rafmacalaba/datause-encoder-data huggingface dataset:
Hummels, David L. and G. Schaur (2013), “Time as a Trade Barrier,” _American Economic Review_ , Vol. 103, 1-27.

- Hummels, David L., Peter Minor,  Matthew Reisman and Erin Endean  (2007),  “Calculating Tariff Equivalents for Time in Trade,” Arlington, VA: Nathan Associates Inc. for the United States Agency for International Development (USAID). Available at: <u>http://www.krannert.purdue.edu/faculty/hummelsd/research/tariff_equivalents.pdf</u>

- Jafari, Yaghoob (2014a), “Ad Valorem Equivalents of Barriers to Services Providers in Kenya,” mimeo, March 22, 2014. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing

- Jafari, Yaghoob (2014b), “Ad Valorem Equivalents of Barriers to Services Providers in Tanzania,” mimeo, March 22, 2014. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing

- Jafari, Yaghoob (2014c), “Foreign Discriminatory Barriers to Services Providers and Estimates of the Ad Valorem Equivalents (AVEs) of the Regulatory Barriers in the 11 Services Sectors for 103 countries,” mimeo. March 22, 2014.  Available at:

- https://drive.google.com/folderview?id=0B0V-t-Bs4-

hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing

- Jafari, Yaghoob (2014d), “Calculation of the Weighted Average AVEs of the Services Barriers in Ten Regions,” mimeo, March 18, 2014. Available at: https://drive.google.com/folderview?id=0B0V- <u>t-Bs4-hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp=sharing</u>

- Jafari, Yaghoob (2014e). “Different Region’s Ownership Shares of Tanzanian and Kenyan Services Sectors,” mimeo, January 13. Available at: https://drive.google.com/folderview?id=0B0V-t-Bs4=

- <u>hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&usp sharing</u>

- Jensen, Jesper, Thomas Rutherford and David Tarr (2010), “Modeling Services Liberalization: the Case of Tanzania,” _Journal of Economic Integration_ , Vol. 25 (4), December, 644-675.

- Jensen, Jesper, Thomas Rutherford and David Tarr (2007), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,” _Review of Development Economics_ , Vol. 11 (3), August, 2007, 482-506.

- Jensen, H. G. and R. Sandrey, 2011, The Tripartite Free Trade Agreement: A computer analysis of the impacts, Chapter 4 in “Cape to Cairo: Making the Tripartite Free Trade Area Work”, Trade Law Center for Southern Africa, National Agricultural Marketing Council 2011.

- Jensen, H. G. and R. Sandrey, 2013, A New Approach to a regional Free Trade Agreement in East Africa: willing participants, TRALAC Working Paper No. S13WP09/2013, June. Available at: http://www.tralac.org/files/2013/07/S13WP092013-Jensen-Sandrey-A-new-approach-to-aregional-Free-Trade-Agreement-in-east-Africa-20130626-fin.pdf

34
|VARIABLES|(1)|(2)|(3)<br>Weight-for-|(4)<br>age z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
|_Structure of house (refere_|_nce category:_|_Kacha)_|||||
|Pacca|0.111|0.112|0.111|0.111|0.119|0.120|
||(0.113)|(0.113)|(0.113)|(0.113)|(0.113)|(0.114)|
|Mixed|-0.067|-0.067|-0.067|-0.071|-0.066|-0.063|
||(0.110)|(0.110)|(0.110)|(0.110)|(0.110)|(0.111)|
|_Asset index (reference cat_|_egory: poores_|_t)_|||||
|Asset index 2|-0.076|-0.073|-0.076|-0.057|-0.063|-0.065|
||(0.133)|(0.132)|(0.132)|(0.133)|(0.133)|(0.133)|
|Asset index 3|0.112|0.117|0.110|0.137|0.108|0.109|
||(0.139)|(0.138)|(0.139)|(0.139)|(0.139)|(0.138)|
|Asset index 4|0.122|0.130|0.119|0.167|0.115|0.111|
||(0.152)|(0.152)|(0.151)|(0.154)|(0.153)|(0.152)|
|Asset index 5|||||||
|(wealthiest)|0.155|0.167|0.152|0.210|0.150|0.153|
||(0.170)|(0.169)|(0.169)|(0.171)|(0.177)|(0.167)|
|Child suffered from|||||||
|diarrhea|-0.308**|-0.307**|-0.308**|-0.302**|-0.307**|-0.307**|
||(0.142)|(0.142)|(0.142)|(0.142)|(0.142)|(0.142)|
|Other illness|-0.228**|-0.227**|-0.228**|-0.230**|-0.223**|-0.221**|
||(0.108)|(0.108)|(0.108)|(0.108)|(0.108)|(0.108)|
|rural or urban|0.218**|0.215**|0.217**|0.203*|0.220**|0.227**|
||(0.108)|(0.109)|(0.108)|(0.108)|(0.108)|(0.108)|
|Punjab|-0.323*|-0.326*|-0.323*|-0.305|-0.324*|-0.312*|
||(0.186)|(0.187)|(0.187)|(0.187)|(0.187)|(0.187)|
|Sindh|-1.310***|-1.315***|-1.309***|-1.297***|-1.314***|-1.307***|
||(0.205)|(0.205)|(0.204)|(0.204)|(0.204)|(0.204)|
|Khyber Pkahtoon Khaw|0.126|0.118|0.128|0.152|0.119|0.139|
||(0.212)|(0.215)|(0.212)|(0.214)|(0.213)|(0.213)|
|Constant|<br>-1.489***|<br>-1.485***|<br>-1.487***|<br>-1.538***|<br>-1.521***|<br>-1.543***|
||(0.288)|(0.287)|(0.288)|(0.291)|(0.289)|(0.289)|
|Observations|2,182|2,182|2,182|2,182|2,175|2,175|
|R-squared|0.110|0.110|0.110|0.111|0.109|0.109|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the caloric share of non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure in total expenditure.

