rafmacalaba/datause-encoder-data huggingface dataset:
from US universities in the 1970s in the ...elds of Science and Engineering returned to Taiwan (Kwok and Leland, 1982) or Korea, a proportion that rose to about one half to two-thirds in the course of the 1990s, after two decades of impressive growth in these countries. Is it due to the economic boom at origin or to changes in the immigration policy at destination? Recent evidence is quite mitigated.

On the one hand, the ...gures for Chinese and Indian PhDs graduating from US universities in the same ...elds during the period 1990-99 are fairly identical to what they were for Taiwan or Korea 20 years ago (stay rates of 87% and 82%, respectively) (OECD, 2002). This would seem to be con...rmed by a recent survey which shows that in the Hsinchu Science Park in Taipei, a large fraction of companies have been started by returnees from the USA (Luo and Wang, 2001). In the case of India, Saxeenian (2001) shows that despite the quick rise of the Indian software industry, only a fraction of Indian engineers in Bangalore are returnees. According to these papers, return skilled migration appears relatively limited, however, and is often more a consequence than a trigger of growth.

On the other hand, a more recent and comprehensive survey of India’s software industry was more optimistic and con...rmed the presence of network e¤ects and the importance of temporary mobility (strong evidence of a brain exchange or a brain circulation), with 30-40% of the higher-level employees having relevant work experience in a developed country (Commander et al., 2004). In their survey on medical doctors working in the UK, Kangasmieni et al (2004) found that ”many” intend to return after completing their training.

# 3.4 Uncertainty

Before 1965, the US immigration policy was based on country-speci...c quotas. This quota system was abolished but various types of requirements and restrictions imposed by the US and other countries’ immigration authorities render the migration decision very uncertain. Implicit or explicit size-quotas are e¤ectively in place, and receiving an immigration visa, whether temporary or permanent, requires being in a close relationship either with relatives or employers who must then demonstrate that the migrant’s skills can hardly be found among native workers. Moreover, in some countries, point-systems are used to evaluate the potential contribution of immigrants to the host economy. This means that at all stages of the immigration process, there is a probability that the migration project will have to be postponed or abandonned. Individuals engaging in education investments with the prospect of migration must therefore factor in this uncertainty, creating the possibility of a net gain for the source country. The conditions required for this possibility to materialize have been the subject of a number of theoretical contributions (Mountford, 1997, Stark et al., 1998, Vidal, 1998, Beine et al., 2001).

To account for this within our general framework, assume that the probability of migration depends solely on the achievement of a given educational requirement, 21
exported. In all cases we regress _Yt_ against a constant, a time trend, and a trend break in 2008 (given by equation (5)). The coefficient _β_ 2 tests whether a linear trend or a linear trend with a break during the policy period fits the data better. All standard errors are heteroskedasticity and autocorrelation consistent (HAC).

Table 3 shows the results of the time series regressions. Column (1) of Table 3 shows results when the dependent variable is production, while column (2) shows results when the dependent variable is exports, both in millions of short tons (mmst) of coal. Column (3) has the share of coal exported annually as the dependent variable.

All three columns show a statistically significant upward trend throughout the sample period. In column (1) however, the trend break coefficient is insignificant, even though the point estimate is positive. Thus, the model fails to reject no change in coal production during the policy period. However, the coefficient on the trend break in both exports and the share of coal exported is significant, indicating an increase in exports during the policy period. In Appendix H we show a visual representation of the time series models for production and exports, estimated with equation (5). The figures show the data in Figure 9 with fitted values from the models in Table 3 overlaid. Consistent with the previous discussion, production follows a relatively steady upward trend throughout the entire sample (including the policy period). Exports show a more marked trend break in the policy period.

