rafmacalaba/datause-encoder-data huggingface dataset:
We compute the covariance of productivity (in logs) and firm size by industry and we aggregate these covariance terms using employment shares. This aggregate covariance value represents the level of resource misallocation. We obtain a covariance term of 0.06 and 0.01 using the WBES2013 and WBES2007 respectively. This result implies a decrease in market distortions. Then, we obtain the same result than the one found using the HK methodology.

Even though the covariance term increased between the two surveys, the value (0.06) is much lower (implying higher market distortions) than the US, Germany, and the Netherlands (respectively 0.51, 0.28, and 0.30; Bartelsman, Haltiwanger, and Scarpetta, 2013) and slightly lower compared to the mean covariance of 52 MIC and LIC (0.12; Inklaar, Lashitew, and Timmer, 2015). These two findings are in line with the results obtained using the HK approach.

With respect to the TFP gain from reallocation, only a back-of-the-envelope calculation is possible. Bartelsman, Haltiwanger, and Scarpetta (2013) finds the aggregated covariance term for the US to be 0.51. The authors show that in an accounting sense, this implies that productivity in the average US manufacturing industry is 51 percent higher than it would be if the size of firms were determined randomly within industries. Analogously, the most recent covariance term value of 0.06 implies that productivity in the average Moroccan manufacturing industry is only 6 percent higher than it would be if the size of firms were randomly determined within industries. Although this is only an approximation, we could argue that if in the Morocco’s manufacturing sector the level of market distortions were reduced to the level found in the US, this sector will increase productivity by 45 percent (51-6). This value (45 percent) is very close to the value found using HK from eliminating distortions up to the level of three relatively undistorted economies (55.6 percent).

# **6 Policy Discussion**

Effective market institutions that limit the extent of product and factor market distortions could be gauged on the basis of at least three objectives: improvement in competition and business climate, low structural unemployment and high economic participation, and strong international economic and financial integration. In all these areas, there is scope for removing market distortions and gradually improving Morocco’s manufacturing TFP (World Bank, 2017a), which will help to reach the goals of the Industrial Acceleration Plan (2014-2020).

## _(i) Improved Competition and Business Climate_

Morocco could strengthen its market institutions to promote and guarantee market openness as well as free competition. Morocco could act in the following two strategic areas to improve resource allocation: strengthen competition and tackle rent seeking; and improve the business environment.

First, steps need to be taken to increase the competition regulatory authorities’ autonomy and clarify their powers, and reduce visible rents (for example, land, 18
# _Estimation using city-pair data_

The growth in country-level bilateral air passenger traffic generated by air services liberalization can be explained by growth along the extensive margin (i.e., the number of direct city-pair aviation services), and along the intensive margin (i.e., growth in traffic within a city-pair). We take advantage of the level of disaggregation in the ICAO dataset to estimate a regression model similar to equation (1) using city-pair air traffic data. This exercise corresponds to an analysis of air services liberalization along the intensive margin. We modify the regression in equation (1) slightly in order to reflect city specific characteristics. First, we replace the country level bilateral distance terms with city-pair distances. Second, we add information on city population levels at origin and destination.<sup>26</sup> Last, we construct a city-specific variable to capture the “hub-ness” of a city by measuring the fraction of all the international departures from a given country that originate in that city. All else equal, consumers attach great value to the flight options of a hub city, while airlines can economize greatly on the fixed cost of ground operations when serving passengers out of the same city. Thus, we expect city “hub-ness” measured at origin and destination to enter positively in the regression.

One advantage of the city-pair data sample is that it allows us to investigate how air services liberalization influences market entry, and thus _the probability that a given city-pair becomes connected through a direct flight service._ We exploit the patterns of ones and zeros for the existence of a direct flight service within a city-pair to estimate a logit regression model.<sup>27</sup> Therefore, we define: and estimate the following logit regression model:

> 26 We keep in the city-pair regression specifications the population variables in order to capture some information on the spatial distribution of population within each country. For example, if a significant fraction of a country’s population lives outside a gateway airport, the city size variable is not going to be sufficient in order to predict the size of city-pair passenger flows.

