rafmacalaba/data-use-mentions-extended huggingface dataset:
significant impact on transaction costs, which in turn affect trade. To examine bilateral trade, the authors use an augmented gravity model that, in addition to distance and the GDP of each country, includes dummy variables for common border, common language, common trade area, and common religion and a set of variables representing institutional quality (voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption). The analysis controls for multicollinearity and includes a composite institutional quality index. Results show that increasing the overall quality of institutions above its mean would significantly increase bilateral trade.

This paper extends the analyses of de Groot et al. (2004) and Hummels (2001) in several respects. First, it uses bilateral trade data from 2003, thus updating the data by five years.<sup>2</sup> Second, it uses a new data set, the first of its kind, which contains quantitative, country-level data on important aspects of trade logistics, including inland transport and trade-related transactions, using concrete metrics of cost and time. Thus it extends Hummels’s model by expanding the transport time measure to include time for document processing, customs clearance, and inland transport, and to include timeliness (or variability in time) rather than average time only.

# **Data**

The data set, compiled by the World Bank in 2005, contains detailed country-level data on the time and cost of moving a typical 20-foot FCL container from the port of entry to a firm in the most populous or commercially active city in the country—or to the port of exit from a firm in that city.<sup>3</sup> The use of a disaggregated supply chain framework makes it possible to measure time and cost for such activities as trade document processing, approvals needed for import or export transactions, customs clearance, technical clearances, inland transport, terminal handling, and container security measures. In addition, the data illuminate underlying policy
**<u>Table 7: O&M Cost Recovery Index</u>**

|**Indicators**|**Jalani**<br>**Sche**|**dhi**<br>**mes**|**Other Co**<br>**Managed**|**mmunity**<br>**Schemes**|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|Revenue as Proportion to O&M Costs|1.21|0.77|1.74|2.28|
|ProportionofSchemes with FullO&MCostRecovery|0.47|0.5|0.5|0.51|

Performance of Jalanidhi schemes in most of the dimensions of institutional sustainability is better compared to other community driven schemes (Table 8). Almost one-third of the BG executive committee members of Jalanidhi schemes are women with 9 percent of the BGs having a woman president and 14 percent having a woman secretary. Almost half of the Jalanidhi BGs have women treasures compared to one-fourth for the other community managed schemes. Jalanidhi BGs also have more regular elections for the executive committees and 74 percent of these BGs reported preparing and presenting Annual Reports in general body meetings compared to 64 percent of the BGs for the other community managed schemes. Similarly, a larger majority of the Jalanidhi BGs reported having a savings bank account and the average balance in these accounts is almost three times compared to other community managed schemes. However, the average balance even for the Jalanidhi BGs is only ₹ 17,161 which severely limits the ability of the majority of the BGs to undertake big investments when necessary.

**<u>Table 8: Institutional Sustainability Index</u>**

|**Indicators
Table 3 Impact of Hurricanes on GDP per capita (SS ≥ 3)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Linear<br>Regression|Linear<br>Regression<br>correcting for<br>Serial & Spatial<br>Autocorrelation|Panel Data<br>with Controls<sup>1/</sup>|Panel Data<br>correcting for<br>Dynamic<br>Models|
|MSWS|‐0.0115***|‐0.0115|‐0.0113***|‐0.0123***|
|Standard Error|(0.00195)|(0.00855)|(0.00324)|(0.00330)|
|R‐squared|0.367|0.434|0.111|n.a.|
|IHD|‐0.0151***|‐0.0167**|‐0.0169***|‐0.0186***|
|Standard Error|(0.00251)|(0.00840)|(0.00314)|(0.00328)|
|R‐squared|0.405|0.476|0.191|n.a.|
|WEI|‐0.0127***|‐0.0135|‐0.0134***|‐0.0145***|
|Standard Error|(0.00211)|(0.00914)|(0.00319)|(0.00329)|
|R‐squared|0.379|0.449|0.139|n.a.|

