rafmacalaba/datause-encoder-data huggingface dataset:
33

# B. Gravitv Flow Analysis of NAFTA's Effects

The possibility exists that the (partial equilibrium) trade simulations analyzed in the previous section may not adequately incorporate the longer term trade changes that may result from NAFTA. This would be true if NAFTA were to induce important changes in international or domestic investment patterns, if it were to create new opportunities for some industries to achieve substantial economies of scale, or if the arrangement were to change other key variables like exchange rates or the terns of trade -- all of which are factors that cannot be incorporated in a partial equilibrium analysis. To assess the potential importance of these effects, we employ a modified version of a gravity flow model.<sup>6</sup> The model, which was developed using data drawn from 95 industrial and developing countries, incorporates the experience of other nations when customs unions or free trade arrangements like the EC or EFT1A were formed. In addition, it also allows one to incorporate the influence of factors like distance, level of economic development, height of trade barriers when each arrangement was formed, or the sharing of a common language or border. Appendix II provides a fuU technical description of the model and its specification along with the empirical estimates. Table 6 summarizes the model's projections for the NAFTA-induced increase in non-oil exports from Canada and Mexico to the United States as well as the value of trade of East Asia and other country groups that could be displaced.

## (MFA) over a 10 years period.

26 For a technical discussion of gravity-type models and their use in trade simulation exercises, see Anderson (1979).
**Figure** 2

## MARGINAL EFECTS OF EDUCATION -- WGE ENPLOYEES

# Lima

## Earnings relative to no educatlon

<!-- Start of picture text --> 4.1<br>3.3 | <Signiflcant at 6% In 2-tallod toest<br>University<br>2.7<br>2.2 PN<br>1.8<br>1.5 - coooiday Webbiesl<br>o_ _....... ... ... ^,--- -- -<br>1.2<br>1.0 a a a a . I I<br>o Pl P2 P3 P4 Pb S1 S2 83 84 86 Ti T2 T3 T4 T6<br>Highest education completed<br><!-- End of picture text -->

# Other Urban Areas

Earnings relative to no education <!-- Start of picture text --> 2.7 Signitloant at 5% In 2-taled teat<br>Significant at 10% In 2-tailed teatP<br>2.2 _2_<br>1.8 scondry tchnlcl<br>1.5 ... ...<br>1.0<br>O Pl P2 P3 P4 P6 S1 S2 83 S4 S6 Ti T2 T3 T4 T6<br>Highest education completed<br><!-- End of picture text -->
# **_Appendix: Data on Quality of Education_**

The analysis in this paper relies upon a variety of cognitive achievement tests. The central analysis of growth and macroeconomic performance uses information from the international tests of a set of nations voluntarily participating in a cooperative venture under the International Association for the Evaluation of Educational Achievement (IEA) and from the Organisation for Economic Co-operation and Development (OECD). The most recent of the IEA tests is the Trends in International Mathematics and Science Study (TIMSS) for 2003, although there is a much longer history identified below. The OECD tests, called the Programme for International Student Assessment (PISA), began in 2000 and cover all OECD countries plus others.

These tests have different groups of countries, sampling of students, and perspectives on what should be tests (see Neidorf, Binkley, Gattis, and Nohara (2006)). Our approach is to aggregate across the variety of tests for each country in order to develop a composite measure of performance. Perhaps the most important issue is whether or not the tests are measuring a common dimension of cognitive skills. The TIMSS tests of math and science are developed by an international panel but are related to common elements of primary and secondary school curriculum, while the PISA tests are designed to be assessments of more applied ideas. In our development of a common metric we also employ data from the U.S. National Assessment of Educational Progress (NAEP). That test, which is conceptually closest to the TIMSS tests except that it relates more directly to U.S. curriculum, provides information over time on a consistent basis.