> 27 Out of the various ways to model binary response variables, we choose a logit regression model because of its estimation performance when there is a very large number of zeros in the sample.

16
the main crops grown in affected areas. Finally, a large CCT program targeted mostly to rural households could have also helped protect their basic welfare in the aftermath of the shock.

A number of checks confirm the robustness of the findings to the trajectories of the outcomes preceding the shock between the treatment and control groups, endogenous compositional changes, nonrandom migration, possible issues of measurement error in the shock variable, alternative shock indicators based on different critical precipitation thresholds and different household-to-weather station matching criteria.

The magnitude of the effects documented in this paper is not trivial. In 2012, Guatemalan authorities reported an increase in the national poverty rate from 51% to 53.7% between 2006 and 2011. Government officials and most analysts have attributed such increase to the collateral effects of the global financial crisis. We, however, argue that this is only part of the story. Since a large fraction of households were bunched right above the poverty line before the shock, the fall in consumption per capita was enough to push nearly 80,000 additional families into poverty. Thus, this natural disaster is one of the key explanatory factors behind the increase in poverty recorded in urban Guatemala between 2006 and 2011. Ignoring the detrimental consequences of natural disasters on human welfare will limit the effectiveness development policy, in particular anti-poverty strategies.

# **References**

- Baez, Javier, Alejandro de la Fuente, and Indhira Santos. 2010. "Do Natural Disasters Affect Human Capital? An Assessment Based on Existing Empirical Evidence." Discussion Paper No. 5164, IZA, Bonn, Germany.

- Bustelo, Monserrat, Mary Arends-Kuenning, and Leonardo Lucchetti. 2012. "Persistent Impact of Natural Disasters on Child Nutrition and Schooling: Evidence from the 1999 Colombian Earthquake." Discussion Paper No. 6354, IZA, Bonn, Germany.

- Dercon, Stefan. 2004a. "Growth and Shocks: Evidence from Rural Ethiopia." _Journal of Development Economics_ 74 (2): 309-29.

- Dercon, Stefan and John Hoddinott. 2004. "Health, Shocks, and Poverty Persistence." In _Insurance against Poverty_ , edited by Oxford, UK: Oxford University Press.

23
Table 5. Regression Estimation of the Impact of Women’s Decision-making Power on Child School Enrollment

||Children|Girls|Boys|
|---|---|---|---|
|Theta|0.489***|0.779***|0.237**|
||[0.074]|[0.108]|[0.099]|
|Ln (per capita consumption||||
|expenditure)|0.400***|0.431***|0.367***|
||[0.011]|[0.016]|[0.014]|
|Age|0.235***|0.213***|0.249***|
||[0.008]|[0.011]|[0.010]|
|Ln (# of people in HH)|0.040***|0.019|0.053***|
||[0.009]|[0.014]|[0.012]|
|Observations|17,696|8,569|9,127|

Note: 1. Absolute value of z statistics in brackets. 2. * significant at 10%; ** significant at 5%; *** significant at 1%3. Other variables controlled for but not presented include child age, child age square, female, household size, demographic characteristics of household members, household’s asset composition, and region fixed effect.

29
More precisely, there will be at most _JR_ thresholds _z_ 0 _,F ≤ z_ 1 _,F ≤ ... ≤ zJR−_ 2 _,F ≤ zJR−_ 1 _,F_ such that agents with skills in the segment <u>[</u> _<u>z, z</u>_ 0 _,F_ ] will become workers (employed for a wage); the set of agents with ability in [ _z_ 0 _,F , z_ 1 _,F_ ] always operate informally (never transition into formal entrepreneurship); agents with abilities in [ _z_ 1 _,F , z_ 2 _,F_ ] operate informally until the last period of their working life; agents with abilities in [ _z_ 2 _,F , z_ 3 _,F_ ] operate informally until the last two periods of their working life; and so on. Entrepreneurs with abilities in [ _zJR−_ 1 _,F ,_ ̄ _z_ ] operate in the formal sector their entire working life. Notice that there may not be any agent who would like to transition into formality for the last periods of their working life as they don’t have much time to invest on their entrepreneurial ability and grow. In these cases, the occupation choices of agents who are not assigned to a tax official are characterized by fewer threshold ability levels.