*** p<0.01, ** p<0.05, * p<0
## **2.2 Energy-Centric Management Practices**

In contrast to general management practices, specialized management practices target a specific aspect of the firm’s operations. Some examples of energy management practices include the introduction of target-setting and monitoring practices related to energy use, an explicit requirement to adhere to energy conservation norms, rewards for employees that promote energy efficiency, emphasis on energy management as a priority, and encouraging innovation in controlling energy use. Based on data from 190 firms in the United Kingdom, Martin et al. (2012) find that climate-friendly management practices are associated with lower energy intensity and higher productivity. Climate-friendly practices in this setting included 33 practices, including the stringency of external GHG reduction targets faced, adaptation measures undertaken, and energy use target-setting and monitoring. Using surveys of environmental management practice adoption at 500 facilities, Delmas and Toffel (2008) find that distinct external constituents prompt firms to adopt specialized environmental management practices. In both cases, the emphasis is on studying the correlation of environment-friendly management practices with energy use, using a limited sample size. We are unaware of any study that explicitly measures energy-specific practices, such as tracking physical quantity alongside expenditures and encouraging cost-effective investments in energy efficiency. To the best of our knowledge, our study is the first to examine the emergence of these practices using data that include many developing country firms, and in this sense our work makes an important contribution to this literature.

# **3 Data Preparation and Exploration**

## **3.1 Data Preparation**

The recent wave of the World Bank Enterprise Surveys conducted in 2018 and 2019 for 31 countries collected nationally-representative firm data on a number of financial, operational, 8
regional building inventories such as the AIR Tropical Cyclone Model for the Caribbean.<sup>15</sup> The resulting construction, occupancy mix, and height distribution of the building stock is a fundamental determinant of the region’s vulnerability and the reason why many Caribbean islands have suffered deep impacts from tropical storms and hurricanes in the last 20 years.

**Figure 2. Tropical storms and hurricanes in the Caribbean, by country and category (2000–12)**

<!-- Start of picture text --> Cuba 11 11 9 9 8 6<br>Bahamas, The 11 8 8 7 8 6<br>Haiti 10 10 10 10 4 3<br>Dominican Republic 10 10 8 7 5 2<br>Bonaire, St. Eustatius and Saba 12 6 2 3 5 3<br>Puerto Rico 8 6 6 6 3 1<br>St. Vincent and the Grenadines 11 6 5 3 2 1<br>Jamaica 6 4 4 5 4 5<br>Virgin Islands, U.S. 6 5 5 5 3 2<br>British Virgin Islands 6 6 5 3 3 2<br>St. Kitts and Nevis 8 7 3 3 2<br>Cat. 0<br>Trinidad and Tobago 13 3 1 2 2 1<br> Cat. 1<br>Guadeloupe 13 6 2 1<br>Grenada 10 4 2 2 2 2 Cat. 2<br>Barbados 12 5 3 11 Cat. 3<br>Antigua and Barbuda 8 5 4 2 2 1 Cat. 4<br>St. Lucia 11 3 1 2 2 2 Cat. 5<br>Cayman Islands 7 2 2 3 3 4<br>St. Martin 5 6 5 11 2<br>Bermuda 3 4 3 2 4 4<br>Sint Maarten 5 5 5 11 2<br>Montserrat 9 7 3<br>Anguilla 5 4 4 3 1 2<br>
# **Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning**<sup>*</sup>

David Newhouse<sup>§</sup> Joshua D. Merfeld<sup>†</sup> Anusha Pudugramam Ramakrishnan<sup>‡</sup> Tom Swartz<sup>§</sup> Partha Lahiri<sup>+</sup>

**Keywords:** _poverty, small area estimation, poverty mapping, satellite data, machine learning_

**JEL codes:** C53, C83, I32

> * We are indebted to Boris Babenko and Jonathon Hersh who made substantial contributions to previous versions. We thank Shiva Makki and the World Bank’s Research Support Board for financial support and James Crawford and Orbital Insight, Inc. for support in providing AGEB-level estimates of land classification and poverty. We thank Keith Garrett and seminar participants at the Northeast University Development Conference, the World Bank, the KDI School, and Sungkyunkwan University for helpful comments. All remaining errors are ours.

