rafmacalaba/datause-encoder-data huggingface dataset:
should expect a broad-based skill set, and the desire to juggle many tasks simultaneously, to be more important in a developing country context.

All of these factors collectively suggest that (self) selection of entrepreneurs from the population may differ in developing and developed countries. Unfortunately, the manner in which the entrepreneurial skill set has been assessed across countries is rather ad hoc. This makes comparison of the factors associated with selection into entrepreneurship difficult to compare internationally.

The combined theoretical and empirical literature therefore suggests a rich set of factors differentiating entrepreneurs from wage workers in both unconstrained and constrained environments. We draw on this literature to identify the relevant factors for placing the own account workers on the wage worker – entrepreneur scale. Our interest in undertaking this exercise is practical: From a policy perspective, should we focus on the own account workers as a potential source of job growth? Or should we instead focus on increasing incomes of the self employed where possible, in the interest of poverty alleviation, while looking elsewhere for the generation of jobs which these own account workers will fill?

# **Data and empirical approach**

We use data from a series of surveys carried out in Sri Lanka between 2005 and 2007. The first is a survey of 618 own account workers, selected from three districts in south and southwestern Sri Lanka, which we refer to as the Sri Lankan Microenterprise Survey (SLMS). The own account workers were surveyed quarterly between April 2005 and April 2007, and then again in October 2007. Each survey included questions on the operating performance of the firms during the month preceding the survey, and questions on additional topics which varied from quarter to quarter. For example, in the July 2005 survey, we played a lottery game from which we measure attitudes toward risk. In the January 2006 survey, we recorded data on the labor history of respondents; and in the October 2007, we gave the respondents a Raven progressive non-verbal test of reasoning. The survey questions relevant to the analysis here are discussed in more detail in the following section.
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Tuition is subtracted from the private costs in this calculation because it is merely a payment by the student of part of the institutional costs, and that part of costs would otherwise be double-counted. Notice also that since private costs are net of scholarships, the latter are counted among the institutional costs.

Private Costs Information on out-of-pocket costs is available from the CILSS for students who are currently enrolled or were enrolled in the year preceding the survey. Those costs include tuition payments, expenditures for books and other school materials, transportation to and from school, food and lodging at school, uniforms, and miscellaneous contributions. The survey also recorded the amounts of any scholarships received. Table 21 indicates that annual out-of-pocket costs are highest for post-secondary VTE. They are 26% higher than those for teacher training, which in turn are 13% above secondary VTE costs. However, scholarships received compensate 69% of these costs for post-secondary level students, 26% for teacher trainees, but only 14% for secondary level students. As a result the net out-of-pocket costs are lowest for post-secondary students - they are only about half the costs for secondary level VTE students.

Opportunity costs consist of the wages foregone as a result of attending a VTE school or program. They were calculated on the basis of the earnings equation reported in Table 19 which corrects for selection
Customers who score high earned two smiley faces on their statements. “Good” conservation got a single smiley face. (Kaufman, 2009) The utility company conducted an initial assessment of this new strategy after using it for six months. The assessment found “that customers who received the personalized report reduced energy use by 2 percent more than those who got standard statements” ( _ibid._ ). Using various forms of competition among households and groups and feedback on who is doing the best at reducing energy use is a strategy for reducing emissions that is increasingly being adopted by college campuses, small cities, and utility firms around the country.

Contemporary psychological studies have found that framing problems related to resource use in a social context does affect actions. Schultz et al. (2007) and Mumford (2007) have conducted studies finding that messages containing social references are more effective in changing behavior than messages stressing factual information. For example, statements saying that “x% of guests in this hotel recycle towels,” instead of specifying the amount of water saved when one recycles, lead to increased levels of recycling.

# **Complex, Multi-Level Systems to Cope with a Complex, Multi-Level Problem**

Given that the recognition of the danger of climate change among citizens and public officials is still relatively recent, and given the debates about who is responsible for causing the problem and for finding solutions, one cannot expect that an effective polycentric system will be constructed in the near future. But given the slowness and conflict involved in achieving a global solution, recognizing the potential of building even more effective ways of reducing energy use at multiple levels is an important step forward.

