rafmacalaba/datause-encoder-data huggingface dataset:
and the data we use. Relative to Duranton _et al._ (2015) who work at the level of districts, using industries and states leads to a lesser importance of the misallocation of land and buildings and a greater importance of the misallocation of employment. This difference should be kept into perspective nonetheless because the relative size of the coefficients obtained here are somewhat sensitive to the exact weighting scheme used to estimate the regressions. We also use the data at a different degree of aggregation. The misallocation of land and buildings may be more important when we look at a finer degree of spatial resolution, districts instead of states.

# **_5.4 Robustness check: Effects of factor misallocation on output per worker for industries and states_**

Turning to regression (7), we now assess the effect of factor misallocation on output per worker. In column 1 of table 15, we regress output per worker on misallocation for employment, land and buildings, and other fixed assets. The coefficient for the misallocation of employment is negative and highly significant. The coefficient on the misallocation of land and buildings is also negative and significant but smaller by nearly 50%. The coefficient on other fixed assets is instead positive and highly significant. Although of opposite sign, its magnitude is about the same as that on the misallocation of land and buildings. As can be seen for the other columns of the same table, these patterns are extremely robust except for the significance of misallocation of other fixed assets. When imposing both industry and state fixed effects, the coefficient on employment misallocation remains at -0.3 while that on the misallocation of land and buildings remains at -0.2. It is only when we look at the organized sector separately that the patterns are somewhat different with some differences across the two sectors. For the unorganized sectors, the effects of employment and land and buildings misallocation are stronger while those of other fixed assets misallocation are positive and highly significant.

A first explanation for these slightly conflicting results and the estimation of positive is the of The correlation between the coefficients presence multicollinearity problems.

34
short, there is no reason to believe that one-off workshops will be effective, despite their being common in school systems.

_What to teach?_ Relative to theory or general pedagogy, subject-specific pedagogy is likely to be most effective, as different subjects require radically different pedagogies (Villegas-Reimers, 2003). For example, a more scripted approach may work for early grade reading, whereas later grade science will require higher-order thinking skills, and teachers need training in different pedagogies for these methods.

_Where to teach?_ In general, teacher training in the school (“embedded”) is likely to be most effective so that concrete problems faced in the local environment can be raised, and teachers can receive feedback on actual teaching (Wood & McQuarrie, 1999). However, this will depend on the environment. In very difficult teaching environments, some degree of training outside the school may facilitate focus on the part of the trainees (Kraft & Papay, 2014).

# **What works in high-income countries?**

A full review of the literature in high-income countries is beyond the scope of this study. However, it may be useful to highlight recent work on in-service teacher training from the United States – which spends almost $18,000 per teacher and 19 days of teacher time on training each year (TNTP, 2015) – and other high-income countries, in order to ensure that low- and middleincome countries are not ignoring well-established evidence. A recent meta-analysis of 196 randomized field experiments from the U.S. that measure student test scores as an outcome examined the impact of both “general” and “managed” professional development, relative to other interventions (Fryer, 2016). General professional development (PD) – as the name suggests – leaves a fair amount of flexibility, even as it may focus on classroom management or increasing the rigor of teachers’ knowledge. Managed professional development, on the other hand, is much more prescriptive; it prescribes a specific method, with detailed instructions on implementation 6
- Fink, C., Mattoo, A., and Neagu, I. C. (2005). Assessing the impact of communication costs on international trade. _Journal of International Economics_ , 67(2):428 – 445.

- Francois, J. and Hoekman, B. (2010). Services trade and policy. _Journal of Economic Literature_ , 48(3):642.

- Francois, J. and Woerz, J. (2008). Producer services, manufacturing linkages, and trade. –

- _Journal of Industry, Competition and Trade_ , 8(3-4):199 229.

- Hoekman, B., Martin, W., and Mattoo, A. (2010). Conclude doha: it matters! _World Trade Review_ , 9(03):505–530.

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- 94(3):605 627.

