rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 2.3: Sh**|**are of the populati**<br>**0-14**|**on per age g**<br>**(percent)**<br>**15-44**|**roup**<br>|**and by locality,**<br>**45-64**|**rural Mexico,**<br>**65 +**|
|---|---|---|---|---|---|
||**Years of age**|**Years of age**||**Years of age**|**Years of age**|
||||**1980**|||
|**Total Mexico**|43.1|42.9||10.1|3.8|
|**North**|41.9|43.9||10.5|3.7|
|**Capital**|40.6|46.2||9.9|3.4|
|**Gulf**|42.9|43.0||10.3|3.8|
|**Pacific**|44.3|41.6||10.0|4.1|
|**Southr**|44.7|41.2||10.2|3.9|
|**Center North**|46.1|39.9||9.8|4.2|
|**Center**|44.3|40.9||10.5|4.2|
||||**1990**|||
|**Total Mexico**|38.6|46.3||11.0|4.2|
|**North**|35.4|49.0||11.5|4.0|
|**Capital**|34.5|50.4||11.2|3.9|
|**Gulf**|39.8|46.2||10.2|3.8|
|**Pacific**|38.5|46.3||10.8|4.4|
|**South**|43.0|42.2||10.6|4.2|
|**Center-North**|41.3|43.8||10.5|4.3|
|**Center**|44.2|11.0||4.5|4.5|
||||**2000**|||
|**Total Mexico**|34.1|48.1||12.8|5.0|
|**North**|32.3|50.1||13.0|4.6|
|**Capital**|30.0|51.4||13.8|4.9|
|**Gulf**|34.6|48.8||12.1|4.5|
|**Pacific**|33.5|48.4||13.0|5.1|
|**South**|38.4|44.4||12.0|5.2|
|**Center-North**|36.5|46.4||12.0|5.1|
|**Center**<br>Source: Census 19|35.2<br>80,1990,and 2000.|46.9||12.5|5.4|

Fecundity, the number of children per mother, dropped from 2.8 in 1980 to 2.6 in 2000 in rural Mexico (Table 2.4). In the poor South region the fertility rate is still higher than in Mexico as a whole and the Capital region, namely by 0.3 percentage points and 0.7 percentage points, respectively. Women’s increased participation in the labor market (by 3.3 percentage points see Table 2.5) may have been an important factor contributing to the reduction in the fertility rate and drop in the dependency rate. The female labor force participation rate is still lower in the South region (24.2 percent) than in the Capital region (35 percent) and therefore the high fertility rate in the former could be an important factor explaining this difference. Moreover, lack of access to childcare facilities etc. may also play a roll in the large difference in labor market structure among regions.

6
<!-- Start of picture text --> Figure 12: Public, Private and Total Pension Balanees<br>with 6% Contribution to 2nd Pillar (in % of GDP)<br><!-- End of picture text --> <!-- Start of picture text --> 23<br>0.53 1_<br>.1.<br>1997 2003 2009 2015 2021 2027 2033 2039 2045 2051 2057 2063 2069<br>Public Balance (Multipillar) N- N et Private Balance<br>-<br>| Total Pension System Balance<br><!-- End of picture text -->

The final impact of the pension reform on national savings will depend on the reaction of voluntary private savings to the individual measures of the reform, and on the reaction of the Government to the path of the PAYG deficits and surpluses. The reaction of voluntary private savings to the reform is difficult to estimate numerically, although it is possible to identify some of the major changes that might occur. The increase in the retirement age could induce some decline in private savings, whereas the change in indexation would imply a decrease in expected retirement income, inducing some increase in private savings.<sup>13</sup> The expectations of higher returns on the contribution to the second pillar (relative to the PAYG) could have a positive or negative effect on voluntary savings, depending on the relative sizes of the income and substitution effects. Therefore, net impact of all these factors on voluntary private savings is in principle ambiguous, and would in any case be dampened by the existence of liquidity constraints.

The reaction of the Government to the path of future PAYG deficits will be primarily influenced by Hungary's objective of joining the EU by the year 2005. Meeting the objective of EU membership will require an effort to reduce the general Government deficit from around 4-4.5 percent of GDP (the levels that prevailed in the late 1990s) to levels below the Maastrich ceiling of 3 percent of GDP. The expected reduction of the General Government deficit to levels below 3 percent of GDP suggests that the transition will be primarily tax-financed and that savings effects might be stronger. However, additional increases in national savings arising from offsetting reductions in the Government's deficit are likely to be moderate, as the General Government deficit is already close to the Maastrich ceiling, the projected PAYG deficits are moderate, and the actuarial projection even predict a period of PAYG surpluses.