§ Development Economics Data Group, World Bank, and IZA † KDI School of Public Policy and Management, IZA ‡ Consultant, World Bank § Change Research + Joint Program in Survey Methodology and Department of Mathematics, University of Maryland, College Park
Riders and platform respondents both state that the risk of harassment is substantially higher in the public space: the perceived risk of either verbal or physical harassment is about twice as high in the public space as in the reserved space.

# **4.3 Attitudes on Harassment and Gender Segregation**

To document the attitudes women face on their daily commute, we design and implement a social norm survey and a pair of Implicit Association Tests which we administer on a random sample of men and women commuters on the platform (Section 3).<sup>23</sup> We present a few descriptive statistics from these data to guide interpretation of our experimental results.

## **Stated attitudes measures**

Perhaps surprisingly, the majority of men and women commuters openly blame women for inviting harassment or not doing enough to avoid it (Table A6). Most agree with at least one of the statements assigning responsibility to women for avoiding harassment or blaming them for harassment experienced in the public space. While men and women do not differ much in their attitudes, women perceive men and women’s attitudes toward women riding in the public space to be quite different (Figure A5). Women are more likely to believe that men perceive women in the public space as more provocative than men are likely to report such attitude. In contrast, women’s second-order beliefs about other women’s attitudes match women’s beliefs.

## **Implicit attitudes measures**

We find that commuters associate seeking safety more strongly with women in the reserved space than with women in the public space. They also associate being more open to sexual advances more strongly women in the public space with than with women in the reserved space. The IAT D-scores can theoretically range from -2 to +2, with zero indicating no 23The full instruments and details of recruitment are provided in the Online Appendix.

16
# 4. Estimating a vulnerability map of Senegal

A vulnerability map is estimated at the commune level. To achieve this, the steps outlined in section 3 were followed, and an individual model was developed to predict household expenditures in each of the country's 14 regions. Appendix A, table A.1 compares the variables available in the census and the household survey, while regressions for each region are illustrated in appendix B, table B.1. The variables used for each of the regions vary, but present consistent coefficients, such as a negative relationship with household expenditure in the case of household size, the dependency ratio, and rural location, a positive correlation with higher educational attainment and asset ownership, and a positive relationship with dwelling conditions and better access to public services, such as piped water. Another measure of the model's performance is how closely poverty estimates in the census data match the poverty rates observed in the household survey. The difference between the observed and predicted poverty rates is less than 1 percent at the national level while in 10 of the 14 regions it is below 5 percent. Yet, all estimates fall within the survey’s poverty estimate 95 percent confidence interval (table 1).

**Table 1. The observed poverty rate and small area estimation predictions, by region**

|_Region_<br>|_Su_<br>|_rvey_<br>|_Cen_<br>|_sus_<br>|
|---|---|---|---|---|
|<br>_Pov_|_erty rate(observed)_|_Vulnerability (predicted)_|_Poverty rate(predicted)_|_Vulnerability (predicted)_|
|Dakar|9.0|11.0|9.4|16.0|
|Diourbel|43.9|60.6|44.7|61.4|
|Fatick|49.2|74.7|52.6
* , 9726 (2020).

17. Huong, H. T. L. & Pathirana, A. Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. _Hydrology and Earth System Sciences_ **17** , 379–394 (2013).

18. IPCC. IPCC 5th Assessment Synthesis Report. _IPCC 5th Assessment Synthesis Report_ http://ar5syr.ipcc.ch/topic_futurechanges.php (2014).

19. Kasmalkar, I. G. _et al._ When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond. _Science Advances_ **6** , eaba2423 (2020).

20. Kendon, E. J., Roberts, N. M., Senior, C. A. & Roberts, M. J. Realism of Rainfall in a Very HighResolution Regional Climate Model. _Journal of Climate_ **25** , 5791–5806 (2012).

