rafmacalaba/data-use-mentions-extended huggingface dataset:
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``` Notes: This table presents four regressions, where each column reports the findings from an OLS regression. The dependent variables, measured in 1999, are: Bank Development equals claims on the private sector by deposit money banks as a share of GDP; Interest Margin equals net interest income divided by total bank assets; Overhead Costs equals total bank overhead costs as a share of total bank assets; and, Nonperforming Loans equals officially reported nonperforming loans as a share of total banks assets. The regulatory indexes are measured in 1999: Capital Regulatory Index measures the extent and stringency of regulatory requirements; Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Though unreported, the regressions also control for the legal origin of each country by including three dummy variables for Common, French civil, and German civil law countries, as well as a constant term. Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.
```
(e.g. pharmaceuticals and IT) and random businesses, i.e. those that do not have an obvious association with large positive externalities (e.g. boilers and compressors in Bangladesh or beauty and fitness centers in Sri Lanka).

Tax incentives come at a price. They reduce government revenue, add to administrative costs and open the door for political favoritism. Indeed, once tax preferences have been granted to one group of taxpayers, others get organized and put pressure on politicians to receive similar tax treatment. Thus, even if initially tax schemes are designed for a small group of taxpayers, gradually the list of eligible activities, industries and firms almost inevitably grows up. India and Pakistan publish annual revenue foregone statements, which provide an estimate of the cost of tax concessions. This is good practice that would benefit other countries of the region as such reports give greater transparency to the policy making process. In fiscal year 2013‐14, the total cost of all tax concessions (direct and indirect taxes, customs and excise duties) amounted to about INR 5.7 trillion (USD 90 billion) in India and PKR 477.1 billion (USD 4.7 billion) in Pakistan.<sup>60</sup> This is about 4.8% and 2% of these countries’ GDP respectively.

There are no estimates of any potential benefits (e.g. in terms of increase in domestic production, value added or new jobs) that tax expenditures, particularly tax incentives, helped create. Such cost benefit analysis will be very useful in informing public debate on tax incentive policies. Studies in other countries show that where tax incentives have contributed to new job creation, this occurred at significant cost to the economy. A 2008 World Bank study found that the Yemeni government spent about USD 6,000 for each of 8,000 jobs that investment incentives helped create – more than six times the country’s per capita income.<sup>61</
household lives in an EA in which the centroid is covered by 3G technologies.<sup>10</sup> The treatment variables are lagged one year because it is most likely that there is some temporal lag between the arrival of digital infrastructure (e.g., fiber-optic nodes or 3G) and the manifestation (if any) of the welfare effect.<sup>11</sup> Because the first round of the 2011 survey does not reveal information on 3G mobile coverage, the welfare effect of 3G coverage can be tested only using the 2017–18 ELEPS. The analysis of the 4G network is not considered extensively in the main analysis of this paper because Senegal had not yet deployed a full 4G network in 2018, the year the latest household survey was collected. Indeed, 4G was only introduced in Senegal in 2016, and 4G coverage remains limited to a few urban centers of the country.<sup>12</sup> The set of control variables i includes household size, the marital status and sex of household heads, access to electricity, literacy of household heads, housing conditions (as measured in a ii composite index of dwelling characteristicsXX<sup>13</sup> ), and a group of EA-level variables, including nighttime lights, road density, and elevation above sea level.<sup>14</sup> In addition, and are region fixed effects and year fixed effects. Controlling for other types of infrastructure, such as road connectivity or electricity, ii ii is important given that, in most African countries, “a part of the backbone network runs parallel to θθ ττ other infrastructure such as roads or electricity cables” (Hjort and Poulsen 2019, 1053). Descriptive statistics on key outcome variables, treatment, and other covariates and controls are reported in table 1. The main object of interest is coefficient 1, which captures the effect of access to digital infrastructure on welfare and poverty. To account for the existence of heteroskedasticity and serial
natural gas, which are duty-free. The simple average applied tariff including preferences varies between 0.495% for Georgia and 4.564% for Moldova.

