rafmacalaba/datause-encoder-data huggingface dataset:
-7-

TABLE 1 MAJOR ECONOMIC INDICATORS OF KOREAN GROWTL, 1955-1986 (Unit: US-$ and %)

||Per<br>capita<br>GNP|Growth<br>rate<br>of GNP<br>(1975|nflation<br>rate (GNP<br>deflator)|Gross fixed<br>investnent<br>to GNP|National<br>saving<br>to GNP|Grz-rvh<br>rate of<br>exp.4ts|Exports<br>to<br>GNP<br>Ratio|Manufacttring<br>Exports to<br>Tot Exports<br>Ratio|
|---|---|---|---|---|---|---|---|---|
|||<br>Constant<br>Won)|||||||
|55|65|4.1|62.1|10.2|5.2|22.1|2.9|-|
||66|-1.4|34.0|10.3|-1.9|-9.0|2.3|-|
||74|7.6|22.2|10.6|<br>5.5|<br>33.9|2.2|-|
||80|5.5|-1.3|10.2|4.9|24.6|2.8|-|
||81|3.8|1.3|11.0|4.2|_15.0_|3.4|-|
||79|1.1|11.7|10.8|0.8|20.8|4.1|-|
||82|5.6|14.0|11.7|2.9|38.7|6.3|18.2|
||87|2.2|18.4|13.7|3.2|13.0|6.0|27.0|
||100|9.1|29.3|13.5|8.7|9.0|5.4|51.7|
||103|9.6|30.0|11.3|8.7|23.5|6.7|51.6|
||_105_|5.8|6.2|14.8|7.4|35.9|9.5|62.3|
||_125_|12.7|14.5|20.2|11.8|42.4|11.9|62.4|
||142|6.6|15.6|21.4|11.4|32.7|13.6|70.0|
||169|11.3|16.1|25.0|_15.1_|39.5|14.7|77.3|
||210|13.8|14.8|25.8|18.8|36.1|15.4|79.0|
|;70|252|7.6|15.6|24.7|16.2|19.6|15.0|83.6|
|/1|288|9.1|12.9|22.5|14.5|21.1|16.1|86.0|
||318|5.3|16.3|20.4|15.7|36.0|20.6|87.7|
||395|14.0|12.1|23.2|21.4|53.0|30.0|88.2|
||540|8.5|30.4|25.6|19.3|-0.8|28.4|90.2|
||590|6.8|24.6|25.3|16.8|19.0|28.2|88.3|
||797|13.4|21.0|24.4|22.2|41.5|32.0|89.8|
||1008|10.7|15.9|27.3|25.4|23.3|32.7|87.5|
||1392|11.0|21.6|31.3|.7.3|12.5|30.6|89.9|
||1640|7.0|20.0|33.2|26.5|-1.1|27.7|90.1|
||1589|-4.8|25.3|32.3|20.8|10.2|34.4|92.3|
|1|<br>1719|6.6|15.4|28.7|20.5|15.0|37.8|92.9|
||1773|5.4|6.7|30.5|20.9|6.5|36.9|93.7|
||1914|11.9|3.9|31.3|25.3|15.5|37.5|94.4|
||2044|8.4|3.8|31.3|27.9|10.0|38.7|95.0|
||2047|5.4|4.1|30.8|28.6|2.1|37.7|95.4|
||2300|12.3|2.7|31.4|32.6|26.5|42.5|94.6|

not available ource: Choi (1988), Table II-I; Economic Planning Board (1976); Economic Planning Board (1988).
**Table S6:** Program impacts on health and nutrition (beneficiaries)

