rafmacalaba/data-use-mentions-extended huggingface dataset:
all coef_<br>|_ficients to vary by re_<br>|_gion_<br>|
|Credit constraints ECA|0.297*|-0.074|0.152*|-0.376**|
||(0.117)|(0.076)|(0.075)|(0.135)|
|Credit constraints MENA|0.229|0.306|-0.178|-0.357|
||(0.262)|(0.178)|(0.206)|(0.188)|
|Green management ECA|-0.284***|-0.054*|-0.078**|0.416***|
||(0.041)|(0.021)|(0.024)|(0.044)|
|Green management MENA|-0.460***|0.092|0.006|0.362***|
||(0.070)|(0.054)|(0.040)|(0.060)|
|Observations||17|,902||
|Clusters||3|,022||

Note: This table shows the average marginal effect estimates of credit constraints and green management on the type of green investment, based on multinomial IV logistic regression with the type of green investment as a dependent variable. Regression controls for locality-level credit market controls (log local banks’ average asset size in a 15km radius and the number of bank branches in a 15km radius); indicators for no firms in other sectors in a 15km radius with data on credit constraints and green management; population size class; and region and sector fixed effects. In Panel B, estimated coefficients for all firm-level covariates as well as log local banks’ average asset size in a 15km radius and the number
systematic reporting differences may be a concern when using surveys for accountability purposes. Levanon et al. (2015), for instance find that across 12 countries, consumers with high social status tended to register more service failures and to complain more frequently than customers of lower social status. In the literature on power sector reform, many analyses recommend taking political economy considerations into account (Sulistiyanto and Xun 2004; Victor and Heller 2007; Erdogdu 2013).

This study uses data from unbiased electricity outage monitoring devices that collected detailed information alongside survey-based measures. Comparing the automated measures to survey-based measures demonstrates the presence and extent of bias in survey-based reporting of electricity outages in a developing country setting.

The results suggest that bias in reporting electricity outages in Tajikistan primarily takes the form of underreporting outages of short duration. Respondents systematically report fewer instances of electricity outages, and when they do report an outage as having occurred, on average report that it lasted less long than was truly the case. However, the results also suggest that the direction of bias changes during periods of widespread outages of significant duration. During such episodes, 2
, Japanese and Chinese companies were among the fastest growing investors overseas in 2010‖ ( _fDIintellingence)._

> 24 Assessing the country/regional allocation of FDI is known to be difficult and alternative data sources must be used for cross-check.The official data underestimate the FDI in Africa because a large proportion of China‘s OFDI flows to Hong Kong SAR, China and several ―tax havens‖, and then being re-directed to various regions.  Another part of this outflow constitutes the so called ―round-tripping‖ investment back to China. The database by Heritage Foundation addressed this problem by tracing investments from these tax havens to its ultimate destination. For details see Derek Scissors (2010).

49
HICs while the proportion of firms that have a bank loan or line of credit is significantly higher (at 10 percent level) in the UMICs. The percentage of firms’ senior management’s time that is spent on dealing with government regulations ( _Time Tax_ ) is used as a broad proxy measure of the regulatory burden on the firms and the business environment in which the firms operate. _Time Tax_ for a typical firm in UMICs equals 12.7 percent compared to a slightly lower value of 11.5 percent in the HICs, and the difference is significant at 5% level. This indicates that the regulatory burden on firms operating in UMICs is higher.