- Khandelwal, A. and Topalova, P. (2011). Trade liberalization and firm productivity: The case of india. _Review of Economics and Statistics, forthcoming_ .

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- Markusen, J. R. (1989). Trade in producer services and in other specialized intermediate inputs. _The American Economic Review_ , 79(1):pp. 85–95.

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- industry productivity. _Econometrica_ , 71(6):1695 1725.

- Ofer, G. (1973). _The service sector in Soviet economic growth: a comparative study_ , volume 141. Harvard University Press.

- Olley, G. S. and Pakes, A. (1996). The dynamics of productivity in the telecommunications –

- equipment industry. _Econometrica_ , 64(6):1263 1297.

36
# **6.4 Expectations and Investment: A Model**

Investments are actions that lead to costs in the present and, potentially, returns in the future. As such they are a key mechanism by which resurgent violence, if anticipated, can affect economic activity even during peace. Besley and Mueller (2012) propose a model to think about future expectations of violence and find that it can explain housing prices in Northern Ireland quite well.<sup>44</sup> Their main idea is to model violence as a two-states Markov Chain.

The model supposes that there are two states: conflict and peace. Conflict in country _i_ in year _t_ is denoted _sit_ = 1 and peace is denoted _sit_ = 0. Assume that countries are characterized by two probabilities which are assumed constant across time. The first probability _pi_ determines how likely it is that a period of conflict is followed by another period of conflict. Analogously the probability _qi_ captures the likelihood that a period of peace is followed by another period of peace. The following table captures the whole model.

If there is conflict in country _i_ this year then there is a likelihood of _pi_ that the country will be in conflict next year as well. There will be a probability 1 _− pi_ that conflict has ended and the country is in peace. Similarly, if country _i_ is in peace this year there is a probability of 1 _− qi_ that the country will experience conflict next year. The stability of peace is _qi_ in this model. Low levels of _qi_ imply that country _i_ cannot keep the peace for long periods in a row. If investors know this then investment might stay away despite the fact that a country is in peace.

To see the impact of _pi_ and _qi_ on investment, note that the likelihood of peace next year is given by Pr(peace next year) = (1 _− pi_ ) _∗ sit_ + _qi ∗_ (1 _− sit_ ) which is saying that the probability of peace next year switches from 1 _− pi_ to _qi_ when a country transitions enters recovery. That is, expectations change more in transition if _pi_ and _qi_ are large. We expect, for example, little changes in investments if investors can anticipate peace to be unstable (low _qi_ ).

In practice _qi_ could obviously vary by time as well. This will depend on what factors are taken into account by investors. If forecasts of instability are based on factors like political institutions, ethnic composition, climate or the availability of natural resources then we do not expect _qi_ to vary with time as these factors are fixed or only slowly moving. However, given our discussion of refugees, for example, we expect at least some factors to be time-varying.

> 44Besley and Mueller (2015a) show that an adaption of the same model can make sense of investment data more generally.

53
To illustrate the increase in trade costs caused by the multiplicity of pallet sizes, we present data from a case study of a lower middle-income economy, Ecuador. Veenstra et al. (2000) study the cost of the lack of pallet size standardization. They conducted a detailed study for banana exports from Ecuador to Europe. Veenstra et al. (2000) find that banana cartons exported to continental Europe (the Netherlands excluded) have to be repalletized from a 1200*1000 pallet to a 1200*800 Europallet at the port of discharge. The latter pallet is the pallet size almost exclusively accepted in the European market (for the whole chain, see Figure 2).

**Figure 2: The Banana Chain from Ecuador to Europe**

<!-- Start of picture text --> Trucking Sea transport Trucking Trucking<br>Packing Port of Port of Ripening<br>Distrib.Cent.<br>house loading discharge<br>facility<br>Build up pallets Unloading truck Unloading vessel Unloading truck Unloading truck<br>Storage pallets Storage pallets Storage pallets Storage/ripening<br>Loading truck Loading vessel Repalletisation Repacking /VAS<br>Loading truck Repalletisation<br>Loading truck<br><!-- End of picture text --> Source : Veenstra et al. (2000).

