rafmacalaba/datause-encoder-data huggingface dataset:
Cameroon, Congo, the Democratic Republic of Congo, Rwanda, Nigeria, Malawi and Togo; meaning that for these countries the magnitude of high-skilled emigration rates are particularly skewed in favour of women. The only new entries in the right panel in Table 6 (as when compared to Table 5), include Burkina Faso, Norway, Moldova, Japan and Turkey, meaning that these destinations are particularly attractive to college-educated female migrants relative to their natural female population of college graduates.

## [INSERT TABLE 6 AROUND HERE]

# **5.3 Brain Drain and Human Capital**

Our final piece of analysis draws upon the recent contribution of Clemens and Pritchett (2008), who provide comparable measures of income based upon the concept of the natural population. They argue “If economic development is that which raises human well-being, then crossing international borders is not an alternative to economic development; it is a form of economic development.” They estimate income per natural, the mean annual income of persons born in a given country regardless of where that person resides and compare it with the standard indicator of income per capita, based upon a specific geographic area. Since human capital mobility affects both incomes per natural and the more usual measure of income per capita, it is instructive to compare measurements of human capital for both the resident and the natural population.

In line with our earlier expression for the average skill levels of naturals and residents, Figure 5.a graphically compares the high-skilled emigration rates of natives (i.e. naturals) on 39
By way of example, table 1.3 lists the transactions in the private sector in Kenya since the early 1990s. The data include the private sector‘s commitments<sup>4</sup> to the energy, water, and transport sectors, as well as specific cash transactions in the case of telecommunications.

**Table 1.3 Kenya 2006, private participation in infrastructure**

|Name of Project|Modality<br>(concession, BOT,<br>management contract, etc)|Status|Sector|Sub-Sector|Contract<br>period|Investment<br>year or<br>financial<br>closure year|Value of<br>investment<br>commitment (US$ million)|
|---|---|---|---|---|---|---|---|
|**Energy sector**||||||||
|Iberafrica Power Ltd.|Greenfield project|Operational|Energy|Electricity|7|1996|64|
|Mombasa Barge-Mounted Power Project|Greenfield project|Concluded|Energy|Electricity|7|1996|20|
|Kipevu II|Greenfield project|Operational|Energy|Electricity|20|1999|85|
|Ormat Olkaria III Geothermal Power Plant (phase 1)|Greenfield project|Operational|Energy|Electricity|20|1999|54|
|**Telecom sector**||||||||
|Safaricom|Greenfield project|Operational|Telecom|Telecom|15|1999|68|
|||||||2000|68|
|||||||2001|68|
|||||||2002|68|
|||||||2003|68|
|||||||2004|157|
|||||||2005|171|
|Celtel Kenya|Greenfield project|Operational|Telecom|Telecom|15|1999|40|
|||||||2000|40|
|||||||2001|40|
|||||||2002|40|
|||||||2003|40|
|||||||2004|300|
|||||||2005|250|
|Econet Kenya Limited|Greenfield project|Construction|Telecom|Telecom|15|2004|75|
|**Transport sector**||||||||
|Mombasa Container Terminal|Management and lease contract|Canceled|Transport|Seaports|2|1996|-|
|Jomo Kenyatta Airport Cargo Terminal|Greenfield project|Operational|Transport|Airports|N/A|1998|21.4|
|Mombasa Grain Terminal|Greenfield project|Operational|Transport|Seaports|8|1998|32.0|
|Kenya-Uganda Railways|Concession|Operational|Transport|Railroads|25|2006|322.0|

_BOT=Build Owned and Transfer_ _Source:_ World Bank (2006c)

# **Official development assistance (ODA) in infrastructure (template 4)**

Indicative levels and trends of ODA in infrastructure according to various private participation modalities and arrangements can be obtained from the online database of the OECD‘s Credit Reporting System (http://www.oecd.org/statistics/). One template should be completed for each country.

Template 4 sets up an indicative listing of levels and trends of ODA in infrastructure as a percentage of GDP. The OECD Credit Reporting Database is a comprehensive information source for the completion of this template.

> 4 With the exception of the telecommunications sector, the PPI database records commitments as opposed to actual disbursements. The difference between commitments and disbursements is often substantial. For instance, in multiyear projects the initial commitment to an investment is an aggregate, while disbursements correspond to annual flows.

