rafmacalaba/data-use-mentions-extended huggingface dataset:
There seems to be consensus that the effect that currency substitution may have on the banking system is ambiguous. On the one hand, a high level of dollar deposits in an economy in which the dollar is used as a store of value but not as a medium of exchange will result in higher exchange rate risk in banks' balance sheets, since these banks will take deposits in dollars but will typically lend in domestic currency.'" On the other hand, a system that allows the coexistence of domestic currency deposits and FCDs can help prevent a capital outflow by increasing the buffer that banks have, increasing their ability to absorb bad shocks. In this section we will try to determine which effect dominates.

As preliminary evidence, we investigate how the probability of a banking crisis is influenced by the degree of dollarization in the economy. Using the Caprio et al (1999) database which reports the banking crises for a large set of countries, we can then calculate the probability of a banking crisis for all the countries in our study given a certain level of dollarization. In order these conditional probabilities, we then divide our sample between high andi low dollarized economies."<sup>1</sup> We then find that the probability of a banking crisis, of any kind, increases with the degree of dollarization. In particular, the probability of a banking crisis given that the economy is highly dollarized is 0.381. On the other hand, if the economy is not highly dollarized the probability of a banking crisis is 0.130. We then investigate how the degree of dollarization affects the nature of a banking crisis. Using Caprio's classification and considering all the countries in our study, we compute the probability of having a severe banking crisis given a certain degree of dollarization."<sup>2</sup> We find that the probability of a severe banking crisis increases with the degree of dollarization, presumably because of higher exposure. In particular, the probability of a severe banking
_Beyond Unequal Development_ and form part of the dependent population. Fourth, GDP statistics often under-report informal sector activities in which lower income groups are involved. Family-based or public-based redistribution is thus needed to reach these segments. Fifth, GDP is a commodity or "opulencebased" measure of economic welfare that does not include non-market goods (such as political freedom, the psychological value of belonging to community, etc.) and bads (environmental degradation, crime, urban congestion) that also centrally affect human well-being. Sixth, unlike traditional social policies that had a political constituency in the urban working-class, in the strong unions of the middle class (teachers, doctors) and in other interest groups, the new social policies have as beneficiaries poor and vulnerable groups with a weak voice and feeble political organization; this creates tenuous political incentives for active poverty reduction beyond that provided by economic growth and may be an important factor behind chronic poverty.

Another main piece of the new strategy of social policies is targeting. The emphasis on precisely defining the beneficiary groups is a reaction to social policies that often reached, at a high fiscal cost, the non-poor (the middle class and the rich). A basic principle of targeting is to focus social policy on the poor avoiding reaching the non-poor<sup>1 5</sup> . In this context, targeting is more effective in terms of reaching the "real" poor, and at a substantially reduced fiscal cost than untargeted, broad-based, social policy.

However targeting is not problem free either. First, targeting makes the beneficiary a passive "victim" rather than an active agent with policy responses and choices'<sup>6</sup> . Second, informational and incentives problems are serious. Delimiting the beneficiaries and their poverty features that needs to be addressed is not easy (e.g. an information problem). Reaching with the

> **15** See Cornia and Stwart (1996) on an interesting discussion of two types of "errors" in
on frontline workers or streetlevel bureaucrats) [Perry _et al._ 2009, Banerjee _et al._ 2014, Khan _et al._ 2018]. Moreover, Finan _et al._ [2017] review the evidence base on the impacts of monitoring in public organizations, including studies highlighting how this can often lead to gaming/circumventing monitoring systems. Our findings reinforce the evidence base suggesting the possibility that management practices related to incentives and monitoring negatively correlate to outputs of the professional tier of civil service _bureaucrats_ across contexts.

Appendix Table A2 provides a battery of checks on our baseline estimates. These show the results to be robust to alternative codings of completion rates, samples, estimation methods, and fixed e↵ects specifications.<sup>14</sup> Appendix Table A3 further shows the results to be robust to

> 13Given the management practices are positively correlated to each other, this also implies that only controlling for incentives/monitoring leads _γ_ b2 to be biased upwards as CS-autonomy is omitted. Similar biases arise from a traditional public administration perspective that might only focus on the provision of autonomy.

