rafmacalaba/datause-encoder-data huggingface dataset:
variable two years prior to an agreement taking effect. Table 4 shows that trade flows are not statistically correlated with future levels of depth. Depth estimates obtained in Table 3 are unaffected by the inclusion of these additional variables. This evidence suggests a causal relationship between depth and trade.<sup>30</sup> <u>Table 4: Trade Creation Falsification Test</u>

||Depth PPML<br>|<br>|||||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Depth LE|(2)<br>Depth LE|(3)<br>Depth All|(4)<br>Depth All|(5)<br>Depth Core LE|(6)<br>Depth Core LE|(7)<br>Depth Core All|(8)<br>Depth Core All|
|Depth|0.133**|0.124**|0.107**|0.104**|0.062|0.062*|0.057*|0.057*|
||(0.064)|(0.058)|(0.049)|(0.045)|(0.039)|(0.036)|(0.034)|(0.031)|
|Falsification test:|0.077||0.052||0.035||0.035||
|Depth 1 year prior to<br>agreement|(0.058)||(0.042)||(0.037)||(0.032)||
|Falsification test:||0.042||0.033||0.019||0.021|
|Depth 2 years prior to<br>agreement||(0.037)||(0.027)||(0.023)||(0.019)|
|Observations|105,107|104,696|105,107|104,696|105,107|104,696|105,107|104,696|
|Importer-Year FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Exporter-Year FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Country-Pair FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Period|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|

Note: Robust standard errors, clustered at the country-pair level, are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Another concern with our main specification is that the absence of intra-national trade flows limits the identification to the comparison between members of a PTA and country-pairs without PTAs. To address this concern, we follow Bergstrand et al. (2015) and construct intranational trade flows using GDP data from the Penn World Tables.<sup>31</sup> In this specification, the control group comprises country-pairs without trade agreements and countries' trade with themselves, neither of which sees any change in depth. Results in Table 5 suggest that the exclusion of internal flows plays an important role in explaining the relatively small effects of depth we found earlier, as already documented in the trade gravity literature for the PTA dummy (e.g. Dai et al., 2014; Larch et al., 2017). The coefficients of depth on trade are around three times larger than those presented in Table 3. These results are more in line with the literature in which, for instance, the coefficient for a common currency is 0.98 while we find

> 30 In an additional falsification test, we assign random levels of depth to trade agreements in our sample. Figure A2 of Appendix A shows the distribution of the coefficients of random depth variable obtained from 500 random draws of depth, obtained from a PPML model that includes a PTA dummy, is normally distributed. 31 The main advantage of constructing intra-national flows with GDP data is the extensive time and country coverage compared to gross output data (e.g. CEPII’s TradeProd data are available until 2006). The drawback is that GDP is measured as value added which is an imperfect proxy of gross output.

24
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30
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18
**Table S2.4 $1.25/day and $2/day OLS and IV GMM results after restricting the sample to more recent poverty observations (ending in 2003 or later)**

|Regression Number|**1**|**2**|**3**|**4**|
|---|---|---|---|---|
|Poverty line for headcount measure|**$1.25/day**|**$1.25/day**|**$2/day**|**$2/day**|
|Estimator|**OLS**|**IV GMM**|**OLS**|**IV GMM**|
|Change in log of domestic food prices|-22.12|-89.13**|-13.72|-60.18**|
||(13.56)|(37.39)|(9.67)|(26.87)|
|N|145|144|153|151|
|Tests|||||
|Kleibergen-Paap rk Wald F statistic||17.81||19.5|
|Kleibergen-Paap rk LM statistic||0.01||0.02|
|Stock-Wright LM statistic||0.03||0.04|

Source: Author’s estimates.

Notes: The sample consists of observations in which the last poverty observation is in 2003 or thereafter. See text for definitions of the variables. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Standard errors clustered at the country level are reported in parentheses. For the IV GMM, errors are clustered at the country level and the excluded instrument is the change in international prices (see Section 3 for details). All regressions control for time dummies. The null hypothesis of the Hausman tests is that the variable under consideration can be treated as exogenous. The Kleibergen-Paap rk Wald F statistic measures weak instruments, with critical values varying between 5.53 and 16.38, suggesting that the regressions above may suffer from a weak instrument problem. The null hypothesis of the Kleibergen-Paap rk LM statistic is that the equation is underidentified. The null hypothesis of the Stock-Wright LM test has the null hypothesis that the coefficient on the change in real food prices is equal to zero and overidentifying restrictions are valid.

28
sample countries are practicing full capital cost recovery (figure 11a); Among the countries that should be able to cover total costs under existing tariff regimes (contingent on their ability to collect bills) are Burkina Faso, Côte d’Ivoire, Kenya, Namibia, and Senegal.

<!-- Start of picture text --> Figure 11. Cost-recovery capabilities of effective tariff regimes<br>a. Weighted effective tariffs and total costs b. Weighted effective tariffs and OPEX<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Historical costs, US cents/kWh Historical opex costs, US cents/kWh<br>(cents/kWh) (cents/kWh)<br>Weighted Effective Tariffs Weighted Effective Tariffs<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ OPEX = operational expenditure.