The Trade Tariff Restrictive Index (TTRI) and the Overall Trade Restrictiveness Index (OTRI) also provide valuable information on trade protection. The first index measures the equivalent uniform tariff of a country’s tariff schedule that would keep domestic import levels constant, and is calculating using applied tariffs including preferences. The second index is the same as the TTRI but also takes into account NTMs.<sup>4</sup> Table 2 presents the value and ranking corresponding to the indicators, which are calculated as simple averages of the values observed in the 2006-2009 period. The TTRI varies between 2.05% for Kazakhstan and 8.81% for Belarus. According to this measure, the region presents intermediate to high levels of protection as countries’ rankings vary between the 39<sup>th</sup> and 93<sup>rd</sup> positions, out of 125 countries. The higher values of OTRIs compared to TTRIs indicate that NTMs are also economically important trade barriers for a number of imported products. In particular, the uniform tariff ranges from 5.1% for Moldova to 18.99% for Russia. If we compare the TTRI and the OTRI rankings, we observe that Moldova and Belarus have better positions in the latter, showing that NTMs are less restrictive. In contrast, the rest of the economies are located in lower positions of the ranking.

Table 2. Trade Tariff Restrictiveness Index and Overall Trade Restrictiveness Index

||TTRI||OTR|I|
|---|---|---|---|---|
|Country|Value|Rank|Value|Rank|
|Moldova|2.6|41|5.1|11|
|Kazakhstan|2.05|39|12.42|65|
|Belarus|8.
- While there is ample evidence of growing intra-industry trade<sup>14</sup> in the Czech Republic, Slovenia and Hungary, Latvia’s performance is near the bottom of all NMS’s.  This suggests that Latvia’s industries and enterprises are not well integrated with industries and enterprises in other EU countries.  In other words, they are not part of EU value chains.  Compared to the early period of transition (and even more so with the pre-transition period), intra-industry trade between the more advanced NMS’s (the Czech and Slovak Republics, Hungary and Poland) and the EU has steadily increased whereas Latvia is clearly an outsider.  Most of the countries that have relatively low and stable intra-industry manufacturing trade have the largest share of non-manufactured goods in total exports.  In Latvia’s case, the low share of intra-industry trade suggests that a high proportion of country’s manufactured exports consist of relatively simple transformations of raw materials, processes that are not suited to a division of labor and activities across different countries.  The low share of intra-industry trade also suggests that Latvian enterprises do not have the skills required to become suppliers to foreign companies and find a higher value added niche in global value chains.

**Figure 8 Indicators of intra-industry trade with the EU (15)**

<!-- Start of picture text --> 0,8<br>0,7<br>Bulgaria<br>0,6<br>Czech Rep.<br>Estonia<br>0,5<br>Hungary<br>Latvia<br>0,4 Lithuania<br>Poland<br>0,3 Romania<br>Slovak Rep.<br>0,2 Slovenia<br>0,1<br>1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Grubel-Lloyd Index<br><!-- End of picture text --> _Sources_ : Eurostat COMEXT Database, WIIW Industrial Database, own calculations
# **5.2. Digging into the Impacts of Training**

It is already clear from the household results that the training program did not have transformative impacts. We now delve into this further by examining group behavior in more detail, as well as looking at ways that the training may have changed the relative contributions of group leaders. The simplest way to analyze the training experiment at the group level is to use only the Early-treated TASAF groups, within which the training experiment was performed, and compared the trained groups with the untrained. This treatment differential remains cleanly present in this group throughout the study, and so we can use the group-level surveys at RR, midline, and endline to examine the additional effect of training using an interaction between the training dummy and round dummies. Table 3 examines two sets of group-level outcomes; in columns (1) – (4) we look for changes in the types of business practices the training was supposed to engender. At RR and R2 we do see that trained groups are more likely to be keeping written records of group accounts, but these effects fade by R3, and do not carry over to other practices. Trained groups are no more likely to make these accounts available to all group members, to have a written business plan, or to issue written receipts to customers. Untrained groups are likely to have written records that are available to members, and are very unlikely to have written business plans or issue receipts.

Columns (5) – (7) of Table 3 then examine the key entrepreneurial outcomes at the group level (sales over the past month, stock value of group assets, and 6-month profits). All but two of the groups in the early treatment arm are operating an income generating activity at RR– with average total business stocks in the untrained groups valued at approximately $5,564, mean sales during the past month just over $40, and net losses in their first six months of $67. Relative to this base
Togo.

29 Enterprise surveys always include this type of question, providing a list of 15 candidates from the topics mentioned above. The interviewed can provide up to 3 answers: The top, second and third most serious obstacle to business. Note that we in this study report the number of firms expressing a category as the most severe constraint, while the Investment Climate Assessment of Togo 2010 report both the firms that saw it as a main and a severe constraint.

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## ILO

International Labour Organization (International Institute for Labour Studies) (2011) TOWARDS A GREENER ECONOMY: THE SOCIAL DIMENSIONS Lobao, L.; Partridgea, M.; Heana, O.; Kellya, P.; Chunga, S.; Ruppert Bulmerb, E. (2021). Socioeconomic Transition in the Appalachia Coal Region Some Factors of Success New Hampshire Employment Security, Economic and Labor Market Information Bureau. (2011)New Hampshire Green Jobs Survey.