# **<u>Panel A:  Baseline Dropouts</u>**

=1 if Suffers from Psychological Distress Number of Times Respondent Ate Protein Rich Foods During the Past 7 Days (out of 21)

||During<br>Program|End of<br>Program|Two Years<br>After<br>Program|During<br>Program|End of<br>Program|Two<br>Years<br>After<br>Program|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|=1 if Conditional Schoolgirl|-0.002|0.010|0.038|0.326|0.224|0.228|
||(0.039)|(0.036)|(0.042)|(0.202)|(0.192)|(0.181)|
|Mean in Control Group|0.463|0.314|0.424|3.678|3.989|3.741|
|Sample Size|698|715|743|698|718|744|
|**Panel B: Baseline Schoolgirls**|||||||
|=1 if Conditional Schoolgirl|-0.068**|-0.037|-0.030|0.385**|0.596***|0.072|
||(0.032)|(0.047)|(0.032)|(0.195)|(0.174)|(0.141)|
|=1 if Unconditional Schoolgirl|-0.139***|-0.026|-0.002|0.445**|0.338**|-0.043|
||(0.035)|(0.054)|(0.046)|(0.199)|(0.153)|(0.240)|
|p-value UCT vs. CCT|0.068|0.860|0.552|0.814|0.215|0.672|
|p-value Treatment|0.000|0.677|0.627|0.023|0.001|0.858|
|Mean in Control Group|0.372|0.313|0.369|3.967|4.052|4.134|
|Sample Size|1,963|2,013|2,045|1,967|2,018|2,047|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Psychological distress is equal to one if the summed GHQ- 12 score is equal to three or higher, and is zero otherwise. Protein rich foods are defined as those containing animal proteins, i.e. meat, fish, and eggs. The number of days each item was consumed over the past week are summed to create the outcome variable. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
21 Table 4: Reason for not available to work during the survey week (1997-2005)

|Reason/Year||Perc<br>|entage of res<br>|pondents, gi<br>|venthat the<br>|ywerenot a<br>|vailable to <br>|work<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||1997|1998|1999|2000|2001|2002|2003|2004|2005|2006|
|Having household, family duty|30.9|30.3|28.7|28.1|28.9|29.3|29.5|30.3|30.9|29.5|
|In school, student|30.4|31.0|30.6|30.3|30.8|32.1|32.6|32.4|31.2|31.3|
|Seasonal worker|4.6|5.1|5.0|5.3|4.2|2.7|2.2|1.3|1.5|3.4|
|Too young or too old|21.3|21.8|21.9|22.1|21.9|21.9|22.5|22.0|22.1|22.0|
|Illness, disability|6.5|5.7|5.7|5.5|6.6|6.3|6.5|7.1|7.1|6.4|
|Resting|3.3|3.4|3.8|3.7|2.4|2.7|2.8|2.9|3.0|3.1|
|Other reasons|2.9|2.6|4.2|5.0|5.2|5.0|4.0|4.0|4.2|4.3|

Source: raw data from the Thai LFS, authors' tabulation.
therefore impossible to distinguish between immigrants who were educated at the time of their arrival and those who acquired education after they settled in the receiving country; for example, Mexican-born individuals who arrived in the US at age 5 or 10 and graduated from US high-education institutions are counted as highly-skilled immigrants. Hence, our de...nition of the brain drain is clearly determined by data availability. Existing data do not allow us to systematically eliminate foreign-born individuals arrived with completed schooling or after a given age threshold.

In the US, the proportion of foreign born who arrived before age 15 (i.e. those who spent part of their schooling time in the US) represents 7.5 percent of the immigration stock. Since we have no systematic data for the other countries, we cannot control for children immigration.

Finally, the concept of immigration is not fully homogenous between countries. When the information exists, we de...ne migration on the basis of the country of birth rather than citizenship. This concept is recommended by the United Nations<sup>8</sup> and better captures the decision to emigrate<sup>9</sup> . Another reason is that the concept of country of birth is time invariant, contrary to citizenship which changes with naturalization. The OECD statistics report that 14.4 millions of foreign born were naturalized between 1991 and 2000. Countries where the number of acquisitions of citizenship is particularly high are the US (5.6 million), Germany (2.2 million), Canada (1.6 million), Australia and France (1.1 million). Data by country of birth are available in a large majority of OECD countries. However, for some countries, immigrants are only classi...ed by citizenship (Germany, Greece, Italy, Japan and Korea).