21
9

**Figure 5:  Regional distribution of privatization proceeds:  1990-2003**

<!-- Start of picture text --> Billions of USD<br>40<br>SAS M ENA 35<br>4%<br>SSA 5% ECA 30<br>3% 25% 25<br>EAP16% 20<br>15<br>10<br>5<br>-<br>LAC 1990 1993 1996 1999 2002<br>47%<br>ECA LAC EAP<br>SSA SAS MENA<br><!-- End of picture text -->

**Figure 6:  Regional distribution of privatization proceeds:  1990-1999 and 2000-2003**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LAC ECA EAP MENA SAS SSA<br>1990-1999 2000-2003<br><!-- End of picture text -->

# _Europe and Central Asia_

15. Europe and Central Asia—with $101 billion or 25 percent of total proceeds from 4,620 transactions—grew its share over time, raising double the proceeds of Latin America since 2000 and making it the leading revenue earner since then. The early to mid-1990s focused on small-scale privatization, with large telecoms, power, and oil and gas deals in the mid 1990s taking place in a relatively small number of countries (Hungary and Russia). During this period, tens of thousands of enterprises were transferred to the private sector using the smallscale or “voucher” method or through management-employee buyouts (as these did not involve any cash transactions or very little in the latter method they are not included in this paper). These forms of divestiture fundamentally altered the ownership structure of former Soviet Union economies. In Russia alone, over 15,000 companies were privatized using the mass-
# **1. Introduction**

Countries in the Middle East and North Africa (MENA) exhibit some of the largest gaps between men and women in observed rates of labor force participation (LFP; Figure 1).<sup>1</sup> How large would the gains in GDP per capita in MENA be from increasing female employment rates to close these gaps? In this paper we answer this question using three neoclassical growth models with different assumptions, time horizons, and adjustments for workforce skill. Across models, gains tend to increase with the magnitude of the employment gender gap. The long run gain for a typical country in MENA is very large—around 50%—though gains are more modest in the short and medium terms and vary substantially across countries.

**Figure 1:** The gender gap in rates of labor force participation across the development spectrum.

<!-- Start of picture text --> IRQ<br>60<br>SAU<br>IRN<br>PSE EGY<br>MARJOR DZA OMN<br>BHR<br>40 TUN LBN<br>KWT QAT<br>ARE<br>DJI<br>LBY<br>20 MLT<br>ISR<br>0<br>$1,500 $5,000 $15,000 $30,000 $80,000<br>GDP per capita (PPP, constant 2017 international $)<br>(15+, national estimates)<br>Gender gap in labor force participation<br><!-- End of picture text --> Note: Difference in labor force participation rates between men and women in percentage points. MENA countries in our paper are labelled. Source: WDI (we use NY.GDP.PCAP.PP.KD for GDP PC, SL.TLF.CACT.FE.NE.ZS for Female LFP and SL.TLF.CACT.MA.NE.ZS for male LFP).

**Methodology.** The simplest estimates of the impact on standards of living from closing gender employment gaps in MENA build on the Gender Employment Gap Index (GEGI) of Pennings (2022). That paper uses a standard Cobb-Douglas production function and the assumption of a constant long-run MMPK or savings rate to show that the long-run GDP per capita gains from closing gender gaps _equal_ the size of the employment gaps themselves (expressed as a fraction of baseline employment). However, because the policy conversation tends to focus on shorter time horizons and MENA exhibits relatively low rates of private sector investment, we also produce

> 1Note that for the rest of the paper we focus on employment rates, rather than labor force participation, due to the difficulty of measuring job searching efforts in developing countries.

2
29 .

Table 5 .

