rafmacalaba/data-use-mentions-extended huggingface dataset:
slowdown. Most studies using 2000 and 2010 census data (Liang et al., 2014; Liang and Ma, 2004) fail to pick up the rate of change over these years. Longer distance migration is likely driven by differences in growth rates, and consequent increases in regional inequality and wage differences, between the coastal and the inland regions (Kanbur and Zhang, 1999; Liu and Xu, 2017), though internal migration spells to large cities within home provinces are less costly and easier to arrange. Recent studies also provide evidence that China’s access to WTO in 2001 and the resulting reduction in trade policy uncertainty faced by Chinese exporters to the U.S. facilitated economic structural transformation in China (Erten and Leight, 2021; Facchini et al., 2019). Increases in trade after 2001 are associated with more migration of skilled urban labor, not unskilled rural labor (Facchini et al., 2019). Based on the RCRE household data from 2009-2017 in which migration destination information was collected in a consistent manner, Guangdong, Zhejiang, Shanghai, Jiangsu, and Beijing 10
safely take system quality as given in its decisions. In contrast, there is no particular reason to expect the causal impact of quality of country systems on use of country systems to vary by donor size. Therefore, if coefficients on control of corruption in Tables 1 and 2 mostly reflect endogeneity bias, the strength of association should vary positively with donor size. We add the appropriate interaction term in equation 2.5 to test this hypothesis.<sup>16</sup> Results are highly inconsistent with the argument for reverse causation: the coefficient on the interaction term is negative (and significant at the .10 level for a two-tailed test), not positive.

In our second variant of this identification strategy, we distinguish donors by selectivity in aid allocations. Specifically, we posit that donors that are more sensitive to quality of country policies and systems when allocating aid will also be more sensitive to the quality of systems when deciding whether to rely on them for managing their aid. In contrast, there is no particular reason to expect the strength of any reverse causation from use of country systems to vary with donors‟ sensitivity to policy in allocating aid. An empirical finding of heterogeneous impacts in this instance can be explained better by the causal argument than by reverse causality (from use of systems to system quality). Equation 3.1 includes an interaction term, to test the hypothesis that Control of Corruption will affect use of country systems more for donors that are more policy selective in allocating aid among recipients.<sup>17</sup> For this purpose, we use the donor policy selectivity scores from Knack, Rogers and Eubank (2011), based on aid disbursements in 2007. The interaction coefficient is positively signed, as hypothesized, and significant at the .05 level.

> 16 The interaction term is equal to the product of the deviations of Control of Corruption and donor aid share from their respective means.  The coefficient on Control of Corruption thus indicates its marginal effect conditional on the mean
poverty and welfare statistics, we also propose to deviate slightly from the sampling design and estimate standard errors using the 16 strata originally defined for the HIES 2010. Using this approach to estimate standard errors of poverty rates and welfare statistics will allow for full consistency in the way these statistics are produced across time.

# **8. Conclusion**

This paper describes the different steps involved in the design of the sample for the Bangladesh HIES 2016/17, whose main objective is to produce reliable poverty and welfare statistics at different levels. Designing a sample to achieve multiple objectives is not a simple exercise as objectives might not necessarily be mutually consistent. How to prioritize the objectives of a survey and address any potential inconsistencies that might arise between multiple objectives is important to ensure that the final selected sample does not compromise statistical precision for all levels of interest to policy makers.

The HIES 2016/17 sample was designed to provide reliable poverty and welfare statistics at three different levels: (i) annual poverty estimates at the district level; (ii) annual poverty estimates at the division level for urban and rural areas; and (iii) quarterly poverty estimates at the national level. The sampling follows a two-stage clustered design. In the first stage, we allocate an equal number of PSUs to each of the 64 districts in Bangladesh to ensure that the sample estimates district-level poverty and welfare statistics with similar levels of reliability across districts. In the second stage, we allocate the sample across three sampling strata (urban, rural, and City Corporation) for each district independently and select households from each of the selected PSUs. To allocate the sample across strata in each district, we modify the standard Neyman allocation rule that allows the achievement of an optimal allocation for clustered stratified samples (the standard Neyman allocation rule only allows an optimal allocation for stratified samples). In the process of selecting the final sample, we also discussed how field data collection protocols are taken into consideration and illustrate their importance in fine tuning the final allocation of
by Current Procedural Terminology (CPT) codes. For example, the facility fee for a toe deformity repair (CPT 29290) performed on an outpatient basis is $1,525, while that for a shoulder joint surgery (CPT 23470, 23472) averages $5,696. See appendix 2 for details on hospital/facility payments for all our selected procedures.

