rafmacalaba/data-use-mentions-extended huggingface dataset:
that certain areas of banking have been hit harder than others.**

- Less profitable, capital-intensive projects are disproportionately affected, including infrastructure finance and loans to SMEs. As discussed, the most recent ECB SME lending survey suggests that SMEs are experiencing difficulties in accessing bank credit across Europe. High lending rates are also discouraging UK SMEs from borrowing from banks, which reportedly failed to meet their SME lending targets in late 2011 (under ―Project Merlin‖). Access to credit is particularly difficult for SMEs in Central and Eastern Europe (CEE) and Central Asia.

- Similarly, global trade financing volumes were down 6 percent in the first nine months of 2011 compared to 2010. This decline was in part due to limited access to U.S. dollar funding by European banks, which account for about one-third of the trade financing market.<sup>8</sup> Anecdotal information suggests that trade financing experienced a significant

> 8 Large French banks, in particular, provide a significant share of trade financing in emerging Europe, Asia, Latin America, and West Africa (figure 13).
**5. Final Remarks**

Measuring the monetary value of the flow of services that households receive from living in their dwellings poses a challenge to researchers and practitioners. On the one hand, a growing consensus requires that imputed rents should always be included in the welfare aggregate. On the other hand, imputed rents are often neglected, mainly because of inherent technical difficulties in rent imputation as well as due to lack of relevant information in household surveys. Moreover, when imputed rents are indeed included in the welfare aggregate, researchers and practitioners frequently overlook analyzing the distributional impacts of alternative rent-imputation approaches.

This paper is the first attempt to assess the distributional impact of including housing in the welfare aggregate comparing two rent imputation methods in four developing countries at different levels of development. First, the paper introduces a concise theoretical discussion of the expected distributional implications of including rent in the welfare aggregate and concludes that unambiguous predictions of the impact on poverty, inequality, and shared prosperity are possible only under strong assumptions. Second, it provides estimates of the distributional effect of including estimated rental values in the consumption aggregates of four countries.

Results indicate that understanding the distributional impact of including housing in the welfare aggregate is ultimately an empirical question. Including rental values determines statistically significant changes in poverty and inequality indicators, yet such changes are only occasionally larger than one percentage point. Shared prosperity exhibits sizable changes, which might also determine international re-rankings. In countries with low headcount rates, any churning around the first decile may imply a change in the profiling of the poor. Although most socio-economic variables characterizing the poor are affected by the inclusion of rents, changes are rarely substantial. Although any distributional analysis shall include housing in the construction of the welfare

> 13 Precisely, we will be discussing quasi-non-anonymous implications since we are not considering changes affecting any particular individual in the distribution, but rather groups of individuals: e.g. individuals belonging to the same decile in the distribution, or individuals belonging to the set of poor individuals.

> 14
# **4.2 Robustness and Treatment Heterogeneity**

In our gravity analysis, we cannot confirm the robust trade-creating role for non-tariff provisions in TAs that has been reported by the literature so far, while tariffs—once properly measured—are robustly shown to be potent inhibitors of trade, yielding plausible trade elasticities in the estimation. We show next that this result is very robust to alternative empirical specifications and is not driven by treatment heterogeneity.

We test if the results might be driven by the definition of depth. To do so, we use alternative measures that are standard in the literature. The results remain virtually unchanged in columns (8) to (14), for which we use the number of legally enforceable provisions to determine the depth of trade agreements. When using DESTA (results can be found in Table A1, Panel (A)) to determine the depth, the NTB effects of the “no tariffs” and “WITS” columns are much smaller than when using the World Bank’s DTA data—the coefficients almost halve. This result is most likely due to the more precise measure of depth in the DTA data compared to DESTA; nevertheless, the NTB effect of deep trade agreements does not survive when controlling adequately for tariff cuts while the discriminatory changes in tariffs remain to be a significant driver for trade.