In sum, there are at most _JR_ + 1 threshold ability levels that determine the occupational choices in equilibrium (one for agents who are assigned to a tax official and _JR_ for those who are not assigned to a tax official).

# **4. Calibration**

We set the model to 5 to the intervals used in our data period years, corresponding analysis. In this framework, agents work for 40 years (8 model periods) and then spend 15 years in retirement (3 model periods). We set the following parameter values from Leal (2014): depreciation rate _δ_ = 0 _._ 055, discount factor _β_ = 0 _._ 945, and tax rate _τ_ = 0 _._ 25. After calibrating the span-of-control parameter _γ_ , we set _α_ such that the importance of capital in the production ( _αγ_ ) equals 0.33, the standard value in the development literature. We assume that entrepreneurial ability follows a log-normal distribution with parameters _μz_ and _σz_ . We normalize the mean parameter _μz_ = 0 and the productivity _A_ = 1. parameter These parametric assumptions leave six parameters to be determined: _γ, σz, θ_ 1 _, θ_ 2 _, b,_ and _φ_ . We jointly calibrate these parameters to match key features of the establishment size distribution in the Mexican census data, with particular attention to the low growth rate of informal plants. Our calibration targets the following moments: (i) the overall mean establishment size; (ii) the mean size of informal establishments; (iii) the fraction of establishments with fewer than 10 workers; (iv) the employment share of establishments with more than 50 workers; (v) the life-cycle growth rate of formal establishments (from ages 0-4 to 35-39); and (vi) the life-cycle growth rate of informal establishments (from 17
**Policy Research Working Paper Series**

|**Tltle**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS**1**02**Did**the Debt Crisis or Declining<br>Oil<br>Prices Cause Mexico's Investment<br>Collapse?|Andrew M. Warner<br>|February 1993|M. Divino<br>33739|
|WPS**1**03 Capital Mobility in Developing<br>Countries: Some Measurement<br>Issu<br>and Empirical Estimates|Peter J. Montiel<br>es|February 1993|R. Vo<br>31047|
|WPSI 104 Trade Policy Reform in Latin Americ<br>and the Caribbean<br>In the 1980s|a Asad Alam<br>Sarath Rajapatirana|February 1993|J. Troncoso<br>37826|
|WPS1105 Estimating<br>Quasi-Fiscal<br>Deficits in<br>a Consistency<br>Framework:<br>The Case<br>of Madagascar|Philippe Le Houerou<br>Hector Sierra|February<br>1993|N. Velasco<br>34346|
|WPS1106 Improving Women's Access to<br>Higher Education:<br>A Review of World<br>Bank Project Experience|Haui Dundar<br>Jennifer Haworth|February 1993|S. David<br>33752|
|WPS**1**07 Financial Reform Lessons and<br>Strategies|Gerard Caprio, Jr.<br>kzak Atiyas<br>James Hanson|February<br>1993|W. Pitayatonakarn<br>37664|
|WPS**1**08 Public Output and Private Decisions<br>Conceptual Issues in the Evaluation<br>of Government<br>Activities and Their<br>Implications<br>for Fiscal Policy|: Thanos Catsambas<br>|February<br>1993|A. Correa<br>38549|
|WPS1109 Risk Management<br>and Stable<br>Financial Structures|Andrew Sheng<br>Yoon Je Cho|March 1993|M. Raggambi<br>37664|
|WPS1110 What Would Happen<br>if All Developin<br>CountriesExpanded<br>Their Manufact<br>Exports?|g Will Manin<br>ured|March 1993|D. Gustaf3on<br>33714|
|WPS1 111 Foreign Investment<br>Law in Central<br>and Eastern Europe|Cheryl W. Gray<br>William Jarosz|March 1993|M. Berg<br>314350|
|WPS**1**12 Privatization,<br>Concentration,<br>and<br>Pressure for Protection: A Steel<br>Sector Study|Ying Qian<br>Ronald C. Duncan|March 1993|S. Lipscomb<br>33718|
|WPSI 1113 The Lucky Few Amidst Economic<br>Decline: Distributional<br>Change in<br>C6te d'lvoire As Seen Through Panel<br>Data Sets, 1985-88|Christiaan Grootaert<br>Ravi Kanbur|March 1993|E. Vitanov<br>38400|
|WPS1114 Does Price Uncertainty<br>Really<br>Reduce Private Investment?<br>A Small Model Applied to Chile|Anita George<br>Jacques Morisset|March 1993|D. Bievenour<br>37899|
**Figure 11. As a Consequence of a Higher Investment Share of Oil Revenue,** **<u>Consumption and Income Levels Will Increase</u>**