Physician payment rates were calculated from the Medicare physician fee schedule, which are listed by CPT code and setting (facility or physician office) for 92 different regions of the US. We used the national average across regions for each procedure in a facility setting to calculate the physician’s fees. For example, the national average physician’s fee for skin lesion removal (CPT 11426) was $238, while that for a cataract procedure (CPT 66983) was $592 (see appendices 2 and 3). Medicare payment rates to providers are lower than those paid by private insurers, and much below what is paid by the uninsured; in fact, Medicare operates _de facto_ as a large government administered PPO with a very aggressive rate structure for provider payments. In 2001, Medicare physician payment rates were 83 percent of average private rates, having risen from 64 percent in the mid 1990s (see e.g., Medicare Payment Advisory Commission, 2001).

Anesthesiology fees are calculated based on the number of base units and time units (one time unit is 15 minutes of time) for a procedure, which is then multiplied by an anesthesia conversion factor that varies by region (see Medicare Claims Processing Manual). We obtained the number of base units by anesthesia CPT code from the Medicare anesthetist fee schedule, assumed a uniform four time units for each procedure (as suggested by the

> 23 National Average Hospital Payment rate reported by the DRG Expert (2005) is calculated by multiplying the current relative weight of the DRG by the national average hospital Medicare base rate of $4,555.

17
and other regional assessment data, is robust to data availability by grade. We also include a variable with grade information in the database to make this transparent.

<!-- Start of picture text -->
-100 -50 0 50 100<br>Coefficients<br>Effects, All Grades<br>Effects, Grade-Fixed<br>Country-Year Dummies<br><!-- End of picture text -->

<!-- Start of picture text --> SUPPLEMENT FIGURE VIII<br>EGRA, Grade-Fixed Effects<br><!-- End of picture text --> _Notes:_ We run a regression with and without grade-fixed effects comparing mean scores relative to a given country which participates across all three grade levels 2-4 using EGRA data. We only include nationally representative data. The darker “All Grades” bar represents the confidence interval without grade-fixed effects. The lighter “grade-fixed effects” represents the confidence interval with grade-fixed effects.

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.2|
|Distribution losses|% production|33.0|**40.0**|23.1|
|Cost recovery|% total costs|56.0|**58.0**|80.6|
|Total hidden costs as % of revenue|%|130.0|**173.9**|84.9|
||||Ot|her developing|
|U.S. cents per m3|**Kenya**|Scarce water|resources<br>|<br>regions|
|Residential tariff||**38.9**|60.26||
|||||30 – 600|
|Non-residential tariff||**45.7**|120.74|. .|

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

**Figure 5. Growing reliance on surface water and static trend in improved sources**

<!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>access method<br>Kenya Sub-Saharan Africa<br>% of population<br><!-- End of picture text -->

Source: Gulyani, Sumila, Talukdar, and Jack 2009.

What is particularly worrisome is that whereas only 0.3 percent of the population is gaining access to some improved form of water service each year, as much as 1.05 percent of the population is moving into reliance on surface water. What this means is that between the mid-1990s and the mid-2000s, Kenya 13
lender-time level (see Table A3 in the appendix for variable descriptions and data sources). The sample includes only non-financial borrowing companies. Throughout the analysis we use a monthly frequency in the time series dimension. If the information about a bank’s share in a loan is missing, the loan is discarded in constructing the volume variable. In addition, we exclude the years before 1995, because Dealscan contains significantly fewer observations in these years. Our sample spans the period from January 1995 to March 2015. Since we focus on cross-border lending, we also exclude observations if the borrower’s and lender’s country of location coincide. Following the literature, e.g. de Haas and van Horen (2012), we define the nationality of a bank based on the location of the ultimate parent. Table 1 shows that the average borrower-lender loan volume is US$55.6 million and ranges between US$1 million and US$410 million. As seen in Figure 1, the total volume of cross-border syndicated loans rose rapidly before the crisis, fell back substantially during 2008-2009, and subsequently recovered to pre-crisis levels towards the end of the sample period.

We also examine various non-volume terms of the loan contracts. First, Maturity is the maturity of a facility in months. In Table 1 we can see that the average maturity of loans in our sample is 60.6 months, or about 5 years. Next, Spread is the loan spread over the reference rate in basis points for drawn credit of a facility with a sample average of 245.7 basis points. The next loan characteristic variable is Collateral, which is a dummy variable indicating that a loan is collateralized.<sup>2</sup> Table 1 reports that about 81.7% of loans are collateralized in our sample. Finally, Covenant is a dummy variable indicating that there is a net worth or financial ratio covenant in the loan contract. About 29% of loans to nonfinancial companies have at least one of these
|5.1|2.6|3.1|3.3|7.4|5.7|35.5|3.6|
|Services FDI stock as % of GDP|31.6|27.7|17.6|17.6|7.7|60.7|26.8|37.8|16.6|26.1|9.4|8.2|7.3|
|Notes: NMS: New EU Member State<br>mining/energy;*** Includes hotel<br>_Source_: wiiw-wifo Database on F|s; * In<br>s and r<br>DI, Ju|cludes<br>estaur<br>ly 200|financ<br>ants;<br>4 edit|e and<br>****<br>ion.|busine<br>Not i|ss servi<br>ncludin|ces. **<br>g utili|Cover<br>ties.|s all in|dustry,|inclu|ding||