21. Hirabayashi, Y. _et al._ Global flood risk under climate change. _Nature Climate Change_ **3** , 816–821 (2013).

22. University Catholic Louvain-Brussels. EM-DAT The OFDA/CRED international disaster database. https://public.emdat.be/ (2021).

23. Jongman, B., Ward, P. J. & Aerts, J. C. J. H. Global exposure to river and coastal flooding: Long term trends and changes. _Global Environmental Change_ **22** , 823–835 (2012).

24. Visser, H., Petersen, A. C. & Ligtvoet, W. On the relation between weather-related disaster impacts, vulnerability and climate change. _Climatic Change_ **125** , 461–477 (2014).

25. Hallegatte, S., Rentschler, J. & Rozenberg, J. _Lifelines : The Resilient Infrastructure Opportunity_ . (World Bank, 2019).

26. Winsemius
# **1 Introduction**

After decades of uneven privatization across countries, the recent Covid-19 pandemic has brought back discussions about the role of state-owned enterprises (SOEs) and firms with state participation in the economy. Increasing economies’ resilience to shocks, using state participation as a mechanism for the diffusion of technologies for climate mitigation and adaptation, and ensuring the production of critical health products, technologies, and infrastructure in the midst of growing geopolitical tensions are being used to justify an increased presence of Businesses of the State (BOS) - defined as firms with direct or indirect state participation of at least 10% - in the economy.

In the case of Brazil, despite the large privatization program in the 1990s and the recent privatization processes carried out by the latest administrations, BOS are still important in some sectors - especially in infrastructure, extractive industries, and postal activities - and the state footprint has expanded to investments to private firms in competitive sectors. However, little is known about the impact of these BOS on markets and how effectively they play some of the roles they are supposed to play. This paper studies the effects of BOS in Brazil from different angles, particularly their effects on the labor market and business dynamism. The contribution of this paper is twofold. First, in addition to the traditional analysis of SOEs, the analysis uses a unique dataset that allows describing BOS in their different modalities, from fully public enterprises to private companies with minority state participation operating in competitive sectors. Second, in addition to measuring the effects of specific roles of the state footprint on the labor market, we also study the impact that BOS presence has on business dynamism.

Specifically, we first provide a descriptive analysis of BOS and find that, on average, BOS pay higher wages, are older, and are larger than their private peers. Next, we perform a series of estimates controlling for firms’ characteristics to examine how BOS differ from private firms in terms of innovation, employment and employment growth. Using data from 2016
health (Clark & Royer, 2013; Powdthavee, 2010), their propensity to get a divorce (Oreopoulos & Salvanes, 2011), and positively affecting attitudes toward climate change (Angrist et al., 2024).

We use labor force and household surveys to show the returns to education overall, and for men and women. Further, we control for endogeneity and use the compulsory schooling law to causally estimate the returns to schooling. We complement our analysis of the return to schooling by using macro-level indicators to estimate the return of an additional year of schooling in the population on gross national income per capita, which we then use to simulate the economic loss from restricting female education. The returns to education in Afghanistan are low on average and the labor force participation rate of women is low. The return to schooling is lowest in Afghanistan among all countries studied (Montenegro & Patrinos, 2023). At only 4 percent, it is dramatically low compared to a worldwide average of 10 percent (Psacharopoulos & Patrinos, 2018). However, women have a much higher rate of return to education. Thus, every year of schooling produces a larger increment in earnings for women than it does for men. Women’s returns are much higher at 13 percent compared to just 3 percent for men. Their contribution to the economy is therefore proportionately larger. We project that banning women from education and work could cost the country annually more than $1.4 billion.

