rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Trends in Capital Income Shares

||ICT S|hare|Non-IC|T Share|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Ann. Trend Growth (%)|3.197***|3.197***|0.0534|0.0534|
||(0.444)|(0.233)|(0.319)|(0.0700)|
|Industry FEs||yes||yes|
|Observations|855|855|855|855|

_Notes:_ The table shows regressions of 100 times the log ICT and non-ICT shares on a time trend for an annual panel of 19 broad sectors over the period 19682012. Columns 1 and 3 report pooled regressions while columns 2 and 4 condition on a complete set of fixed effects for 19 broad sectors. Sector specific income shares are based on the BEA fixed asset accounts and the BEA’s estimates of sector specific value added and constructed as in Eden and Gaggl (2015). Standard errors are HAC robust and significance levels are indicated by * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, and *** _p <_ 0 _._ 01.

3 and 4 illustrate that this finding does not change after we control for a complete set of fixed effects. In fact, we find that the levels of ICT intensity vary vastly across the various industries, but the differential trends in ICT and non-ICT income shares are predominantly a within industry phenomenon.

# 4. A Calibrated Production Function

This section lays out a procedure for calibrating an aggregate production function based on observed trends in income shares. The underlying assumption is that long-run trends in the _relative_ income shares of ICT capital, routine- and non-routine labor are driven by the interactions between labor and ICT capital.

Using lower case letters to indicate variables expressed in per-worker terms, we assume an aggregate production function given by: where _y_ is output, _kn_ is non-ICT capital and _x_ is an input produced by routine inputs ( _xr_ ) and 22
distant family members and have di¤erent impacts on household expenditures at origin. In a study on South Africa, Collinson (2003) shows that employed men remit 25 percent less than employed women. Regarding the determinants of migration, it is also argued that women and men do not respond to push and pull factors with the same intensity. Social networks are usually seen as more important for women who rely more strongly on relatives and friends for help, information, protection and guidance at destination. Without a gendered assessment of the brain drain, it is obviously impossible to conduct a complete analysis of these issues.

In this paper, we build on the DM06 data set, update the data using new sources, homogenize 1990 and 2000 concepts, and introduce the gender breakdown. We provide revised stocks and rates of emigration by level of schooling and gender. Our gross data reveal that the share of women in the skilled immigrant population increased in almost all OECD destination countries between 1990 and 2000. Consequently, for the vast majority of source regions, the growth rates of skilled female emigrants were always bigger than the growth rates obtained for unskilled women or skilled men. The evolution was particularly in the least developed countries. This feminization of the South-North brain drain mostly re‡ects gendered changes in the supply of education. We show that the cross-country correlation between emigration stocks of women and men is extremely high (about 97 percent), with women’s numbers slightly below men’s ones. However, these skilled female migrants are drawn from a much smaller population. Hence, in relative terms, the correlation in rates (88 percent) is much lower than in stocks. On average, women’s brain drain is 17 percent above men’s. This gender gap in skilled emigration rate is strongly correlated with the gender gap in educational attainment of the source population, re‡ecting unequal access to education. Although causality is hard to establish, it is very likely that equating men and women’s educational attainment at origin would strongly reduce the gender gap in skilled migration.

The remainder of this paper is organized as follows. Section 2 provides a brief survey of existing data sets on the brain drain. Section 3 then describes our methodology and presents the measure of emigrant stock in 1990 and 2000. Section 4 analyzes emigration rates. Section 5 summarizes the main results.

# 2 Background

The ...rst serious e¤ort to put together harmonized international data set on migration rates by education level was by Carrington and Detragiache (1998, 1999). They used US 1990 Census data and other OECD statistics on international migration to construct estimates of emigration rates at three education levels for 61 developing countries (including 24 African countries). Adams (2003) used the same technique to build estimates for 24 countries in 2000. Although Carrington and Detragiache’s study initiated new debates on skilled migration, their estimates su¤er from a number of limitations. The two most important ones were: i) they transposed the education 4
||Mean|Mean|Mean|Mean|Year|Source|
|---|---|---|---|---|---|---|
|Rural|23.8|22.2|22.3|25.0|2016|UNHS|
|Urban|9.0|9.6|10.2|9.6|2016|UNHS|

# 6.2. Empirical evaluation of the longitudinal estimates

Panel surveys produce net change and gross change estimates (Section 2.2.2). Gross change offers valuable insights into net change, so it is important that the cross-sectional estimates achieved by transition matrices are consistent with the estimates obtained with the overall UNPS 2015/16 sample.