<!-- Start of picture text --> 0.4660 0.47<br>0.4650<br>0.45<br>0.4640<br>0.4630<br>0.43<br>0.4620<br>0.4610 0.41<br>0.4600<br>0.39<br>0.4590<br>0.4580<br>0.37<br>0.4570<br>Oil revenue as a share of output<br>0.4560 0.35<br>1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>Income per capita Total consumption by period (right axis)<br>Income per capita<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

Figure 12 reveals that the Pre-Salt oil revenue will only be sufficient to drive Brazil all the way to its efficient capital stock from the point where the two trajectories intersect. This corresponds with the finding that all the oil revenue must be invested — and nothing consumed — if the oil revenue does not increase beyond approx. 10 percent of GDP. On the other hand, if oil revenue reached more than 10 percent of GDP then the capital labor ratio could reach its efficient level (Figure 12), there would be revenue left to be transferred for consumption (Figure 3), and growth in income per capita could be enhanced (Table 3).

**Table 3. Growth Implications of (Pre-Salt) Revenue Investment Policies**

|Oil revenue/GDP|2%|3%|4%|5%|6%|7%|8%|9%|10%|11%|12%|13%|14%|15%|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Investment Share|_100%_|_100%_|_100%_|_100%_|_100%_|_100%_|_100%_|_100%_|97.8%|92.1%|87.4%|83.5%|80.1%|77.3%|
|**Growth**<br>(incomeper capita)|**2.1%**|**3.1%**|**4.0%**|**4.9%**|**5.8%**|**6.7%**|**7.6%**|**8.4%**|**9.0%**|**9.2%**|**9.5%**|**9.7%**|**10.0%**|**10.2%**|

Source: Author’s simulations based on a general equilibrium model with overlapping generations calibrated for Brazil.

24
8. Agnolucci, P. _et al._ Impacts of rising temperatures and farm management practices on global yields of 18 crops. _Nat. Food_ **1** , 562–571 (2020).

9. Deryng, D., Conway, D., Ramankutty, N., Price, J. & Warren, R. Global crop yield response to extreme heat stress under multiple climate change futures. _Environ. Res. Lett._ **9** , 034011 (2014).

10. Gourdji, S. M., Sibley, A. M. & Lobell, D. B. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. _Environ. Res. Lett._ **8** , 024041 (2013).

11. Barlow, K. M., Christy, B. P., O’leary, G. J., Riffkin, P. A. & Nuttall, J. G. Simulating the impact of extreme heat and frost events on wheat crop production: A review. _Field Crops Res._ **171** , 109–119 (2015).

12. Teixeira, E. I., Fischer, G., Van Velthuizen, H., Walter, C. & Ewert, F. Global hot-spots of heat stress on agricultural crops due to climate change. _Agric. For. Meteorol._ **170** , 206–215 (2013).

13. Ritchie, H. Opinion | 3 charts show how better buildings save lives in earthquakes. _Washington Post_ (2023).

14. Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. _Nature_ **529** , 84–87 (2016).

15. Pirlea, A. F., Serajuddin, U., Wadhwa, D., Walsh, M. & eds. _Atlas of Sustainable Development Goals 2023_ . (World Bank, 2023).

16. FAO. _The impact of disasters and crises on agriculture and food security: 2021_ . (FAO, 2021). doi:10.4060/cb3673en.