81
...cation.<sup>43</sup> We then aggregate tari¤s (taking the simple average) and imports (taking the sum) to the EORA sector level. We then sum imports across countries of origin to obtain import weights which are used to compute an auxiliary measure of tari¤s on imports of BEC intermediates at the reporting country-EORA sector-year level. Then, based on the US Input-Output table for 2005 from WIOD which was aggregated to the eight EORA manufacturing sectors, we construct for each sector the share of its total intermediate inputs that originates in each of the eight EORA sectors. Finally, we combine these I-O table weights with the auxiliary tari¤s on imports of BEC intermediates to construct a weighted-average tari¤ on intermediate inputs at the reporting country-EORA sector-year level.

Third, to construct tari¤s imposed in destination markets on exports of the sector, we aggregate tari¤s (taking the simple average) and exports (taking the sum) to the EORA sector level. Then, we sum exports across destination markets to compute export weights at the reporting country-EORA sector-year level which allows us to construct a weightedaverage of the tari¤s the destination markets impose.

Finally, we also rely on the fDi Markets Database which allows us to construct a proxy for FDI at the country-sector-year for a limited sample period 2003-2015, following the approach of Hallward-Driemeier and Nayyar (2019). Speci...cally, we de...ne a measure of the number of cross-border green...eld FDI projects announced cumulating it from 2003 onwards for each country-sector (summing across the projects from any origin country).

# References

Braun, M. 2003. “Financial Contractibility and Asset Hardness,”UCLA, Mimeo.

Couharde, C., Delatte, A-L, Grekou, C., Mignon,V., and Morvillier, F. 2017. “EQCHANGE: A World Database on Actual and Equilibrium E¤ective Exchange Rates,” CEPII Working Paper 2017-14.

Felbermayr, G., Teti, F., and Yalcin, E. 2019. “Rules of Origin and the Pro...tability of Trade De‡ection,”Journal of International Economics 121: 103248.

Hofmann, C., Osnago, A., and Ruta, M. 2017. “Horizontal Depth: A New Database on the Content of Preferential Trade Agreements,”Policy Research Working Paper 7981, World Bank, Washington, DC.

Melitz, J., and Toubal, F. 2014. “Native Language, Spoken Language, Translation and Trade,”Journal of International Economics 93 (2): 351-363.

Nunn, N. 2007. “Relationship-Speci...city, Incomplete Contracts, and the Pattern of Trade,” Quarterly Journal of Economics 122 (2): 569–600.

Rauch, J. 1999. “Networks Versus Markets in International Trade,”Journal of International Economics 48 (1): 7-35.

> 43The UN BEC classi...es goods into capital, consumption and intermediate goods.

3
feasibility specifications. The decision support tool then reports the cumulative effects of each optimal portfolio and supports the comparisons of projects selected for each portfolio. The analytical figures in this paper are screenshots from the tool. It is available online at https://goo.gl/BRojPW.

# 5 Baseline Vulnerability

We present the results of this study by stepping through a series of questions about the proposed investments and trade-offs among portfolios. In this section, we assess the performance of the baseline vulnerabilities of the current system and the Master Plan. In section 6, we generate and evaluate portfolios of projects under different budgets. In section 7, we use these portfolios to develop a decision tree that guides SEDAPAL through an adaptive set of no-regret investments.