Part of the analysis on individual returns relied on the International Adult Literacy Survey (IALS), a set of tests given to 20 countries between 1994-1998. These tests cover several functional areas including: Prose Literacy – the knowledge and skills needed to understand and use information from texts including editorials, news stories, poems, and fiction; Document Literacy – the knowledge and skills required to locate and use information contained in various formats, including job applications, payroll forms, transportation schedules, maps, tables, and graphics; and Quantitative Literacy – the knowledge and skills required to apply arithmetic operations, either alone or sequentially, to numbers embedded in printed materials, such as balancing a checkbook, calculating a tip, completing an order form, or determining the amount of interest on a loan from an advertisement. They were designed to be very practical.

Interestingly, the TIMSS tests with their curricular focus and the PISA tests with their real-world application focus are highly correlated at the country level. For example, the correlation coefficients between the TIMSS 2003 tests of 8<sup>th</sup> graders and PISA 2003 tests of 15-year-olds across the 19 47
# **A GLOBAL PERSPECTIVE ON EFFECTIVENESS OF AID FOR TRADE**

Maria Berrittella<sup>a,</sup> and Jian Zhang<sup>b*</sup> _a Dipartimento di Scienze Economiche, Aziendali e Finanziarie, Università degli Studi di Palermo, Italy._ _b The World Bank, 1818 H street, NW, Washington, DC 20433, USA._

**JEL Classification:** C68; F13; F59; H77.

# **Sector Boards: Economic Policy (EPOL)**

**Keywords:** aid for trade, computable general equilibrium model, income inequality, trade balance, welfare.

> ***Contact Author:  Jian Zhang,** **_The World Bank, 1818 H street, NW, Washington, DC 20433, US, Contact information: jzhang7@worldbank.org;_**
9

|||_Table 1 (continued)_|
|---|---|---|
|**Country**|**Tax Revenue**|**Non-tax Revenue**<br>**Grant**|
||**Own-Taxes**|**Tax-Sharing**<br>**General Purpose**<br>**Specific**|
|**Lithuania**|-None|-Mainly taxes on income,<br>profits and capital gains.<br>-Small<br>contribution<br>from<br>taxes on property<br>-Mostly fees<br>-S.n. governments set rates<br>-Objective criteria without<br>own tax effort<br>-Important decrease in 1999<br>due to raise of income tax<br>revenues<br>-Oriented to State social policy<br>implementation<br>-Conditional grants based on<br>standard costs reduced in 1999<br>(30 %)<br>-Conditional grants based on<br>actual costs raised in 1999<br>(70 %)|
|**Poland**|-Mainly taxes on property<br>(Real estate tax)|<br>-Mainly taxes on income,<br>profits and capital gains<br>-Service fees is the major<br>source of revenue<br>-No<br>central<br>government<br>control on revenues<br>-Objective criteria without<br>own tax effort<br>-Redistribution<br>and<br>education purposes<br>-Mainly operating grants<br>-60 % based on actual costs<br>-40 % based on standard<br>costs|
|**Romania**|-Mainly taxes on property<br>-Small contribution of taxes<br>on<br>income,<br>profits<br>and<br>capital gains|<br> <br>-Mainly taxes on income,<br>profits and capital gains<br>-Small<br>contribution<br>from<br>taxes on property<br>-Service fees is the major<br>source of revenue<br>-Fees<br>are<br>regulated<br>by<br>subnational governments<br>-None<br>-All grants are conditional<br>and based on actual costs<br>-Mostly capital grants|
|**Slovak**<br>**Republic**|-80<br>%<br>from<br>taxes<br>on<br>property<br>-20 % from taxes on goods<br>and services|<br> <br>-Mainly taxes on income,<br>profits and capital gains<br>-Small<br>contribution<br>from<br>taxes on property<br>-Mainly fees, sales, and<br>fines<br>-Prices of services set by s.n.<br>governments<br>-Administrative fees, rents<br>and waste disposal charges<br>are<br>set<br>by<br>central<br>government<br>-None<br>-All unconditional<br>-Operating grants|
|**Slovenia**|-Mainly taxes on property<br>-Small<br>contribution<br>from<br>taxes on goods and services|<br>-Mainly personal income tax<br>-Some taxes on property and<br>goods and services<br>-Rental revenue is the major<br>source<br>-The majority of non-tax<br>revenues are within the<br>competence<br>of<br>s.n.<br>governments<br>-Objective criteria without<br>own tax effort<br>-Financial<br>adjustment<br>purposes<br>-Conditional grants based on<br>standard costs serves for<br>promotion of bilingualism<br>-Conditional co-investment<br>grants are based on actual<br>costs|