14More precisely, we first redefine project completion rates to be the average of the codings of the two enumerators designated to each progress report. The result in Column 2 shows the baseline results continue to hold even when we reduce the variation in completion rates this way. Column 3 excludes projects implemented by the largest organization in terms of number of projects; Column 4 excludes the five smallest organizations by number of projects. Columns 5 and 6 exclude organizations at the top and bottom of the CS-autonomy and CS-incentives/monitoring management scales respectively. Column 7 uses only the 30 organizations for whom we have coded project completion data for, to define the management z-scores (and so does not define the z-scores based on all 45 organizations for which these scores are available based on our civil servant survey). Column 8 reports the result of estimation 15
# **_<u>________________________________________________________</u>_**

||total number of<br>trucks/locations surveyed||
|---|---|---|
|Reported occurrence<br>of corruption|Number of cases when a<br>driver makes or is asked to<br>make an unauthorized<br>payment compared to the<br>total number of survey<br>responses|Information to calculate this indicator is derived<br>from records of received reports of corruption<br>maintained by the Customs administrations.|
|Revenue collected/<br>Customs staff|Total revenues collected /<br>Total number of customs<br>employees|Information obtained from records maintained by<br>the Customs administrations.|
|Total Customs<br>administration cost/<br>revenue collected|Total budget of the<br>administration (including<br>salaries, overtime, bonuses<br>and benefits)/ Total<br>revenue collected<br>irrespective of its<br>destination|Information obtained from records maintained by<br>the Customs administrations.|
|Revenue collected/<br>Salaries|Total agency salaries,<br>overtime, bonuses and<br>benefits/ Total revenue<br>collected irrespective of its<br>destination|Information obtained from records maintained by<br>the Customs administrations.|
|Trade<br>volume/Customs<br>staff|Trade Volume / number of<br>customs employees|Information obtained from country statistical data.|
|Annual number of<br>declarations/Customs<br>staff|Total number of<br>declarations (import,<br>export, suspense regimes,<br>but excluding transit)<br>irrespective of the number<br>of items/Total staff<br>employed by Customs|Information obtained from records maintained by<br>
................................ 4<br>a. Deriving the basic ERP formula ............................................................................... 5|
|b. Adding input subsidies – reduced energy costs – to the ERP calculation ................ 6|
|4. A Discussion of Energy Subsidies and Tariff Data Sources and Implications for the<br>Level of Disaggregation ................................................................................................... 9<br>|
|5. Estimating Non-tariff Barriers for Selected Activities ............................................... 10|
|6. Results: Levels and Dispersion of ERPs across Selected Industries, Tariffs Only<br>(without Energy Subsidies), Comparing 2009 with the Late 1990s ............
and self-administered instruments through phone messages.

Finally, existing evidence documents heterogeneity in data quality obtained from different survey modes by some respondent characteristics (Bardasi et al., 2011; Beegle et al., 2012; Gigliotti and Dietsch, 2014; LeFevre et al., 2020).<sup>8</sup> For example, Bardasi et al. (2011) document that responses by proxy of employment rather than self-report have differential effects on employment rates by gender in Tanzania. Similarly, Beegle et al. (2012) experimentally test alternative survey designs in face-to-face surveys that measure household consumption. The authors find that under-reporting is particularly apparent for illiterate households and for urban respondents. We contribute to this literature by experimentally testing survey mode – instead of survey design – and variation in response rate by respondents’ characteristics that are standard in the literature – gender and age – and by others that are novel and relevant due to the policy implications of the findings – phone availability and access of electronic-transfer technologies.

The remainder of the paper is organized as follows: Section 2 describes the research design, including details on the recruitment process, and experimental design. Section 3 presents the data collected and our implementation process. In section 4, we describe our main empirical approach. Section 5 summarizes the main results obtained from specifications presented in section 4, and section 6 describes our robustness checks. Finally, in section 7 we briefly conclude.

# **2 Research Design**

We embedded a survey-mode experiment in a midline survey of the impact evaluation of a positive-parenting and stress-management intervention for caregivers in El Salvador (Amaral et al., 2021). The midline survey sought to assess if caregivers who received the intervention were incorporating its concepts. We took the opportunity of the survey to conduct the survey-mode experiment and generate evidence on the most convenient method (CATI vs. self-completion) to collect data on sensitive topics. We used the results of this

> 8See De Weerdt et al. (2020) for a literature review
a first-stage decision variable for the activity (e.g. water storage for activity _i_ , irrigated agriculture or hydropower).

cost associated with (e.g. cost associated with storing water for ); at the time is chosen state of nature of water availability (e.g. if water supply is the random variable, then below mean, mean, above mean flow) a second-stage decision variable for the activity given that the _k_<sup>th</sup> state of nature occurs (e.g. acres of crop to irrigate or units of hydropower to produce, given state has been realized) a coefficient associated with activity _yin_ (e.g. revenue per acre of crop or unit of hydropower generated, given the _k_<sup>th</sup> state of nature is realized) the probability of the _k_<sup>th</sup> state of nature occurring (taken from the BWI analysis) resource availability in the first stage (e.g. total land available, or area of sites for storage) a coefficient associated with activity (e.g. acre-inches of water required per acre of crop given the _k_<sup>th</sup> state of nature is realized) resource availability when given the _n_<sup>th</sup> state of nature is realized (e.g. total irrigation water available) a coefficient linking second-stage activity levels to first-stage activity levels (e.g. acres of crop planted in the second-stage cannot exceed acres prepared for crop in the first stage, or storage used cannot exceed available storage, in which case 1).