The ...rst six columns in table 1 describe the availability of immigration data in OECD countries as well as the statistical sources. Some of these data are not available yet. Hence, provisory assumptions are used in this release of the database.

> 8In the International Migration Report (UN, 2002), the concept of country of birth is used for 159 countries while citizenship is used for 51 countries.

9In some receiving countries such as Germany, immigrants’ children keep their foreign citizenship.

9
<!-- Start of picture text --> Figure 6: Firm Productivity in Figure 7: Firm Productivity By Ownership<br>Moscow Vs Other Cities Type<br>Other Cities Moscow State-Owned Municipal-Owned<br>TFP Private-Owned<br>16 TFP<br>15<br>15<br>14 13<br>13<br>11<br>12<br>9<br>11<br>10 7<br>2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

- Fifth, productivity is negatively correlated with both **very young firms** (under the age of one year) **and exiting firms.** This suggests that _existing (or incumbent) firms enjoy certain advantages_ , which may be due to either weak competition or other productivity and competitiveness advantages. But with many existing firms consisting of state and municipal companies with lower productivity than in private firms, it appears that firm survival is not correlated with high productivity. Internal managerial incentives in incumbent firms may not be strongly favoring productivity in the presence of weak competitive pressures. Managers of existing firms can maintain sub-optimal use of factor inputs. By contrast, intensified competition pressures force managers to speed up the adoption of new technologies in order to survive.

- And sixth, **market share** (at the NACE industry level four) matters and is correlated with productivity. This is corroborating empirical evidence in Szegedi et al (2009) that firms can increase productivity by raising the market share. In fact, market share has the largest coefficient and in our model is the single most important driver of manufacturing productivity in Russia. But the estimated effect of the broader measure of industry concentration, the Herfindahl index, while having the theoretically correct negative sign, is not significant.

14
WPS3991

# : **University-Local Industry Linkages**

## The Case of Tohoku University in the Sendai-Area of Japan<sup>1</sup>

### Juan JIANG

Research Fellow, Management of Science and Technology Department Graduate School of Engineering, Tohoku University

### Yuko HARAYAMA

Professor, Management of Science and Technology Department Graduate School of Engineering, Tohoku University

### Shiro ABE

Professor Emeritus, Tohoku University Professor, Tohoku Fukushi University

### Abstract

This paper focuses on Tohoku University in Sendai in the non-metropolitan area of Japan. Both a long historical and comparative perspective and a spacial perspective are essential to discuss the relevance of university-industry linkages to local-regional economic development. The conjunction of these linkages and economic development has been affected by two evolutionary processes: institutional configurations and territorial dynamics in the national innovation system. In addition, university-industry linkages have been complicated by top-down regionalization and bottom-up regionalism.

Key Words: Japan, Sendai, Tohoku University, University-Industry-Linkages, Local-economic-development

### World Bank Policy Research Working Paper 3991, August 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. 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 this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 This paper was originally written for a research project for “Universities as Drivers of Urban Economies in Asia” sponsored by the Social Science Research Council, New York and the Development Economics Research Group of the World Bank, Washington, D.C., presented in Washington, D.C. (November 17-18, 2005).

1
functions). Ravallion et al. (2009) find that there are relativist gradients in both the food and nonfood components of the national poverty lines for developing countries.