Further, one of the important strategies for reducing the level of CO2 in the atmosphere is developing more effective policies for protecting ecosystem services—particularly those related to carbon sequestration. Developing effective and adaptive programs, however, requires 38
education seems to serve graduates better in entering wage employment (and the formal sector in general), compared with secondary education. This finding is more pronounced in Bolivia (72 percent of participation in wage employment compared to 40 percent for those with secondary education), China (86 compared to 70 percent) and Vietnam (57 compared to 36 percent).

Table 3: Returns to Schooling by Country <u>(male wage earners, 25-65 years)</u>

|_Country_|_Average return (%)_<br>_(OLS)_|_90_<sup>_th_</sup>_-10_<sup>_th_</sup>_quantile_|
|---|---|---|
|Cambodia|38.3|-44.6|
|China|12.1|-4.7|
|Indonesia|11.4|-0.9|
|Mongolia|8.5|-4.5|
|Philippines|11.6|-3.3|
|Singapore|11.9|4.3|
|Thailand|15.2|-5.3|
|Vietnam|7.2|-4.4|
|_East Asia Mean_|_14.5_|_-7.9_|
|_East Asia mean excluding Cambodia_|_11.1_|_-2.7_|
|Argentina|11.0|4.2|
|Bolivia|10.3|6.2|
|Brazil|15.7|6.4|
|Chile|12.0|7.0|
|Colombia|10.4|5.5|
|Guatemala|12.6|5.3|
|Mexico|11.3|2.4|
|Venezuela|9.9|3.3|
|_Latin America Mean_|_11.6_|_5.0_|

Note: Full results are presented in Annex 3 *Includes Polytechnic diplomas

# **Results**

Looking at returns to schooling for the average individual (from OLS regressions), the return to an additional year of schooling for males is higher in the East Asian countries under study (8 country average) at 14.5 percent, compared to the Latin American group of countries (11 percent), mainly due to the high returns in Cambodia (see Table 3). Generally speaking, returns are considerable for all countries, especially in Cambodia, Thailand and Brazil. In only three cases are the estimated returns less than 10 percent. The second column of Table 3 presents the differences in returns estimated for the bottom and top quantile. A negative entry implies that the returns are higher at the bottom of the distribution, meaning that education 12
**Table 1:** Comparing rebating mechanisms

||Total rebate|Margina|l rebate|
|---|---|---|---|
|||Intensity|Output|
|Type|_R_(_q, μ_)|_−Rμ/q_|_Rq_|
|LSR|_R_|0|0|
|ABR|_s_(_μ_0_q_0_−μq_)|_s_|_−sμ_|
|OBR|_τμbq_|0|_τμb_|
|IBOR|_z_(<br>_μ_ _−μ_)_q_|_z_|_z_(<br>_μ_ _−μ_)|
|IBER|_ρ_<br>_μ−μ_<br>_μ−μ_<br>_τμq_|_ρ_ <sup>2</sup><sup>_μ−_</sup><br>_μ_<br>_μ__~~−~~μ_<br>_τ_|_ρ_<br>_μ−μ_<br>_μ−μ_<br>_τμ_|

**Table 2:** Revenue-neutral rebating mechanisms

|Type|Scaling factor|Opportunity<br>cost of emissions|Output price|
|---|---|---|---|
|LSR|_R_<sup>_∗_</sup>=_τμ_<sup>_∗_</sup><br>LSR<sup>_q∗_</sup><br>LSR|_τ_|_c_(_μ_<sup>_∗_</sup><br>LSR<sup>) +</sup><sup>_τμ∗_</sup><br>LSR|
|ABR|_s_<sup>_∗_</sup>=_τ_<br>_μ_<sup>_∗_</sup><br>ABR<sup>_q∗_</sup><br>ABR<br>_μ_0_q_0_−μ_<sup>_∗_</sup><br>ABR<sup>_q∗_</sup><br>ABR|_τ_<br>_E_0<br>_E_0_−E_<sup>_∗_</sup><br>ABR|_c_(_μ_<sup>_∗_</sup><br>ABR<sup>) +</sup><sup>_τμ∗_</sup><br>ABR<br>_E_0<br>_E_0_−E_<sup>_∗_</sup><br>ABR|
|OBR|_μ_<sup>_∗_</sup><br>_b_ <sup>=</sup><sup>_μ∗_</sup><br>OBR|_τ_|_c_(_μ_<sup>_∗_</sup><br>OBR<sup>)</sup>|
|IBOR|_z_<sup>_∗_</sup>=_τ_<br>_μ_<sup>_∗_</sup><br>IBOR<br>_μ__~~−~~μ_<sup>_∗_</sup><br>IBOR|_τ_<br>_μ_<br>_μ−μ_<sup>_∗_</sup><br>IBOR|_c_(_μ_<sup>_∗_</sup><br>IBOR<sup>)</sup>|
|IBER|_ρ_<sup>_∗_</sup><br>_μ__~~−~~μ_<sup>_∗_</sup><br>IBER<br>_μ−μ_<br>= 1|_τ_<br>_μ_<sup>_∗_</sup><br>IBER<br>_μ−μ_<sup>_∗_</sup><br>IBER|_c_(_μ_<sup>_∗_</sup><br>IBER<sup>)</sup>|