Kuhn-Tucker conditions of the Lagrangian of (4)-(8) provides: , and where are the shadow values of constraints (5)-(7), respectively.

By combining and re-arranging we get . This means that in the optimal solution the expected value from an additional unit of activity in the second stage should 19
key argument is that, in a globalized economy, _all_ sectors can improve by learning from those at the technology frontier, and that many possible sources of comparative advantage exist (Dadush, 2015). In particular, advanced infrastructure (ICTs) creates new opportunities to boost the productivity and tradability of a range of services.

The current debate on industrial policy in developing countries (see Pack and Saggi, 2006, and Naudé and Szirmai, 2012) opposes two views: the neo‐liberal view, which maintains a critical perspective of any intervention (selective or not) and the neo‐structuralist view, which argues in favor of selective protection of industries.

There is growing consensus that promoting industry cannot be limited to “passive” interventions to enforce the rules that contribute to the functioning of markets. It is also recognized that traditional interventions in the industrial sector, often aimed at promoting the emergence of national champions, are not always desirable or optimal. The new forms of industrial policy do not fit into either a traditional interventionist approach, nor into actions that would fit the definition of an appropriate regulatory framework. They are a mixed attempt, whose goal is to create the conditions for businesses to invest in technologies and create markets, without interfering with the nature of the products themselves.

More generally, the primary objective of the “new” industrial policy – viewed as an important dimension of growth policy – is to diversify the economy and create new comparative advantages. To weed out programs or operations that have bad results, the use of success or failure indicators is essential. At the same time, the support granted by the state should be limited in time and be granted on activities rather than sectors themselves. Moreover, these activities must be distinguished by their potential to generate technological and informational spillovers. In the same vein, the new industrial policy must avoid public activism involving the choice of products and players. It aims to foster competition and promote innovation.

In the context of the new industrial policy, a key issue
Telework programs for sure reduce firms’ environmental footprint but part of it is transferred to workers, and workers may be just doing the same sort of tasks as they were doing at the firm location. Besides this issue, it is not clear in the survey how workers should be counted when involved in GTPs (should all teleworkers or e-car drivers be counted as green workers?) or how to capture the intensity with which a worker may be performing green and nongreen tasks.

Applying the BLS process approach to developing countries also brings its own challenges. Besides the challenges already noted, this approach falls short of capturing the informal sector and the agriculture sector, both usually large in the developing world. Moreover, it should be added that the cost associated with these surveys is high, especially as they may require large sample sizes. While a large percentage of firms have adopted GTPs, very few have workers who dedicate time to them. For example, in the U.S., the BLS GTP survey found that 75 percent of firms adopted a GTP and that 0.7 percent of workers use these more than half of their time.<sup>27</sup> Hence, for the occupational module to be relevant, the survey needs to have a large enough sample size to generate statistical power. As one could imagine, these proportions are even lower in developing countries, exacerbating the sample size requirements.<sup>28</sup> For example, we added a few questions similar to the BLS GTP survey in the Environmentally Friendly Industry Study (SIRL) survey conducted in 2021 in Indonesia. It was found that about half of firms have

> 25 Stoevska & Hunter (2012) also raises this issue.

> 26 BLS (2012b).

> 27 BLS (2012b).

> 28 Of course, the statistical power also depends on the standard deviation of the variables of interest, which even if small they contribute little to reduce the sample size.

11
**<u>Table 1: Distribution of sample villages across states and reservation status</u>**

|**Region**|**No. of**<br>**villages**|**Never**|**No. of tim**<br>**Once**|**es reserved**<br>**Twice**|**Trice**|**Pe**<br>**Now**|**riod when res**<br>**Previous**|**erved**<br>**Past prev.**|
|---|---|---|---|---|---|---|---|---|
|North|55|11|29|14|1|23|14|23|
|East|37|20|15|2|0|10|8|1|
|West|75|17|55|3|0|22|19|20|
|South|66|35|21|8|2|16|20|7|
|**Total**|233|83|120|27|3|71|61|51|

_Source:_ Own calculation based on REDS/NCAER 2007 Survey.

Note: The North includes the states of Haryana, Himachal Pradesh, Punjab and Uttar Pradesh; the West includes Gujarat, Maharashtra, Madhya Pradesh and Rajasthan; the East includes Bihar, Chhattisgarh, Orissa and West Bengal; and the South includes Andhra Pradesh, Karnataka, Kerala and Tamil Nadu.

20
>

# 2.3 Irrigation Data

Gridcells included in this analysis are restricted to those where it is believed that agriculture is irrigated. This ensures that the EC measured in the surface water is like the EC in the water which is feeding the crops. In India, data on irrigation by district were obtained from the Ministry of Agriculture and the International Crops Research Institute for the Semi‐Arid Tropics. Only districts for which greater than 50 percent of agricultural land is irrigated by surface irrigation are included in the analysis (see Figure 2).