We also include some country-level variables in our regressions. These are intended to account for differences across countries in the level of human capital, market size, quality of governance, and institutional strength. For human capital, we use three different measures, namely: gross primary enrollment rate, gross secondary enrollment rate, and gross tertiary enrollment rate taken from World Development Indicators, World Bank. For market size, we use the log of the total population in the country. For the quality of governance, we use the Transparency International’s Corruption Perception Index ( _Corruption_ ), and to capture the institutional strength and protection of private property against expropriation by state and private agents we use the Law and Order index from ICRG. Note that higher values of all the macro-level variables described above imply a more favorable business environment from the firms’ point of view. As depicted in Table 2, market size is lower in the HICs than the UMICs (by about 59 percent) and the difference insignificant at the 1 percent level. The enrollment rate in primary education is also higher in the UMICs than in HICs by about 5.7 percentage points, and the difference is significant at the 1 percent level. However, all the remaining macro-level indicators including corruption, law and order, enrollment rates in secondary and tertiary education, HICs outperform UMICs, with the difference significant
MR-GE model in which temperature change caused by CO2 emissions related to consumption enter negatively in the consumer’s utility function.<sup>21</sup> In this set-up, lower consumption directly raises welfare as temperature rises less and indirectly as the country’s terms-of-trade improves because of reduced demand for imports. Calibrating the utility function parameters on the Stern (2005) damage estimates, they show that a 10% damage (i.e. reduction in GDP) from a 1% reduction in consumption would be necessary for the US or China to participate if they are linked through trade. In the absence of trade, the 18Drawing on data for 38 countries (26 of which have ratified Kyoto), and 12 sectors over the period 1995-2005, Aichele and Felbermayr (2010) examine the impact of different GHG policies on trade flows and emissions. They estimate that carbon imports are on average 12% higher if the importer has ratified Kyoto and his trading partner not. Confirming previous work, the effect is most important in energy intensive industries, where robust evidence for carbon leakage was found for seven sectors. Their findings suggest that, even though the volume of trade “caused” by Kyoto is rather small, on average about 40% of carbon savings due to the ratification of the Kyoto protocol has been offset by increasing emissions in non-committing countries.

19Dong and Whalley (2010) examine reasons for carbon-motivated RTAs and requirements for WTO compatibility of such agreements (no selective actions against high-emitting countries and the possibility of invoking article XX to avoid covering all trade). Then, these RTAs could include extension of preferences to low-emission products, but also BTAs to offset anti-competitive effects. As under the Montreal Protocol , the RTAs could also include penalties against those who do not join the agreement.

20De Melo and Mathys (2010, appendix) review the results from simulation models, focusing mostly on MR-GE models, all assuming CED product-differentiation by country of origin (the ‘Armington’ assumption) which produces strong terms-of-rade effects of any
We pinpoint three measurable ways this rapport may be measured and evidenced over time in the data. Table 3 summarizes the chosen IVs, which we describe and support in the paragraphs below. Appendix Figures A.4 and A.5 illustrate the temporal path of the three IVs aggregated over regions from 1980 to 2018.

Table 3: Summary of Instrumental Variables

|**Closeness to donors**|**IV**|**Supported in**|
|---|---|---|
|Foreign policy|UN General Assembly voting|Bailey et al. (2017)|
|Trade|Relative trade value aggregates|(Rufín, 2003)|
|Trade|Trade agreements in place||

_Sources: Bailey et al. (2017); United Nations Comtrade dataset via the Database for International Trade Analysis (BACI), by the Center for Prospective Studies and International Information (CEPII); European Commission._ We consider that it may take time to implement policies following an increase in closeness measured through our IV. Therefore, we use a moving average over five years of the IVs. Although bilateral relationships can be relatively slow to change, administration changes in democracies may result in more abrupt changes, so we also consider the moving average over three years of the IVs.

# **Foreign Policy Closeness IV**

To represent changes in foreign policy preferences, we use a dyadic dataset behind the Affinity of Nations index by Bailey et al. (Bailey et al., 2017). They use a dynamic ordinal spatial model on a single dimension to estimate state preferences toward the US-led liberal order, as reflected through the United Nations General Assembly voting. The measure is the _Ideal Point Index_ .