OECD (2012) The jobs potential of a shift towards a low-carbon economy FINAL REPORT FOR THE EUROPEAN COMMISSION, DG EMPLOYMENT Stoevska, V. Hunter, D. (2012) Proposals for the statistical definition and measurement of green jobs. Discussion paper prepared for informal consultation, November 2012. International Labour Office. Geneva.

- <u>Stoveska (2017).  ILO measuring conference Measuring green jobs current proposals.pdf</u>

The Institution of Engineering and Technology (2021). IET skills for net zero and a green recovery 2020 survey. Examining the engineering skills needed to meet net zero.

UNEP, ILO, IOE, ITUC (2008). Green jobs: Towards decent work in a sustainable, low-carbon world. United Nations Environment Program (UNEP), ILO, International Organisation of Employers (IOE), International Trade Union Confederation (ITUC).

Vidican Auktor, Georgeta V. (2020). Green Industrial Skills for a Sustainable Future. Vienna: UNIDO World Bank (2018). Managing Coal Mine Closure. Achieving a Just Transition for All World Bank (2021) Supporting Transition in Coal Regions A Compendium of the World Bank’s Experience and Guidance for Preparing and Managing Future Transitions

# **Appendix D. ISCO classification system**<sup>**65**</sup>

The level of analysis is the occupation at the 4-digit KBJI level. Indonesia adopted the KBJI classification following the International Standard Classification of Occupations 2008 (ISCO-08), which is a hierarchically structured system classifying and aggregating occupational
# _Impact of a single 2022/2023-sized drought_<sup>_9_</sup>

## **For a single drought event, the impact on the livestock sector is less acute than in the agriculture sector,**

**but more persistent** .  Crops have a shorter production cycle than livestock. They can typically be planted, grown, and harvested within a few months to a year, whereas livestock, particularly beef cattle, require several years to reach maturity. This means that the impact of drought on some crops may be felt more acutely in the short term, but the recovery period can also be shorter. Furthermore, crops can be replanted more easily than livestock can be replaced. If crops are lost due to drought, farmers can often replant the same or different crops in the same field in the next growing season. In contrast, if livestock are lost due to drought, farmers may need to invest significant time and resources into rebuilding their herds, which can take years.

**The first exercise describes the impulse-response functions for selected macroeconomic variables of a one-in-a-hundred-year drought such as the 2022/23 drought.** The cumulative impact of this drought on the L&A sector is calibrated to US$1,800 million, which is the estimated direct losses from the current drought in Uruguay. Using public data by MGAP, we assume that 42 percent of these damages are materialized in the crops sector, with the remainder coming from the livestock sector. These losses are spread over three years (with most of the impact occurring in the first year) to account for the persistent effects of the drought, particularly in the livestock sector. For the crops sector, 87 percent of the losses are realized in year one of the drought, and the remaining 13 percent are realized in the subsequent year. For livestock, 60 percent of the losses are realized in year 1, with 30 percent and 10 percent realized in years two and three respectively. These dynamics are driven by
capita is only about 5% of Korea’s GDP per capita. Note again that we are not comparing the performance of one country with another; rather, we are comparing each country with its potential.

A third example relates to the indicator of financial depth. The raw indicator suggests that the United States is top 2 in the world with a financial depth ratio of 190% of GDP, while Fiji is placed number 39 with a financial depth ratio of 79% of GDP. In contrast, the SF model places Fiji as the top 2. That is, considering that Fiji is a small state with GDP per capita only 7% that of the United States, Fiji is operating close to its potential.

The ranking comparisons can be done for different country groupings. These comparisons may add some insight regarding a country’s OTE relative to its peers. For instance, Table 6 presents top 5 and bottom 5 countries for each indicator by geographic location. Alternative groupings –by income level, for example—are also possible.

Next, we present a broad perspective on where countries stand regarding their OTE for each indicator. We do so by showing a color-coded world map for each indicator. We group countries into five categories according to OTE (high, medium-high, medium, medium-low, and low) and assign a color for each category. Figure 2 presents this collection of world maps, one for each indicator. Let’s consider a couple of examples. The measles immunization map shows that countries with more scope for improvement are concentrated in Africa and South Asia. The same pattern is observed for life expectancy. The income inequality map shows that countries with more scope for improvement are in Latin America and SubSaharan Africa. Within Latin America, the countries with more scope for improvement are Honduras, Colombia, Guatemala and Brazil. While in Sub-Saharan, the countries with more scope for improvement are South Africa, Eswatini, Lesotho, and Rwanda.

