rafmacalaba/data-use-mentions-extended huggingface dataset:
inconvenient for further comparative analysis due to "hyper-mobility". According to **the** findings of surveys held in Moscow, **the** share of such households in Moscow was 0.9, 1.4, **1.8** and 1.7% in 1992, 1993, 1994 and 1995 respectively. **In** Vladimir and **Gorodets, the** proportion ofthe renter households was much lower.

**19 One** can overlook the possibility that over a short period households may have moved more than once, this is very uncommon in Russia.
frequently purchased items. They find that each additional day of recall results in about 3 percentage points decline in reported daily expenditure.<sup>8</sup> Other experiments on recall period that were identified are all based on surveys in which the same household reports expenditures across two different recall periods _within_ the same survey. This is potentially problematic if there is conditioning bias due to households or interviewers imposing consistency between their responses.<sup>9</sup> Grosh et al _._ (1995) compare consumption estimates from the Ghana LSMS reported by short (2-week) recall periods and longer recall. The latter were “explicitly normative” questions in which households report the number of months purchases were made in the last year, how often they were made, and how much was usually spent each time (referred to here as “usual-month” questions). While food expenditures were not different, non-food expenditures were 72 percent higher based on short recall, contrary to expectations. Similar analysis is presented for Jamaica (Grosh et al _._ , 1995), Côte d’Ivoire, Vietnam, and Pakistan (Deaton and Grosh, 2000). Deaton and Grosh (2000) conclude that there is only a slight sensitivity to the choice of these recall periods. In much earlier work from Malawi in 1970, Plewis (1972) presents admittedly weak evidence that 7-day food recall is consistent with 24-hours recall (in some sense, this is like a diary). However, it is not clear to what extent conditioning bias is driving these results. The Indian National Sample Survey (NSS) experimented with using a “last week” versus a “last month” recall and found that for the all-food aggregate the estimates based on weekly recall were 21 percent higher (NSSO 2003).

In short there are numerous features of survey design that can result in different reported consumption for the same household over the same period of time. Usually based on ad hoc or opportunistic studies, clear divergences arise in
family as an exclusion restriction frequently invoked in the female labor force participation decision. This is like the studies by Martins (2001), Chang (2011) and Huber and Mellace (2014). This variable potentially impacts the caregiving activities of women but does not affect earnings. Several previous studies also find higher selectivity corrected estimates than the OLS estimates for Turkey (see, for example, Tansel 1994, 2001, 2005, 2010).

Second, returns to education for females are larger than those to males. This is also found in the previous studies for Turkey cited above. In their global survey, Psacharopoulos and Patrinos (2018) also report higher returns for females than for males as a general pattern for many countries. Dougherty (2005) investigates the reasons for this finding and attributes this general observation to discrimination, tastes and circumstances.

Third, returns to education in the public sector are higher than that in the private sector. This is contrary to what is observed for many countries of the world by Psacharopoulos and Patrinos (2018). We can get some idea about the situation in the public versus private sectors although a comparison of the mean wages in these two sectors is not the same as the comparison of the returns to education in these two sectors. The mean wages in the public sector is larger than the mean wages in the private sector in the HLFS data in 2017 and in several years before. This observation is also confirmed with the recent Survey of Income and Living Conditions (SILC). Casual observation also supports this fact. Most people in the private sector work at the minimum wage while public sector salaries are much higher than the minimum wage. There are two points to note here. One is that the presence of larger numbers of Syrian refugees since 2011 has lowered the private sector wages substantially. Second is that there is a large informal sector in Turkey. Almost 30-35 percent of the wage earners are in the informal sector. It is well-known that the wages in the informal private sector
(2016). _Poverty in a rising Africa_ . World Bank Publications.

- Carpenter, J. R., Bartlett, J. W., Morris, T. P., Wood, A. M., Quartagno, M., & Kenward, M. G. (2023). _Multiple imputation and its application_ . John Wiley & Sons.

- Christiaensen, L., Ligon, E., & Sohnesen, T. P. (2022). Consumption subaggregates should not be used to measure poverty. _The World Bank Economic Review_ , _36_ (2), 413-432.