## 5.1 Does Lima’s Current System Achieve Water Reliability?

Lima’s current water demand is approximately 855 Mm<sup>3</sup> . Using WEAP to evaluate the performance of the current system under current and historical flow conditions and current demand reveals that SEDAPAL’s existing system just achieves water reliability. Lima meets 90.4% of the demand 90 percent of the time. (Recall that water reliability is achieved when the 90th percentile of monthly met demand is above 90%.)

## 5.2 Could SEDAPAL Achieve Future Water Reliability Without Further Investment?

It is useful to assess whether Lima could achieve water reliability in the future without the proposed projects. This analysis reveals the need for future investments.

We assessed whether SEDAPAL could achieve future water reliability by evaluating the performance of the current system in WEAP in each of the 300 plausible demand and streamflow futures.

Without the proposed projects, SEDAPAL cannot achieve water reliability in 2040 if demand exceeds approximately 920 Mm<sup>3</sup> , regardless of future flows. This is somewhat more than current levels of demand but much less than SEDAPAL’s projection for 2040 demand of 1,125 Mm<sup>3</sup> and far less than the highest plausible demand of 1,800 Mm<sup>3</sup> . If future flows decrease, SEDAPAL may not be able to ensure water reliability even if future demand were less than today’s demand. These results confirm SEDAPAL’s need to undertake major investments in either new water supply or efficiency and demand management to achieve water reliability for Lima.

Figure 5-1 shows the results. Each mark represents a future. The size of each mark indicates the 90th percentile of monthly met demand—smaller marks indicate lower met demand and hence worse performance. Marks are also colored differently—blue circles 17
Policy Research Working Paper 10980

# **Abstract**

Can skills-based programs promote women’s economic - inclusion? This study randomizes access to a program teach ing entrepreneurship skills in rural Uganda. The program covers record-keeping, identifying business opportunities, raising capital, and soft skills like perseverance and confidence, but it provides no access to cash or capital. Treated women are 17 percent more likely to generate income from their own businesses 18 months post-program. They heavily re-invest in their businesses. High-frequency data show that treated women also fare significantly better during the COVID-19 lockdown than women in the control group. Exploiting social network data, this paper detects positive network-based spillovers to the control group and provides novel tools to adjust estimates accordingly. Although the program is not transformative, the results indicate an important role for skills-based programming in efforts for economic inclusion among rural, low-income women.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mlang@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
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<mark>Wellalage, Nirosha and Stuart Locke (2017). “Access to credit by SMEs in South Asia: Do women entrepreneurs face discrimination.”</mark> _<mark>Research in International Business and Finance</mark>_ <mark>, 41 (October): 336-346</mark> <mark>World Bank (2011).</mark> **<mark>World development report 2012: Gender equality and development</mark>** <mark>.</mark> _<mark>The World Bank</mark>_ <mark>.</mark> 33
**_Implication 10:_** There are diminishing marginal returns to adding more post-treatment rounds in terms of the gain in power they give. The greatest gain is from moving from one to two posttreatment rounds, and the gains are smaller the higher is the autocorrelation.

_Proof:_ One can easily determine that the derivatives of (14) with respect to both _r_ and _ρ_ are both negative.

Given the symmetry of the role of pre-treatment and post-treatment surveys in difference-indifference estimation, we see the same holds for adding more pre-treatment rounds. For ANCOVA estimation, the gain from adding pre-treatment rounds is different from that from adding post-treatment rounds in general, and the gain from moving from one to two pretreatment rounds is less than that from moving from one to two post-treatment rounds when

# **3.4. Numerical illustrations**

To illustrate how much the required cross-sectional sample size can be reduced when more survey rounds are undertaken, Table 3 presents the minimum treatment group size _n_ obtained under power calculations with different values of _r_ , _m_ , and ρ _._ I consider a hypothetical experiment intended to detect a 10 percent increase in business profits from a baseline value of 100. Microenterprise profits are noisy, so it is common for the standard deviation to be of the same order of magnitude as the mean. I therefore take the standard deviation as 100, and fix the power at 80 percent and size at 5%. Panel A then shows the treatment group sample sizes required for post estimation, Panel B for difference-in-differences estimation, and Panel C for ANCOVA estimation, for ρ ε{0, 0.25, 0.5, 0.7, 0.95}. It is assumed that the control group is the same size as the treatment group in these calculations.