The forgoing snapshot of trends in the share of services in GDP, employment, output per worker, trade and FDI reveal both substantial convergence towards European countries, but also a distinct difference between Central European/Baltic states and Central Asian and CIS economies. Given that trade and FDI in services can be expected to be associated with the acquisition of new technologies, higher service standards and more effective delivery, these differences should help explain the observed higher labor productivity performance in services in the former. The question explored in the rest of this paper is whether these services developments are determinants of the aggregate growth performance of countries. The services outcome variables are of course endogenous, influenced by the policy stances of governments, so that the focus is on the impact of services policy reforms.

# **2. Policy Stances and Service Sector Reforms**

Service
households face the greatest crises, that gender differentials in outcomes are most likely to open up [Duflo 2012]. There is a long established literature from across disciplines discussing the issue using observational data [Goldin and Katz 2002, Doepke _et al._ 2012, Duflo 2012, Jayachandran 2015], and a nascent experimental literature evaluating interventions designed to empower women in periods of stability, not crisis [Ashraf _et al._ 2017, Buchmann _et al._ 2017, Bandiera _et al._ 2018, Dhar _et al._ 2018].<sup>8</sup>

> 6Existing work on the economics of Ebola has focused on monitoring impacts during the crisis through live trackers [Thomas _et al._ 2014, Glennerster _et al._ 2016], on the political economy of government responses [Maffioli 2017, Fluckiger _et al._ 2018], and on how formal sector firms were impacted [Bowles 2016, Casey _et al._ 2017].

7Fallon and Lucas [2002] review cross-country evidence from the 1990s financial crises in East Asia, and Mexico. The general pattern they find is that employment falls less than production, there is churning across sectors, large falls in real wages, reductions in school enrolment and the use of health facilities. In the context of the Mexican Peso crisis (a forced devaluation of the Peso in December 1994), McKenzie [2003] documents various household coping strategies, including changes in household structure (fertility), schooling, labor supply, and interhousehold transfers. All these channels can break down in the face of an aggregate shock.

> 8Ashraf _et al._ [2017] evaluate the effects of interventions targeting adolescent girls enrolled in primary schools in Zambia, providing them with negotiation-skills training and a safe-space to meet. The negotiation skills training led to long-run improvements in human capital outcomes of treated girls, but the safe space program had no effect. Neither intervention affected pregnancy rates. They do not report effects on girls’ labor market outcomes.
applied, would **not entirely** close off a sector-mode (e.g. combination of ENT, LMT or other quotas for mode 4 entry, assuming that such entry is allowed in the first place).

# **Aggregation**

In the first version of the World Bank database (2008-11), the associated STRI relied on an assessment of groups of measures, which were included in a ‘policy summary’. The automatized aggregation methodology described here follows a similar approach. The overall restrictiveness of a group of policy measures, say at the sector-mode level for a given economy, results both from the **types** of measures applied as well as from the **number** of measures applied. The severity of measures is captured directly in the scores assigned to individual measures, as shown in Table 1. A major restriction, such as a quantitative limit, may attract a score of 0.5 whereas a minor measure affecting only transparency would be scored much lower (0.125). In a second step, the set of individual measure scores needs to be aggregated for a given sector-mode or even an entire economy.

Aggregation of this kind is a challenge in a wide range of fields. In consumer theory, individuals aggregate the utility from the consumption of a bundle of goods. In production theory, firms produce a good by combining a range of intermediate inputs. In each case, the aggregation of utility or inputs is achieved by a mapping—the utility function or the production function, respectively—that exhibit well-established properties. In particular, the marginal contribution of an additional unit to the aggregate (utility, output, or indeed restrictiveness) is typically positive but smaller than that of a previous unit.