The judgments made in setting the various parameters of a poverty line are likely to reflect prevailing notions of what poverty means in each country. And those norms clearly go well beyond the “physiological minimum necessary for survival.” The basal metabolic rate implies a positive lower bound to the cost of nutritional requirements (for all positive food prices). The cost of the (food and non-food) goods required for social needs must also be bounded below. The poverty lines found in many poor countries are certainly frugal. For example, World Bank (1997) gives the average daily food bundle consumed by someone living in a neighborhood of India’s national poverty in 1993. The daily food bundle per person comprised 400 grams of course rice and wheat, 200 grams of vegetables, pulses and fruit, plus modest amounts of milk, eggs, edible oil, spices and tea. After buying such a food bundle, one would have left with about $0.30 per day (at 1993 PPP) for non-food items.

India’s official line is clearly too low to be acceptable in middle-income (and certainly in rich) countries, where higher overall living standards naturally mean that higher standards are used for identifying the poor. Consider instead the daily food bundle used by Bidani and Ravallion (1993) for constructing Indonesia’s poverty line for 1990. This comprised 300 grams of rice, 100 grams of tubers and similar amounts of vegetables, fruits and spices as in the India example; but it also included fish and meat (about 140 grams in all per day), and the overall diet was more varied and probably preferable by the tastes of most consumers.<sup>17</sup> This bundle would in turn be considered too frugal for defining poverty standards in many middle-income countries.

So there is ample scope for (implicit) relativism to emerge via the methods used to set “absolute” poverty lines in practice.<sup>18</sup> As noted in section 2, in principle it could be either the welfare norms (for utility or other functionings) that are relative or it could be welfare itself, while treating the norms as absolute in the welfare space. This difference is not identifiable by looking at aggregate data on national poverty lines, although sections 4 and 5 will point to evidence from micro data that provides some support for the idea that relative position matters directly to welfare, though probably less so for very poor people.

> 17 Vegetarians would presumably need to be compensated for the meat and fish by similarly protein-rich foods, and would then prefer this version of the Indonesian bundle over the Indian bundle described above.

> 18 This is true within countries as well as between them. Though it is not a topic for this paper, this implicit relativism can considerably distort national efforts to reduce aggregate poverty, as discussed in Ravallion (1994).

13
# **F. References**

Batini, N., Eyraud, L., and Weber, A. (2014). “A Simple Method to Compute Fiscal Multipliers”. IMF Working Paper No. WP/14/93.

Bolder, D. J. (2002). “Towards a more complete debt strategy simulation framework”. Bank of Canada Working Paper No. 2002-13.

Bolder, D. J. (2003). “A stochastic simulation framework for the government of Canada’s debt strategy”. Bank of Canada Working Paper No. 2003-10.

Bolder, D. J. (2005). “Measuring and managing foreign currency risk in a sovereign liability portfolio”. Manuscript. Financial Markets Department, Bank of Canada.

Bolder, D. J. (2008). “The Canadian debt-strategy model”. Bank of Canada Review, pp. 3-16.

Briceño-Garmendia, C. M., and D. A. Benitez (2011). “Cape Verde’s Infrastructure. A Continental Perspective”. World Bank Policy Research Working Paper No. 5687.

Cabral, R., and M. Lopes (2005). “Benchmark for public debt: Two alternative approaches”. Manuscript. Annual Meeting of the Brazilian Society of Finance.

Cabral, R., Lopes, M., Baghdassarian, W., Alves, L. F., Souza Junior, P., and A. Santos (2008). “A benchmark for public debt: The Brazilian case”. Manuscript. Latina America and the Caribbean Debt Group, IDB.

Di Bella, G. (2008). “A Stochastic Framework for Public Debt Sustainability Analysis”. IMF Working Paper No. WP/08/58.

Di Giovanni, J., and Gardner, E. (2008). “A Simple Stochastic Approach to Debt Sustainability Applied to Lebannon”. IMF Working Paper No. WP/08/97.

Fatás, A. (2001). “The Effects of Business Cycles on Growth”, working paper prepared for the 5<sup>th</sup> annual conference of the Central Bank of Chile, November, 2001.

Government of Cape Verde (2011). “Medium-Term Debt Management Strategy MTDS Report”.