Note: the second repalletization is generally a retailer’s policy.

They first examine the inefficiencies linked to the lack of standardization and then estimate their cost. In Table 1, a combined scenario is compared to the benchmark case of a harmonized ISO pallet chain. The three inefficiencies identified concern pallet material, handling and equipment:

1. Pallet material: the Europallet is the smallest ISO size, which means more Europallets are needed to transport the same number of cartons (inefficiency of space and additional pallets required),

2. Handling: pallets have to be handled on several occasions (see Figure 2), 7
strongly constrained in our model. Regarding formal housing, inertia in housing supply slows down potential formal housing relocations. As a result, urban reconfigurations have a low potential for emissions reduction in our model. We conducted two sensibility checks (Appendix B). First, we implemented a fuel tax of the same monetary amount (10cts) per kilometer for all transportation modes (Table 9). Second, we look at the result after 20 years, to let formal housing supply the time to adjust despite inertia (Table 10). In these cases, scenario 1 remains more efficient in terms of emissions mitigation, and urban reconfigurations play a small role compared with modal shares.

As a result, taxing polluting public transportation modes might be less efficient in terms of emissions mitigation than taxing private cars only, and policy makers should in priority account for potential modal shifts induced by the tax. This result is in line with Avner et al. (2014), which finds that, in Paris, reducing transportation emissions requires a lower level of gasoline tax if a dense network of public transportation is available: indeed, having public transportation options allow to play on modal shift, in addition of urban reconfiguration, to reduce emissions.

|||Pas<br>|senger-km var<br>|iations<br>||Emissions<br>|
|---|---|---|---|---|---|---|
||Walking|Train|Private car|Minibuses/<br>taxis|Buses|variations|
|**Scenario 1**|**+0.45%**|**+1.24%**|**-9.34%**|**+4.52%**|**+9.13%**|**-5.04%**|
|-<br>modal shares<br>variations|+0.15%|+1.06%|-11.74%|+3.49%|+7.63%||
|-<br>average distances<br>variations|+0.30%|+0.18%|+2.72%|+1.00%|+1.40%||
|**Scenario 2**|**+22.26%**|**+46.79%**|**+1.64%**|**-28.97%**|**-23.49%**|**-0.59%**|
|-<br>modal shares<br>variations|+19.63%|+49.94%|+5.41%|-10.91%|-18.99%||
|-<br>average distances<br>variations|+2.20%|-2.10%|-3.58%|-20.27%|-5.56%||

_Table 8 - Impact of the fuel tax on transportation demand and estimations of its impact on transportation emissions._ The outputs of the model also allow us to provide a rough estimate of the monetary value of the improved health through reduced air pollution due to the tax (Appendix C). Overall, scenario 1 allows to gain 3.1 million USD from improved health, and scenario 2 allows to gain 2.1 million USD from improved health, partially offsetting the welfare impact of the tax.

# 8. Discussion

Beyond income inequalities, this work allows decomposing the spatial dynamics resulting from the implementation of a climate transportation policy. In the short run, the climate policy results in an increase in transportation costs, strongly impacting households living in the periphery of the city and commuting by polluting transportation modes. Low-income households are also strongly impacted by the tax, which weighs heavily in their budgets. Then, equilibrium effects tend to mitigate the impacts of the fuel price increase: workers adjust employment centers and transportation mode choices, rents increase in the city center and decrease in the periphery, and households adjust locations and housing types to, overall, live closer to the city center and job-dense locations.