Table 6.5 shows the dynamics of employment across the 2009/ 2015 and 2013/2015 panels. In particular, the first three columns report the employment status in 2015 with respect to the status in 2009. Only individuals present in both 2009 and 2015 are represented in the table. The last three columns show the same statistics for those individuals in 2015 who also responded to the survey in 2013.

Focusing on the 2009/2015 estimates, we note that all estimators produce upward estimates with respect to the estimates presented in table 6.2. This result suggests that the individuals who left across the years – characterizing most of the attrition – were largely unemployed. The three estimators indicated that the panel comprised 78.6%, 77.6%, and 81.4% employed persons. The refresh operation rebalanced the panel. Focusing on the cross-sectional estimates given by the 2013/2015 panel, 77.4% of individuals were employed according to the unweighted estimator, 76.7% for the current UNPS estimator, and 80.8% for the calibrated GWSM base weight estimator.

We can state that the last estimator produces more stable and consistent estimates when considering different sub-samples, i.e., the 2009/2015 and the 2013/2015 panel and the entire sample observed in the 2015 with a percentage relative variance (ratio of standard error to mean) among the the three estimates equal to 0.8%, The UNPS estimator has 1.0% of relative error and the unweighted estimator 1.2% of standard error.

Table 6.5. Employment transition matrix estimates by unweighted, current UNPS, and calibrated GWSM base estimators

||Unweighted|2009/1<br>UNPS|5<br>Calibrated<br>GWSM base|2<br>Unweighted|013/15<br>UNPS|Calibrated<br>GWSM<br>base|
|---|---|---|---|---|---|---|
|Unemployed in|||||||
|2009 (or 2013)<br>and 2015|5.1|5.4|3.7|11.6|12.1|9.3|
|Unemployed in<br>2009 (or 2013) and<br>employed in 2015|6.0|5.6|5.8|8.5|8.1|7.6|
|Employed in 2009|||||||
|(or 2013) and 2015|72.6|72.0|75.6|68.9|68.6|73.2|
|Employed in 2009<br>(or 2013) and<br>unemployed in<br>2015|16.3|16.9|14.9|11.0|11.2|9.9|

29
# **6.2 Predicted changes in overfishing status**

Today, 16 of the 18 FAO Regions experience overfishing (Figure A11). We find that a 10% reduction in Chinese distant water fuel subsidies would not eliminate overfishing in any FAO Region in the sense of reducing _F/Fmsy_ from above 1 to 1 or below (Figures 6 and 7). At best, our 10% reform scenario could reduce overfishing by up to 14% in some regions (e.g., the Southeast Pacific [FAO Region 87]). In contrast, consider our high reform scenario where Chinese distant water fuel subsidies are reduced by 50%. This would move two FAO Regions (the Southeast Pacific [87] and the Antarctic Atlantic [48]) below the overfishing threshold of _F/Fmsy_ = 1 and result in a further reduction in overfishing status in other regions by up to 20% (East Central Pacific [77] and West Central Pacific [71]). Figure 6 shows the changes in overfishing status by FAO Region for the three scenarios of subsidy reduction. We predict the largest decreases in overfishing in FAO Regions in the Pacific Ocean, where Chinese vessels account for large shares of fishing (Figure A10 and Table A5). Using the stock-level data, we can also calculate the proportion of stocks that would be subject to overfishing under each scenario. Currently, 64.5% of stocks (N = 3,042 stocks) are estimated to experience overfishing (Costello et al., 2016). A 10% reduction in fuel subsidies to China’s distant water fishing fleet would see this fraction decrease to 63.8%, while a more ambitious 50% reduction in subsidies to China’s distant water fishing fleet would result in 61.6% of stocks experiencing overfishing. While few stocks would no longer experience overfishing (N = 29 to 132 stocks [0.65% - 2.8% of total], depending on the level of the subsidy reduction), these stocks account for much of the reported catch from stocks currently experiencing overfishing,<sup>31</sup> and thus translate into large reductions in FAO Region-level overfishing status.