Source : OECD, Fiscal Design Across Levels of Government, 2001, 2002.
# **3. Findings**

Each of the three evaluation questions posed by the report is addressed by triangulating analysis obtained across data collection activities.

## **QUESTION 1: HOW FREQUENTLY IS VALUE FOR MONEY ANALYSIS INCORPORATED INTO PUBLISHED IMPACT EVALUATIONS?**

We estimate that 18.9 percent of the World Bank’s impact evaluations include any kind of VFM analysis, while the predicted proportion in the 3ie data set is 14.1 percent. Our prediction indicates that the 3ie sample has a somewhat lower, yet not statistically different, proportion of VFM studies compared to the sample of World Bank IEs.

The estimated percentage of IEs with any kind of VFM analysis in the 3ie sample has not changed much over time. The estimated percentage of IEs that include VFM was:

- 16.8 percent of IEs published prior to 2004;

- 11 percent of IEs published between 2005 and 2008; and

- 14.5 percent of IEs published between 2009 and 2012.

A majority of the World Bank’s IEs with efficiency analysis —around 80 percent— conducted a CBA or CEA, and the remaining conducted a general efficiency analysis.

The quality of cost ingredients reporting was mixed in the sample of 30 World Bank IEs. About 13 percent of the CEAs and CBAs listed only the main cost components, whereas just over half listed several cost ingredients; and the remaining third simply reported a Cost estimate without detailing much of what went into it. About 40 percent of World Bank IEs classified as category II (those with any kind of VFM) reported the cost of a program in relation to the number of beneficiaries—a common metric used in VFM analyses.

Fewer than 20 percent of CEA and CBA studies reported on unit cost per impact. These percentages are very low, considering that they are included in impact evaluations where at least one measure of impact is supposed to be very available to be used in these comparisons.

We find that just 25 percent of the CEAs and CBAs reported opportunity costs. However, because policy makers craft decisions based on real budgets and financial costs, excluding opportunity costs may be theoretically imperfect but practically correct.

The transparency of reporting is very low among the 30 World Bank IEs that included any kind of VFM analysis. Descriptions of the Costing data, methods and assumptions are often vague or incomplete. Just 13 percent reported unit costs of inputs, and only 17 percent reported unit costs per impact. Only 8 percent of IE projects conducted over a year reported discount rates and only 21 percent reported adjusting for inflation. There was also a lack of consistency in reporting discount rates, currency conversions, and inflation adjustments. For example, 58 percent of the analyses did not report the exchange rate used to convert from local to the currency of analysis. In addition, very few studies—around 13 percent—provided 14
with the error terms. Both the Sagan overidentifying restriction test and the Hausman exogeneity test statistics are shown for each regression result in the Annex tables.

The IV estimation results are broadly consistent with the DID regression results (Table 14). Rural accessibility to a populated area would likely be improved, but not to a school. Overall use of public transportation increased, as did the use of individual cars. However, the impact on individual car ownership disappeared. There was a visible positive impact on girls’ school attendance. In contrast to the above DID result, the IV estimation indicates that the project might have resulted in increased jobs in both the service and public sectors.

Although economic impacts exist, they may be statistically weak. The DID regression estimated a significant coefficient of household income increase only in the Bico do Papagaio and Southeast regions. When evaluated at the sample means, the marginal effect is appraised at R$59.8, with a standard error of 36.5. This is smaller than the previous estimate, but is more or less consistent.

