rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 3: Differential Dropout and Attrition

||(1)<br>% Students Dropped Out<br>_b/se_|(2)<br>% Students Non-Attritted<br>_b/se_|(3)<br>% Teachers Non-Attritted<br>_b/se_|
|---|---|---|---|
|Treatment|_−_2_._841|0_._176|_−_0_._943|
||(2_._14)|(2_._00)|(2_._11)|
|Constant|34_._264_∗∗∗_|84_._833_∗∗∗_|80_._866_∗∗∗_|
||(1_._52)|(1_._45)|(1_._53)|
|Observations|337|337|337|
|R-sqr|0_._005|0_._000|0_._001|

Data Source: MLSS 2021. Shares of longitudinal sample. Analytical sample restricted to MLSS schools observed in baseline and endline. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 4: Impact of the Intervention on Test Scores

||(1)|(2)|(3)|
|---|---|---|---|
||Math Score|English Score|Total Score|
|Treatment|12_._979_∗∗_|8_._566|10_._443_∗_|
||(6_._094)|(6_._355)|(5_._940)|
|Constant|339_._098_∗∗∗_|325_._052_∗∗∗_|305_._597_∗∗∗_|
||(9_._468)|(10_._726)|(10_._788)|
duced primarily by thermal (oil, gas, or coal) plants (World Bank, 2010). If expensive oil is increasing the price of electricity within countries, it should do so less where hydro is the most likely source. World Bank (2010) also reports the location of power plants, by type. Column 3 of Table 5 restricts attention to the 251 cities in those countries that have both hydro and thermal power plants and adds to the baseline specification a term interacting _distance_ ( _Primate_ ) _∗Poil_ with an indicator that the closest plant to the city is a hydro plant. The coefficient on the triple interaction is small and insignificant and has very little effect on the coefficient on _distance_ ( _Primate_ ) _∗ Poil_ .<sup>43</sup> Proximity to a hydro plant has no effect on the relationship between transport costs and lights.

A related concern is that cities far from the primate might not be on the power grid, and therefore might be more likely to rely on non-grid electric lights fueled by diesel generators. High oil prices could reduce diesel generator use, lowering lights more in faraway cities than near ones. Data on the location of electrical transmission lines are available from World Bank (2010) for 13 of 15 sample countries. Transmission lines pass through 184 of 260 cities (71%) in these 13 countries. In column 4, when the sample is restricted to these 184 cities least likely to rely on diesel generators, the results are very similar to the baseline. This suggests that diesel generators are not driving my results.<sup>44</sup> As noted above motorists use gasoline and diesel, not oil, so as a further check on my result, I can use the price of diesel instead of the price of oil for the subset of countries and years for which it is available. However, countries often subsidize diesel, and this introduces potential reverse causality because countries may subsidize in part to prevent the isolation of hinterland cities. The
In the absence of distortions, _TFPRsi_ should be equalized across establishments within in each industry.

The actual TFP at industry level can be calculated as where _TFPRs_ is a geometric mean of the average marginal revenue product of capital and labor: When _Asi_ (= _TFPQsi_ ) and _TFPRsi_ are jointly lognormally distributed, HK show that To empirically implement the HK framework, we require information for several parameters. The primary parameter we need to fix is the elasticity of substitution – _σ_ . There is little agreement in the literature on the plausible magnitude of this parameter. We follow Hsieh and Klenow (2009) and set a conservative estimate _σ_ = 3.<sup>4</sup> . Again, following Hsieh and Klenow (2009), we set _R_ = 10% assuming a real interest rate of 5% and depreciation rate of 5 %. There is some evidence that the cost of capital is high in Africa. But the different values of _R_ only affect the average capital distortion but not the differences between firms in a given industry. Thus, it doesn’t affect our calculation of gains from reallocation. For the industry-level factor share, _αs_ , we use NBER Productivity Database. We assume factor intensities are the same as those of the corresponding U.S. industries, which is assumed to be undistorted.<sup>5</sup> After obtaining the capital share at four-digit level, we combine it with our firm-level datasets.<sup>6</sup> Once these parameters are fixed, the wedges can be computed as follows:

> 4Note that this parameter doesn’t affect the basic measure of dispersion but only alters their effect on aggregate productivity

> 5HK point out that the labor share in this dataset underestimates the labor compensation because it doesn’t include fringe benefits and employer social security contribution. Following Hsieh and Klenow (2009), we inflate the labor cost by a factor of 3/2.