While overfishing status is an important measure of sustainability, it may also be useful to understand the implications of these reductions on fish stocks themselves. To get a backof-the-envelope estimate of the effect on fish stocks, we derive a new elasticity. A common model used to assess fishery sustainability is the Pella-Tomlinson model, for which the steady

> 31In the 50% fuel subsidy reduction scenario, the 132 stocks that cease experiencing overfishing account for 7.97% of total biomass and 6.56% of total catch derived from stocks currently experiencing overfishing.

36
- 6 -

There are many potential reasons for this poor relationship between infrastructure and agricultural growth. First, aggregation of agricultural outputs may not be suitable for addressing a question on the infrastructure impact on farming productivity. One of the most traditional approaches in this area is to estimate an aggregate production function of agriculture (e.g., Antle, 1983; Mundlak _et al_ ., 2002; Mundlak _et al_ ., 2004; Bravo-Ortega and Lederman, 2004). However, such estimates may not be straightforward to be interpreted from the governmental policy point of view, unless only one farm product is sufficiently dominant in the economy.<sup>8</sup> Each agricultural product must have a unique production function and thus require different inputs. For the same reasons, it is also unlikely that different commodities would benefit identically from a particular type of public infrastructure.

Second, the conventional infrastructure variables may also be inappropriate in the sense that they do not represent agriculture-specific infrastructure. For instance, agricultural growth is unlikely to be stimulated even if water access is improved. This is because the majority of agricultural activities are concentrated on rural areas while water access mostly benefits urban dwellers.<sup>9</sup> Most direct agriculture-related “infrastructure” may be rural roads and irrigation if applicable (World Bank, 2005; Williams _et al_ ., 2006; Buys _et al_ ., 2006; Broadman, 2007). Without effective access to the input and output markets, agriculture production could not be viable. In this context, the most relevant infrastructure proxy would be the “rural access index” (Roberts _et al_ ., 2006). Irrigation water is a major input to traditional staple crops; a good proxy may be the share of irrigated land to total cropland, which is available by _World Development Indicators_ ( _WDI_ ) or _FAOSTAT_ .

Infrastructure that farmers indirectly rely on differs from commodity to commodity. If inputs and outputs are mass-transported, railways may be an essential infrastructure. If a modern

> 8 The production function estimation approach _is_ advantageous to investigate the (very) long-run overall productivity growth in agriculture, e.g., total factor productivity, with relatively less constrains on data requirement.

> 9 Even by the traditional aggregate infrastructure proxies, overall growth would be affected differently. Calderón and Servén (2004) show that growth is promoted especially by telecommunications network development. This is basically consistent with Fay and Yepes (2003), which investigate the reverse direction.
**Chart 6: Benefit incidence curves**

<!-- Start of picture text --> Estonia Lithuania Slovakia Poland<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>Decile Decile Decile Decile<br>Czech Republic Hungary Slovenia<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>Decile Decile Decile<br>60 60 60 60<br>40 40 40 40<br>benefits share 20 benefits share 20 benefits share 20 benefits share 20<br>0 0 0 0<br>60 60 60<br>40 40 40<br>benefits share 20 benefits share 20 benefits share 20<br>0 0 0<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions 23. The redistributive impacts of the tax and benefits systems by country are summarized in Table 4, which illustrates the extent of income redistribution attributable to its tax and benefit system.<sup>14</sup>