On the other hand, the Ideal Point Distance is the difference between the Ideal Points for all country dyads that participate in the United Nations General Assembly (e.g., France 16
of migration on educational investments (Dabalen and Miluka 2010), female empowerment, (Stecklov _et al._ 2010), and occupational mobility (Carletto and Kilic 2011) also seem to have been limited. At the same time, migration networks create options for the skilled to leave, possibly draining rural areas of talent and entrepreneurial skills. Finally, although the number of plots per farm in Albania remains low in comparison to China or Vietnam (Deininger and Jin 2005, Deininger and Jin 2008), concerns about inefficiencies due to an overly fragmented land ownership structure have been raised (Lusho and Dhimiter 1998). Fragmentation has also been linked to abandonment of potentially productive plots (Lemel 2000) through a number of channels (SabatesWheeler 2002) including broader social change and low agricultural profitability (Sikor _et al._ 2009). Whether fragmentation is to blame or some other issue is at the root of the problem is an important issue that will have implications for the policy options pursued to deal with the problem.

# **2.2 Characteristics of Albania’s rural economy**

Data from the 2005 Albania LSMS, administered from April to November 2005 by the Institute of Statistics (INSTAT) in collaboration with the World Bank, to 3,640 households in 480 enumeration areas

> 4 Certificates include a basic sketch and description of the parcel and its boundaries, the name of the household head, and the issuing authority.

5
sectors that are not scheduled). Most members have not complied with this notification requirement. Thus, in the last 10 years Albania has notified some 120 measures under Art. III:3, as compared to just a handful by Canada, the EU and the US combined.

Until very recently it was therefore not possible to assess the extent to which commitments implied a locking in of applied policies. A new database on services trade barriers compiled at the World Bank now allows such an assessment to be made. The database reveals that applied policies of 102 surveyed countries in a number of sectors are much more restrictive than in others, with mode 4 being the least open.<sup>2</sup> Borchert, Gootiiz and Mattoo (2010) build on earlier work by Gootiiz and Mattoo (2009) and use the database to compare applied policies and the GATS commitments of the 93 WTO Members surveyed.<sup>3</sup> They conclude that there is a very significant gap between applied and “bound” policies (the commitments) because most services liberalization around the world has been undertaken unilaterally. The policy commitments (bindings) made in the GATS during the Uruguay Round by the 93 WTO members surveyed are on average 2.3 times more restrictive than currently applied policies – i.e., countries could more than double their average levels of restrictiveness without violating their commitments.

Figure 1 is a scatter diagram using this database where the location of each country reflects the overall restrictiveness of its services trade policies and its per capita income. A number of patterns can be seen. High‐income countries are clustered together at the bottom‐right, showing that they are quite open overall. There is much more variation in the restrictiveness of services policies for low‐income countries. Some of the poorest countries, like Ecuador, Ghana, Nigeria, Senegal, and Mongolia are remarkably open, while some of the most restrictive policies today are visible in the fast growing economies of Asia, including
specific domestic producer or consumer subsidies have played a more limited role (because of their much greater cost to the treasury), with a few exceptions most notably in rich-country agriculture. With the freeing up of most foreign exchange markets over the past two decades, the phasing out of most export taxes,<sup>2</sup> and the conversion of many non-tariff trade barriers into tariffs, the task of measuring the extent of distortions to goods markets is made much easier in that attention can focus on import tariffs and agricultural subsidies. In principle services trade and foreign investment distortions also could distort incentives in the agricultural and industrial sectors, but they are ignored here partly because much controversy still surrounds their measurement and how they should be modelled, and partly because they do not seem destined for major reform under the current Doha Development Agenda of multilateral trade reform by WTO members.

The latest release of the GTAP dataset, Release 6.05, includes estimates of bilateral tariffs and of domestic and export subsidies as of 2001 for 87 countries and country groups spanning the world. This is a substantial improvement over Version 5 of the GTAP dataset, which relates to 1997. The new protection data come from a joint CEPII (Paris)/ITC (Geneva) project. The product of this joint effort, known as MAcMaps, is a HS6 tariff level detailed database on bilateral protection that integrates trade preferences, specific and compound tariffs and a partial evaluation of non-tariff barriers such as tariff rate quotas (TRQs).<sup>3</sup> The new GTAP database has lower tariffs than the previous database. This is because of the inclusion of bilateral trade preferences, as well as the major reforms between 1997 and 2001 such as continued

> 2 Apparently only a few minor export taxes remain – see Piermartini (2004) and also Thiele (2004).