> 6Note that industries in Ethiopia, Ghana and Kenya are classified according to
.|
|Country-specific school<br>closure information|Yes. UNESCO school closure tracker has country-<br>specific information.<sup>15</sup>|No. All countries were assumed to have the same length<br>of school closures, depending on the simulation<br>scenario.|
|School closure assumptions:<br>Optimistic|Observed country-level school closures, partial closures<br>are assumed to affect 75% of the student population.|Schools assumed to be closed uniformly for 3 months<br>in a 10-month school year.|
|School closure assumptions:<br>Intermediate|Observed country-level school closures, partial closures<br>are assumed to affect 85% of the student population.|Schools assumed to be closed uniformly for 5 months<br>in a 10-month school year.|
|School closure assumptions:<br>Pessimistic|Observed country-level school closures, partial closures<br>are treated as full closures.|Schools assumed to be closed uniformly for 7 months<br>in a 10-month schoolyear.|
|School closure assumptions:<br>Very Pessimistic|N/A|Schools assumed to be closed uniformly for 9 months<br>in a 10-month school year.|

> 15 We impute missing values for share of school system closed by using the regional average by income level for countries with learning data (LAYS, LP, or PISA). The countries are: Belarus; Burundi; the Democratic Republic of Congo; the Republic of Congo; the Arab Republic of Egypt; Hong Kong SAR, China; the Islamic Republic of Iran; the Republic of Korea; Kosovo; Macao SAR, China; Micronesia, Fed Sts.; Nauru; St Kitts and Nevis; St Lucia; St Vincent and the Grenadines; Tajikistan; and the Repbulci of Yemen.

6
of GDP in private investment for infrastructure; but a number of other African peers have done even better, capturing two to three times as much in proportional terms. Nevertheless, the largest funding gaps in transport and water may be less amenable to private finance. One option would be for Sudan to draw increasingly on its Chinese and Arab partners to support transport and water investments, and allow the private sector to play a larger role in the power sector, where it has not been active to date. Nevertheless, the funding gap remains substantial relative to the Sudanese economy.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Sudan. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Sudan, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Sudan is a relatively well-off low-income state richly endowed with oil; therefore, both low-income and resource-rich benchmarks will be used to evaluate its performance. Detailed comparisons will also be made with immediate regional neighbors in the East African Community (EAC) and with countries in North Africa in some cases.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006–07 (or the most recent year available), while financial data are

> <sup>2</sup>
the specification in Equation 1 using the matched sample, the coefficient βB recovers the causal impact of the program while βL recovers the impact of the secondary market on non-beneficiaries in lottery villages.

Column 8 of Table 7 reports for completeness the results using the index of agricultural outcomes using the sample of 427 households with data in both the baseline and endline samples. The point estimates are comparable to those in column 7 using the full sample and somewhat less precisely estimated due to the lower power. Panel A of Table OA7 in the Online Appendix reports the individual items of the index for the sample of 427 households with baseline and endline data. Overall, the results in Panel A of Table OA7 in the Online Appendix are comparable to those in columns 1 to 6 of Table 7.

Column 9 of Table 7 reports the impact of NAIVS on agricultural outcomes using the matched sample. Compared to the results on column 7, we see that the point estimates are smaller in magnitude and not statistically significant. This suggests that the impacts detected in column 7 are mostly driven by the exante differences between beneficiaries and non-beneficiaries in non-lottery villages, rather than the NAIVS program.