**Table 4: Impact of tax and benefit systems on income inequality in EU countries**

|**Country**|**G**<br>**Pre-tax-benefit**<br>**incomes**|**ini coefficient o**<br>**Post tax**<br>**incomes**|**f income inequality**<br>**Post tax-benefit**<br>**incomes**|**Difference in pre**<br>**/ post tax Ginis**|**Ratio of pre-tax**<br>**to post-tax /**<br>**benefit Gini**|
|---|---|---|---|---|---|
|**EU8 countries:**||||||
|Slovenia|0.471|0.369|0.239|0.232|1.97|
|Czech Republic|0.485|0.340|0.249|0.236|1.95|
|Hungary|0.568|0.429|0.324|0.244|1.75|
|Slovakia|0.474|0.339|0.279|0.195|1.70|
|Poland|0.558|0.440|0.329|0.229|1.70|
|Lithuania|0.540|0.413|0.349|0.191|1.55|
|Estonia|0.499|0.391|0.329|0.170|1.52|
|**EU15 countries:**||||||
|Sweden|0.482|0.335|0.240|0.242|2.01|
|Denmark|0.481|0.316|0.241|0.240|2.00|
|Netherlands|0.503|0.330|0.258|0.245|1.95|
|Austria|0.492|0.336|0.254|0.238|1.94|
|Finland|0.507|0.372|0.264|0.243|1.92|
|Belgium|0.524|0.390|0.279|0.245|1.88|
|Luxembourg|0.486|0.362|0.276|0.210|1.76|
|United Kingdom|0.526|0.376|0.324|0.202|1.62|
|Ireland|0.520|0.386|0.322|0.198|1.61|
|Spain|0.478|0.372|0.310|0.168|1.54|

_Source:_ World Bank staff calculations based on data from the 2006 EU Survey of Income and Living Conditions 14 Two important caveat need to be borne in mind with regard to the analysis presented here. First, this tax and benefit incidence analysis does not take into account general equilibrium considerations arising from changes in the marginal tax or benefits rates. Second, even though tax and benefit systems play an important redistributive role, it is important to bear in mind that they impose efficiency costs too (e.g. through lowering labor force participation rates, greater informality in the economy, etc.) 13
24