# L

|Private saving: Alternat<br>(Dependent variable:|ive samples<br> Gross priva|, system est<br>te saving/GPD|imator<br>I)||
|---|---|---|---|---|
||||2|3|4|
|Sample|Full|Bounded*|LDC|OECD|
|Lagged private saving rate|0.587<br>|0.494<br>|0.476<br>|0.674<br>|
||(9.254)|(10.330)|(17.820)|(12.704)|
|Real per-capita GPDI*|0.049|0.035|0.071|0.013|
||(2.458)|(2.408)|(7.473)|(0.382)|
|Realgrowth rate ofper-capita GPpbI|0.450|0.379|0.425|0.285|
||(5.828)|(6.103)|(13.282)|(2.036)|
|Real interest rate* °|-0.253|-0.162|0.002|0.020|
||(-5.011)|(-3.408)|(.084)|(0.313)|
|M2/GNP|-0.020|-0.007|0.024|0.028|
||(-0.562)|(-0.262)|(1.001)|(1.989)|
|Terms of trade*<br>-|0.078|0.060|0.044|0.068|
||(5.096)|(5.921)|(4.875)|(3.641)|
|Urbanization ratio|-0.382|-0.241|-0.240|-0.080|
||(-3.538)|(-3.452)|(-5.101)|(-1.751)|
|Old dependency ratio|-0.655|-0.555|-1.370|-0.218|
||(-3.068)|(-4.531)|{(-4.321)|{-1.42)|
|Young dependency ratio|-0.299|-0.275|-0.279|-0.068|
||(-4.017)|(-5.607)|(-5.816)|(-0.639)|
|Governmentsaving/GPDI|-0.285|-0.172|-0.238|0.142|
||(-5.097)|(-3.782)|(-8.333)|(-2.782)|
|Private credit flow/GPDI|-0.318|-0.316|-0.508|-0.085|
|,|(-3.989)|(-5.791)|(-9.955)|(-2.427)|
|Inflation rate* °|0.143|0.127|0.177|0.157|
||(2.034)|(3.325)|(4.181)|{2.963}|
|Waid test ofjoint significance (p-vaiue)|9.000|0.000|0.000|0.000|
|Sargan test (p-value)|0.400|0.174|0.292|0.942|
|Test for 1st-order serial correlation (p-value)|9.001|0.001|0.000|0.013|
|Test for 2nd-order serial correlation (p-value)|0.121|0.362|0.690|0.157|
|Test for 3d-order serial correlation (p-value)|0.221|0.404|0.353|0.889|
|‘Numberofobservations(Numberof countries)|872(69)|+~—«<br>845(73)|+475(49)|—«-397(20)|

## Notes: T-statistics (in brackets) computed with heteroskedasticity-consistent standard errors.

* Observations more than 4 Standard Deviations away from mean of variables are dropped. * Expressed in logs (log of [1+x] for the real interest rate and the inflation rate). ° Measured by the first difference of the log. “ Both the real interest rate and the inflation rate are bounded between —50 percent and percent.
Table 3. Break Even Contribution Rates for Reformed PAYG<sup>Defined Benefit System</sup> (percentage of wage bill)

|||1995|2000|2010|2020|2030|2050|2100|
|---|---|---|---|---|---|---|---|---|
|(1)|Current system<sup>--</sup><br>baselinea|20|22|21|27|37|49|60|
|(2)|Current system<sup>-</sup><br>slow growth|20|23|25|30|40|54|68|
|(3)|Reduced replacement<sup>ratio</sup>|20|20|17|21|28|36|45|
|(4)|Increased retirement age'|20|21|20|24|29|33|42|
|(5)|Price indexation|20|19|17|23|32|42|50|
|(6)|Price indexation<sup>-</sup><br>slow growth|20|22|24|29|38|50|63|
|(7)|Rapid interest rate<sup>reform</sup>|20|22|21|27|37|49|60|
|(8)|Delegated interest<sup>rate reform</sup>|20|22|21|27|37|49|60|
|(9)|Combined reforms'|20|17|13|15|19|21|27|
|(10)|Combined reforms with<sup>coverage</sup><br>expansion|20|12|7|12|16|20|26|

- a Current system means<sup>80% replacement</sup> rate; retirement age<sup>55for women, 60</sup> for men; 50% real wage<sup>indexation.</sup> See Table 2 for baseline<sup>and reform</sup> assumptions.

- b Retirement age for women<sup>raisedto 60 by 2010. Retirementage for men and</sup> women raised to 65 by<sup>2040.</sup>

- Combined reforms mean 60%<sup>replacement rate, retirement age raised to 65 for</sup> men and women by 2040, price<sup>indexation, rapid interest rate reform.</sup>

- d See Table 2 for increased<sup>rate of migration to urban areas and coverage</sup> expansion in rural areas.

39
schools where more than 75 percent students completed at least 80 percent of the survey questions were entered into a lottery for one of twenty-five computers. Despite this incentive, we did not have a higher number of schools participate in follow-up 2 than in follow-up 1. However, a greater number of students within the participating schools answered the surveys in follow-up 2 than in follow-up 1. The number of students surveyed per school in follow-up 2 is still lower than in the baseline (about 22 vs. 28 students per school). One reason for this decline in the number of students per school is that the drop-out rate is quite high in our sample (about 10 percent per year at baseline).

Note that student rotation is also common in our study sample. As shown in Table 1, 30 percent of students reported that they had repeated at least one school year at baseline. In part due to high drop-out and repetition rates, some schools reshuffle classrooms from one year to the next. In our sample, the majority of students in follow-up 1 were present in baseline (follow-up 1 was implemented in the same semester as the baseline, about four months apart). However, only about 60 percent of students in follow-up 2 were present at baseline (follow-up 2 took place about 16 months after baseline). For the financial education program, this high rotation implies that more than a third of the sample was not exposed to the material for a full three semesters, but rather for only one or two semesters.

