution of incumbent and new firms, products and export market destinations, as well as unit values to total export growth in the short run and in the long run. In addition, Section IV also briefly reviews the estimation equation of the gravity model of trade and relates it to the role of unit values in affecting the “costs” of distance to export markets. Section V concludes with a brief summary of the results.

# **II. Costa Rica’s Export and Growth Performance in Comparative Perspective, 19972007**

Table 1 shows average growth rates of exports (measured in current U.S. dollars) and gross domestic product per capita (adjusted for purchasing power parity with international prices of 2005) for the period 1997 – 2007.

Costa Rica’s average export growth during this period was 9 percent per year. This rate was a bit higher than the average export growth rate for Latin America and the Caribbean region, clearly superior to Central America and the Caribbean, but lower than the average for other regions. Within Latin America, Costa Rica’s export growth performance is dominated by Peru and Chile, which achieved export growth rates of 16.4% and 15.7% respectively.

The picture is slightly better when looking at GDP per capita growth rates. Costa Rica’s economic growth rate of 3.3% was higher than Peru’s and Chile’s, and higher than the average for all regions except Europe and Central Asia and South Asia. This suggests that Costa Rica’s overall economic performance was relatively more impressive than its export growth rate.

4
the entire household of the assigned individual, and collected information on all members of the assigned individual’s household. The household survey questionnaire resembles other labor force surveys in Latvia and includes questions on education, employment status of household members, detailed questions for WWS participants, household expenditures, and asset ownership; data were collected through face-to-face interviews. The sample comprises 1,166 households in T1; 1,016 households in C1; 463 households in T2; 396 households in C2. Due to the time lapse between sampling and the actual interview, about 396 assigned persons in group T1 finished WWS participation, and about 222 assigned persons from group C1 began participating in WWS. We dropped those assigned persons who have different status than actually assigned. This is done to avoid contamination bias. In addition, about 64 assigned persons in group T1 have an additional household member enrolled in WWS besides the assigned individual. We also dropped these assigned individuals to avoid over estimation of the impact of WWS on household welfare. Similarly, we dropped 22 assigned workers from group C1 as some other household member was enrolled in the WWS program.<sup>14</sup> The final sample size used in the analysis is: T1 - 721; C1 - 769; T2 - 463; and C2 - 396.<sup>15</sup> Omitting some of the assigned individuals (from T1 and C1) to avoid contamination bias raises concerns about selectivity bias. However, as shown in appendix Table A1, we do not find significant differences in the characteristics of individuals who were dropped from T1 from the remaining individuals in T1. We find significant differences only in 3 out of 28 characteristics of those individuals from C1 who were dropped and those who remained in C1. Hence, it is safe to assume that omitting observations to avoid contamination bias does not introduce a selection bias. In this paper, we focus on targeting and short-term income impacts of the WWS program, and for this we compare assigned workers in T1 and C1.<sup>16</sup> The WWS Household Survey collected aggregate household income information and in-

> 14 We are grateful to Mihails Hazans for his advice on sampling.

> 15We keep only those assigned workers in T1 group who have participated in WWS for at least one month. This led to further dropping of 12 assigned individuals from group T1.

> 16Assigned individuals in group T2 and C2 were surveyed to study the medium/longer-term employability impacts of the WWS program.

8
In developing countries, implicit stratification is often introduced in household surveys by sorting clusters in a certain geographical order within each explicit stratum, since frame lists of EAs may not contain other variables correlated with the phenomenon under investigation. In this way, the classical construction of a _serpentine_ of clusters, is the most effective method that has been implemented so far for the preparation of the list frame of clusters from which the systematic sampling is conducted. In the present paper we aim to further improve the implicit stratification of a geographic serpentine, by ordering the clusters according to another socio-economic characteristic: a specific description of the living conditions of the clusters included in the so-called _poverty mapping_ . This approach will be further described in Section 4.

However, it is well-known in the literature that this design of systematic selection of clusters in the first stage makes it impossible to find the exact formula for an unbiased estimator of the variance in (2), which could be renamed as V s �, referring to systematic sampling. Wolter (1985) and Cochrane (1977) proposed ss VV�YY<sup>�</sup> several methods of approximate estimators of V s �, while in the last two decades, replication methods have reached more precise and unbiased estimators, ss VV�YY<sup>�</sup> which include Taylor Linearization (TL), Jack-knife Repeated Replications (JRR), Balanced Repeated Replications (BRR), and Bootstrap and Random Groups (RG). Recently, Verma and Betti (2011) have proposed a developed version of JRR, while Arnab _et al._ (2015) have proposed some more refined methods of variance estimation in the case of systematic sampling in the first stage. They show that such proposed methods and JRR perform better than other methods that are usually adopted by National Statistical Offices in developing countries.