**Table 2: Treatment effects on poor cases as a proportion of total output**

|Dependent variable: Poor cases as a|proportion o|f total cases|||
|---|---|---|---|---|
|I|II|III|IV|V|
|Treatment: Non-poor bonus<br>-0.214***|-0.210***|0.286|0.294*|0.314|
|(0.05)|(0.05)|(0.17)|(0.16)|(0.19)|
|Treatment: Equal bonus<br>-0.030|-0.025|0.300*|0.281*|0.280|
|(0.07)|(0.07)|(0.17)|(0.16)|(0.16)|
|Treatment: Poor bonus<br>0.070|0.068|0.203|0.198|0.236|
|(0.06)|(0.05)|(0.18)|(0.19)|(0.21)|
|Pro-poor preference|0.189*|0.584***|0.587***|0.619***|
|Donation topoor school/total donation|(0.09)|(0.16)|(0.15)|(0.17)|
|Interaction: Non-poor bonus X||-0.775***|-0.804***|-0.833***|
|Pro-poor preferences||(0.25)|(0.23)|(0.27)|
|Interaction: Equal bonus X||-0.505**|-0.486**|-0.488**|
|Pro-poor preferences||(0.23)|(0.22)|(0.22)|
|Interaction: Poor Bonus X||-0.210|-0.206|-0.274|
|Pro-poorpreferences||(0.26)|(0.27)|(0.30)|
|Effort cost ratio (ability round)|||0.245**|0.256**|
|Score with poor cases / Total score|||(0.10)|(0.11)|
|Time cost ratio (ability round)|||0.209|0.220|
|Time spent withpoor cases/Total time spent|||(0.22)|(0.22)|
|Occupation: Midwife|||-0.004|0.013|
||||(0.04)|(0.05)|
|Occupation: Doctor|||0.197***|0.176**|
||||(0.05)|(0.06)|
|Occupation: Other|||0.018|0.041|
||||(0.05)|(0.05)|
|Female||||-0.054|
|1 = Female||||(0.04)|
|Age (years)||||-0.002|
|||||(0.00)|
|Income (in CFA)||||0.000|
|||||(0.00)|
|Constant<br>0.422***|0.299***|0.042|-0.204|-0.175|
|(0.04)|(0.09)|(0.12)|(0.13)|(0.23)|
|Pseudo R2<br>0.076|0.086|0.110|0.123|0.130|
|Log Likelihood<br>-167.7|-165.7|-160.5|-157.7|-156.2|
|P<br>0.000|0.000|0.000|0.000|.|
|Observations<br>379|379|379|379|379|

````Notes: OLS regressions. Dependent variable is the proportion of output that are poor cases (number of poor divided by total cases). * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by day) in parentheses. The specification in column V also includes controls for current state of personal finances, confidence in payment to schools, and interest in task. None of these are significant.
26 Anderson, K., M. Kurzweil, W. Martin, D. Sandri and E. Valenzuela. 2008.

- “Measuring Distortions to Agricultural Incentives, Revisited.” _World Trade Review_ 7(4):1–30.

Anderson, K. and E. Valenzuela. 2008. _Global Estimates of Distortions to Agricultural Incentives, 1955 to 2007_ . Database available at www.worldbank.org/agdistortions.

Feenstra, R.C. 1995. “Estimating the Effects of Trade Policy” in _Handbook of_

- _International Economics_ , vol. 3, G.N. Grossman and K. Rogoff (Eds.), Amsterdam: Elsevier.

- Harberger, A.C. 1959.  “Using the Resources at Hand More Effectively.” _American Economic Review_ 49(2):134–46.

- Kee, H.L., A. Nicita and M. Olearraga. 2008.  “Import Demand Elasticities and Trade Distortions.” _Review of Economics and Statistics_ 90(4):666–82 _._

- Kee, H.L., A. Nicita and M. Olearraga. 2009.  “Estimating Trade Restrictiveness Indexes.” _Economic Journal_ 119(534):172–99 _._

- Lloyd, P.J., J.L. Croser and K. Anderson. 2009.  “Global Distortions to Agricultural Markets: New Indicators of Trade and Welfare Impacts, 1955 to 2007.” Policy Research Working Paper xxxx, World Bank, Washington DC, March.

Lloyd, P.J. 1974. “A More General Theory of Price Distortions in an Open Economy.” _Journal of International Economics_ 4(4):365–86.
**Table 7. Investment to Capital Regressions, Listed Firms data**

||(1)<br>IK|(2)<br>IK|(3)<br>IK|(4)<br>IK|(5)<br>IK|(6)<br>IK|(7)<br>IK|
|---|---|---|---|---|---|---|---|
|Log of net sales|0.04***<br>(0.001)|||||||
|Volatility of Sales||0.02<br>(0.813)||||||
|Sales Growth|||0.17*|||||
||||(0.075)|||||
|Total Debt to||||||||
|Assets||||0.06||||
|||||(0.672)||||
|Interest Rate|||||0.21<br>(0.141)|||
|Gross Margin||||||-0.01<br>(0.930)||
|Net Income|||||||0.44<br>(0.152)|
|year==2002|-0.13|-0.09|-0.21***|-0.16**|-0.13|-0.17**|-0.16*|
||(0.143)|(0.262)|(0.006)|(0.044)|(0.225)|(0.036)|(0.059)|
|year==2003|-0.16**|-0.21**|-0.21***|-0.22***|-0.16**|-0.19***|-0.17**|
||(0.039)|(0.022)|(0.001)|(0.006)|(0.038)|(0.008)|(0.016)|
|year==2004|-0.20**|-0.25**|-0.22***|-0.27***|-0.14|-0.19**|-0.18**|