Figure 2 depicts the relative marginal incentives for reducing emission intensity—the opportunity cost to the individual firm of increasing emissions by one unit—given an emissions price and revenue-neutral rebating. For LSR and OBR, the opportunity cost of producing emissions is exactly equal to the emissions price, _τ_ . Proposition 4 revealed that revenueneutral IBOR, given an emissions price, incentivizes more intensity reductions than either ABR or IBER. Because ABR also drives additional output and emissions reductions compared with intensity-based rebating, ABR has fewer revenues to rebate, meaning that in revenue-neutral applications the equilibrium abatement subsidy may be smaller. Opportunity costs for IBER, as previously noted, are increasing with intensity. Although it is not possible to definitively rank equilibrium outcomes with ABR against IBER, for the purposes of illustration, in Figure 2 we have placed ABR slightly above (consistent with the parameterization of our subsequent numerical exercise).

16
# **References**

Austen-Smith, David and Roland G. Fryer. 2005. “An Economic Analysis of “Acting White”, _Quarterly Journal of Economics_ , 120(2): 51-583.

Bertrand, Marianne and Sendil Mullainathan. 2004. “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination”, _American Economic Review_ , 94(4): 991-1013.

Blau Francine D. and Lawrence M. Kahn. 2005. "Gender and Assimilation among Mexican Americans," National Bureau of Economic Research Working Paper 11512.

Bleakley, Hoyt and Aimee Chin. 2004. “Language Skills and Earnings: Evidence from Childhood Immigrants”, _Review of Economics and Statistics_ , 86(2):481-496.

Bleakley, Hoyt and Aimee Chin. 2007. “English Proficiency and Social Assimilation Among Immigrants: An Instrumental Variables Approach”, Center for Comparative Immigration Studies Working Paper 147, University of California, San Diego.

Bleakley, Hoyt and Aimee Chin. 2008. “What Holds Back the Second Generation? The Intergenerational Transmission of Language Human Capital among Immigrants.” _Journal of Human Resources_ , forthcoming.

Borjas, George J, Jeffrey Grogger and Gordon H. Hanson. 2006. "Immigration and African-American Employment Opportunities: The Response of Wages, Employment, and Incarceration to Labor Supply Shocks," National Bureau of Economic Research Working Paper 12518 Butcher, Kristin F. 1994. “Black Immigrants in the United States: A Comparison with Native Blacks and Other Immigrants”. _Industrial and Labor Relations Review_ , 47 (2): 265-284.

Card, David. 2001. "Immigrant Inflows, Native Outflows and the Local Labor Market Impacts of Higher Immigration." _Journal of Labor Economics_ , 19(1): 22-64.

Card, David. 2005. “Is the New Immigration Really So Bad?” _Economic Journal_ , 115(507):F300-F323.

Card, David and John DiNardo. 2000. “Do Immigrant Inflows Lead to Native Outflows”, _American Economic Review_ , 90(2): 360-367.

Card, David, John DiNardo, and Eugena Estes. 2000 **.** "The More Things Change: Immigrants and the Children of Immigrants in the 1940s, the 1970s, and the 1990s. **"** in George J. Borjas, editor, Issues in the Economics of Immigration. Chicago: University of Chicago Press.