- De Weerdt, J., Gibson, J., & Beegle, K. (2020). What can we learn from experimenting with survey methods?. _Annual Review of Resource Economics_ , _12_ , 431-447.

- Dang, H. A. H., & Lanjouw, P. F. (2023). Regression-based imputation for poverty measurement in data-scarce settings. In Silber, J. (Ed.), _Research handbook on Measuring Poverty and Deprivation_ (pp. 141-150). Edward Elgar Publishing.

- Dang, H. A. H., & Verme, P. (2023). Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation. _Journal of Population Economics_ , _36_ (2), 653-679.

- Dang, H. A. H., Lanjouw, P. F., & Serajuddin, U. (2017). Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country. _Oxford Economic Papers_ , _69_ (4), 939-962.

- Dang, H. A., Kilic, T., Abanokova, K., & Carletto, C. (forthcoming). Poverty imputation in contexts without consumption data: a revisit with further refinements. _Review of Income and Wealth_ .

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exclusive and possibly both carry an element of truth. The implications of these explanations are consistent with the comparative performance of firms in the years 1980-82 and 1984. In the 1980-82 period, debt to asset ratios increased because lower asset returns dominated the effects of increased financial costs. In 1984, however, both rates of return on assets and financial costs were high, and, firms responded by lowering their indebtedness.

4 - CONCLUSION There were, basically, two somewhat related stories laid out in this paper. The first one was about the response of the banking system to deregulation. It was argued that the complete absence of a regulatory framework allowed insolvent banks to avoid bankruptcy by offering high rates to depositors, using funds thus collected to finance their obligations and refinance non-performing loana. The second one was related to the response of firms. Contrary to the expectations of policy makers, firms that made losses were shown to increase their debt to asset ratios even though cost of borrowing had increased. It is not possible to clearly demonstrate the correspondence between these two stories. Although in the CMB data set there were some observations with negative book values of net worth, it is not clear how many firms that had low earnings were actually insolvent.

However, there is a unifying theme between the two stories: The unexpected consequences of deregulation and increases in the level of indebtedness of firms seemed to be generated by a drop in the earnings of the corporate sector that resulted from stabilization policies and the radical changes in the economic environment. Once firms and banks are hit by
MR, Siegel JE, Russell LB, Weinstein MC. (eds). 1996. Cost-Effectiveness in Health and Medicine. Oxford and New York: Oxford University Press.

- Haacker, M., Hallett, T. B., & Atun, R. 2019. On discount rates for economic evaluations in global health. Health Policy and Planning, czz127. https://doi.org/10.1093/heapol/czz127

- Hall, G., & Patrinos, H. A. (Eds.). 2006. Indigenous peoples, poverty, and human development in Latin America. Palgrave Macmillan.

- Hernandez, D. J. 2011. Double Jeopardy: How Third-Grade Reading Skills and Poverty Influence High School Graduation. Annie E. https://eric.ed.gov/?id=ED518818

- Hodges, Charles,  Stephanie Moore, Barb Lockee, Torrey Trust and Aaron Bond. 2020. The Difference Between Emergency Remote Teaching and Online Learning. Available online at <u>https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teachingand-online-learning%C2%A0%C2%A0</u>

- Jaume, D. and Willén, A. 2019. The Long-Run Effects of Teacher Strikes: Evidence from Argentina," Journal of Labor Economics 37, no. 4: 1097-1139. <u>https://doi.org/10.1086/703134</u>

- Jerrim, John, Luis Alejandro Lopez-Agudo, Oscar D. Marcenaro-Gutierrez, and Nikki Shure. 2017. "What happens when econometrics and psychometrics collide? An example using the PISA data," Economics of Education Review, Elsevier, vol. 61, pages 51-58.

Jones, S. 2017. Do schools in low income countries really produce so little learning?

- Kaffenberger, M. 2020. Modeling the Long-Run Learning Impact of the Covid-19 Learning Shock: Actions to (More Than) Mitigate Loss. RISE Insight Series. 2020/017. https
Neither of the two meta‐analyses contains a discussion of potential differences in φ between developed and developing countries. However, Calderon, et al. (2015),<sup>24</sup> using a relatively extensive cross‐country sample, find that the long‐run elasticity of infrastructure does not seem to vary with countries’ per capita income levels, infrastructure endowment or population size.