||(0.019)|(0.020)|(0.003)|(0.008)|(0.126)|(0.026)|(0.029)|
|year==2005|-0.18**|-0.15**|-0.17***|-0.17**|-0.16*|-0.15**|-0.16**|
||(0.018)|(0.034)|(0.009)|(0.018)|(0.057)|(0.030)|(0.030)|
|year==2006|-0.20***|-0.17**|-0.20***|-0.17**|-0.15|-0.18**|-0.19**|
||(0.009)|(0.035)|(0.007)|(0.041)|(0.113)|(0.018)|(0.016)|
|year==2007|-0.21***|-0.17**|-0.22***|-0.20**|-0.19*|-0.18**|-0.18**|
||(0.009)|(0.031)|(0.006)|(0.028)|(0.061)|(0.026)|(0.035)|
|year==2008|-0.12|-0.09|-0.15|-0.12|-0.09|-0.12|-0.15|
||(0.186)|(0.323)|(0.104)|(0.239)|(0.404)|(0.200)|(0.123)|
|Constant|-0.06|0.27***|0.29***|0.29***|0.24***|0.29***|0.26***|
||(0.645)|(0.001)|(0.000)|(0.000)|(0.008)|(0.000)|(0.000)|
|Observations|221|226|215|231|204|215|217|
|R-squared|0.142|0.039|0.090|0.038|0.043|0.031|0.049|

39
# **TABLE A25:  State Department’s Trafficking in people Report (TPR)**

## **A25: State Department's Trafficking in People Report**

http://www.state.gov/g/tip/rls/tiprpt/ Initiated in 2001, the Trafficking in People Reports cover "severe forms of trafficking in persons" defined as: (a) sex trafficking in which a commercial sex act is induced by force, fraud, or coercion, or in which the person induced to perform such act has not attained 18 years of age; or (b) the recruitment, harboring, transportation, provision, or obtaining of a person for labor or services, through the use of force, fraud or coercion for the purpose of subjection to involuntary servitude, peonage, debt bondage, or slavery.

In preparing the reports, the Department of State in Washington asked for information from US embassies and consulates around the world. The embassy reports reflect discussions with host governments, local nongovernmental organizations, immigration officials, police, journalists, and victims, in addition to reviews of government, press, and NGO reports. The State Department's Bureau for International Narcotics and Law Enforcement Affairs; the Bureau of Democracy, Human Rights, and Labor; the regional bureaus; and the Office of the Legal Adviser, with assistance from the intelligence community, reviewed reporting from U.S. embassies and consulates overseas. The Department also reviewed information from other sources including, but not limited to, UNICEF, UNHCR, the International Organization for Migration, Human Rights Watch, Amnesty International, the Protection Project, media reports and other U.S. Government agencies.

In the table below we list the variables included in each of the governance indicators. We use data from 2001 to 2006 Reports (which refer to years 2000-2005).

|**Table A25: State Department(14**|**9 developed and developing count**|**ries)**||||||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**<br>NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**<br>NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Trafficking in People|X|X|X|X|X|..|..|
|**Control of Corruption**<br>NA|..|..|..|..|..|..|..|
|_Country coverage_|149|142|131|116|82|..|..|

76
# **TABLE 1: TEST FOR RANDOMIZATION**

Comparison of Ex-ante characteristics of principal applicants in successful and unsuccessful ballots

||Successful<br>Ballots<br>Sample<br>APPLI|Unsuccessful<br>Ballots<br>Means<br>CANTS|T-test<br>of equality<br>of means<br> p-value|
|---|---|---|---|
|Age|33.6|33.7|0.91|
|Years of schooling|11.9|11.5|0.37|
|Proportion male|0.55|0.51|0.52|
|Proportion born on Tongatapu|0.75|0.79|0.54|
|Proportion who had been to NZ before 2000|0.39|0.35|0.63|
|Proportion who are married|0.60|0.62|0.77|
|Height|171.6|169.3|0.16|
|Proportion selling fish in 2003|0.03|0.06|0.40|
|Proportion selling crops in 2003|0.22|0.26|0.52|
|Income in 2003/before moving|103.7|88.0|0.32|
|Proportion with the following family members||||
|living in NZ at time of last application:||||
|Father/Father-in-law|0.38|0.44|0.45|
|Mother/Mother-in-law|0.40|0.35|0.46|
|Brother/Brother-in-law|0.72|0.71|0.78|
|Sister/Sister-in-law|0.64|0.60|0.63|
|Aunt or Uncle|0.65|0.55|0.17|
|**Total Sample Size**|**120**|**78**||

Average first quarter 2005 exchange rate of 1 Pa'anga = 0.729 NZ Dollars used for comparing mean incomes 41