18
# Table ls Trends in Real Basic Starting Salaries ia the Public Sector in Selected African Countries

|||An|nual Rate|of Growth (2)|
|---|---|---|---|---|
|Cou|ntry/Level|Period|Salary|RealGDP/Capita|
|Gha<br>|na<br>|1977-1983||-<br>4.8|
|2<br>5|Principal Secretary/Director<br>Office/Administrative<br>III/<br>Senior Works Superintendent||-30.8<br>-26.9||
|10|Messenger/Watchman/Laborer||-14.3||
|Mal|awi|1975-1983||1.2|
|S5|Undersecretary||- 5.3||
|<br>A3|<br>University Graduate||- 3.7||
|D6|Messenger||- 2.0||
|Nia|eria|1975-1983||-<br>2.1|
|17|Permanent Secretary||-13.9||
|8|University Graduate||-11.4||
|1|Unskilled Laborer||- 5.5||
|Sen|egal|1976-1984||- 2.6|
|A|With university degree||- 3.7||
|B|With secondary school diploma||- 2.1||
|E|No diploma||1.5||
|Sie|rra Leone|1975-1980||0.2|
||Deputy Secretary||- 9.1||
||Technical Officer||- 6.8||
||Messenger||0.7||
|Sud|an|1975-1983||-5.2a|
|4|Deputy Undersecretary||-14.4||
|9|University Graduate||-13.5||
|14|Secondary School Graduate||-13.3||
|18|Unskilled Worker||-12.3||
|Uta|nda|1976-1983||-3.2|
|Ul|Permanent Secretary/Under<br>||-34.8||
||Secretary||||
|US|Entering University Graduate<br>Group Employee||-33.3<br>-23.5||
|Zam|bia|1975-1983||-2.4|
|S3|Undersecretary||- 9.5||
|S12|Entering University Graduate||-10.6||
|S21|Lowest Salaried Employee<br>Laborer||- 2.3<br>- 1.6||
|a.|1975-1982.||||
|So|urce: Lindauer, Meesook and Suebs|aeng (1987); Ta|ble 3||
**Table 6: Instrumental Variable Regressions**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|ln (Tota<br>|l export)<br>|ln (Average<br>expo<br>|value of<br>rts)<br>|ln (Number<br>expor<br>|of product<br>ted)<br>|
||OLS|2SLS|OLS|2SLS|OLS|2SLS|
|Internet adoption<br>exporter (log)|0.157***|0.203***|0.0282|0.00174|0.129***|0.201***|
||(0.0209)|(0.0487)|(0.0193)|(0.0457)|(0.0112)|(0.0272)|
|Internet adoption<br>importer (log)|0.0395*|0.0429|0.0701***|0.154***|-0.0305***|-0.111***|
||(0.0214)|(0.0495)|(0.0193)|(0.0461)|(0.0114)|(0.0268)|
|GDP exporter (log)|0.561***|0.543***|0.420***|0.417***|0.141***|0.126***|
||(0.0325)|(0.0382)|(0.0291)|(0.0353)|(0.0176)|(0.0202)|
|GDP importer (log)|0.249***|0.242***|0.396***|0.365***|-0.146***|-0.123***|
||(0.0326)|(0.0385)|(0.0292)|(0.0356)|(0.0177)|(0.0206)|
|Observations|81,311|81,031|81,311|81,031|81,311|81,031|
|R-squared|0.887|0.885|0.757|0.755|0.887|0.883|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Standard errors in parentheses are clustered at the country-pair. *** p<0.01, ** p<0.05, * p<0.1

# (b) Alternative Measure of the Internet

An additional concern about the above results is that the regressions do not control for the quality of the Internet access, resulting in an omitted variable bias. The level of Internet adoption may be the same in two countries but one country may have faster broadband Internet that allows them to communicate more and transfer information faster compared to the other country with slower fixed line connection. We control for the quality of Internet access in a country in the regression by using data on download speeds. The data is obtained from the Net Index dataset based on the Ookla Speedtest and provides information about the download speed measured in kilobytes per second for a wide set of countries starting from 2008.<sup>16</sup> The logs of the download speed in the importing and exporting countries is used an alternative measure of the Internet in our baseline regressions. In order to facilitate comparison of estimates, given the different time period available in the Net Index dataset, we also report the OLS estimate of our baseline regressions using Internet adoption for the same time period 2008-13 where data are available for the download speed. The results are reported in Table 7.