> 13 These are the responses predicted by the overlapping generations model. See, e.g., Kotlikoff (1989).

17
|**Table 4: Impact on Firm Survival**|||||||||
|---|---|---|---|---|---|---|---|---|
||Sample<br>Size|Before<br>Subsidy|During<br>Subsidy|Year1<br>A|Year2<br>fter Subsid|Year3+<br>y|p‐value<br>equality|p‐value<br>all zero|
|**Panel A: Self‐employed in Survey Round**|||||||||
|Assigned to Treatment|5005|‐0.005<br>(0.023)|‐0.009<br>(0.018)|0.056*** <br>(0.021)|0.074***<br>(0.025)|0.058**<br>(0.027)|0.004|0.006|
|Control Mean||0.927|0.958|0.888|0.858|0.834|||
|**Panel B: Self‐employed, assuming that firms w**<br>|**hich close and**<br>|**are never**<br>|**observed**<br>|**again have**<br>|**stayed clo**<br>|**sed**<br>|||
|Assigned to Treatment|5044|‐0.006<br>(0.023)|‐0.010<br>(0.018)|0.054**<br>(0.021)|0.066**<br>(0.026)|0.056*<br>(0.028)|0.009|0.014|
|Control Mean||0.927|0.958|0.888|0.855|0.817|||
|**Panel C: Self‐employed, assuming that all attr**|**itors are closed**||||||||
|Assigned to Treatment|5139|‐0.008|‐0.012|0.052**|0.058**|0.055*|0.015|0.029|
|||(0.023)|(0.018)|(0.022)|(0.028)|(0.030)|||
|Control Mean<br>Notes: robust standard errors in parentheses,<br>strata, variables used for re‐randomization, a<br>*, **, *** indicate significance at the 10, 5, an<br>p‐values are for test that the treatment effect<br>effect is zero in all four periods.|clustered at th<br>nd contain sur<br>d 1 percent lev<br>is equal in the|0.888<br>e firm leve<br>vey round<br>els respect<br>during, yea|0.879<br>l. All regre<br>dummies.<br>ively.<br>r 1, year 2|0.820<br>ssions con<br>, and years|0.827<br>trol for ran<br>3 to 4 peri|0.788<br>domization<br>ods; and th|<br>at the tre|atment|

40
Great Britain, it is possible to see a tendency in the polarization in the work market, with growth in high-income cognitive work and low-income manual occupations, accompanied by a reduction in routine tasks with medium incomes. In this sense, given a reduction in the prices of computing equipment, problem-solving skills are becoming relatively more productive, which explains the growth in occupations which require the performance of cognitive tasks by a qualified labor force (Katz and Murphy, 1992 and Acemoglu, 2002).

# 3. Methodology and source of information

The source of information used in this study is the database developed by O*NET (Occupational Information Network), which provides information related to the content of occupational tasks. Since the year 2003 O*NET has been compiled in the United States for approximately 1,000 occupations based on SOC - Standard Occupational Classification, and since then, until the year 2014, it has been updated periodically.4 Following Acemoglu and Autor (2011) four sets of O*NET data are used: skills, work activities, work context and capacities. Each one of them contains descriptors which aim to measure, using a scale of importance, the level, or extent, of the activity. For this, the O*NET data from 2003, 2005 and 2015 are used to try and identify the change in the content of the tasks within each occupation within that time period.5 At the same time, the Permanent Household Surveys from the years 1998, 2003 are 2015 are used for Argentina, and the Continuous Household Surveys from the years 1995, 2003 are 2015 are used for Uruguay.

In order to estimate the content of the tasks of the occupations, the elements of the tasks provided by O*NET are mapped to the classification of occupations coming from the household surveys from Argentina and Uruguay. In general, each country has its own specific version of the International Standard Classification of Occupations (ISCO) or at least, in the cases where a national classification is used, an equivalent to ISCO is applied.

The classifications used in the different household surveys are the National Occupation Classifications revision 2001 and 1991 (CNO 01 and CNO 91) in the case of Argentina and the

> 4 O*NET is the successor to DOT (Dictionary of Occupations) which is no longer updated. O*NET was started in 1998 on the OES base of codes Occupational Employment Statistics. In 2003 this was changed to SOC which means that the consistent measures of task content are calculated from 2003.