For the Mekong River basin analysis, as well as the global analysis, data on irrigation were obtained from Food and Agriculture Organization’s Global Map of Irrigated Areas (GMIA) version 5.0.<sup>6</sup> GMIA gives a global raster of area equipped for irrigation for the year 2005 at a resolution of 0.083 degrees. These data were aggregated up to the 0.1‐degree gridcell described in the previous section, and gridcells are only included if some land area is equipped for irrigation according to GMIA. GMIA does not distinguish between groundwater irrigation and surface water irrigation. For the Mekong analysis, this is not a concern, as surface water irrigation comprises 91 percent of total irrigation in Thailand, 99 percent in Lao People’s Democratic Republic and Vietnam, and 100 percent in Cambodia (FAO 2016). Thus, we can be reasonably confident that nearly all the NPP included in the analysis is irrigated using surface waters.

For the wider, global study, there is unfortunately no way to distinguish between ground and surface irrigated crops. However, i <mark>t is well‐known that ground and surface‐water bodies interact with each other. Various studies in the hydrological literature have begun to show that groundwater can be a major and potentially long‐term contributor to contamination of surface water (e.g. Yu et al. 2018; Delsman et al., 2015; De Louw et al.,
Acknowledgments: Robin Audy performed excellent work to produce administrative and conflict data sets; Horacio Vera Cossio provided outstanding research assistance. We thank Christian Bodewig, Paul Christian, Alice Duhaut, Marcus Holmlund, Arianna Legovini, John Loeser, the Sahel Adaptive Social Protection team, and seminar participants at the World Bank (DIME) for their helpful comments. The study was supported by the Sahel Adaptive Social Protection Program at the World Bank. Dominic Rohner also gratefully acknowledges financial support from the ERC Starting Grant POLICIES FOR PEACE-677595. DIME provided editorial support and verified the computational reproducibility of the results. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the government they represent.

2
source for a third approach in the climate change and crop yield literature that employs statistical modeling based on observed harvest data at varying levels of aggregation (e.g. Lobell, Schlenker and Costa-Roberts, 2011; Imran, Zurita‐Milla and Stein, 2013; Liu _et al._ , 2016).

Survey data may be a valuable source of complementary information for several reasons.

- 1) They can provide information down to the plot-crop level and thus possibly higher microlevel accuracy than most grid-based models to date. This opens up opportunities for better understanding of micro-level dynamics that can inform disaster risk reduction policies.

- 2) They provide high data density. Relative to site-based models, this reduces the need for strong assumptions about the generalizability of the sample for larger aggregates (though it may not eliminate it entirely) (van Wart _et al._ , 2013; van Bussel _et al._ , 2015).

- 3) They provide an approach that may be more suitable to the peculiarities of smallholder farming in LMICs (see above).

- 4) They are able to pick up non-meteorological sources of crop losses such as pests and diseases (Liu _et al._ , 2016).

- 5) Their information can be effortlessly integrated with other information collected in surveys that make understanding, for example, behavioral components of crop management

> 6 In comparison to gridded models that rely on remote-sensed data of sufficiently high resolution, site-based models typically use sensor data collected at several distinct experimental sites, e.g. farms or even single plots. While the former can thus cover a larger geographic area and may be more representative for aggregate measurement, the latter has the advantage of more fine-grained measurement based on data observed “on the ground”.

5
.gpo.gov/nara/cfr/ waisidx_00/31cfrv2_00.html). Parts 351</u> and 352 define the offering terms for and rules governing series EE savings bonds. Parts 359 and <u>360</u> define the offering terms for and rules governing series I savings bonds. Part <u>363</u> contains the regulations governing securities held in the new TreasuryDirect system. (For a complete list of department circulars under Title 31, see <u>http://www.publicdebt.treas.gov/sav/savdeptcircular.htm.)</u> <u>Part 356, on the sale and issue of marketable book-entry Treasury bills, notes, and bonds,</u> defines book-entry securities, establishes the Treasury’s authority to issue and sell such securities, and lays out the rules on denomination, taxation, tenders, offering announcements, and submission of competitive and noncompetitive bids. It defines book-entry securities as those represented by an accounting entry or electronic record, and not by a paper certificate. These are held in either TRADES—a commercial book-entry system—or the legacy _TreasuryDirect_ system. In October 2002 holdings in legacy _TreasuryDirect_ totaled about $66 billion or 2 percent of outstanding marketable debt. Part 356.3 sets out the terms and conditions under which the Treasury can sell and issue marketable book-entry bills, notes, and bonds to the public. For _TreasuryDirect_ , book-entry securities of account holders are identified and maintained directly in the records of the Bureau of Public Debt.

10