In light of the above, the variance formulas (2) and (3) are not applicable when estimating complex measures from complex sample designs, but they are very useful as benchmarks for comparison (Kalton _et al._ , 2015). Such comparisons could also be done with reference to Simple Random Sampling (SRS). In fact, Kish (1965) firstly defined the so-called _design effect_ as “...the ratio of the variance of any estimate, say, y , obtained from a complex design to the variance of y that would 8
placement and underperformance of migrants are due to the fact that diplomas earned in their home countries are of no value in the destination countries. Whether this arises from poor education quality or acquisition of non-transferable skills, it requires policymakers to rethink national education policies and global creation of human capital (Mattoo et al. 2008).

The next section reviews the literature on the performance of male migrants United States and the determinants of their performance. Section 3 focuses on the dynamic analysis of the occupational placement over time using census data. It includes three sub-sections: the first introduces the empirical strategy and describes the data; the second presents the results; and the third is dedicated to robustness checks. The impact of country attributes on the occupational performance is evaluated in section 4, which is divided into two sub-sections, one presenting the empirical framework and the other describing the results. Section 5 summarizes the findings and discusses their policy implications.

# **2. LITERATURE REVIEW**

Literature on the performance of immigrants in destination labor markets<sup>3</sup> mostly focuses on their earnings, especially relative to those of the natives with similar education and demographic characteristics. One of the main findings is that migrants in the United States tend to earn less than natives and this gap depends on the country of origin. For example, immigrants coming from countries that more closely resemble the United States (such as Canada and Western Europe) tend to have higher initial earnings relative to those from other countries such as Mexico<sup>4</sup> .

The debate, however, is not on the extent of the initial earning gaps but on what happens to them over time. This is related to the “assimilation effect” which receives special attention as it relates to the accumulation of destination specific human capital (language skills, cultural knowledge and social capital) that increases wages and other measures of performance over time. Chiswick (1978) and subsequent papers<sup>5</sup> claim that, as time passes, immigrant’s existing and newer skills are gradually more valued in the destination country labor markets, and consequently their earnings increase even though, at the time of migration, they suffer from a poor transferability of their skills. As a result, immigrants’ earnings grow faster than those of natives’, which they eventually may even overcome. This can happen because, as suggested by Chiswick

> 3 Borjas (1994) and LaLonde and Topel (1997)

> 4 Chiswick (1977, 1978, 1986),  Borjas (1987, 2000)

> 5 Further elaborated by Chiswick(1986), Duleep and Dowhan (2002), Duleep and Regets (1994, 1997, 1999)

4
# **6. Conclusions**

The strong and independent causal role of finance in promoting growth has been demonstrated in recent literature using quantitative evidence from around the world. A large body of empirical work has also demonstrated the importance of good governance ownership structures and market competition for value creation by firms.

Understanding how an effective financial system contributes to economic development and which characteristics of the business environment promote good governance is crucial to optimize policy recommendations to help develop financial sectors that promote growth. Since innovation responses to a changing economic environment are widely considered to be the main driver of the growth process, understanding the links between finance and firm innovation is an important step in identifying the channels through which financial development contributes to economic development.

Using firm level data for over 19,000 firms in 47 developing countries, this paper provides evidence that a firm’s access to finance is an important determinant of the extent of innovation it undertakes. We define innovation broadly, to include not only core innovation activities (introducing new technology, new product lines and upgrading them), but also other types of activities that promote knowledge transfers (such as signing joint ventures with foreign partners and obtaining new licensing agreements), as well as opening or closing plants, discontinuing product lines, and activity sourcing decisions, which reflect the overall dynamism of firms.

Our results indicate that the more innovative firms are younger but larger, and are exporting firms characterized by private ownership, highly educated managers with midlevel managerial experience, and access to external finance. By contrast, firms that do not innovate much are typically state owned firms who do not have foreign competitors. Identity of the controlling shareholder seems to be particularly important for the introduction of new technology and those private firms whose controlling shareholder is a financial institution, tend to be the least innovative. We find evidence that the use of external finance is associated with greater innovation by foreign firms, yet state owned firms with external financing are even less innovative. Financing from foreign banks is associated with higher levels of innovation compared to financing from domestic banks 26
WPS4243 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 16</mark>

# Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis*

Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW)

## Clionadh Raleigh

## CSCW, PRIO & University of Colorado at Boulder

## **Abstract**

Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses.

## World Bank Policy Research Working Paper 4243, June 2007

_The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the PostConflict Transitions Project can be found at http://econ.worldbank.org/programs/conflict_ ). _An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._

* Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre@prio.no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115/V10.