The results also hold for a longer time horizon. The second release of the ITPDE extends the number of available years and starts as early as 1989. The advantage of the longer sample is that we can cover more trade agreements entering into force. However, data on services are only available starting in 2000. With this extended data at hand, we redo the baseline analysis including the years 1989 to 2015 for the two broad sectors manufacturing and agriculture. The coefficients, which are shown in Panel (B) of Table 1, report the same pattern as in the baseline and are also robust to alternative definitions of depth (c.f., columns (8)
02|
|_Men_|||||||||||
|Cross-section (ind level)|-0.22|-0.21|-0.20|-0.18|-0.17|-0.17|-0.17|-0.16|-0.16|-0.16|
|Panel (RE)|-0.13|-0.12|-0.12|-0.11|-0.10|-0.10|-0.10|-0.10|-0.10|-0.10|
|panel (FE)|-0.07|-0.06|-0.06|-0.05|-0.05|-0.05|-0.05|-0.04|-0.04|-0.04|
|_Women_|||||||||||
|Cross-section (ind level)|-0.33|-0.30|-0.27|-0.30|-0.31|-0.31|-0.31|-0.29|-0.29|-0.28|
|Panel (RE)|-0.17|-0.16|-0.14|-0.15|-0.15|-0.14|-0.14|-0.15|-0.14|-0.13|
|Panel (FE)|-0.07|-0.07|-0.04|-0.05|-0.05|-0.03|-0.04|-0.05|-0.03|-0.03|

_Source_ : Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16.

_Note_ : Demographic
WHO (World Health Organization), 2000. “The world health report 2000 - Health systems: improving performance” World Bank, “Health Nutrition and Population Statistics” Washington, DC: World Bank.

__________, “World Development Indicators,” Washington, DC: World Bank.

__________, “Global Financial Inclusion (Global Findex) Database.” Washington, DC: World Bank __________, 2013. “World Development Report 2014: Risk and Opportunity.” Washington, DC: World Bank.

__________, 2015. “A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals.” Policy Research Report. Washington, DC: World Bank.

Yepes, Tito and Pierce, Justin and Foster, Vivian, 2009. “Making Sense of Africa's Infrastructure Endowment: A Benchmarking Approach” World Bank, Policy Research Working Paper 4912.

18
Figure 2. Gender gap in time allocated to paid and unpaid work (men minus women). Hours per day <!-- Start of picture text --> 3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>Albania Argentina Bangladesh Colombia Ecuador Ghana Guatemala Iraq Mexico Moldova Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data. Notes: Paid and unpaid work include market work, housework and childcare.

Figure 3. Time allocation by activity in countries with largest and smallest gender gaps. Hours per day.

<!-- Start of picture text --> 30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>W M W M W M W M W M W M<br>Iraq Guatemala Mexico Moldova Mongolia USA<br>Largest gender gaps Smallest gender gaps<br>Market work Unpaid domestic work Personal care Leisure, social and study<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data.

40
# 2. Estimating trading times using network analysis

To estimate the shipping times between two BRI locations, we use georeferenced data combined with GIS analysis on a country’s transport network.<sup>5</sup> The main objective of the network analysis is to quantify the connectivity between capitals, ports and major cities (>100K) of BRI countries before and after various planned interventions (see Figure 1).<sup>6</sup> Trading times are assessed with the transport network in 2013 (i.e. before the official launch of BRI) for a set of 1,818 cities across 71 BRI economies listed in Appendix Table A. 1. This section reviews the main steps that are performed to assess the time it takes to trade across countries before the Belt and Road interventions (the assessment of the updated transport network on trade times is discussed in Section 4). It also highlights the main statistics deriving from this exercise.

A network analysis provides a more accurate measure of the real time it takes to ship goods across countries, as it takes into account factors such as the quality and quantity of infrastructure, physical obstacles (rough terrain) or other barriers (border and other delays). It also provides a more realistic description of the travel routes of goods that are shipped between cities compared to other measures such as the distance between capitals or main cities that are commonly used in the trade literature.

> 5 The details of the methodology and data are discussed in de Soyres et al. (2018).

> 6 The list of interventions under the BRI has been identified in Reed and Trubetskoy (2018).

5
Kurasha, F.M.N. (2021). Wealth, health, and inequality: a study of hyperinflation and households in Zimbabwe. Faculty of Commerce, School of Economics.

Catherine Larochelle, Jeffrey Alwang, Nelson Taruvinga (2014). Inter-temporal Changes in Well-being During Conditions of Hyperinflation: Evidence from Zimbabwe, Journal of African Economies, Volume 23, Issue 2.