The results are similar to the baseline regressions with Internet adoption as the measure of the Internet. Faster Internet in the exporting country increases bilateral exports. A 10 percent increase in download

> 16 The dataset provides daily measures of the speed of download and upload. The daily value is calculated as the average of the last available 30 days of test. For our purpose, we construct a yearly variable by averaging the speeds of the last day of each month. See http://www.netindex.com/ for additional information about the available indexes.

13
Policy ReseaRch WoRking PaPeR 4613

# **Abstract**

This paper updates and extends the Docquier-Marfouk data set on inter-national migration by educational attainment. The authors use new sources, homogenize definitions of what a migrant is, and compute genderdisaggregated indicators of the brain drain. Emigration stocks and rates are provided by level of schooling and gender for 195 source countries in 1990 and 2000. The data set can be used to capture the recent trend in women’s skilled migration and to analyze its causes and consequences for developing countries. The .findings show that women represent an increasing share of the OECD immigration stock and exhibit relatively higher rates of brain drain than men. The gender gap in skilled migration is strongly correlated with the gender gap in educational attainment at origin. Equating women’s and men’s access to education would probably reduce gender differences in the brain drain.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to analyze the impact of migration on poverty and economic development.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at frederic.docquier@uclouvain.be.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Estimation Equations

For purposes of estimation we assume a more flexible functional form than is the case in (2) and (4) by allowing a constant term.' Thus the two estimation equadons used in this paper, expressed in logs, are as follows: Log Q = B0 + B1 Log K + B2 Log L + B3 Log TP _(5)_ and LogDif Q = CO + C1 LogDif K + C2LogDif L + C4LogDif TP (6) where LogDif X = (Log Xt - Log Xt.1).

The pure cross-section estimations using period-average or period change data apply equations _(5)_ and (6) as shown. The pooled fixed effect estimates based on annual data also use country dummies to capture country-specific differences in economic performance, but are not shown in equations _(5)_ and (6).

# Data

The choice of countries and the total number of countries in the regression analysis was dictated wholly by data availability. IThis relates to limitations on data for physical capital stock and openness measures. The period under study is 1967-87 but since a lot of the trade reforms occurred 'AII growth in output may not be captured directly by the arguments of the production function. For example a Hick's neutral technical change not measured directly by the production inputs suggests a non-zero constant term in the production function.

1'Two of the openness measures were available for a shorter period 1978-87.

17
# CONCLtJSION

This paper tests Hayami and Ruttan's (1985) induced innovation hypothesis using data for South African agriculture. The two stage CES production function leads to estimating equations that directly test the inducement hypothesis, by making factor ratios functions of factor price ratios. The model also incorporates the variables that generate the new technologies (public R&D and extension and private patents) and variables that affect the factor saving biases, such as farm size, negative interest rates and tax concessions.

The use of cointegration methods allows the inducement hypothesis to be subjected to a series of tests. First, since the variables are non-stationary, the time series properties of the variables are determined to ensure that cointegration is possible. Then, cointegration is established and an error correct model constructed, which separates short run effects, such as factor substitution, from the long run equilibrium, which incorporates induced technological change. Finally, the ECM is subjected to causality tests to determine the direction of the long run relationships in the inducement mechanism. All of these tests corroborate the inducement hypothesis and the empirical results suggest that average farm size, research and extension expenditures, favourable tax and interest rate policies are also important determinants of the observed rates and biases of technological change.

In the new South Africa, more attention should be focused on the technological needs of small scale farmers, as the combination of the lobbying power of the large scale commercial farmers and the policies followed by the apartheid regime must have influenced the allocation of R&D expenditures between labour and land-saving technical change. This will have distorted the technological bias towards labour-saving technical change, which is hardly appropriate for a labour-surplus economy, in which the small farmers in the ex-homelands face chronic land scarcity. These results show that factor prices do matter in agricultural production and the selection of production technology, at least in South Africa. This is a clear indication that the usual policy prescription of the World Bank in many economies where prices are controlled and distorted, namely structural adjustment and market liberalization, has merit.

24