Panel B of Table OA7 in the Online Appendix report the individual items of the agricultural index for the matched sample. Column 4 suggests that beneficiaries only increase total fertilizer use by 7.6 Kg from a base of 18.3 Kg among non-beneficiaries in non-lottery villages. This increase is small and not statistically significant. Since roughly the same proportion of beneficiaries in the matched sample report receiving the vouchers as in the full sample (column 3 of Table 4) we conclude that the increase in fertilizer use due to the NAIVS is small. Using maize yields and input use we estimate a nutrient use efficiency of 8.2 Kg of maize per Kg of nitrogen from a base production of 383 Kg of maize.<sup>11</sup> This estimate is consistent with the response rates from other evaluations of ISPs in Sub-Saharan African countries in the range of 8–
S., Fortin, N., & Lemieux, T. (2009a). Supplement to "Unconditional Quantile Regressions": Estimation and Testing. _Econometrica, 77_ (3), 953-973.

- Firpo, S., Fortin, N., & Lemieux, T. (2009b). Unconditional Quantile Regression. _Econometrica, 77_ (3), 953-973.

- Food and Agricultural Organization, U. N. (2008). The State of Food Insecurity in the World 2008: High food prices and food security – threats and opportunities.

- Hampel, F., Ronchetti, E., Rousseeuw, P., & Stahel, W. (1988). _Robust Statistics: The Approach Based on Influence Functions_ . New York: Wiley

- Haroun, M. (2010). Food Crisis and its Impact on Afghanistan's National Security. In S. Ahmed & H. G. P. Jansen (Eds.), _Managing food price inflation in South Asia_ . Dhaka: The University Press.

- Islamic Republic of Afghanistan, C. S. O. C., and the World Bank Economic Policy and Poverty Sector,. (2011). Setting the Official Poverty Line for Afghanistan. Kabul: Islamic Republic of Afghanistan, CSO.

- Islamic Republic of Afghanistan, M. o. E., and the World Bank Economic Policy and Poverty Sector. (2010). Poverty Status in Afghanistan: A Profile Based on the National Risk and Vulnerability Assessment (NRVA) 2007/08. Kabul: Islamic Republic of Afghanistan, Ministry of Economy, and the World Bank Economic Policy and Poverty Sector.

- Jensen, R. T., & Miller, N. H. (2008). The impact of food price increases on caloric intake in China. _Agricultural Economics, 39_ , 465-476. doi: 10.1111/j.1574-0862.2008.00352.x

- Kish, L. (1965). _Survey Sampling_ . New York: Wiley.
only changes in relative prices are coming from the trade policy changes including those linked to BRI. Moreover, most inputs are assumed to be consumed in fixed quantities. All else equal, the scale effects should line up with increases in GDP on a country basis. The composition effects are likely to be large as policies engendered by BRI lead to changing comparative advantage with significant changes in both the internal and external composition of output. These are not necessarily easy to trace in a modeled economy with many sectors and countries. If production moves to relatively ‘clean’ sectors and countries, the composition effects may counteract the scale effects, or vice versa.

Figure 2 provides an overall picture for the changes in emissions at the global level from the BRIUB scenario. GDP growth is some 0.12%, thus this scenario suggests that for most gases, the composition effect dominates the scale effect. There are some notable exceptions—F-gases, NMVB, and SO2. F-gases are linked in particular to electronics manufacturing, thus this suggests that re-allocation of electronics manufacturing would lead to somewhat cleaner production. SO2 is often related to coal-based electricity production. A decline in SO2 would bring some health benefits.

> 12 Emissions covered in Envisage include: Greenhouse gases: Carbon dioxide (CO2), Nitrous oxide (N2O), Methane (CH4), F-gases (HFCs, PFCs, SF6, NF3) and Other gases: Black carbon (BC), Carbon monoxide (CO), Ammonia (NH3), Non-methane volatile organic compounds short carbon cycle (NMVB), Non-methane volatile organic compounds long carbon cycle (NMVF), Nitrogen oxides (NOx), Organic Carbon (OC), Particulate matter 10 (PM10), Particulate matter 2.5 (PM2.5) and Sulfur dioxide (SO2). 13  Also referred to at times as the Kyoto gases as they were targeted in the Kyoto Protocol Agreement, signed in 1997.