International Monetary Fund (2012). “Cape Verde. Second Review Under the Policy Support Instrument and Request for Waivers of Nonobservance of assessment Criteria”. EBS/12/7.

International Monetary Fund and International Development Association (2012). “Revisiting the Debt Sustainability Framework for Low-Income Countries”. SM/12/10. (Washington).

International Monetary Fund and International Development Association (2004). “Debt-Sustainability in LowIncome Countries-Proposal for an Operational Framework and Policy Implications”. SM/04/27. (Washington).

Pradelli, J., and Baghdassarian, W. (2013). “Cape Verde: Building Resiliense in a Small Island State”. CEM – Cape Verde. World Bank.

Tielens, J. Van Aarle, B. and Van Hove, J. (2014). “Effects of Eurobonds: A stochastic Sovereign Debt Sustainability Analysis for Portugal, Ireland and Greece”. Discussion Paper Series DPS 14.10, Ku Leuven Center for Economic Studies.

27
costs could be due to several reasons including improvements in transportation technologies, reduction in fuel costs, and/or improvements in road infrastructure. When focusing on infrastructure improvements, it is unrealistic to design a counterfactual in which transportation costs are reduced in a uniform way across all locations. Most governments have limited fiscal space and implementation capacity for country-wide infrastructure programs. Therefore, we design a counterfactual that quantifies the dynamic gains from reductions in trade frictions due to _optimal_ investments in road infrastructure in the LAC countries and projects the economic adjustments to these changes over the subsequent 100 years.

We obtain the geolocations where bilateral trade costs are reduced in an optimal way and the size of these reductions from the work of Gorton and Ianchovichina (2021). They use data from multiple sources and the general equilibrium spatial framework of Fajgelbaum and Schaal (2020) to identify the endogenous change in trade costs due to optimal expansions of the domestic road networks in most Latin American countries. Gorton and Ianchovichina (2021) demonstrate that optimal investments are more likely along road segments linking more productive and populous locations, which in LAC tend to have relatively high bilateral costs, as shown in Section 4. Since these costs ζ<sup>nn</sup> (s, r) vary across goods n, we calculate the change in average trade costs from optimal investments along each link ( _s_ , _r_ ) as follows: where _N_ is the number of goods transported in the economy and is good _n_ ’s share of all goods _N_ traded along route ( _s_ , _r_ ). ππ<sup>nn</sup> (ss, rr) In Figure 7, we compare the performance of LAC relative to the global economy in terms of utility and GDP and the evolution of these metrics over the next century in the baseline (shown in red) and the counterfactual of optimally reduced trade costs (shown in blue). The results suggest that optimal reductions in domestic trade costs are likely to increase LAC’s share in the world’s utility and GDP in a sustained and re-enforcing way. The regional effect is expected to be substantial, largely because we assume that such investments are not made in the rest of the world. Latin America’s share in GDP rises relative to the baseline in the first period and is 62 percentage points higher relative to the baseline in 2100. Lower transportation costs have a direct and immediate positive effect because fewer 21
al. (2015). <mark>It is therefore very likely that on average most if not all</mark> _<mark>injera</mark>_ <mark>baking (but perhaps not all</mark> <u><mark>cooking) is done on the Mirt stove and therefore captured by our monitoring.</mark></u> <mark>Finally, it is very unlikely that any increase in real income due to Mirt stove adoption would be realized as significant increased cooking. Our study areas do not have refrigeration, so households that cook</mark> _<mark>injera</mark>_ <mark>must eat them within one to three days to avoid possible bacterial infection. It is, of course, possible that as real income increases, households over time are able to add extra coffee or stews (typically eaten with</mark> _<mark>injera</mark>_ <mark>) to their diets. These effects – due to the nature of the cuisine and the Mirt technology - are expected to be limited, however, causing us to not be overly concerned with leakage.</mark> In terms of empirical methods, because our stove distribution is random at the household level and behavioral treatments are also randomized, we begin our empirical analysis with differences in means, rank-sum and differences in median tests. Using OLS with monitoring round and district fixed effects and (in Appendix A) random effects with district fixed effects, we also evaluate the factors affecting estimated fuel savings at the household level. Perhaps not surprisingly given our random sampling, randomized behavioral treatments and CCT experimental methodologies, which explicitly adjust for unobservable differences, few socio-demographic factors affect fuelwood savings. Table 1 presents the variables used in the regression models.