Table 3: **Production, Exports regressions**

|VARIABLES|(1)<br>Production|(2)<br>Exports|(3)<br>Export Share|
|---|---|---|---|
|Trend break (2008)|2.73|10.09***|0.007**|
||(1.70)|(1.92)|(0.003)|
|Time|11.62***|8.34***|0.007***|
||(0.25)|(0.24)|(0.001)|
|Constant|100.34***|32.55***|0.445***|
||(5.18)|(4.85)|(0.025)|
|Observations|39|39|39|
|SE|HAC(4)|HAC(4)|HAC(4)|

25
- 75 -

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**Table 5: Models by Product – Specification 2**

|**Vars**|**Carpets**|**Shawls**|**Jewelry**|**Handmade**<br>**Paper**|**Medicinal**<br>**Herbs**|**Dentifrices**|**Cardamom**|**Pipes I&S**|
|---|---|---|---|---|---|---|---|---|
|**_Lagged Log Tourists_**|0.866|3.647***|0.592|0.926*|6.366|0.765|-0.158|-3.357|
||(0.616)|(1.153)|(0.589)|(0.456)|(3.73)|(0.865)|(0.65)|(2.023)|
|**_Lagged Log Expenditure_**|0.779*|3.029***|-0.237|0.997***|-4.021**|-0.983*|-0.122|-2.306|
|**_per day per Tourist_**|(0.409)|(0.77)|(0.371)|(0.274)|(1.099)|(0.536)|(0.485)|(1.933)|
|**_Log World Imports of_**|0.745**|-0.104|0.482*|1.790***|2.301|0.846**|1.189**|3.099*|
|**_the Same Product_**|(0.299)|(0.38)|(0.235)|(0.302)|(4.887)|(0.327)|(0.456)|(1.383)|
|**_Log Real Exchange Rate_**|-0.328***|-1.306***|-0.417***|-0.380**|0.518|-1.071***|-0.162|-2.606|
||(0.113)|(0.289)|(0.134)|(0.162)|(0.583)|(0.187)|(0.116)|(1.766)|
|**_Constant_**|-11.99|-32.58*|-10.06|-24.33***|-121.8|-18.39*|-3.834|-6.903|
||(9.07)|(16.52)|(7.149)|(7.937)|(103.2)|(10.23)|(5.841)|(35.11)|
|**_Observations_**|95|99|60|20|9|19|20|14|
|**_R-squared_**|0.207|0.741|0.222|0.915|0.67|0.678|0.794|0.437|
|**_Number of Products_**|5|5|3|1|2|1|1|1|

Robust standard errors in parentheses

*** p<0.01, ** p<0.05,

*p<0.1

16
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17
detailed system of registration and reporting of demographic changes at the local level. He also had every adult participate in the _Umuganda_ (weekly communal labor), and institutionalized weekly animation sessions in honor of himself (Verwimp, 2003a).

A key characteristic of the Habyarimana regime was its doctrine on the relation between population and land. The president had never been an advocate of a family planning policy. On several occasions he declared that children were the wealth of every Rwandan family. Groups set up by the Ministry of the Interior attacked pharmacies that sold condoms. The president was fully supported by the Catholic Church, which was omnipresent in Rwanda. The fertility rate of Rwandan women was among the highest in the world and the average size of cultivated land per family was shrinking rapidly from 1.2 ha in 1984 to 0.9 ha in 1990 (National Agricultural Surveys, 1984 and 1989-1991). Many families had not enough land to earn a living and feed their families. In 1986, when discussing the fate of the 1959-1962 refugees, the Central Committee of the MRND said that their return was not possible because the country was overpopulated.

During the civil war preceding the genocide (1990-1994), a number of local massacres occurred in which a total of 2,000 Tutsi were killed. These massacres were not spontaneous outburst of violence from a poor peasant population but were organized by the national power elite. On April 6<sup>th</sup> , 1994 Habyarimana’s plane was shot down. After that, the genocide broke out.