Table 3 illustrates numerically Implications 1-6, and Implication 10. Comparing the sample sizes in Panel A and B, we see Implication 1 and 3. When there is only one baseline, difference-indifferences requires larger sample sizes than post analysis when ρ<0.5, the same sample sizes for ρ=0.5, and lower sample sizes for ρ>0.5. The difference in sample size can be large. For example, with a single baseline and follow-up, when ρ=0.25, one would need a treatment group size of 2355 with difference-in-differences, compared to 1570 with post analysis. Adding a second pre-treatment survey reduces the sample size for difference-in-differences to 1766, 18
-6-

that '...sooner or later, these diminishing return effects overpower .ociety's interest in stimulating additional cost reductions by extending the patent life. Therefore, in all but some special limiting cases there exists a finite socially optimal patent life To be more explicit, Nordhaus (1969) assumed that: B (RD) **a** B RDa With this assumption and using a social discount rate of 20X, he determined the optimal life of a patent. The figures are shown in Table 1.

Table 1: OPTIMAL LIFE OF A PATENT (In num er of years)

|value of<br>Demand<br>B<br>Elasticity|0.001|0.01|0.02|0.05|0.10|
|---|---|---|---|---|---|
|0.25|34.0|22.5|19.1|14.7|11.6|
|0.50|30.5|19.1|15.8|11.6|<br>8.6|
|0.75|28.5|17.1|13.9|9.8|<br>7.2|
|1.0|27.0|15.8|12.6|8.7|<br>6.2|
|1.5|25.0|13.9|10.8|7.2|<br>5.0|
|2.0|23.6|12.6|9.6|6.2|<br>4.2|
|4.0|20.2|9.6|6.9|4.2|<br>2.9|
|10.0|15.8|6.2|4.2|2.6|<br>1.8|

Source: Abridged from Table 5.1. in Nordhaus (1969).

Based on this simple model, the following conclusions can be made:

- Pirst, for a given percentage cost reduction, the welfare cost paid by society for the innovation (EAN) is higher the higher is the price elasticity of demand. Thus, ceteris paribus, a high

1/ For a discussion of the productivity of RD and of patents in the U.S., see Criliches (1989).
Bourguignon and Morrisson (16 countries)<sup>8</sup> , we increase the coverage to about 90 percent, the number of independent data points to 767, and the global Gini goes up to 43.3. This will be our estimate for the early 19<sup>th</sup> century global inequality. It is not far from the BFLZ (2009) estimate of 47 (see their Table 4). As I shall argue below, Bourguignon and Morrisson estimate of Gini 50 for 1820 seems implausibly large.

In comparison with today’s inequality, global inequality in the period 1750-1880 was much lower. The most recent global inequality estimate, calculated from individual household survey data around year 2002, is 65 Gini points, using the “old” PPPs which are based on the same ("old") benchmark as the 1990 PPPs used by Maddison. If we use the more recent PPP values (from the 2005 International Comparison Project), the global Gini is 70.7. The increase is due to a significant reduction of GDPs per capita or average household incomes for China, India and several other Asian countries (see Milanovic, 2009).

It is interesting to focus on the highest incomes in our sample. Incomes above $PPP 70,000 per capita, which would place such individuals into the top global percentile _today_ , are registered in the Netherlands, Java (which was a Dutch colony then), Chile and (a practically negligible number) in Brazil. The total number of people with such high incomes is minute however. It was less than 5,000 (out of 650 million people). Yet it is clear that enormously rich people, by today’s standards, lived then too.

> 8 The “countries” are Austria-Czechoslovakia-Hungary, Australia-Canada-New Zealand, the Balkans, Ivory Coast-Ghana-Kenya, Germany, Egypt, Japan, Korea-Taiwan, Nigeria, Philippines-Thailand, Poland, Russia, Scandinavia, Turkey, USA and South Africa.

8
- 25 -

**Average Caribbean Catastrophe Rates - Commercial Properties**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>XL Bahamas<br>so (0 (%Jamaica<br>E ' I -i- Cayman<br>*j60<br>40<br>f or caatoh-rmr-rmu ae n omrilpoet hr oaiity DRs<br>20-<br>on Global Carib- OECS Barbados Domin. Trinidad The Jamaicaarbado<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 est.<br><!-- End of picture text --> This patter was experienced across the wider<sup>Caribbean as the above chart shows</sup> for catastrophe primary premium rates on commercial<sup>property where volatility was</sup> particularly apparent in rates charged in countries<sup>such as Jamaica but also Bahamas and</sup> Cayman Islands. The volatility parameters<sup>are summarized below:</sup>