One of the most widely used mappings in economics for both utility and production functions is the ‘Constant Elasticity of Substitution’ (CES) functional form, which generically combines an array of elements according to � .<sup>24</sup> NN
In multi-stage, stratified clustered designs, the probability of being selected must be reflected in each of the different sampling stages. In the case of a two-stage, stratified clustered design, such as HIES 2016/17, the probability of being selected into the sample is a function of: 1) the probability of a PSU being selected in the first stage plus; and 2) the probability of a household being selected within each PSU in the second stage. This can be calculated as follows:<sup>27</sup> where ���� is the probability of household _j_ , in stratum _h,_ and PSU _i_ to be included in the sample; p1 is the probability of the PSU to be selected in the first stage; p2 is the probability of a household being selected in the second stage; kh is the number of PSUs selected in stratum _h_ ; mhi is the number of households selected in PSU _hi;_ and �� is the total number of households in stratum _h_ .

The probability sampling weights in Equation (3) are called _ex-ante_ weights—that is, done before the survey field work—and closely follow the sampling design. It is common, however, to adjust sampling weights _ex-post_ —that is, after survey data collection—to correct for imperfections and bias in the sample identified during field work. If not corrected, these imperfections can lead to errors that detract from understanding the reference population. These adjustments usually include the following: (i) corrections for household non-response conducted at the PSU level; (ii) corrections for sample frame errors generally linked to outdated information, which are usually take place at the PSU level; and (iii) post-stratification to match official projected population statistics at a chosen level of aggregation.<sup>28</sup> The final expression below accounts for the _ex-post_ adjusted sampling weights:

> 27 Since the probability of being selected in each stage is a function of the cluster size and the number of units sampled in
Based on assumptions, and the projected evolution of the population by simple gender and age, the actuarial projection (Figure 17) shows a sustained growth of total expenditures, shifting from 3% of the GDP in 2019 to 6.9% by 2050, until reaching 13.8% by 2080. This relates to an increase in elderly population while the country moves forward in this demographic transition. At the same time, income per contributions made by workers would keep this trend relatively stable at about 3.3% of the GDP during the whole term being studied. As a consequence of these trends, the pure financial result, that is to say, the result arising from the difference between contributory resources and total expenditures on pensions, would be positive at approximately 3% of the GDP until the beginning of the 2030s. From that decade on, it would become negative and it could reach 3% of the GDP by 2050 and almost 11% by 2080.

Figure 17. Resources, Expenditures and Financial Result of Pension Insurance. 2018 – 2080 <!-- Start of picture text --> 15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>Contributions Public transfers (40%) Expenditures<br>Pure Financial Result Financial Result<br>2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064 2066 2068 2070 2072 2074 2076 2078 2080<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS Maintaining the financial equilibrium purely contributory would require, by keeping the rest of the system parameters constant, an increase in total contribution tax rate as of the first year in which a deficit is recorded (Figure 18).

26
rural _Hukou_ and lives and works in an urban location, there is no consistent definition of “migrant” in the China literature. For example, when using the Population Census data to study migration trends, scholars often define a migrant as an individual who has crossed county boundaries and has stayed at a destination for no less than six months without a local _Hukou_ (Liang, 2016; Liang et al., 2014). The census data have clear definitions and offer a clean approach to estimating changes in the migrant population over long periods of time, but do not contain sufficient information, especially longitudinal data, that may be used to examine migrant impacts.

A number of household surveys offer promise for study of the impacts of migration. When using these surveys, interpretations may turn on how they define household membership, definition of a migrant, and approach to sampling. In contrast to common standards for household membership followed internationally, as in the World Bank’s Living Standards Measurement Survey (LSMS) and LSMS-type surveys, definition of household membership and migrant status are less clear in China’s household surveys. Categorization as a household member often follows the National Bureau of Statistics (NBS) standards for the NBS Rural and Urban Household Surveys that were implemented annually from the late 1980s through the early 2010s. Under longstanding NBS practice, household membership was based on whether a family member was co-resident for more than 6 months in a year _or_ was a family member with prior residence and who continued to have an interdependent economic relationship with the household. Most important for migration studies, non-resident children who still have a rural _Hukou_ and pool earnings with the household are counted as household members. Some surveys, like the China Health and Retirement Longitudinal Survey (CHARLS), and the NBS surveys starting in 2013, use residence in the household for six months of the year as the criterion for household membership.

When different criteria are applied to identify migrants, one finds different estimates related to migration. Even different waves of the same dataset
75, and, the<br>markup ratio of one standard deviation below the mean)|.0|Level<br>Rate|-.022<br>.100**|(.104)<br>(.050)|

Note. Reported in the parentheses are the standard errors for the estimated coefficients. *, ** and * represent significance levels of 10, 5 and I per cent respectively..

t. Here the mean or the percentile of wage elasticity is based on the PC-participants sample.

9R. When evaluated at a positive wage elasticity, the percentage of firms refers to that with wage elasticity equal to or greater than the specified threshold. Note that when evaluated at 0 wage elasticity, however, it refers strictly to firms with zero wage elasticity.

38