1
<!-- Start of picture text --> Figure 1<br>Yn Equity-Efficiency Tradeoffs<br>N Distribution-neutral Growth:<br>Income of non-intervention or one-shot current transfer<br>the non-poor<br>Capital transfer strategy<br>A ~ ~ ~ ~ ~ ~ ~ ~ ~ L<br>IS-ncm lines (L)<br>2~~~~~~~~~~~~~~~~~~~~~~<br>2~~~~~~~~~~~~~~L0<br>/ Indifferencj Social , >,, \ ~~~~L 02<br>1~~~~~~~~~~~~~~~~~~~~~~~~~L<br>/2. \ L B2<br>Y<br>LAi P<br>Income of the poor<br>-<br>B = current transfer; C1 capital transfer {Investment in human capital of the poor)<br><!-- End of picture text -->
# **Table 8**

**Drivers of Gross Capital Inflows: DEVELOPING COUNTRIES, 1980‐99 vs. 2000‐17** _Dependent Variable: GROSS Inflows, Total and By Type (% GDP) Sample: 56 countries, 1980‐2017 (annual) Estimation method: Instrumental Variables (IV)_

|Variables|Total<br>1980-99|Total<br>2000-17|FDI<br>1980-99|FDI<br>2000-17|PI<br>1980-99|PI<br>2000-17|OI<br>1980-99|OI<br>2000-17|
|---|---|---|---|---|---|---|---|---|
|_Domestic Factors_|||||||||
|GDP Growth|0.00872|0.0215***|0.000858|0.00332|0.000247|0.00103|0.00761|0.0171***|
||(0.0110)|(0.00589)|(0.00330)|(0.00373)|(0.00242)|(0.00121)|(0.0103)|(0.00417)|
|GDP per capita|0.244|0.164***|0.0282|0.00387|-0.00377|-0.0108|0.219|0.171***|
|_(in logs)_|(0.403)|(0.0633)|(0.121)|(0.0397)|(0.0891)|(0.0128)|(0.379)|(0.0448)|
|CPI Inflation|-0.000221|0.00116|-0.000298|0.000574|-0.000233|-8.73e-05|0.000310|0.000644|
||(0.000899)|(0.000760)|(0.000271)|(0.000464)|(0.000199)|(0.000153)|(0.000847)|(0.000538)|
|Primary Balance|-0.00583*|-0.000704|-0.000114|-0.000960|-0.000176|-0.000718**|-0.00554*|0.00104|
|_(% of GDP)_|(0.00302)|(0.00177)|(0.000911)|(0.00108)|(0.000668)|(0.000355)|(0.00285)|(0.00125)|
|Exchange rate flexibility|-0.00481|0.00192|1.34e-05|-0.000675|-0.000105|-0.000543|-0.00472*|0.00336*|
||(0.00300)|(0.00264)|(0.000905)|(0.00163)|(0.000664)|(0.000538)|(0.00283)|(0.00187)|
|Investment Profile|0.00102|0.0163***|0.00186|0.00342|0.00135|0.000964|-0.00219|0.0118***|
||(0.00451)|(0.00423)|(0.00136)|(0.00261)|(0.000997)|(0.000869)|(0.00425)|(0.00299)|
|Trade Openness|0.0702|-0.0831**|-0.00770|-0.0186|0.00598|-0.0110|0.0720|-0.0526*|
|_(% of GDP, logs)_|(0.0755)|(0.0388)|(0.0228)|(0.0237)|(0.0167)|(0.00793)|(0.0711)|(0.0274)|
|Financial Openness|0.0105|0.0104|0.00642*|0.00792*|-0.00212|0.000868|0.00624|0.00151|
|_(Chinn‐Ito Index)_|(0.0109)|(0.00730)|(0.00329)|(0.00449)|(0.00241)|(0.00149)|(0.0103)|(0.00515)|
|_External Factors_|||||||||
|Foreign Growth|0.000395|0.00202|9.77e-05|-6.42e-05|-0.00153|-0.000697**|0.00183|0.00285**|
||(0.00750)|(0.00173)|(0.00226)|(0.00107)|(0.00166)|(0.000353)|(0.00706)|(0.00122)|
|VIX Index|0.00733|0.0889**|0.0136**|-0.00158|-0.00451|-0.00748|-0.00181|0.0987***|
||(0.0214)|(0.0381)|(0.00644)|(0.0237)|(0.00473)|(0.00758)|(0.0201)|(0.0269)|
|US Economic Policy|0.00110|-0.000786***|-0.000149|-0.000115|3.68e-05|0.000120**|0.00121*|-0.000787***|
|Uncertainty|(0.000765)|(0.000278)|(0.000231)|(0.000169)|(0.000169)|(5.51e-05)|(0.000720)|(0.000196)|
|Commodity Terms of|0.214|-0.00829|-0.192|-0.00372|0.0453|-0.000365|0.361|-0.00225|
|Trade (in logs)|(0.463)|(0.0316)|(0.139)|(0.0195)|(0.102)|(0.00647)|(0.436)|(0.0223)|
|<br>Oil Price|0.0611|0.0245|0.00928|-0.0138|-0.00838|0.0111|0.0602|0.0280|
|(in logs)|(0.0401)|(0.0358)|(0.0121)|(0.0221)|(0.00887)|(0.00722)|(0.0378)|(0.0252)|
|<br>Minerals and Metals|0.0269|0.0346|-0.0121|0.0424*|0.0128|-0.00888|0.0262|0.000342|
|Prices (in logs)|(0.0675)|(0.0407)|(0.0203)|(0.0250)|(0.0149)|(0.00810)|(0.0635)|(0.0288)|
|Observations|203|907|203|924|203|929|203|909|
|Number of cnum|37|55|37|56|37|56|37|55|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 26