# <u>5.2.2.  Initial capital‐to‐output ratios and depreciation rates</u>

Our default data source for aggregate data on capital stocks and depreciation rates is the PWT 8.1, which is also used in the Standard LTGM. Unfortunately, PWT 8.1 does not include the split into public and private capital so we rely on data from the IMF for the relative shares of public and private capital stocks (see IMF 2015 and <u>http://www.imf.org/external/np/fad/publicinvestment/).</u><sup>25</sup> Given different computation methodologies, aggregate K/Y ratios differ between the PWT 8.1 and the IMF, and are on average lower in the latter case.<sup>26</sup> We assume that public capital is mostly structures, and so apply the 2% structures depreciation rate from PWT 8.1. The private capital depreciation rate is a residual determined by the country‐specific PWT 8.1 depreciation rate for aggregate K: δ�δ<sup>��</sup> �<sup>��</sup> + δ<sup>��</sup> �<sup>�</sup> ~~.~~

# = = <u>5.2.3.  Congestion (ζ 1) vs pure public good (ζ 0) and actual vs measured TFP growth</u>

Users of the LTGM‐PC must choose either a calibration
estimates we discussed earlier (Table 4).

In light of these estimates and the event studies presented earlier, we believe that the most plausible interpretation of our findings is that increased competition from Venezuelan migrants adversely affected the quality of employment and earnings of young, low-educated Ecuadoran workers.

# **7 Conclusions**

Using data from Ecuador’s labor force survey (ENEMDU) merged with new data on the geographic distribution of Venezuelan migrants across Ecuador, this paper has analyzed the location choices of Venezuelans within Ecuador and the labor market consequences of these choices.

Our analysis has shown that Venezuelans’ location choices have been fundamentally driven by the size of regional economies, measured by the wage bill at the canton level. We have also grouped Ecuador’s cantons as a function of the size of the Venezuelan population living in the canton relative to its population size, and produced differencein-difference estimates of the labor market effects of Venezuelan inflows. The estimates clearly indicate that young, low-educated Ecuadoran workers have been adversely affected. Specifically, they have experienced reductions in the quality of their employment, increases in informality, and declining earnings, relative to workers with similar characteristics living in cantons with very low or non-existing inflows of Venezuelans.<sup>25</sup> Our findings are in line with recent studies analyzing the labor market effects of Syrian refugee inflows into Turkey that also find that the newly arrived migrant workers have found employment mainly in informal jobs, placing the burden of the adjustment disproportionately on the more vulnerable workers in the main host regions. These effects are also highly localized in a few regions, suggesting the use of targeted interventions to alleviate the economic burden falling on the vulnerable groups of workers in those regions.

Last, it is important to highlight that these adjustment costs can lead to an increase in economic prosperity, both for the migrants and the hosting regions. The inflow of migrant labor is likely to spur investment and the creation of new businesses, generating economic opportunities that can benefit both migrant and domestic workers.
high values are problematic for charge-offs, we look at P90 and max statistics instead of P10 and min, respectively.

# **4.1.1 Bank Conditions**

The bank equity ratio (equity/GTA) and similar ratios are frequently employed in banking research as summary measures for bank health and the ability to absorb shocks. As noted above, it is consistently measured over time and is not subject to temporary easing of what counts as regulatory capital during COVID-19. We find that the equity ratio is stable for the banking industry for the entire set of five quarters. The mean equity ratio for the U.S. banking industry stays in the tight range of less than one percentage point variation between 12.56 and 13.41 percent, suggesting that the industry remained well capitalized throughout. Focusing next on the P10 points to see if there is trouble with the least capitalized set of banks, the figures range between 8.30 and 8.98 percent, showing relatively little movement, no sign of a banking crisis, and much higher than in troubled times of the past.