# **3. Methodology and Data Description**

We use the 1992 and the 2000 Demographic and Health Surveys (DHS) for Rwanda. The 2000 survey includes a module collecting information on all the siblings of the respondent, including siblings who have died. The respondent is always an adult 6
Table A30: Medium-term heterogeneous impacts by membership in a social organization

|_Dep. Var. (Y):_|_Global_<br>_index_|_Knowledge_|_Gender_<br>_norm-_<br>_s/roles_|_VAW_<br>_attitudes_|_Beliefs_<br>_others’_<br>_attit._|_Intent to_<br>_update_<br>_picture_|_Actual_<br>_picture_<br>_update_|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Drama|0.039|-0.031|0.086<sup>_∗_</sup>|0.002|-0.000|0.033|0.024|
||(0.038)|(0.065)|(0.046)|(0.075)|(0.096)|(0.048)|(0.032)|
|Documentary|0.036|0.018|0.054|0.014|0.106|0.058|0.076<sup>_∗∗_</sup>|
||(0.036)|(0.062)|(0.043)|(0.079)|(0.091)|(0.050)|(0.037)|
|Drama * ORG member|0.049|-0.015|0.006|0.189|0.288|0.173|0.054|
||(0.107)|(0.182)|(0.107)|(0.203)|(0.228)|(0.123)|(0.074)|
|Documentary * ORG member|-0.052|0.045|-0.129|0.025|-0.038|0.111|-0.016|
||(0.104)|(0.186)|(0.110)|(0.197)|(0.235)|(0.127)|(0.076)|
|ORG member|-0.015|0.140|-0.019|-0.130|-0.016|-0.058|-0.063|
||(0.077)|(0.142)|(0.075)|(0.150)|(0.166)|(0.087)|(0.046)|
|R-squared|0.535|0.132|0.481|0.289|0.074|0.021|0.002|
|Observations|619|619|619|619|619|619|619|

Notes: Heteroscedasticity-robust standard errors in parentheses. The Global index is composed by indexes for individuals’ knowledge, gender norms/roles and VAW attitudes; excluding individuals beliefs on others’ attitudes. Higher values of the indexes in (1) to (4) stand for more progressive stances. The index "Beliefs on others’ attitudes" reported in (5) takes higher values when the respondents believe that a larger share (out of 10) of their closest Facebook friends have progressive attitudes. Baseline controls described in the notes to Table 2 are included in all regressions. * p < 0.10, ** p < 0.05, *** p < 0.01 42
20 plant sizes. That is, the apparent changes in overall eificiency were probably not exclusively attributable to small inefficient plants expanding or dropping out.

Taken together, the rank corelations in table 3 are wholly consistent with the table 2 finding that industries undergoing the most reduction in protection showed the most productivity improvement. They also appear to confirm the adjustment processes discussed in connection with figure 1. Nonetheless, there are alternative plausible explanations for our findings. Consider the scale result first. Changes in returns to scale are negatively associated with changes in average plant size across the two censuses, as one would expect.<sup>18</sup> So it appears that expansions in plant size do partly account for reductions in estimated returns to scale. But other factors may also be at work. Specifically, our data deflation is based on the presumption that products are homogeneous at the 3-digit level. This is obviously unlikely, and could lead to misinterpretations. That is, when value added is deflated by an industry price deflator, we are not really isolating a physical quantity, even if intermediate inputs are always used in fixed proportion to output. This means that if big firms achieve larger mark-ups because of market power, they will appear to have more output per unit input -- that is, they will appear more scale efficient. Moreover, if reductions in protection force these firms to price competitively, it will appear that returns to scale in the