With respect to the parent questionnaire, Table 2 shows that participation was quite high, considering that this questionnaire was self-administered at home. About 88 percent of students returned parent questionnaires at baseline. In the follow-up surveys, this number dropped to about 76 percent.

To test whether survey attrition differed across treatment and control schools, we perform the following analysis for follow-up 2. We use enrolment counts at the beginning of the fall 2011 semester to calculate response rates to the survey conducted at the end of the semester. These enrolment counts were available for 631 schools in our sample. We then run a regression of response rates on a treatment dummy and randomization school pair dummies. The results are reported in Table 3 and show that survey attrition is uncorrelated with treatment status for both the student and parent surveys. The response rate for the student (parent) survey was about 80 (55) percent in both treatment and control schools.

# **7. Analysis and Discussion of Main Results**

# **7.1. Student Financial Proficiency**

Table 4 shows the impact of the school financial education program on students’ financial proficiency score. The proficiency tests were tailored to the program’s material and objectives, and the same tests were administered in both treatment and control schools. Tests were constructed using item response theory (which is also used to construct the GRE and SAT in the United States), and were comparable across students and across time. This implies that a series 17
# **7. References**

Alcala, Francisco and Antonio Ciccone (2004), “Trade and productivity,” Quarterly Journal of Economics, 119(2), pp.613-646.

Anderson, James E. and Douglas Marcouiller (2002), “Insecurity and the pattern of trade: An empirical investigation,” Review of Economics and Statistics, 84(2), pp.342-352.

Anderson, James E. and J. Peter Neary (1994), “Measuring the restrictiveness of trade policy,” World Bank Economic Review, 8(2), pp.151-169.

Anderson, James E. and J. Peter Neary (1996), “A new approach to evaluating trade policy,” Review of Economic Studies, 63(1), pp.107-125.

Anderson, James E. and Eric Van Wincoop (2003), “Gravity with Gravitas: A Solution to the Border Puzzle,” American Economic Review, 93(1), pp.170-192.

Berg, Andrew and Anne Krueger (2003), “Trade, Growth, and Poverty: A Selective Survey,” IMF Working Paper 03/30, International Monetary Fund.

Bernard, Andrew B., J. Branford Jensen, Stephen J. Redding, and Peter K. Schott (2007), “Firms in International Trade”, Journal of Economic Perspectives, 21(3), pp.105-30.

Clarke, George R. G. (2005), “Beyond tariff and quotas: Why don’t African manufacturing enterprises export more?” World Bank Policy Research Working Paper 3617, World Bank.

Chang, Roberto, Linda Kaltani, Norman V. Loayza (2009), “Openness can be good for growth: The role of policy complementarities,” Journal of Development Economics, 90, pp.33-49.

Djankov, Simeon, Caroline Freund, Cong S. Pham (2010), ”Trading on Time,” The Review of Economics and Statistics, 92(1), pp.166-173.

Dollar, David and Aart Kraay (2003), “Institutions, trade, and growth,” Journal of Monetary Economics, 50(1), pp.133-162.

Dollar, David, Mary Hallward-Driemeier, and Taye Mengistae (2006), “Investment climate and international integration,” World Development, 34(9), pp.1498-1516.

Francois, Joseph and Miriam Manchin (2007),”Institutions, infrastructure, and trade,” world Bank Policy Research Working Paper 4152, World Bank.

Frankel, Jeffrey A. and Andrew K. Rose (2000), “Estimating the effect of currency unions on trade and output,” NBER Working Paper 7857.

Frankel, Jeffrey A. and David Romer (1999), “Does Trade Cause Growth?” American Economic Review, 89(3), pp.379-399.

12
# **Figure A-3: Industrial Upgrading by Asian Economies in the Apparel Value Chain**

**Countries Segments of Apparel Value Chain**

|Japan|**_Garments_**<br>1950s &|**_Textiles_**|**_Fibers_**<br> <br>|**_Machinery_**<br>(spinning,<br>weaving<br>cutting, sewing)|
|---|---|---|---|---|
||early 1960s|1960s onwa|rd<br>|1970s onward|
|Hong Kong<br>South Korea<br>Taiwan|**_Garments_**<br>late 1960s, 1970s<br>& early 1980s|**_Textiles_**<br>late 1980s onw|**_Fibers_**<br>ard||
|China<br>Indonesia|**_Garments_**|**_Textiles_**|||
|Thailand|||||
|India|late 1980s|1990s|||
|Pakistan|||||
|Bangladesh<br>Cambodia|**_Garments_**||||
|Vietnam|mid-1990s||||
||to late 2000s||||

Low High VALUE-ADDED Notes: _Dotted arrows_ refer to the sequence of production and export capabilities within economies. _Solid arrows_ refer to the direction of trade flows or foreign direct investments between economies.