# **Volatility Indicators of Global and Caribbean**<sup>**Reinsurance**and</sup><sup>**Primary Insurance Rates**</sup>

||Global<br>XL<br>Rate<br>Price<br>Index|Carib-<br>bean XL<br>Rates<br>(50%<br>mid-<br>point)|OECS<br>XL<br>Rates<br>(50%<br>mid-<br>point)|Barbados<br>Commer-<br>cial<br>Rates|Domin.<br>Republic<br>Comm.<br>Rates|Trinidad<br>&<br>Tobago<br>Comm.<br>Rates|The<br>Bahamnas<br>Comm.<br>Rates|Jamaica<br>Comm.<br>Rates|Cay-<br>man<br>Islands<br>Comm.<br>Rates|
|---|---|---|---|---|---|---|---|---|---|
|Standard<br>Deviation|1.3%|1.6%|2.1%|0.13%|0.17%|0.16%|0.34%|0.45%|0.34%|
|Rate Mean|2.7%|5.2%|4.4%|0.65%|_0.59%_|0.6%|0.92%|0.83%|0.79%|
|Standard<br>Deviation<br>Normalized<br>to Mean|49%<br>|31%|48%|20%|29%|27%|37%|54%|42%|

T'he key indicator to take account of above, is the<sup>standard deviation normalized</sup> to the mean (coefficient of variation). This shows<sup>the actual percentage deviation of the</sup> rates from the mean over the period. As can<sup>be seen, the global excess-of-loss</sup> reinsurance (XL) price index had a percentage standard<sup>deviation of 49% around the</sup>
results in about 33.1% points decline in the chance of engaging in non-agricultural business enterprise.

|**Table 5:**|**2SLS: Effect o**<br>|**f mobile use an**<br>|**d activity on bu**<br>|**siness enterpris**<br>|**e**<br>|
|---|---|---|---|---|---|
|DV:**Business**|(1)|(2)|(3)|(4)|(5)|
|**Enterprise**||||||
|MOBILE use activity|-0.339**<br>(0.097)|-0.351***<br>(0.099)|-0.3364***<br>(0.102)|-0.321**<br>(0.129)|-0.318**<br>(0.129)|
|**1**[Sex = female]||0.041<br>(0.042)|0.046<br>(0.042)|0.056<br>(0.187)|0.060<br>(0.069)|
|**1**[Married = Yes]||-0.018<br>(0.035)|-0.031<br>(0.035)|-0.032<br>(0.034)|-0.030<br>(0.035)|
|Mobile X**1**[Sex =||||-0.040<br>|-0.048<br>|
|female]||||(0.187)|(0.187)|
|Age in Quantiles|No|No|Yes|Yes|Yes|
|Any Education?|||||Yes|
|Constant|0.256***<br>(0.039)|0.239***<br>(0.066)|0.228**(0.098)|0.225**<br>(0.102)|0.047<br>(0.134)|
|R–squared (%)|23.81|24.83|25.33|25.34|25.40|
|# of Clusters/OBS<br>Fixed Effects|231<br>(1337)<br>Cluster+ Time|229<br>(1294)<br>Cluster+Time|229<br>(1294)<br>Cluster+Time|229<br>(1294)<br>Cluster+Time|229<br>(1294)<br>Cluster+Time|
|1<sup>st</sup>Stage Estimates<br>[-0.052,-0.125]**<br>n X R-squared ≥ 51.6 √<br>No evidence of_“weak_<br>_instruments”_||||||

_Notes_ : Table reports estimates from regressions of “engagement in non-agricultural business enterprises” on mobile use and activity. Columns differ based on the combination of models type and fixed effects included. All standard errors are clustered at the region level <u>and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%. Unit of Analysis:</u> **<u>cluster-level</u>** Most of the control variables included in the various regressions have their expected signs; but not all are precisely estimated. In the OLS, the gender variable which is equal to one if the survey respondent is a female and zero otherwise is positive throughout all specifications. This implies that female run households are more likely to engage in business enterprises compared to their male counterparts; highlighting the gender aspects and vital role that women play. But this estimate is not significant. Marital status which takes a value equal to one if the survey respondent is married is positive and statistically significant at 5% in all but the last specification. The implication is that the married are more likely to engage in non-agricultural enterprises than the unmarried households. Column 4 examines heterogeneous responses across gender by interacting MOBILE with the gender variable. The estimate of the interaction is negative but indistinguishable from zero, suggesting limited evidence in favor of heterogeneous ‐ 15 ‐