> 3 More information on the MAcMaps database is available in Bouët et al. (2004) and at

> <u>http://www.
Policy Research Working Paper 5995

# **Abstract**

This paper analyzes the subjective impact of the global economic crisis on households in Europe and Central Asia and relates subjective impacts to consumption, actual shocks, and coping strategies, using the 2010 Life in Transition Survey. Two-thirds of respondents in Europe and Central Asia report their household was subjectively affected, primarily through the labor market. The findings underscore the limitations of crosscountry comparisons of subjective perceptions, due to reporting biases. Within countries, richer households felt a decline in their relative income position, consistent with evidence from household budget surveys that the crisis reduced the consumption of the middle and upper classes. But the analysis also finds that poorer households report being (subjectively) affected by the crisis more. Differences in the feasibility of coping strategies may help explain variations in subjective perceptions: the poorest were forced to reduce their staple food consumption and health spending, and tended to depend on public safety nets. Richer households had more options to cope, pursuing so-called “active strategies” (such as increasing their labor supply), borrowing, and cutting spending on non-essentials. Transition countries differed significantly from western European comparator countries in that public safety nets had lower coverage, private safety nets and informal insurance mechanisms could not meet the shortfall in income, and a large proportion of their populations reduced the consumption of basic necessities. The paper finds subjective perceptions of the impact of the crisis to be relevant to socio-political outcomes: the harder the impact, the lower the life satisfaction level and the more negative the assessment of government performance.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at mfdiagne@worldbank.org.

_The Policy Research Working
>-10 -5 0 5 10<br>Average GDP growth in 2009-2010 (percent)<br>Change in average life satisfaction (2006-2010)<br><!-- End of picture text --> Source: LiTS I (2006) and LiTS II (2010), IMF WEO (October 2010 Update).

Note: Change in average life satisfaction is the difference between the 2006 and 2010 average life satisfaction, measured on a 1-5 scale (see footnote 20).

19
|**Table 1.1. Use of Poverty and Welfare**|**Measures in Ac**<br>|**ademic, Policy, and Public**<br>|**Discourse**<br>|
|---|---|---|---|
||**Academic Use**|**Policy Use**|**Public Discourse**|
||_EconLit*_|_Multilateral Development_<br>_Banks at nlp4dev.org* _|_Google Ngram**_|
|**Poverty Measures**||||
|Poverty Rate OR Poverty Headcount ***|40832|26730|40.49|
|Poverty Gap|4438|4609|6.84|
|Squared Poverty Gap|473|681|0.32|
|Watts Index|47|52|0.09|
|**Welfare Measures**||||
|Average income|61493|15492|36.11|
|Atkinson Index|744|122|0.49|
|Sen Index|258|70|0.12|

**Notes** : * Number of documents. ** 1 million times fraction of corpus in 2019. *** Search results in last column for “poverty rate” only as Ngram viewer does not support Boolean search. Search results retrieved March 22-24, 2023.

One plausible reason for the limited uptake of distribution sensitive measures is that they are difficult to explain in non-technical terms. For example, it is challenging to define the Atkinson index as anything other than a constant elasticity of substitution aggregate of individual incomes. Compounding the problem, distribution sensitive measures also often have non-intuitive units. For example, the squared poverty gap is measured in squared percent deviations from the poverty line, while the Watts poverty index is measured in log-point differences from the poverty line that cannot easily be interpreted as percent differences unless they are
where poverty is increased and others where poverty is decreased. The notable exceptions are most African countries where yield impacts of climate change are severe and no single stratum experiences significant poverty reductions.

The study by Hertel et al (2010) provides a promising approach for studying the impacts of climate change taking into account general equilibrium effects between agricultural productivity, cost of living, and earnings effects. However, as is the case for most models, there are serious trade-offs associated between the tractability of the general equilibrium effects and the heterogeneity incorporated into the model.