17. World Bank. _Poverty and Shared Prosperity 2020: Reversals of Fortune_ . (World Bank, 2020). doi:10.1596/978-1-4648-1602-4.

18. Park, J., Bangalore, M., Hallegatte, S. & Sandhoefner, E. Households and heat stress: estimating the distributional consequences of climate change. _Environ. Dev. Econ._ **23** , 349–368 (2018).

19. Tol, R. S. J. The Economic Impacts of Climate Change. _Rev. Environ. Econ. Policy_ **12** , 4–25 (2018).

17
Table 8: Comparison after wave 1: ending the experiment vs. conducting a second wave.

|_Panel A: _|_Posterior Estimates af_|_ter Wave 1._|
|---|---|---|
||Exploration|Sampling|
||Posterior|Posterior|
|Treatment|expected policy<br>regret|probability<br>optimal|
|T1B|0.12%|74.14%|
|_Panel B: _|_Posterior Estimates af_|_ter Wave 2._|
||Exploration|Sampling|
||Avg. posterior|Avg. posterior|
|Wave|expected policy<br>regret [median]<br>|probability<br>optimal [median]|
|1 and 2|0.08% [0.05%]|77.86% [83.01%]|
|_Panel C: _|_Average Realized Value_|_s._|
||Exploration|Sampling|
||Average|Percentage|
|Wave|policy|best arm|
||regret|identifed|
|1|0.13%|71.00%|
|1 and 2|0.06%|81.00%|

Notes: The table summarizes the results of 200 simulated samples based on _β_<sup>_E_</sup> , _κ_<sup>_E_</sup> and _η_<sup>_E_</sup> drawn from their posterior distribution after wave 1.

# **7 Conclusion**

This paper shows a concrete application of the exploration sampling algorithm to demonstrate the successful use of adaptive sampling in real-world policy choice problems. The experiment we conducted provides an opportunity to answer many implementation questions surrounding this new method. For instance, as part of the IVR experiment, we give two examples of Bayesian modeling for the outcomes of interest – here call engagement and oral reading fluency – and show how to use such models to compute the assignment shares in each wave and the posterior expected regret that is used to choose one arm for implementation. We discuss some of the constraints on the research design that are unique to adaptive experiments as well as the approaches to choosing between alternative designs based on simulations.

Our sample application tests six different designs for a new parent outreach method, interactive voice response calls, to encourage home reading with children in Kenya, which is known to improve early literacy. Even though the time and budget for the experiment were limited, the adaptive design is able to identify 43
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why we have not been able to employ such information. Its entries have been extracted from country reports by the Asian Disaster Reduction Center. The reports generally focus on summary information rather than specific information for emergency preparedness by type of disaster (e.g., floods, droughts). In the case of Japan, for example, much of the $34 billion expenditure is clearly for earthquake-related measures. The listed funds for Bangladesh are more than twice China’s and four times Indonesia’s, and they include both emergency food assistance and disaster management. Much of the Indonesian fund undoubtedly relates to geologic disasters (earthquakes, volcanic eruptions, tsunamis) as well as weather-related disasters.

Most reports do not provide time series information, nor do they go beyond reporting for single funds or national-level agencies.<sup>9</sup> Local expenditures are not included.

In summary, the available data are far too spotty, non-specific, non-standardized, and temporally limited to permit estimation of cost functions that could be used for projection. In addition, they cover relatively few countries. There is simply no way to construct a reasonable cost analysis from such information.

# **7.2  The Education Alternative**

Since direct cost measures cannot be derived from the available data, we turn to an indirect approach. As the panel results in Table 6 show, improvements in female education are powerfully associated with reductions in disaster risks once changes in weather and income are accounted for. In this section, we exploit this relationship to address the adaptation cost question indirectly.

Our approach applies straightforward algebra to equation (1). Given an anticipated change in precipitation, we calculate the increase in education that will be just sufficient to restore the risk level prior to the precipitation change. With subscripts B for the baseline case and N for the risk-neutralizing case, we impose the following constraint on the relevant elements of equation (1) (the others cancel because they remain unchanged):

> 9  Our thanks to Tim Essam for his help with gathering this information.

18