21
**Figure 3: Response Rates by Month, Type of Phone, Incentive and Participants by Response Pattern**

<!-- Start of picture text --> 100 Full Intermittent Non<br>Drop-outs<br>compliers compliers compliers<br>90<br>Full Sample<br>80 30.5 28.8 23.8 16.9<br>70 Nokia + 10 SDG<br>27.2 26.5 24 22.3<br>60<br>Nokia + 5 SDG<br>50<br>37 24.8 24 14.2<br>40<br>Solar + 10 SDG<br>30<br>31.3 28.8 19.5 20.4<br>Nokia + 5 SDG<br>20<br>Nokia + 10 SDG Solar + 5 SDG<br>Solar + 5 SDG<br>10 27.4 34.7 26.7 11.2<br>Solar + 10 SDG<br>0 [Month] Full Sample [Percent]<br>Dec Jan Feb Mar 0 20 40 60 80 100<br>[Response Rate (%)]<br><!-- End of picture text --> Among respondents receiving Nokia phones, those offered 10 SDG credit were about 10 percent less likely in each round to complete the survey compared to respondents offered the 5 SDG credit (Figure 3). There was not a similar pattern among respondents using solar-rechargeable phones. Both Nokia and Safaricom solar phone users completed surveys with the same frequency: among 4,028 possible completed surveys across all four survey rounds the overall response rate for users of both phones was 59 percent. This suggests that the constraint of having to charge the Nokia phone using an electrical outlet did not influence survey completion.

Three probit regression models were used to explore in more detail factors associated with successful completion of the survey. Model 1 in Table 5 assesses the likelihood that a respondent completed the questionnaire given personal characteristics and SSEPS design features. Models 2 and 3 in Table 5 assess the association of these same factors with the likelihood a participant completed all four surveys and with the likelihood a participant completed none.

The decline in response over the course of the four month implementation period is evident in the regression results. Controlling for other variables, respondents were 7, 16, and 20 percent less likely to have completed a questionnaire in January, February, and March relative to December. Operational data tracked by the Horizon Contact Center suggests that this result was in part due to declining network availability over the four month period.

12
# **THE POLITICS OF POWER: The political economy of rent-seeking in electric utilities in the Philippines**

## **Zahid Hasnain**<sup>*****</sup> **Yasuhiko Matsuda**<sup>**†**</sup>

### **JEL Codes: H41, H54, D72**

**Acknowledgements:** This paper has benefitted from comments from Phil Keefer, Alan Townsend, Victor Dato, Beatriz Arizu, Takeshi Kawanakta and Motoky Hayakawa

> * The World Bank

> † The World Bank
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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43
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 12. Benchmarking air transport indicators for Angola and selected countries**<sup>**2**</sup>

|**Country**|**Angola**|DRC|Zambia|Mozambiqu<br>e|Namibia|Republic of<br>Congo|
|---|---|---|---|---|---|---|
|**Traffic (2007)**|||||||
|Domestic seats (seats per year)|**1,199,016**|327,988|437,658|1,144,644|84,162|443,634|
|Seats for international travel within Africa (seats per year)|**484,179**|468,217|1,459,766|582,836|877,812|351,882|
|Seats for intercontinental travel (seats per year)|**588,978**|193,414|113,217|91,637|242,736|117,962|
|Seats available per capita|**0.134**|0.24|0.168|0.087|0.574|0.016|
|Herfindahl-Hirschmann Index—air transport market (%)|**33.25**|22.65|17.53|31.54|39.39|30.79|
|**Quality**|||||||
|Percent of seat-km in newer aircraft|**59.7**|74.7|63.8|57.0|79.0|73.3|
|Percent of seat-km in medium or smaller aircraft|**13.9**|39.3|50.6|42.5|28.3|40.5|
|Percent of carriers passing IATA/IOSA Audit|**1 (as of**<br>**2009)**|0|0|100.0|100.0|0|
|FAA/IASA Audit Status|**No audit**|Failed|No audit|No audit|No audit|No audit|

_Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data).

_Note:_ The Herfindahl-Hirschmann Index is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. An HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent.

FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit; DRC = Democratic Republic of Congo.