- 11 -
further _exploratory_ analysis of the impact of advertised P4P. Specifically, we estimate regressions of the form given in equation (2) for four additional teacher attributes: age, gender, risk aversion, and an index capturing the Big Five personality traits.<sup>28</sup> Results are reported in Table A.3, with details of the variable construction provided in the table-note. We are unable to reject the sharp null of no advertised P4P treatment effect for any of these exploratory outcomes.

**Student learning induced by placed recruits** The skill and motivation of placed recruits on arrival are policy relevant insofar as these attributes translate into teacher effectiveness. To assess this, we combine experimental variation in the advertised contracts to which recruits applied, with the second-stage randomization in experienced contracts under which they worked. This allows us to estimate the impact of advertised P4P on the student learning induced by these recruits, holding constant the experienced contract—a pure compositional effect (Hypothesis IV).

Our primary test is derived from estimates on student-subject-year level data. The advertised treatment about which a given student’s performance is informative depends on the identity of the placed recruit teaching that particular subject via qualification type and district. We denote this by _Tqd_<sup>_A_for teacher</sup><sup>_j_with qualification</sup> type _q_ in district _d_ , and suppress the dependence of the teacher’s qualification _q_ on the subject _b_ , stream _k_ , school _s_ , and round _r_ , which implies that _q_ = _q_ ( _bksr_ ). The experienced treatment is assigned at the school level, and is denoted by _Ts_<sup>_E_.We</sup> pool data across the two years of intervention to estimate a specification of the type for the learning outcome of student _i_ in subject _b_ , stream _k_ , school _s_ , and round _r_ . We define _j_ = _j_ ( _bksr_ ) as an identifier for the teacher assigned to that subjectstream-school-round. The variable _Ij_ is an indicator
US adult population from 2002 to 2013: insights from the medical expenditure panel survey. JAMA Cardiol. 2017;2(1):56–65.

Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and uncertainty, 1(1), 7- 59.

Saposnik, G., Redelmeier, D., Ruff, C. C., & Tobler, P. N. (2016). Cognitive biases associated with medical decisions: a systematic review. BMC medical informatics and decision making, 16(1), 1-14.

<mark>Schneider, P., Popkin, B., Shekar, M., Eberwein, J. D., Block, C., & Okamura, K. S. (2020). Health and economic impacts of overweight/obesity.</mark> _<mark>Health and Economic Consequences of an Impending Global Challenge</mark>_ <mark>, 69</mark> Scott, I. A., Soon, J., Elshaug, A. G., & Lindner, R. (2017). Countering cognitive biases in minimising low value care. Medical Journal of Australia, 206(9), 407-411.

<mark>Serrano Fuentes, N., Rogers, A., & Portillo, M. C. (2019). Social network influences and the adoption of obesityrelated behaviours in adults: a critical interpretative synthesis review.</mark> _<mark>BMC public health</mark>_ <mark>,</mark> _<mark>19</mark>_ <mark>(1), 1-20.</mark> Skov, L. R., Lourenco, S., Hansen, G. L., Mikkelsen, B. E., & Schofield, C. (2013). Choice architecture as a means to ‐ change eating behaviour
I’m less likely to get asked for a bribe than in the old days.”<sup>37</sup> However, as would be expected, significant corruption still exists, especially in customs. According to a previous Commissioner General (CG), “elaborate schemes of fraud exist.”<sup>38</sup> Yet in order to reduce corruption further, additional resources and perhaps additional autonomy, will be required.

> 36 Survey data from Taliercio (2000).

> 37 Interview, Nairobi, Kenya, 7/6/2001.

> 38 Interview, Nairobi, Kenya, 7/4/2001.

26
enhance the overall performance of the banking industry. We test whether the presence of entry barriers amplifies the negative effect of bank market power on access to finance for financially constrained firms by interacting an index of entry into banking with our external finance dependence measure. Our entry into banking index is taken from Barth et al. (2013) and measures a country’s requirements of entering into banking based on the amount of information required to apply for a banking license. The values of the index of entry into banking range from 0 to 8, with higher values indicating greater stringency. Accordingly, we should expect a negative sign for our interaction variable, i.e. lower stringency is associated with higher value added of financial constrained firms. Results are reported in column 3 (labeled “Bank entry”). While the estimated coefficient is always positive, we do not find a statistically significant impact of entry barriers in the banking sector on growth.