Malin A (2016). Inflation and Its Influence of Households Livelihoods in Mogadishu – Somalia. European Journal of Business and Management, 8(7).

Narayan, Deepa, Raj Pattel, Kai Schafft, Anne Rademacher, and Sarah Koch-Schulte. 2002. “Voices of the Poor. Can Anyone Hear Us?” Oxford: Oxford University Press.

Nordhaus, W.D. (1973). The Effect of Inflation on the Distribution of Economic Welfare. Journal of Money, Credit and Banking, Vol. 5 (1).

Pape, Utz Johann; Mistiaen, Johan A.. (2018). _Household expenditure and poverty measures in 60 minutes: a new approach with results from Mogadishu (English)_ . Policy Research working paper; no. WPS 8430. Washington, D.C. : World Bank Group.

Pape, U., Parisotto, L. (2019). Estimating Poverty in a Fragile Context - The High Frequency Survey in South Sudan. Policy Research Working Paper, 8722. World Bank.

Reinhart, C., & A. Savastano, M. (2003). The Realities of Modern Hyperinflation, Finance & Development, 40(002).

Republic of South Sudan Inclusive Growth Country Economic Memorandum, World Bank (2017).

Rozanski Alan, James A. Blumenthal and Jay Kaplan. 1999. “Impact of Psychological Factors on the Pathogenesis of Cardiovascular Disease and Implications for Therapy.” _Journal of the American Heart Association._ Daniel J Brotman, Sherita H Golden, Ilan S Wittstein. 2007. The Cardiovascular Toll of Stress. _The Lance_ t: Vol. 370: 1089
inefficiency inasmuch as a fixed transfer of groundwater necessarily leads to a misallocation across farms once the state of nature is revealed. Our model yields the sharp prediction that as groundwater supply uncertainty increases, long-term contracts become unattractive relative to spot arrangements.

The assumption that long-term (ex-ante) contracts deter holdup appeals to the referencepoint insight of Hart and Moore (2008) and Hart (2009) wherein a contract establishes what each party in the transaction is entitled to; opportunism thus leads to deadweight losses (see also Herweg and Schmidt, 2014, for a related model).<sup>4</sup> In the earlier property rights theory of the firm associated with Grossman and Hart (1986) and Hart and Moore (1990), renegotiation is efficient so that hold-up is virtually inevitable (Hart, 1995). Since there is, consequently, no functional difference between contracts agreed upon ex-ante and those agreed upon ex-post, _payoff_ uncertainty, in Hart’s (2009) terminology, can play no role.

A key contribution of this paper lies in quantifying the contracting distortion, as well as its impact on the return to water-saving technology, using a structural econometric model. A rather unique feature of a groundwater economy that allows us to do this is that buyers and sellers are _both_ agricultural producers, cultivating side-by-side with the same technology. Our model of agricultural production under stochastic groundwater supply accounts for the choice between seasonal contracts and per-irrigation sales, for water transfers through leasing, as well as for the area irrigated under each such arrangement. We use data from a large sample of borewell owners across six districts of Andhra Pradesh and Telangana states. The specially-designed survey instrument takes particular care to elicit from each well owner a subjective probability distribution of their borewell’s discharge near the end of the season conditional on its initial discharge. The structural parameters of the model are identified principally from the variation across borewells in this conditional probability distribution.

To assess external validity of the structural model, we retain two _nonrandom_ holdout samples corresponding to
back office in the same department, while another third of institutions place them in completely separate departments. The remaining central banks opt for a hybrid approach (Anasashvili et al. 2020).

# **3. Approach and Objectives**

Little quantitative research is available on the link between overall central bank governance and reserve management, despite the critical role that sound reserve management plays in helping, supporting, and maintaining confidence in monetary management (Al Hassan, Farahmand, and Papaianou 2014). As shown above, most publications on reserve management governance are prescriptive and qualitative. Therefore, we contribute to the reserve management governance discussion with data-driven analysis using RAMP's unique survey data on governance and organizational arrangements of central banks and asset allocation and risk measures. Specifically, we investigate whether governance arrangements impact investment policies and central bank risk taking and, if so, precisely which arrangements matter. We also analyze whether organizational arrangements impact central banks' investment policies and whether reporting structures influence central banks' investment policies and risk taking in their reserve management operations. This paper's ultimate goal is to empirically analyze the relationships between a central bank's governance structure for its reserve management operations and its investment policies and risk taking.