Lack of full cost recovery can be partly explained by the large weight of residential customers in utilities’ revenue structure, whose charges – for political and social reasons – often fail to reflect costs. Excluding South Africa, roughly 50 percent of the total power billing in Africa is associated with households, for just over 40 percent of the total power supplied (figure 13). This is a very high share by global standards. In South Africa, by contrast, the total share of residential billing stands much lower at 17 percent versus only 8 percent of total power supplied. Only in 30 percent of the sample countries do residential tariffs allow for 100 percent cost recovery (figure 12a). While residential tariffs do better at covering operating costs (figure 12b), this is not enough to guarantee financial sustainability in the medium to long term.

It is important to underscore that setting tariffs at cost-recovery levels is one thing and actually recovering costs is another. African utilities are characterized by bill-collection rates of well below 100 percent. This translates to financial losses that can amount to more than the entire turnover of a utility (see annex 14 for details).

Have African utilities been improving their cost-recovery rates over time? Power tariffs increased substantially over the period 2001 to 2005, but not fast enough to keep pace with rising costs (figure 14). Indeed, by 2005, average revenues had only just caught-up with the average costs in 2001.

22
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32
# **Expediting Trade: Impact Evaluation of an In-House Clearance Program***

Ana M. Fernandes Russell Hillberry Claudia Berg World Bank World Bank

# JEL codes: F13, F14, F15, C22.

# Keywords: Trade facilitation, Customs, Trade costs, Synthetic controls, Program evaluation.

a Ana Margarida Fernandes. The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: afernandes@worldbank.org.

> b Russell Hillberry. The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: rhillberry@worldbank.org.

c Claudia Berg, The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: cberg@gwmail.gwu.edu.

* The authors would like to thank the Serbian Customs Authority for providing us with data, Predrag Arsic for valuable clarifications about the data and procedures.  Michael Ferrantino, Laura Puzzello and Ben Zipperer and seminar participants at University of Virginia and the World Bank provided very helpful comments on an earlier draft.  Violane Konar-Leacy, Lazar Ristic, Brankica Obucina, and Alejandra Mendoza Alcantara assisted with the data collection and contributed with very helpful discussions. Special thanks to Nenad Popadic for providing us with details on the Serbia in-house clearance program, and to Erhan Artuc for help with simulations of the Irwin-Hall distribution. We are grateful for the support of the International Finance Corporation (IFC) and the Governments of UK, US, and Canada through the Investment Climate Impact Program.  Research for this paper has in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development, the Strategic Research Program on Economic Development, and the i2i Fund for Impact Evaluation.

1
Table 15 – Educational Attainment of Migrants and Non-migrants

|Education level|Non-Migrants|Migrants|Total|
|---|---|---|---|
|Less than 6 years|22.8|2.7|21.6|
|Illiterate|19.6|26.2|20.0|
|Read & Write|12.6|11.4|12.6|
|Less than Intermediate|16.0|13.9|15.9|
|General Secondary|3.6|4.1|3.7|
|Tech. Secondary|15.9|21.9|16.3|
|Above Intermediate|2.0|3.8|2.1|
|University|7.3|15.5|7.8|
|Above University|0.2|0.6|0.2|
|Total|100|100|100|

Source: Authors’ calculations using Labor Force Survey

# **Agricultural Sector Involvement**

The second stylized fact that can explain Egypt’s low internal migration rate is involvement in agricultural activities that ties people to the land, often as unpaid family workers. Agricultural workers have lower migration rates than workers with other occupations (see Table 16).

Agricultural activities are typically characterized by low productivity (output per input) and lower wages (see Figure 4) than in other sectors. Using the share of agriculture in total employment as a proxy for productivity, as in Gollin, Parente and Rogerson (2008), it can be seen that governorates with high shares of agriculture, and hence lower productivity, are associated with lower wages.

Figure 4- Wages and Share of Agricultural Sector Employment by Governorate <!-- Start of picture text --> suez<br>cairo 6-Oct<br>red sea<br>helwan<br>north sinai<br>south sinaiport said<br>giza damietta sharkia<br>alexandriaqualiobia dakahlia sohag<br>menia<br>luxor ismailia<br>garbeyya qenaaswan<br>new valley<br>assiut<br>fayoum bani suef<br>matrouh<br>menoufiakafr el sheikh<br>beheira<br>0 20 40 60<br>agriemp<br>lmwage Fitted values<br>7<br>6.8<br>6.6<br>6.4<br>6.2<br>6<br><!-- End of picture text --> Source: Authors’ calculations using Labor Force Survey 14
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41
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Policy Research Working Paper 8916

# **Abstract**

This study conducted a large-scale, representative survey of social norms for female labor force participation in three governorates of Jordan. The social norms measures are disaggregated into thematic clusters, empirical and normative expectations, and interpersonal expectations within the household. The measurements satisfy reasonable tests for internal consistency, external validity, and test-retest reliability. The survey shows that the great majority of men and women favor women’s labor force participation, although support falls under specific scenarios. Most non–working women would like a job. Among married women, the strongest correlates of working are the woman’s expectations of her husband’s views and the husband’s personal beliefs. Among unmarried women, empirical expectations of the number of women working correlate strongest with labor force participation. The study findings indicate that information campaigns highlighting hidden support for women working could be effective, although distinct messages for men, married women, and unmarried women may be useful.

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 vgauri@worldbank.org.

_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
# AGSS Sample Kebeles (2014)

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