We next turn to the bank liquidity ratio, which includes cash, due from, and securities normalized by GTA, for potential signs of trouble that might signal a liquidity problem that would require fire sales or reduced credit supply. The mean bank liquidity ratio varies from 30.33 to 35.27 percent, rising over time as banks become more liquid after the crisis starts. The P10 also rose from 12.69 to 17.34 percent over time as the industry becomes more liquid, clearly indicating that the COVID-19 crisis did not induce liquidity problems in the U.S. banking industry.

The U.S. G-SIBs, whose data are shown on the right side of the table, normally maintain significantly lower capital ratios and higher liquidity ratios than other banks. The mean capital ratio ranges between 9.13 and 10.54 percent, with the minimum varying between 6.96 and 8.35
Table 7 Percentage of Providers Observed during Consultation from Voucher and Nonvoucher Facilities That Adequately Offered Various Services to Clients in 2010

|Indicator|Voucher facilities|Comparison facilities|
|---|---|---|
|ANC|(_N_= 96)|(_N_= 51)|
|Adequately created rapport<br>(three items)|79|67*|
|Adequately took account of<br>client’s general history (four<br>items)|26|26|
|Adequately took account of<br>client’s medical history (six<br>items)|18|10|
|PNC|(_N_= 59)|(_N_= 24)|
|Adequately created rapport<br>(four items)|20|13|
|Adequately took account of<br>client’s general history (four<br>items)|3|0|

_Source:_ Population Council, Health Facility Survey 2010–11.

_*_ Difference between voucher and nonvoucher facilities is statistically significant at 5 percent. For each indicator, percentages refer to the proportions of consultations at which the SP performed all the itemized tasks.

The results from the observations of CPIs further show that significantly higher proportions of SPs from voucher facilities conducted most of the clinical examinations for ANC and PNC clients compared with their counterparts at nonvoucher facilities (table 8).

31
|18.0|17.9|10.7|
||Natural resources,excl agricgoods|72,332|100.0|41.1|||||||
|0-97|Allgoods|1,005,555||8.3|||||||

Source: UN COMTRADE database.

Moreover, just as WTO bindings constrain the scope for preferential access to markets, a WTO provision – GATT 1994 Article XVII on State Trading Enterprises – is meant to prevent state traders and investors from departing from the MFN principle. Thus, in principle, even a stateowned firm based abroad could not divert its output in a discriminatory fashion to its home country. However, even though this provision extends to all such enterprises ―wherever located,‖ it is not clear that WTO rules can oblige a country to ensure its firms honor its obligations even when they are located outside its territory. How this provision is interpreted and applied will have a crucial bearing on how much scope countries like China have to secure privileged access to resources by investing abroad.

21
**Table 2. Microfinance Scale by Region**

|||**Number of**<br>|**Active Borrowers/**<br>|||
|---|---|---|---|---|---|
||**MFI**|**active**|**Working age**|**Gross Loan**|**GLP/**|
||**Providers**|**borrowers**|**population(%)**|**Portfolio(GLP)**|**Total Credit**|
|**MENA **||**2,807,116**|**1.99**|**1,505,414,132 **|**0.47**|
|East Asia & Pacific|160|11,294,660|4.74|5,195,228,561|6.12|
|Europe & Central Asia|263|2,603,513|4.11|9,375,908,169|5.37|
|Latin America & Caribbean|327|12,781,340|4.93|15,334,944,785|7.14|
|South Asia|148|39,274,147|5.90|4,293,510,584|4.72|
|Sub-Saharan Africa|208|6,732,155|1.48|3,162,740,041|4.71|

Source: Microfinance Information Exchange Database and Sanabel. Data for MENA is for 2009, other regions 2008.

**There were 2.81 million microcredit borrowers at the end of 2008, with a total loan portfolio of US$1.5 billion** . Microcredit is concentrated in two
structural and distributional implications of economic shocks and policies. In particular, _the framework should account for interdependence between policy issues and instruments, and for the distribution of economic welfare among socioeconomic groups, households and individuals_ . A general equilibrium framework offers an attractive point of departure because it has both macro and micro elements upon which to base the analysis of some macroeconomic shocks and policies.