# **Challenges**

There is relatively limited competition in the air transport sector. The dominant position of the national carrier TAAG keeps the Herfindahl-Hirschmann Index for Angola’s air transport market relatively high. But this is not to suggest that Angola relies on TAAG alone. Namibian carriers also provide much of the capacity.

Rather than capacity or competition, Angola’s biggest challenge in the air transport sector is safety oversight. All airlines from Angola are on the European Union (EU) blacklist, with exceptions for specified 777s and one 737 operated by TAAG. The latest International Civil Aviation Organization (ICAO) audit results still show significant room for improvement, with Angola being below international averages in nearly all categories except primary aviation legislation.

> 2 All data are as of 2007, based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of worldwide traffic, but a percentage of African traffic is not captured by these data.

27
# **Contents**

|**1**|**Intr**|**oduction**<br>**3**|
|---|---|---|
|**2**|**Em**|**pirical Approaches**<br>**5**|
||2.1|Identifcations and their Drawbacks . . . . . . . . . . . . . . . . . . .<br>6|
|||2.1.1<br>Long-Run Restrictions . . . . . . . . . . . . . . . . . . . . . .<br>6|
|||2.1.2<br>Max-Share<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>6|
||2.2|New Approaches<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>7|
|||2.2.1<br>Non-Accumulated Max-Share (NAMS) . . . . . . . . . . . . .<br>8|
|||2.2.2<br>Spectral Identifcation . . . . . . . . . . . . . . . . . . . . . .<br>9|
|||2.2.3<br>Limited-Horizon Spectral Identifcation<br>. . . . . . . . . . . .<br>11|
|**3**|**Mo**|**del Evaluation**<br>**11**|
||3.1|DSGE Monte Carlo simulations . . . . . . . . . . . . . . . . . . . . .<br>11|
||3.2|Monte-Carlo DSGE Results . . . . . . . . . . . . . . . . . . . . . . .<br>12|
||3.3|An Illustrative Two Variable Data-Generating Process . . . . . . . .<br>14|
|||3.3.1<br>Motivating the Choice of Stochastic Processes . . . . . . . . .<br>16|
|||3.3.2<br>SVAR Performance . . . . . . . . . . . . . . . . . . . . . . . .<br>17|
||3.4|The Case of Unit Roots . . . . . . . . . . . . . . . . . . . . . . . . .<br>19|
||3.5|Summary of Identifcation Performance<br>. . . . . . . . . . . . . . . .<br>22|
|**4**|**SV**<br>|**AR Performance in the Framework of Chari, Kehoe, McGrattan**<br>|
||**(20**|**08)**<br>**23**|
|**5**|**Ap**|**plication to US Data**<br>**27**|
|**6**|**Ap**|**plications to Emerging Markets**<br>**31**|
|**7**|**Det**|**ecting Business-Cycle Shocks**<br>**32**|
|**8**|**Con**|**clusion**<br>**34**|
|**9**|**Ap**|**pendix**<br>**35**|
||9.1|What shock is Max-Share capturing?: Sources of bias<br>. . . . . . . .<br>35|
|||9.1.1<br>Low and high-frequency drivers of forecast errors . . . . . . .<br>35|
|||9.1.2<br>Solving the maximization problem . . . . . . . . . . . . . . .<br>36|
||9.2|DSGE Model Specifcation . . . . . . . . . . . . . . . . . . . . . . . .<br>39|
||9.3|Alternative simulations. . . . . . . . . . . . . . . . . . . . . . . . . .<br>44|
|||9.3.1<br>More persistent confounding shocks<br>. . . . . . . . . . . . . .<br>44|
|||9.3.2<br>Changing the target horizon for Max-Share and NAMS<br>. . .<br>45|
|||9.3.3<br>Where do long-run restrictions work well? . . . . . . . . . . .<br>46|
||9.4|CKM (2008) simulations . . . . . . . . . . . . . . . . . . . . . . . . .<br>47|
||9.5|US Data Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>50|

2