_Dates_ refer to a country’s peak years for exports of specific products.

Source: Adapted from Gereffi, G. (2005): 172.

40
# Table 5: Correlation Between Pollution, Road Size, and Firm Density

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Any Firm|Any Firm|log(Firm Density)|log(Firm Density)|log(Firm Density)|log(Firm Density)|log(Firm Density)|
|Median Road Size|0.0391<sup>_∗∗_</sup>||0.131<sup>_∗∗∗_</sup>||0.121<sup>_∗∗∗_</sup>|||
||(0.0170)||(0.0449)||(0.0451)|||
|Avg log(Pollution) Residual||0.202<sup>_∗∗∗_</sup>||0.269<sup>_∗_</sup>||0.243<sup>_∗_</sup>||
|||(0.0536)||(0.143)||(0.136)||
|Avg Man. Score|||||0.0137<br>(0.0659)|0.0111<br>(0.0676)||
|log(Pollution) Residual|||||||0.164<sup>_∗∗∗_</sup><br>(0.0454)|
|N|972|972|420|420|420|420|57569|
|R2<br>Fixed Efects|.2981<br>Subcounty|.3048<br>Subcounty|.4422<br>Subcounty|.4365<br>Subcounty|.4877<br>Subcounty|.4825<br>Subcounty|.4683<br>Subcounty|
|Level of Observation|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Poll. Measure|
|SE clustering|SHAC|SHAC|SHAC|SHAC|SHAC|SHAC|Grid Cell|

Notes: Standard errors are displayed in parentheses (* p _<_ 0.10, ** p _<_ 0.5, *** p _<_ 0.01). SHAC standard errors are Bartlett (spatial weighting kernel decaying linearly in distance) and the distance cutoff for spatial correlation is 5km. Man. Score is a standardized index of managerial ability constructed using our survey (see Appendix A.1 for details). We control for log distance to the main city in the region. In regressions at the grid cell level we also control for whether the grid cell contains any road, whether the grid cell is incomplete (i.e., _<_ 500m x 500m), its area, as well as a dummy for whether it is in our main surveyed area. In columns 5 and 6, we also control for missing managerial score (dummy). The top and bottom one percent of pollution residuals are trimmed. Regressions at the pollution measure level have the same geographical coverage than regressions at the grid cell level. Road size goes from 1 (Trail/Track) to 5 (Highway). The procedure to construct pollution residuals is detailed in section 5.1.
concentration of female-managed firms reflects comparative advantages (rather than entry barriers). Female-managed firms are also able to take advantage of being in service sectors (other than retail), which contributes to a 72 percent narrowing of the labor productivity gap between female-managed and malemanaged firms. However, these effects are overcompensated by counteracting differences in location fixed effects (gender gap increasing) and regression intercepts (gender gap reducing), which illustrates that much of the differences in labor productivity between female- and male-managed firms cannot be explained by our model. Thus, the overall structural difference between male-managed and female-managed firms leads to a 125 percent increase in the labor productivity gap between female-managed and male-managed firms.

# **6. Robustness**

The regressions in Table 3 use information on whether a firm purchased fixed assets in the last fiscal year to proxy for physical capital, which is the only variable related to capital use that is available for the full sample of firms. However, such binary data do not provide any information on the value of the firm’s capital stock. In addition, the regressions in Table 3 do not control for the firm’s labor cost (that is, spending on wages and benefits), because this information is not available for the full sample. However, the World Bank’s Enterprise Surveys do collect data on the hypothetical repurchase value of machinery and the cost of labor for manufacturing firms, and these factors may explain some of the differences in labor productivity between male- and female-managed firms. For example, there is evidence from prior studies that female entrepreneurs face disproportionate constraints in access to capital (Klapper and Parker 2010) and that controlling for capital intensity significantly reduces the size of the gender gap in labor productivity (Hallward-Driemeier 2013).

The first robustness check explores what happens to the gender gap in labor productivity if we control for the repurchase cost of machinery and labor cost. Because this type of financial data is only available for a subset of manufacturing firms, this comes at the cost of a significant decline in sample size, from 46,876 in Table 3, column 1, to 18,358 in Table 8, column 2. To separate the effect of the additional control variables from that of sample composition, Table 8 column 1 re-estimates the base specification on the subsample of manufacturing firms with financial data. For this sample, female-managed firms are 20 percent less 18