# **4. Methodology and Data**

We use correlations and regression analysis to find links between specific governance arrangements and variables related to central banks' reserves investment policies. We start by analyzing correlations between the variables described above and testing for their statistical significance. We then use regression analysis to analyze whether some of the correlation results hold when adjusting for reserve adequacy and indicators that describe the macroenvironment.

We use data groups that capture the governance arrangement, investment policies, and measures of risk taking of individual central banks to test whether governance affects investment policies. First, we use data on governance arrangements for central banks' reserve management operations as an independent variable collected through the RAMP surveys. Second, we utilize RAMP survey data on the composition and risk of reserve portfolios as dependent variables describing
_Table 2.3 Educational Distribution_ _<u>by Gender, 1988, 1997 and 2004 (percentage)</u>_

|_Educational_<br>_Group_|_Primary_<br>_incomplete_|_Primary_<br>_complete_|_Lower_<br>_secondary_<br>_complete_|_Upper_<br>_secondary_<br>_complete_|_University_<br>_complete_|
|---|---|---|---|---|---|
|_1988_||||||
|Male|19.0|30.1|24.5|14.6|11.8|
|Female|17.3|22.2|23.2|29.1|8.2|
|Total|18.5|27.7|24.1|18.9|10.7|
|_1996 _||||||
|Male|13.3|26.2|27.6|17.8|15.1|
|Female|12.3|20.9|21.8|29.2|15.8|
|Total|12.9|24.4|25.7|21.6|15.4|
|_2002_||||||
|Male|10.1|23.3|31.5|18.8|16.2|
|Female|9.7|19.2|25.4|28.1|17.6|
|Total|10.0|21.9|29.5|21.9|16.7|

_Source:_ Calculations based on the ENEU survey (third quarter).

_Figure 2.2 Cumulative Educational Distribution by Gender, 1997_ <!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.
51)** such vehicles would not be sampled at all, and would therefore not be accounted for hours **per** week in the above population adjustment. Now **Figure 8** we address the issue of the true population _Source:_ Author's survey of taxicabs. of vehicles within Ulaanbaatar, and the methods used to estimated what percentage of vehicles are _never_ used as taxicabs.

108. The number of vehicles in Ulaanbaatar is enumerated by the Ulaanbaatar City Road Police, and can be broken down into two categories depending on the type of "numbers""s with which each vehicle is registered. There are frequent, random, checks of registration papers by the traffic police, annual fees for registrations, and annual technical inspections, so it is reasonable to assume that the number of vehicles registered is very close to the actual number of vehicles in operating condition. The population of taxicabs is a subset of the population of vehicles. If we can estimate the proportion of vehicles that are _never_ used as taxicabs, we can identify the population of taxicabs to which we can apply the frequency derived above and arrive at aggregates.

109. Two approaches were used to address the question of how many vehicles in Ulaanbaatar are never used as taxicabs. The first method is to make educated guesses and solicit the guesses of Mongolians. (The final estimates of aggregates are not terribly sensitive to this guess, so broad upper and lower bounds are acceptable.) Mongolians universally estimate the percentage of vehicles never used as taxis to be very close to zero, and certainly no higher than 10%. The second approach is to fit the population distribution derived above to a standard distribution, and estimate the proportion of vehicles used as a taxi zero hours per week, based on the fit of the distribution. Experiments with the Poisson distribution, choosing the Poisson parameter to optimize the fit over the relevant portion of the population distribution derived above, suggest a distribution whose mode is not zero, but some
.

- Dollar, D., I. Giuseppe, and T. Mengistae, 2002, “Investment Climate and Economic Performance: Some Firm Level Evidence from India” Working Paper No. 143, Center for Economic Research and Policy Reform, Stanford University.

- Dollar, D., M. Hallward-Driemeier, and T. Mengistae, 2004, “Investment Climate and Firm Performance: in Developing Economies,” Processed, World Bank, February

- Dubin, M., and D. McFadden, 1984. “An Econometric Analysis of Residential Electric Appliances Holdings and Consumption,” Econometrica, vol. 52, pp. 345-362.