> 18/ The rank correlation between average plant size changes and scale changes is about -.30.
<!-- Start of picture text --> 4%<br>In percent of Birtish GDP (until 1914),<br>in percent of US GDP thereafter<br>3%<br>a<br>a<br>)<br>4<br>2 2%<br>a<br>zo<br>B<br>2<br>&<br>1%<br>0%<br>1815 1840 1865 1890 1915 1940 1965 1990 2015<br><!-- End of picture text -->
Gazprom, however, while not a monopoly in Russia’s domestic market, is clearly a very dominant firm with considerable monopoly power. Until more effective competition is introduced into the Russian market, efficient regulation requires constraining the exercise of that monopoly power. Efficient regulation of monopolies calls for a price in the domestic market at levels that reflect the true alternate economic value of the commodity in question.<sup>11</sup> If there were a world price, the opportunity costs of selling gas domestically would be the world price and it would be optimal **for Russia** to charge a unique price on its domestic and export sales.<sup>12</sup> I have explained above, however, that there is not a world price of natural gas for Russia, and Gazprom must determine its export price independently of its domestic price. In Russia’s domestic market, the opportunity costs then corresponds to the long run marginal costs of natural gas. In 2001, this implied that it was necessary for Russia to raise the domestic price of natural gas to achieve this economically efficient price; otherwise the capital stock will deteriorate and supplies will not be forthcoming over time. Many market economies, in fact, regulate the maximum price of monopolies such as gas and electricity distribution to achieve this pricing objective.<sup>13</sup> The analysis summarized in figure 1 suggests that in 2001, Russia should have allowed Gazprom to raise its domestic prices of natural gas from about $15 to $20 per TCM to the full long run marginal costs (about $35 to $40 per TCM). This would have resulted in benefits to Russia of about $1.24 billion dollars per year.

By 2007, natural gas prices in Russia had increased to between $64 and $72 per TCM.14 Although I do not have an updated estimate of LRMC, it has surely increased considerably due to inflation and the substantial increase in steel and wage costs above

> See “Gazprom is Not the Only Player in the Russian Fields,” _Oil and Gas—Eurasia_ . August 2008. http://www.oilandgaseurasia.com/articles/p/80/articles/684

> 11 This discussion is based on the monopoly structure of the natural gas market in Russia. Of course, production of natural gas is not a natural monopoly and it would therefore be desirable to have additional producers. We discuss below that if alternate producers of natural gas were given access to the gas pipelines, there would be economic gains as well as environmental benefits. Nothing in the argument developed in this paper implies that the current structure of Russia’s gas market is efficient.

> 12 Given a domestic monopoly, unified pricing would call for a tax to prevent monopoly profits.

> 13 See, Scherer (1980, chapter 18) and Carlton and Perloff (2000, chapter 20).

> 14 Estimates based on Rosstat and Ministry of Economy data. According to Gazprom, in 2008, the average price excluding VAT and excise taxes was 1653 rubles per MCM, or about $66  per MCM at 25 rubles to the dollar.  See http://old.gazprom.ru/documents/Background_09.06.09.pdf

8
<u>Evaluating Enterprise Innovation Capability. A recent World Bank study describing how</u> firms innovate and use knowledge evaluates and ranks enterprises on the basis of nine key dimensions of technological capability.<sup>53</sup> These variables encompass such factors as a firm’s ability to develop a coherent technology strategy to support the business, acquire and absorb technologies, form and exploit linkages with networks of suppliers and collaborators, plus several other critical core competencies.

**Figure 33 Nine dimensions of Technological Capability**

<!-- Start of picture text --> Awareness<br>4<br>Linkages 3 Search<br>2<br>Learn 1 Core competence Best practice model<br>0<br>Company x profile<br>Implement Strategy<br>Acquire Assess/select<br><!-- End of picture text --> _Source_ : How Korean Firms Use Knowledge, World Bank processed, 2002.

Firms are then placed in one of four categories based on (i) the degree to which a firms is aware of the overall need to change and (ii) the degree to which management is aware of what to change and how to go about changing it.<sup>54</sup>

> 53 World Bank, <u>Korea: How Firms Use Knowledge, Part A: Firm Level Innovations in the Korean Economy, 2002.</u>

> 54  This discussion is from <u>Korea: How Firms Use Knowledge,  Part A</u> – Firm Level Innovation in the <u>Korean Economy, World Bank processed, 2002 and Erik Arnold, Martin Bell, John Bessant and Peter</u> Brimble, _Enhancing Policy and Institutional Support for Industrial Technology Development in Thailand: The Overall Policy Framework and the Development of the Industrial Innovation System_ , World Bank processed, 2000.

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