# **_3.2 Heterogeneity Galore_**

The study by Jacoby, Rabassa, and Skoufias (2011) applies a flexible framework for quantifying the distributional impacts of climate change in rural economies. In this study welfare is measured by consumption per capita and is modeled as being determined by the resource endowments of the household, such as land and labor, and the returns from farm and non-farm activities.<sup>18</sup> Using a comparative statics framework, the impacts of climate change on household consumption can be expressed as the impact of changes in temperature on the returns to land (used as a summary measure of agricultural productivity) multiplied by the proportion of income derived from owned land, the impacts of temperature on the returns to labor multiplied by the proportion of income derived from labor, and the impacts of climate change on the price of food multiplied by the net consumption ratio, i.e. the value of the net marketed surplus of food by the household.<sup>19</sup> Using micro data representative for all India and following the Ricardian approach proposed by Mendelsohn et al. (1994) the authors estimate the impacts of climate change in 2040 on agricultural productivity and wages taking into account adaptation (i.e. using district-level cross sectional data) and assuming imperfect mobility of labor.<sup>20</sup> They are also able to estimate

> 18 Additional heterogeneity is introduced into the model by distinguishing
.392|0.491|-0.099|0.000|
|Caregiver: Years of schooling|2.628|1.908|0.720|0.000|
|Caregiver: Is illiterate (%)|0.795|0.832|-0.037|0.004|
|Stimulation Index (Activities with children)|7.080|7.482|-0.402|0.001|
|Household Size|4.926|5.369|-0.444|0.000|
|Number of children aged 0-18 in Household|2.968|3.315|-0.346|0.000|
|<br>Number of children aged 3-5 in Household|1.077|1.071|0.006|0.502|
|Household receives remittances|0.102|0.097|0.004|0.668|
|Wealth index of household|-0.358|0.446|-0.804|0.000|
|Observations||3680|||

_Notes_ : The table reports the descriptive statistics of target children in the bottom 50% (column 1) and top 50% (column 2) of the predicted Conditional Average Treatment Effect (CATE) on the skills index. Column 3 reports the difference between column 1 and 2. Column 4 shows the _p_ -value of the t-test adjusted for multiple hypothesis testing.

sured using the ASQ). For preschool attendance and primary school enrollment, target children with lower ASQ scores at baseline benefited more from the program. However, the opposite is true for the skills index: here, target children with higher ASQ scores at baseline benefited more. The difference in results here may be due, in part, to dynamic complementarities in skills formation, where skills produced at earlier ages increase the productivity of investment at later ages (Heckman et al., 2010; Conti et
outcomes specific to social groups, to context and to specific intervention elements. Manning et al. (2016) provide a detailed discussion on these elements. However, the current version of the enhanced CBA APP is capable of more than what we have presented here. Below we describe three additional modules that are currently in various stages of development, testing and implementation.

Module 4 (as shown in Figure 4) improves upon earlier economic tools, such as the MCBT, by drawing upon and exploiting machine learning (ML) techniques. Here, we incorporate a ‘User Input Database’ module, where all data entered by users into the APP is stored as one set of records per project. The enhanced APP will allow the user to store their input data on their server, providing a single data resource for the ML module (i.e. Module 6). Here, we intend to provide source code to allow users to manage their data on their own server, utilizing their own security protocols. Specifically, since Module 1 identified and established a relationship between moderators and outcome, module 4 then captures those relationships allowing the system to learn from these relationships and identify moderators that are relevant to outcomes concerned. This allows for the creation of a more comprehensive database and the possibility of subsequent ML, as described below in Module 6 (e.g. imputation of missing data).

Linked to Module 4, Module 5 will provide code for users to build a database to store the calculated results after analysis. Storing the calculated results is crucial because it provides the ability to model the relationships between all input-relevant cost and benefit data (Module 1) and the output of CBA (Module 3). Therefore, the database in this module of the workflow diagram stores the benefits (that could also be weighted using a harm index) and analysis data, enabling the system to map the relations between input and output, and exploit this to learn and improve CBA over time.

Finally, Module 6 integrates ML techniques