Since the 1970s, Afghanistan has endured continuous instability. The overthrow of the last king triggered the invasion by the Soviet Union in 1979. A Soviet-backed state ruled until 1993, followed by a period of Mujahideen rule, civil war, and the Taliban’s rise to power in 1996. The Taliban regime was toppled by the invasion of the United States in 2001, leading to the establishment of the Islamic Republic of Afghanistan, which fell in 2021, marking the Taliban’s return. These upheavals have caused widespread disruptions, deeply affecting educational opportunities
**Figure 3:** Average prevalence of Rohingya households, for Teknaf and Ukhia by camp and non-camp villages

<!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>Teknaf Camp in No camp in Ukhia upazila Camp in No camp in<br>upazila village village village village<br>Teknaf Ukhia<br>Prevalence: Old Rohingya Prevalence: New Rohingya<br><!-- End of picture text --> Source: IPA Population Count, 2018. The IPA population count counts with 201 villages in Teknaf and 209 villages in Ukhia. Old Rohingya refers to displaced population arrived before September 2017. New Rohingya refers to population arrived after September 2017.

In summary, analysis of the IPA population count indicated a very small presence of recently arrived Rohingya outside camps and in upazilas that did not host camps. The few Rohingya who were reported as living outside camps were located in the adjoining upazilas of Cox’s Bazar Sadar and Ramu that border Ukhia. These persons were almost all from the older cohort of Rohingya who arrived before the 2017 influx.

However, analyses of the count highlighted two challenges of relying on these data alone to conclude that the prevalence of Rohingya displaced outside camps was low. First, due to inconsistencies in the use of geographic codes, 35 percent of the villages covered by the IPA count could not be matched to the official Housing and Population Census host population data, and therefore no refugee prevalence information was available for these villages.<sup>13</sup> Second, the IPA count relied on a non-standard methodology for collecting population numbers. Standard quick counts are typically used by all national households
approximation. This is because the lognormal relies only on one parameter (the standard deviation) to capture the shape of the income distribution, while the other approximations rely on two or more, and are thus better able to capture the tails of the distributions. Whenever possible, we will therefore use the best fit of the General Quadratic and the Beta Lorenz curves (as reported by the _PovCal_ database, and, for LAC by the comparison with actual data), and leave the use of the lognormal approximation only for simulations that will not allow for this.

# **Simulations**

The heart of our analysis lies in simulating counterfactual poverty changes between 1995 and 2008, where we keep country-specific “performances,” but attribute equal “initial conditions” to countries. These two concepts remain specific to the way we structure the simulations, and deserve some attention.

Let us consider a space of income distributions _f(y,_ μ _,G)_ characterized by two parameters: _average income_ μ, and an _inequality index G_ . We then consider a _poverty index H(z,_ μ _,G)_ that can be mapped to each income distribution as follows: where _z_ can be thought of as an absolute reference threshold, which we shall denote as the _poverty line_ , and _h(y,z)_ a function that can be integrated. Observe that common indexes, such as the poverty headcount and the poverty gap, can all be expressed in a form compatible with (1).

Consider then, two income distributions, f(y, μ0, G0) , and f(y, μ1, G1) . Think of the first set of parameters as characterizing the distribution of income in a given country in period zero (i.e., 1995 for our main simulations), and of the second set as characterizing the distribution of income in period one (i.e., 2008). For a poverty line _z_ , changes in poverty between the two
your firm’s sales are sold domestically, exported directly, or indirectly through a distributor?” Firms that report a positive value for either direct or indirect exports are identified as exporters, with the dummy variable _Prob_ ( _Exportijt >_ 0) taking a value of one. The sum of direct and indirect exports yields the value of total export as a percentage of total sales used to estimate Equation 2. For robustness checks, we use alternative definitions of the dependent variables. First we consider excluding exports sold indirectly through a distributor. Second, we measure the dependent variable in Equation 2 as the total value of exports, allowing for a distinction between small and large firms.<sup>5</sup> There is an objective and a subjective measure of the variable _CreditConstraintijt_ . The sub-

> 5Total value of exports is not directly recorded in the BEEPs survey. It is calculated by multiplying the share of exports in total sales and the value of total sales. While export value is a preferable dependent variable to the share of exports in total sales, there are inconsistencies in the way total sales are recorded across the countries and time periods. We cannot dismiss the potentially considerable measurement error and related bias. Hence, we use this measure only for robustness checks.

6