> 5In this study it is assumed that the characteristics that describe each average occupation in Argentina and Uruguay are similar to those prevailing in the United States. This is not necessarily the case and so the results may have some bias.

- 8 -
confidence on the use of _Teach_ in low- and middle-income countries to monitor teaching practices additional studies will need to be conducted in other countries to further corroborate these findings.

# **References**

American Educational Research Association., American Psychological Association., National Council on Measurement in Education., & Joint Committee on Standards for Educational and Psychological Testing (U.S.). (2014). Standards for educational and psychological testing.

Andrabi, T., Das, J., Khwaja, A. I., Vishwanath, T., & Zajonc, T. (2007). _Learning and Educational Achievements in Punjab Schools (LEAPS): Insights to inform the education policy debate_ . World Bank: Washington, D.C.

Araujo. M. C., Carneiro, P., Cruz-Aguayo, Y., and Schady, N. (2016). Teacher Quality and Learning Outcomes in Kindergarten. _Quarterly Journal of Economics_ , 131(3): 1415-1453.

Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. _The Quarterly Journal of Economics_ , 122(3), 1235-1264.

Bau, N. and Jishnu, D. (2017). The Misallocation of Pay and Productivity in the Public Sector: Evidence from the Labor Market for Teachers. Policy Research Working Paper, 8050. World Bank, Washington, D.C.

Bennell, P., & Akyeampong, K. (2007). _Teacher Motivation in Sub-Saharan Africa and South_ Asia. London: DfID.

Berry, D., and O'Connor, E. (2010). Behavioral risk, teacher-child relationships, and social skill development across middle childhood: A child-by-environment analysis of change. _Journal of Applied Developmental Psychology_ , 31(1), 1-14.

Blazer, D. and Kraft, M.A. (2017). Teacher and Teaching Effects on Students’ Attitudes and Behaviors. _Educational Evaluation and Policy Analysis_ , 39(1), 146-170.

Bold, T., Filmer, D., Martin, G., Molina, E., Stacy, B., Rockmore, C., Svensson, J., and Wane, W. (2017). Enrollment Without Learning: Teacher Effort, Knowledge, and Skill in Primary Schools in Africa. _Journal of Economic Perspectives_ , 31(4): 185-204.

Bold, T., Filmer, D., Molina, E., and Svensson, J. (2018). The Lost Human Capital. Working Paper, World Bank, Washington, D.C.

Bosacki, S., and Astington, W. J. (1999). Theory of mind in preadolescence: Relations between social understanding and social competence. _Social Development_ , 8(2), 237-255.

Brophy, J. (1986). Teacher influences on student achievement. _American Psychologist_ , 41(10), 1069-1077.

28
reverse causality from our dependent variable to democracy is unlikely. It is difficult, for example, to imagine that the enactment of a more efficient bankruptcy law would influence the societal bend towards democracy. In contrast, other available indicators of the business environment are aggregate or macro level measures. A possible feedback effect from macro level changes in the business environment to the quality of democracy is plausible. This problem is identified in Glaeser et al. (2004), among others.

# 2.2 _Explanatory variables_

Democracy The main explanatory variable is a measure of the level of democracy. We use data from Polity IV and Freedom House to construct three separate measures of democracy. The “democracy” variable in the Polity IV data provides a score between 0 and 10 for each country with higher scores implying a better quality of democracy. In our sample, countries with the highest level of democracy include Costa Rica, Mauritius, Mongolia, the Netherlands and United States, while Azerbaijan, Belarus, Bhutan, Vietnam and Sudan show the lowest level of democracy.

The democracy scores from Polity IV are available on an annual basis but there is little time variation in these scores over our sample period. Hence, we use scores in year 2003, the beginning of the sample period, to construct _Democracy_ , our main measure of democracy. It equals 1 for a country with a score of 5 or higher (high democracy) and 0 otherwise (low democracy). To check that our results are not sensitive to the high and low democracy classification, we report all results using the democracy scores on the full 7
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The instrument set for the differenced equation consists of TFP, the log of innovation investment (for all, internal, and external innovation), in levels, in periods _t_ -2 and _t_ -3 (for which missing values are treated as zero, and instruments for each period are collapsed), export and FDI status, and year and industry dummies, differenced. The instrument set for the levels equation consists of TFP, the log of innovation investment (for all, internal and external innovation), export and FDI status, a constant, and year and industry dummies.