In terms of employment, the significance of the impact on agricultural jobs disappeared in both pairs of regions. However, the _p_ -value of the coefficient of agricultural employment remains relatively high, at 0.113, in the Bico do Papagaio and Southeast regions. Similarly, in the Jalapão and Northeast regions, the _p-_ value is relatively high for public sector jobs, at 0.162. These figures are consistent with the above simple DID matching estimates.

**Table 13: First-stage regression of IV estimation on household income**

|Dependent variable= _D_|Coef.<br>S|td. Err.|
|---|---|---|
|Municipality having paved state road|-0.384|(0.069) ***|
|lnDistance to paved state road|-0.030|(0.005) ***|
|ln HH size|-0.026|(0.043)|
|Adult share|-0.042|(0.088)|
|HH head sex: Male|-0.036|(0.051)|
|HH head education: Elementary|-0.018|(0.036)|
|HH head education: Secondary|0.026|(0.062)|
|HH head education: University|-0.042|(0.128)|
|Appliance ownership: Refrigerator|-0.040|(0.053)|
|Appliance ownership: Color TV|-0.141|(0.049) ***|
|Appliance ownership: Radio|-0.034|(0.032)|
|Appliance ownership: Gas stove|-0.113|(0.052) **|
|Appliance ownership: Washing<br>machine|0.070|(0.038) *|
|Gas use for cooking|-0.059|(0.037)|
|Power use for lighting|-0.057|(0.053)|
|Tap water for cooking|-0.110|(0.033) ***|
|Constant|1.388|(0.161)***|
|Obs.|859||
|R-squared|0.15||
|F-stat|10.67||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

**Table 14: IV OLS/Probit estimation results**

|Dependent variable|IV OLS or IV probit|
|---|---|
||Coef.<br>Std.Err.|
|Rural accessibility||
|Distance to:||

27
Table 4: Overlap in targeting errors between methods

|**Asset Index**|**Consumption**|**CDR**|**Combined**|
|---|---|---|---|
|_Panel A: Overlap in Errors of _|_Exclusion_|||
|Asset Index<br>100.00%|65.33%|57.33%|66.67%|
|Consumption<br>61.25%|100.00%|56.25%|62.50%|
|CDR<br>51.19%|53.57%|100.00%|63.10%|
|Combined<br>75.76%|75.76%|80.30%|100.00%|
|_Panel B: Overlap in Errors of _|_Inclusion_|||
|Asset Index<br>100.00%|26.67%|22.67%|48.00%|
|Consumption<br>25.00%|100.00%|16.25%|37.50%|
|CDR<br>20.24%|15.48%|100.00%|46.43%|
|Combined<br>54.55%|45.45%|59.09%|100.00%|

_Notes_ : Table measures the extent to which the targeting errors produced by each pair of targeting methods overlap in the matched sample. Evaluation is performed on the matched sample of 535 TUP respondents. Panel A: Overlap between ultra-poor households that are misclassified as non-ultra-poor (errors of exclusion) for each targeting method. Panel B: Overlap between non-ultrapoor households that are misclassified as ultra-poor (errors of inclusion).

27
# **Introduction**

A central driver of economic growth over the past century has been the increased role of women. This growth in the role of women comes in many forms: increased female labor force participation generally,reduced discrimination and wage differentials that encourage greater effort, improved advancement practicesthatpromote talented women into leadership and managerial roles, and similar. Simply put, empowering half of the potential workforce has significant economic benefits beyond promoting gender equality (Duflo 2005, World Bank 2012). This growth stimulus is particularly true for female entrepreneurship and the economic dynamics that entrepreneurship promotes. Yet, as Klapper and Parker (2011) review, we are still just beginning to identify the local business and social factors that unlock female entrepreneurship.