**Table 1. Descriptive Statistics of Covariates used in the Pooled OLS Models**

|Variable|Obs.|Mean|Sd. D.|Min|Max|
|---|---|---|---|---|---|
|Age of respondent|1205|42.09|13.07|20|90|
|Sex of respondent 1 if male, 0 if female|1205|0.88|0.33|0|1|
|Marital of respondent 1 if married, 0 otherwise|1209|0.90|0.30|0|1|
|Education-1 if illiterate, 0 if literate|1209|0.39|0.49|0|1|
|FamilySize in adult equivalent|1209|4.93|1.85|0|10.88|
|Religion -1 if Christian, 0 Muslim|1205|0.73|0.45|0|1|
|Livestock in tropical livestock units(TLU)|1194|5.00|3.56|0|26.23|
|Children under 15|1103|3.09|1.51|1|11|
|Walking Distance from household to nearest road<br>(two-way)in minutes|1205|60.13|97.35|0|840|
|Participation in controlled cookingtest, 1 ifyes, 0 if no|1209|0.30|0.46|0|1|
|Risk coefficient|1197|3.80|1.17|0.08|6.58|
|Average temperature in<sup>0</sup>C|1203|24.75|3.16|15|31|
|Average number of injera baked at a time|1205|19.79|10.25|0|55|
|Use the of improved stove for any other purpose other|1209|0.35|0.48|0|1|

**17 |** P a g e
**(19c) Women**

||N|PAYG=0<br>DC=20|PAYG=5<br>DC=15|PAYG=10<br>DC=20|PAYG=15<br>DC=5|PAYG=20<br>DC=0|
|---|---|---|---|---|---|---|
|Dl|306|.12|**.20**|**.28**|.36|.44|
|D2|305|.20|.26|.32|.38|.44|
|D3|267|.26|.31|.35|.40|.44|
|D4|244|.32|.35|.38|.41|.44|
|**D5**|155|.39|.40|.41|.43|.44|
|D6|131|.44|.44|.44|**.44**|.44|
|D7|111|.51|.49|.47|.46|.44|
|D8|76|.61|.57|.53|**.48**|**.44**|
|D9|57|.73|.66|.59|.51|.44|
|D1O|35|1.07|.92|.76|.60|.44|
|ALL|1687|.33|.35|.38|.41|**.44**|

The final row of Table 19a shows that the assumptions of i = 3% and dY = 1.5% create an approximate equivalence between PAYG and funded pension systems within the demographic parameters of the LIFEMOD model (as can be seen, there is a very slight in-built advantage for funded pensions in this and the subsequent tables). The approximate equivalence condition enables us to make direct comparisons of the distributional outcomes of different combinations of PAYG and funded pension systems in Tables 19-22. Table 19a shows that all individuals in the bottom five deciles of the lifetime earnings distribution would fare better from a pure flat-rate pay-as-you-go pension than from any hybrid system and, not surprisingly, the more the final pension depends on own earnings history,<sup>the lower is the</sup> achieved replacement rate for these bottom five deciles. In general, however, pension outcomes for individuals in the sixth decile of the lifetime income<sup>distribution are</sup> insensitive to the mix of PAYG and defined contribution pension elements.

The decile rankings in Tables 19-22 are by discounted lifetime earnings. In Table 19 the replacement rates for men (19b) and women (19c) are almost identical within<sup>each</sup> decile for each of the five types of pension system. This is not surprising; men and 61