A general equilibrium analysis of the distributional implications of macroeconomic shocks and policies may follow three basic approaches: (1) the standard representative household (RH) approach, (2) the extended representative household approach (ERH), and (3) the microsimulation approach. The standard RH approach limits the analysis of the distributional impact of shocks and policies to their effects on the mean welfare within a few representative socioeconomic groups. In this framework, one can predict the impact of shocks and policies on the functional distribution and on between-group inequality but not on poverty. Poverty analysis requires the specification of the size distribution within groups. One way of extending the RH is to model withingroup size distribution with a density function associated with a well-known distribution such as the lognormal or the beta (Dervis, de Melo and Robinson 1982; Decaluwé, Patry, Savard and Thorbecke 1999). Another way is to use unit record data drawn directly from a household survey to represent the size distribution of economic welfare. This is consistent with the microsimulation approach (Ravallion and Lokshin 2004, Bourguignon, Robilliard and Robinson 2003).

This paper focuses on the ERH approach. However, instead of relying on a parametric distribution, it demonstrates how the Lorenz curve can help link an extended functional distribution to a size distribution of economic welfare to analyze the poverty impact of macroeconomic events. Indeed, the Lorenz curve provides an integrative framework for both the simulation and ethical evaluation of inequality and poverty. Most, if not all, inequality and poverty measures of interest can be computed from the level of income or expenditure **x** , the associated density function **f(
This information was used in table 2.6 to adjust the national accounts figures for the 21 branches to eliminate the impact of their inventory valuation adjustment' and the impact of indirect taxes and subsidies. After eliminating these items in column 5, we arrive at the residual national accounts figure which roughly represents what the census figures would be if purchased services were deducted. This reconciliation is rough for particular branches because the BEA calculates value added as the sum of income flows. Wages and salaries are collected from Bureau of Labor Statistics (BLS) information based on unemployment insurance data. BEA and BLS use the same classification system as the census but establishments whose output-mix is varied, may well be classified in different industries from the census. Profits, depreciation and interest estimates are derived from income tax sources for companies (not establishments). For profits and depreciation there is an attempt to convert to an establishment Lbsis using the "Census-Internal Revenue Service Link Project", but this is not done for interest payments. Hence some of the industry variation in our coefficients in table 2.6 is due to possible

- The census gives the value of inventory changes as reported by the manufacturer. BEA modifies these "book values" with an adjustment which converts them to a replacement cost valuation consistent with its definition of GDP.
In these contexts, improvements in enforced compliance may depend not only on deploying new tools to monitor taxpayers, but on expanding rules-based enforcement efforts by tax collectors.

Improving access to information has been at the heart of efforts to improve monitoring of taxpayers. These strategies have reflected the centrality of information to tax enforcement: it is through access to information on income, wealth and economic transactions that governments are able to identify evasion and avoidance, pursue enforcement, and impose penalties. Some reform efforts have focused on improving the collection, management and sharing of data that is already possessed by governments – including improved data sharing across divisions _within_ tax administrations in order to cross-reference the tax declarations of firms and individuals across tax types. Meanwhile, many countries have also sought to improve access to third-party data. This third-party data can come from a variety of sources: cross-referencing of tax declarations from firms that do business together, data from other government agencies e.g. on land, property or business registrations, or external sources like public contracts, bank accounts, stock exchanges or credit cards. Finally, tax withholding often enables a particularly effective form of data gathering for tax enforcement. In most developing countries the bulk of income taxes are already collected via withholding taxes on salaries. Some countries have correspondingly considered the potential merits of expanding withholding taxes, for the VAT and elsewhere, in order to widen the tax net.

Reforms have also sought to expand audit capacity – while reducing opportunities for taxpayers to engage in tax evasion and avoidance. Along with expanded access to information, governments and donors have invested in the expansion of audit capacity, with a particular focus on the introduction of risk-based audits. The goal has been to improve the technical capacity of audit departments – increasingly focused on challenges related to international taxation – while putting in place strategies to ensure the most efficient targeting of audit resources. Meanwhile, efforts have focused in parallel on simplifying tax rules – and eliminating loopholes – in order to reduce opportunities for evasion and avoidance. For example,