Test results for the AR (equation 2) are quite large, between 0.509 and 0.607, and fail to reject the null hypothesis that the differenced residuals in period _t_ and _t_ -2 are uncorrelated; therefore, autocorrelation in levels might not be a serious concern in this system GMM measurement. However, the _p_ -value of the Sargan/Hansen test of over-identifying restrictions for GMM estimators is close to zero, rejecting the null hypothesis that instruments are exogenous. This might have been caused by the fact that the instrument is exactly identified. The difference-in-Sargan/Hansen test fails to reject similar results that these additional instruments are valid.

Overall innovation has a positive and significant effect on TFP, as seen in column (1) in table 9. The coefficient of internal innovation is statistically insignificant, while the coefficient of external innovation is positive and statistically significant in column (2). Lastly, using both sources of innovation at the same time (synergy) does not have a significant effect on TFP. These results are also consistent with the results of table 5, which support innovation effects on final output via TFP.

# **6. Conclusion**

In this paper, I first identified the determinants of innovation investment and analyzed whether those determinants are different for internal and external sources of innovation for a large number of representative samples of Tunisian manufacturing firms from 1997 to 2007.

As in previous literature on determinants of innovation, my estimation results showed that in Tunisia, larger firms that pay higher wages and participate in exports are more likely to invest in innovation. Meanwhile, having more technicians and a larger market share are 52
24

# **Literature**

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- Beck, T. , 2005, Learning Lessons from Disaster Recovery: The Case of Bangladesh, _Disaster Risk Management Working Paper Series No. 11_ : World Bank: Washington D.C.

- Carter, Michael R. and Christopher, Barrett, 2006, The economics of poverty traps and persistent poverty: An asset‐based approach, _Journal of Development Studies_ , 42‐ 2:178 — 199.

Cavallo, Eduardo and Ilan, Noy, 2009, The Economics of Natural Disasters, Inter American Development Bank, Working Paper 124.

- Christiaensen, Luc, and Kalanidhi, Subbarao, 2005, Towards an Understanding of Household Vulnerability in Rural Kenya, _Journal of African Economies_ , 14‐4: 520‐558.

- Christiaensen, Luc, Nguyen Van Viet, and Ngo Tien Gian, 2009, Natural Hazards Events, Risks, and Vulnerability to Poverty, mimeographed.

- CIA (Central Intelligence Agency).  1972.  World Databank II.  Washington:  Central Intelligence Agency.

- CIESIN (Center for International Earth Science Information Network), Columbia University; IFPRI (International Food Policy Research Institute); The World Bank; and CIAT (Centro Internacional de Agricultura Tropical). 2004a. "Global Rural‐Urban Mapping Project (GRUMP), Alpha Version: Settlement Points". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw.  Downloaded March 17, 2005.

- CIESIN (Center for International Earth Science Information Network), Columbia University; International Food Policy Research Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004b. "Global Rural‐Urban Mapping Project (GRUMP), Alpha Version: Urban Extents". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw.  Downloaded December 22, 2005.

- Costanza, R., and J., Farley, 2007, Ecological Economics of Coastal Disasters: Introduction to the Special Issue, _Ecological Economics_ , 63(3): 249‐253.

- Dartmouth Flood Observatory, 2008, Dartmouth Flood Dataset, Downloaded from ~

- <u>http://www.dartmouth.edu/ floods/Archives/index.html.</u>

- Delavande, A., X., Gine, and D., McKenzie, 2010, Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence, _Journal of Development Economics_ , forthcoming

- De Smith, Michael J., Michael F. Goodchild, and Paul A. Longley.  2007. _Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools_ , 2d ed.  Leicester, UK: Matador.  Web version available at http://www.spatialanalysisonline.com/.
**Table 9. Adequacy regressions with instrumented consumption expenditures**