This paper usesdetailed micro-data on the unorganized manufacturing and services sectors of India in 2000-2005 to explore the spatial factors that promote female entrepreneurship and business ownership. The micro-data are a representative sample of the Indian economy, and the establishment records identify the gender of the owner for proprietary establishments (which account for greater than 95% of establishments). For most surveys, we can also identify establishments that are new entrants. From these micro-data, we develop relative rates of female entrepreneurship and business ownership at the district-industry-year level.

The central focus of our paper is on identifying and quantifying the importance of existing female business networks for promoting subsequent entrepreneurship among women. We primarily evaluate these local conditions using conditional estimations that include industryyear and district-year effects. These estimations isolate variations inincumbent industrial conditions by district-industry-year. We prepare measures of the overall incumbent female business ownership in the district-industry and indices of how favorable the district’s broader industrial structure is to new entrants in terms of typical agglomeration factors: the suitability of local labor markets and the strength of input-output markets for buying and selling goods. These indices link measures of how related two industries are on these two dimensions with the relative presence of industries in each district.

Our estimations emphasize that favorable incumbent industrial conditions increase the subsequent relative rate of female entrepreneurship and business ownership in a district-industry. We further calculate the indices separately using male-owned incumbents and female-owned incumbents. This separation strongly emphasizes that higher relative rates of female entrepreneurship closely follow upon industrial structures with existing female-owned businesses. While we do not observe direct economic exchanges among businesses with our data, these patterns are consistent with hypotheses of the importance of existing business 2
- Ellingsen, T., Johannesson, M., Mollerstrom, J., Munkhammar, S., (2011). ̳Social framing effects: Why do situational labels affect cooperation?‘, mimeo, Stockholm School of Economics. Available at: http://www2.hhs.se/personal/ellingsen/pdf/FramingEconQ1.pdf.

- Etzioni, A. (1985).  ̳Opening the preferences: a socio-economic research agenda‘, _Journal of Behavioral Economics_ , **14** (1), pp. 183-198.

- Eugster, B., Lalive, R., Steinhauer, A. and  Zweimuller, J. (2011).  ̳The demand for social insurance: Does culture matter?‘ _Economic Journal_ , this issue.

- Falk, A., Fehr, E. and Zehnder, C. (2006).  ̳Fairness perceptions and reservation wages—The behavioral effects of minimum wage laws‘, _Quarterly Journal of Economics,_ vol. **121** (4), pp. 1347-1381.

- Falk, A. and Fischbacher, U. (2006).  ̳A theory of reciprocity‘. _Games and Economic Behavior_ , vol. **54** (2), pp. 293-315.

- Fehr, E., Gachter, S. and Kirchsteiger, G. (1997).  ̳Reciprocity as a contract enforcement device: Experimental evidence‘, _Econometrica_ , vol. **65** (4), pp. 833-860.

- Fehr, E. and Schmidt, K.  M. (2003).  ̳Theories of fairness and reciprocity—Evidence and economic applications,‘ in M. Dewatripont, L.P. Hansen, and S. Turnovsky, (Eds.) _Advances in Economics and Econometrics._ vol. 1, Cambridge University Press, pp. 208257.

- Fischbacher, U., Gachter, S. and Fehr, E.(2001).  ̳Are people conditionally cooperative? Evidence from a public goods experiment‘, _Economics Letters_ , vol. **71** (3), pp. 397-404.

- Frederick, S., Loewenstein, G., O‘Donoghue, T. (2002).  ̳Time discounting and time preference: A critical review‘, _Journal of Economic Literature,_ vol. **40** (2), pp. 351-401.

- Gauri, V., Woolcock, M., and Desai, D. (2011).  ̳Intersubjective meaning and collective action in  ̳fragile‘ societies‘, World Bank Policy Research Working Paper 5707, Washington DC.

- Gintis, H. (1972). 'A radical analysis of welfare economics and individual development‘, _Quarterly Journal of Economics_ , vol. **86** (4), pp. 572-99.

- Gneezy A. and Fessler, D. T. (2011).  ̳Conflict, sticks and carrots: war increases prosocial _’_

- punishments and rewards , _Proceedings of the Royal Society B.,_ published online before print. June 8, 2011, doi:10.1098/rspb.2011.080.

- Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., and McElreath, R. (2001).  ̳In Search of Homo Economicus: Behavioral Experiments in 15 Small-Scale Societies‘. _American Economic Review_ , 91(2), 73-78.

21
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE When all these inefficiencies are put together, EDM has one of the highest hidden costs of power utilities in West African countries (figure 17). Hidden costs are about 100 percent of EDM’s revenues (table 12), which is almost twice the figure found in other low-income African countries.

**Table 12. Evolution of hidden costs associated with EDM (power)**

||Net generation<br>(GWh/year)<br>Distribution<br>losses<br>(%)<br>Implicit<br>collection ratio<br>(%)<br>Average total<br>cost<br>(US$/kWh)<br>Average<br>effective tariff<br>(US$/kWh)|Total hidden<br>costs<br>(US$m/year)|Total hidden<br>costs<br>(% revenues)|
|---|---|---|---|
|1999|24.9<br>92<br>0.26<br>0.14|38|95|
|2000|471<br>25.8<br>92<br>0.26<br>0.14|50|113|
|2001|521<br>26.4<br>93<br>0.26<br>0.14|69|119|
|2002|590<br>27<br>94<br>0.26<br>0.15|71|100|
|2003|557<br>26<br>95<br>0.26<br>0.17|70|87|
|2004|639<br>25<br>96<br>0.28<br>0.17|87|92|
|2005|711<br>23<br>94<br>0.29<br>0.17|140|138|
|2006|764<br>23<br>95<br>0.33<br>0.17|147|133|
|2007|836<br>22<br>96<br>0.33<br>0.19|148|110|
|_2008_|_884_<br>_22_<br>_96_<br>_0.39_<br>_0.20_|_202_|_131_|
|Source: Deri<br>Note: Result<br>**Figure 17.**<br>140<br>160<br>180<br>**ues**|ved from Briceño-Garmendia, Smits, and Foster 2009.<br>s for 2008 based on preliminary data.<br>**Hidden costs of Mali’s power utility are among the highest in West Africa**|||
|120<br><br>**reven**||||
|100<br>**of**||||
|60<br>80<br>**tage**||||
|40<br><br>**cen**||||
|0<br>20<br><br>**Per**||||
||Liberia<br>Niger<br>Mali<br>Ghana<br>Burkina Faso<br>Cote d'Ivoi|re<br>Benin||
||Collection Inefficiencies<br>Underpricing<br>L|osses||

Source: Derived from Briceño-Garmendia, Smits, and Foster 2009.

In the long-term, the power utility may be closer to achieve cost recovery, particularly if regional power trade is developed (figure 18). If investments on generation are carried out taking into account the least cost alternatives and best utilization or hydropower potential, Mali can produced electricity with a long-term marginal cost of $0.28 per kWh. If further investments are undertaken to develop optimal power trade arrangements, marginal costs can be decreased to an even lower $0.25 per kWh. This puts cost recovery within the reach of the utility with a modest progressive tariff increase between 10 percent (if regional power trade is honed) and 20 percent (under stagnation) over the existing effective tariff.

32
<!-- Start of picture text --> 2%<br>0%<br>-2%<br>“4% —Baseline scaling up<br>6%8%<br>“10% “temaiary puss<br>-12%<br>-14%<br>16%<br>-18%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 6% —agriculture —construction<br>2% >=<br>0%<br>-2%<br>_ ee<br>4%<br>6%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 4%<br>3% ——<br>i ies —<br>2% ffi Faprete—con: Mteteescntnnan<br>1% i =P rote<br>f ~~total (ax. mining specific inputs)<br>0% SHS<br>“1%<br><!-- End of picture text --> <!-- Start of picture text --> 4%<br>1% —agriculture<br>0% oe<br>Fiano dl<br>“2% bias<br>-3%<br>4%<br>6%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->