National regulators not only license banks but also specify permissible activities. Countries may restrict banks to a narrow range of activities, or allow them to engage in a broad array. The scope of activities helps define what is meant by a “bank” in a country. The possibility for a bank to diversify its business model and offer a wide array of products and services to firms, especially SMEs, may be beneficial for economic growth. We test this hypothesis by interacting an index of activity restrictions with the external finance dependence index. The index of activity restrictions is from Barth et al. (2013) and measures the degree to which national regulations restrict banks from engaging in securities activities, insurance activities, and real estate activities. The index ranges from 1 to 4, where larger values indicate more restrictions on banks’ activities. Results are reported in column 4 (labeled “Act restr”). For all measures of market power in the banking sector, we find that the degree of restrictions on banks’ activities enter the model with a statistically significant negative sign.
is the first randomized controlled trial (RCT) experiment to examine the _Choices_ program’s impact.<sup>6</sup> We report results from both individual-level surveys and a lab-in-the-field experiment to estimate the impact of the _Choices_ program on adolescents’ gender attitudes and behaviors, and examine whether the effects differ for boys and girls. In the randomized experiment we also test whether the participation of their parents in a complementary gender norms program influences the effects of the _Choices_ program on adolescent outcomes.<sup>7</sup> Importantly, we measure persistence of program effects beyond an immediate effect using two follow-up survey rounds: 6 months and 2 years post-intervention. We test a number of key mechanisms examining whether the program affects personally held gender attitudes, perceptions of others’ beliefs, the propensity to conform to gender norms, and the willingness to stand up for their own beliefs, even in the face of opposing views.

First, our results show that the _Choices_ program in Somalia has a positive treatment impact on the personal gender equitable attitudes reported by both adolescent boys and girls. The treatment led to a 0.07 standard deviation increase in an index of gender equitable attitudes in the short-run, and a 0.05 standard deviation unit increase in the longer-run.

> 3The _Choices_ program studied here targets young adolescents aged 10-14. Therefore, study participants may be vulnerable to peer pressure to conform. One may also expect adolescence to be a period of greater rebellion against societal expectations as individuals wrestle with gaining a sense of individuality and identity.

> 4We examine conformity as a behavioral outcome that measures an adolescent’s likelihood of changing their privately-held personal attitudes when grouped with peers of the same or opposite sex in public.

> 5 _Choices_ is a component of the Challenging Harmful Attitudes and Norms for Gender Equality and Empowerment in Somalia (CHANGES) program under the Social Norms and Participation (SNaP) project.

> 6The _Choices_ program has been adapted for use in over 10 countries. The program was evaluated in Nepal
WPS3909

# **Trading on Time**

Simeon Djankov Caroline Freund Cong S. Pham*

**Abstract:** We determine how time delays affect international trade, using newlycollected World Bank data on the days it takes to move standard cargo from the factory gate to the ship in 126 countries. We estimate a modified gravity equation, controlling for endogeneity and remoteness. On average, each additional day that a product is delayed prior to being shipped reduces trade by at least 1 percent.  Put differently, each day is equivalent to a country distancing itself from its trade partners by 70 km on average. Delays have an even greater impact on developing country exports and exports of timesensitive goods, such as perishable agricultural products. In particular, a day’s delay reduces a country’s relative exports of time-sensitive to time-insensitive agricultural goods by 6 percent.

_JEL codes: F13, F14 and F15_ _Keywords: trade facilitation, time costs, gravity models_

## World Bank Policy Research Working Paper 3909, May 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _____________________________

* The authors are from the World Bank. Corresponding author: Caroline Freund, World Bank, Mailstop MC3-301, 1818 H Street, NW, Washington, D.C. 20433, ph:(202) 333-1351, email: <u>cfreund@worldbank.org. We thank Mary Amiti, James Harrigan, Bernard Hoekman, Alan</u> Winters and