Not only individual countries but also groups of countries can be compared
# **2. Issues in the Measurement of Consumption through Surveys**

There is a large literature on methods of collecting consumption data through household surveys.<sup>4</sup> Much of the evidence from survey experiments was generated in developed countries, while studies from lowincome settings typically draw on surveys not specifically designed to systematically compare contrasting methods.<sup>5</sup> The specific experimental design described in the next section was motivated by the desire to carefully investigate the divergences across the main methods of consumption data collection in common use today in a developing country setting. The primary dimensions in which these methods vary are three: the use of diary vs. recall, the level of aggregation or detail in the commodity list, and the reference period. Some existing evidence of the measurement consequences of the three aspects is reviewed in turn; lengthier discussions of this literature can be found in Gibson (2006), Deaton and Grosh (2000), and Scott and Amenuvegbe (1991).

There are many potential sources of reporting error that lead survey estimates of consumption to deviate from actual consumption. Perhaps the most commonly discussed in the literature is recall error, where a household under-reports true consumption over the period of recall due to faulty memory. Presumably the longer the period of recall, the greater the cognitive demand on the respondent and the greater the divergence between reported and actual consumption. Several studies have documented that, all else equal, the longer the period of recall, the lower the reported consumption per standardized unit of time. Closely related to recall error is telescoping, where a household compresses consumption that occurred over a longer period of time into the reference period asked and thus reports consumption greater than the actual value.

A third important source of error is the inability to accurately capture individual consumption by household members that occurs outside the purview of the survey respondent. Clearly this inability may be more significant for certain types of consumption goods such as transport, telecommunication, or meals outside the home
- => Last, the profile shows that there is a gender dimension to poverty in each

country. Female-headed households have higher poverty incidence and gap in each of the six countries.

Without wishing to downplay differences across countries, the common aspects in this profile of poverty suggests that there is a case to be made for a poverty alleviation policy for the- East Europe/FSU region as a whole. Lessons learned in one country are likely to have applicability irrothers. Priorities in targeting (children, elderly, loweducated workers, female-headed households) are similar across countries and the design of targeted interventions can benefit from region-wide experiences. Needless to say, social and cultural differences are important, and must be given their due weight, even if economic behavior and responses-are similar.

The multivariate analysis has corroborated the univariate observations from the poverty profile, and made it possible to compare net effects, controlling for all other factors, and identify the highest pay-off actions. Although there is more variation across countries, some common factors have emerged:

> > education plays a key role for welfare improvements. There is always a significant welfare penalty to having achieved only primary education, and in some countries (those most advanced in the transition process) the penalty extends to vocational and technical education as well. Since it is not practical to quickly upgrade education and/or retrain huge segments of the population, this aspect of poverty will remain a long-term challenge.

103
unconditional measure of volatility: an index of the rolling standard deviations of prices in the same 7 asset markets, along with VIX, used to compute the conditional volatility measure.<sup>17</sup> Results with this alternative measure of volatility are reported in column ( _R4_ ), and mirror the benchmark results, with a significant negative coeeficient on the alternative volatility measure.

As discussed in Section 2, the uncertainty channel variable is intended to measure shocks to uncertainty, as distinct from shocks to risk aversion. However, the two concepts are interrelated, as the effect of uncertainty on behavior depends on the degree of risk aversion; and the degree of perceived risk aversion, as well as the effect of a given degree of risk aversion on behavior, may depend on the level of uncertainty. Furthermore, there is a question of whether risk aversion itself changed during the crisis, or whether behavior to reduce risk exposure during the crisis was predominantly a reaction to heightened risk and uncertainty for a given degree of risk aversion. Results with a variable measuring U.S. loan officers’ risk aversion are reported in columns ( _R5_ ) and ( _R6_ ).<sup>18</sup> While the coefficient is insignificant when risk aversion is included in place of

> 17The results are robust to the method of aggregation. While we report here the weighted average of the first two principal components, using a simple average of the 8 series yields qualitatively similar results.

> 18The risk aversion variable is constructed from a question on the Fed’s Senior Loan Officer Survey that asks loan officers who reported that they tightened or eased credit standards or terms on C&I loans what factors were relevant to that decision, with reduced or increased tolerance for risk given as an option for the reason for tightening or easing, respectively. Banks’ responses are averaged, with each response that a change in risk tolerance was “very important” given twice the weight of a response that it was “important.”

15