- Eifert, B., A. Gelb, and R. Ramachandran. 2005. “Business Environment and Comparative Advantage in Africa: Evidence from the Investment Climate Data,’ mimeo, World Bank Group, Washington DC, January.

- Escribano, A. and J. Guasch. 2004. “Assessing the Impact of Investment Climate on Productivity Using Firm Level Data: Methodology and the cases of Guatemala, Honduras and Nicaragua,” mimeo, World Bank Group, Washington, DC,. November.

35
# **5. CONCLUSIONS**

This paper investigates how patterns in occupational placement of skilled male immigrants on the US labor market vary over time and by country of origin when education and other human capital characteristics are accounted for. By focusing on occupational placement rather than earnings, the analysis reaps the former’s benefit of better comparability across geographic regions within the US and over time.

Employing three US Census samples, 1980, 1990, and 2000, to follow the progress of cohorts who arrived during the 1970s and 1980s, our first finding is that (i) the initial occupational placements by country and (ii) their improvements within the first 10 or 20 years since arrival are highly heterogeneous. Second, immigrants from countries with lower initial occupational placement are shown to improve their performance faster than those from countries with better initial placement, thus there is convergence among migrants. However, our third main result shows that, the magnitude of the convergence factor is not large enough to ensure full catch-up to the levels of those countries which had high initial performance.

Country specific attributes have both qualitative and selection effects on the predicted initial occupational placement. However, their impact on predicted occupational placement improvement over a decade seems to be indirect, in the sense that it is channeled through the initial levels of performance which are shown to be negatively correlated with the growth rate. Once initial occupational placement is introduced in the estimation explaining the predicted improvement in occupational placement, most country specific variables change signs and lose significance.

The findings in this paper may have several policy implications. The first relates to the heterogeneity on the initial occupational performance levels across different countries. It is possible that the variance arises from the fact some migrants are unable to practice their professions due to regulatory restrictions (such as absence of mutual recognition agreements between countries) and migrants struggle to obtain necessary licenses. Then, it is economically and socially efficient to remove those barriers. Another potential constraint are language barriers which means appropriate linguistic training for new migrants can have
and 10).

# 2. Households Receiving Partial Bank Financing

35. The lack of mortgage lending in Korea forces households into complex funding strategies as shown by the annual surveys of units partially financed by the Korea Housing Bank. This subset covers a more fortunate minority of home buyers. Table 3 ranks in order of frequenjc, the most common strategies currently used by Korean households.There are for these borrowers eight major sources of

- 12/ These tables were prepared from the 1985 KHB Survey at my request by Jae-Hye Han. Ms. Han has recently completed an analysis of the Korean tenure choice which draws on a variety of recent housing surveys. See Jae-Hye HAN, (1987). The present analysis was suggested by French surveys of household wealth and mortgage borrowers, the French market being subjected to financial repression and a scarcity of mortgage funds until the early 1970s.
Learning from Developing Country Power Market Experiences: The Case of Colombia

|**2011**|9,295|14,420|55%|2%|9%|58,062|3%|
|---|---|---|---|---|---|---|---|
|**2012**|9,504|14,361|51%|2%|0%|59,211|2%|
|**2013**|9,383|14,559|55%|-1%|1%|61,182|3%|
|**2014**|9,551|15,489|62%|2%|6%|64,014|5%|
|**2015**|10,095|16,420|63%|6%|6%|66,275|4%|
|**2016**|9,904|16,595|68%|-2%|1%|66,318|0%|
|**2017**|9,996|16,779|68%|1%|1%|66,893|1%|
|**CAGR 2010-17**|**1.4%**|**3.4%**||||**2.4%**||

*Net effective generation capacity in the National Interconnected System by December 31st.

Source: Own elaboration based on data collected by the World Bank.

Despite very high and rising reserve margin, supply reliability in Colombia must be assessed considering its dependence on hydro power plants with little multi-year storage capacity. Power generation from thermal power plants increased sharply during 2015, providing 32% of total generation (see Figure 6-13). Average PLFs increased