|||**S**|**ubjective ad**|**equacy of:**|||
|---|---|---|---|---|---|---|
|**Isolation**|**Food**|**Clothing**|**Housing**|**Schooling **|**Health care**|**Income**|
|Travel time to local markets (log)|-0.09437|-0.07013|-0.13636|-0.22105|-0.23553|-0.03827|
||(2.64)**|(1.80)|(3.37)**|(5.02)**|(5.77)**|(0.92)|
|Urban population within 2 hrs travel time|1.00991|1.01245|1.84166|1.19979|1.62492|1.04121|
||(4.42)**|(4.19)**|(6.87)**|(4.12)**|(5.99)**|(4.21)**|
|Population Density (per sqkm)|0.00032|0.00021|-0.00055|-0.00002|0.00013|-0.00026|
||(2.00)*|(1.24)|(3.18)**|(0.13)|(0.72)|(1.58)|
|**Household consumption and controls**|||||||
|<br>Consumption expenditures (log)|1.71018|1.78895|1.29761|1.76904|0.77670|1.39914|
||(4.58)**|(4.35)**|(2.88)**|(3.90)**|(1.73)|(3.27)**|
|Residuals from instrumenting regression|-1.27176|-1.37106|-1.15007|-1.57043|-0.66373|-1.13087|
||(3.40)**|(3.30)**|(2.54)*|(3.46)**|(1.47)|(2.63)**|
|**Other regressors**||as in Table|6 but not sho|wn here to sa|ve space||
|<br>**Likelihood ratio test of independence betwe**|**en the selectio**|**n and satisfa**<br>|**ction equati**<br>|**ons**<br>|||
|Chi2(1) test-statistic|19.05|2.16|0.27|2.45|1.68|0.94|
|p-value|0.00|0.14|0.60|0.12|0.20|0.33|
|**Overidentification test for consumption expe**|**nditures**||||||
|Sargan statistic|3.82|1.87|2.34|3.03|3.01|2.13|
|p-value|0.58|0.87|0.80|0.70|0.70|0.83|

Estimator is Heckman (selection) probit. Absolute value of t statistics in parentheses: * significant at 5%; ** significant at 1% The corresponding selection regressions are not shown to save space. Overidentification tests are computed using OLS.
funded R&D. This has several important advantages. First, it encourages enterprises to start conducting more research. As the innovation survey shows, most Latvian enterprises are not particularly active in this regard. This program can help alter the status quo. Second, it encourages enterprises to find commercial outlets for the governmentfunded research which they conduct. This, in turn, will help to stimulate high tech startups and spin-offs. In the US, the government organizes special seminars to teach firms how to apply for SBIR grants. A similar training program would not only help Latvian enterprises apply for a Latvian SBIR program but it could also be tailored to help them apply for various EU programs as well.

If the government is interested, the World Bank could ask the US National Science Foundation (NSF) to help design this component. (See Annex 8 for further details of the SBIR program.)

**Figure 32 From Academy to Industry**

<!-- Start of picture text --> Reaserch Profit Barrier<br>Profit<br>Enthropy of Knowledge<br><!-- End of picture text -->

**RECOMMENDATION 9.  Establish a fund of funds, similar to Yozma in Israel, Sitra in Finland, or SBIC in the US to help attract venture capital to Latvia.**<sup>52</sup> While

> 52 For details of Sitra’s activities, see <u>http://www.sitra.fi/eng/index.asp?MM=1&DirID=62</u> .  Yozma’s venture capital activities are described in http://www.yozma.com/home/ .  For a general overview of the venture capital industry see, Martin Kenney, Kyonghee Han, and Shoko Tanaka. “Venture Capital Industries in East Asia,” Report prepared for the World Bank, 2003.    Also note that the Netherlands established a matching grant program for seed capital investments in the biotech sector.  Details of this program are available at www.biopartner.nl . This Dutch program is administered for the Government by the Zernike Group whose website address is given in footnote 13 above.  Many US states established similar matching grant programs.  One interesting program is organized by the State of Maryland. Details of the Maryland Venture Capital Trust can be found at <u>http://www.mdarchives.state.md.us/msa/mdmanual/25ind/html/76vent.html</u> .  In addition to the Yozma Fund, Israel recently established a Seed Fund whereby the government matches investments in a start-

62
simultaneity between infrastructure provision and growth, and the possibility of multiple (hence indirect) channels of influence between infrastructure and productivity improvements.

This paper explores these possibilities utilizing panel data for South Africa over the 1970-2000 period, and a range of 19 infrastructure measures. Utilizing a number of alternative measures of productivity, the prevalence of ambiguous (countervailing signs) results, with little systematic pattern is also shown to hold for our data set in estimations that include the infrastructure measures in simple growth frameworks.

We demonstrate, however, that controlling for potential endogeneity of infrastructure in estimation robustly eliminates virtually all evidence of possible overinvestment in infrastructure.

Indeed, controlling for the possibility of endogeneity in the infrastructure measures renders the impact of infrastructure capital not only positive, but of economically meaningful magnitudes.

These findings are invariant between the direct impact of infrastructure on labor productivity, and the indirect impact of infrastructure on total factor productivity.

22