rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 4. The Global Progression of Severity: Daily Severity from COVID-19 and Proportionate Mortality by Top Cause <!-- Start of picture text --> 6<br>3<br>5<br>4<br>5<br>3<br>2<br>1<br>0<br>Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

The pandemic’s severity seems to be lower in countries with large populations (Figure 3.B). The data are choppy, but larger countries tend to have lower numbers. Among the 12 with 100+ million people, China and India (not shown in the figure for presentational reasons) have severity ratios of 0.01 and 0.02 percent whereas others in this category are well below 1 percent except for the United States (15 percent), Brazil (9 percent) and Mexico (6 percent). The low numbers in large countries may reflect a strong regional concentration of the pandemic at the subnational level—compare the severity of the pandemic for China as whole versus the province of Hubei (Fenz and Kharas, 2020). Among countries with smaller populations, the severity ratios appear to be much more spread out.

# **Progression over Time**

To examine the progression of severity over time, we switch from cumulative to daily measures. The daily numbers are calculated by comparing COVID-related deaths and baseline deaths for all or top causes with respect to the period of one single day. The discrete daily data shows significant noise, but they allow us to examine patterns related to the speed and sustained strength of the progression of
and a broader access to or provision of seeds. Although the relation is still positive when using CC measure, the magnitude is considerably different - doubling the seeding rate would increase SR yields by 37 percent while it would only increase CC yields by 19 percent. For household labor inputs, the analysis based on SR yields points to the yield gains associated with increased labor inputs per hectare, suggesting a certain level of misallocation of labor across households. This suggestion, however, does not emerge if the analysis is anchored in CC yields.

These results emphasize the need for adopting objective measurement of production, specifically crop cutting, in household and farm surveys. The adoption of these methods would undoubtedly require additional technical and financial resources, and in the case of national surveys, could present considerable logistical challenges. In these circumstances, it is worth investigating whether crop cutting can be implemented in a sub-sample, with the intention to build an imputation model of CC yields as a function of easier-and-cheaper-to-collect self-reported and geospatial data, and to obtain, in the rest of the sample that is not subject to crop cutting, predicted CC yields that are subject to attenuated NCME and that allow for a less biased assessment of the relationships between yields and inputs. This is what is attempted in the second part of this analysis.

# **4 Estimating the relationships between yields and inputs using machine learning-based imputed crop cut yields**

This section provides an analytical framework on how the production function coefficients associated with agricultural inputs are expected to be modified as a result of using machine learning-based imputed crop cut yields (henceforth referred to as ML yields) vis-`a-vis CC and SR yields. This is followed by the presentation of the empirical results from the regressions of SR, CC and ML yields, verifying the predictions stemming from the analytical framework.

## **4.1 Analytical framework**

Recall that we are interested in estimating: Where _Y_<sup>_∗_</sup> is the log of production measure and _X_
# **1 Introduction**

In the 2000’s, services sector in transition countries experienced rapid development due to major regulatory changes. Deregulation allowed new firms to enter the market resulting in rapid expansion of services as a share of GDP. The focus of this paper is on analyzing the impact of those changes on productivity of manufacturing firms. This question has recently received considerable attention due to the importance of services in the global economy and due to the ongoing debates on the Doha Agenda (Hoekman et al., 2010). The literature documents a positive effect of services deregulation on productivity of manufacturing firms in the Czech Republic (Arnold et al., 2011) and in Chile (Fernandes and Paunov, 2011). Still, as pointed out by Francois and Hoekman (2010), works that try to establish a causal link from services to increase in productivity are plagued with the endogeneity problem and with the problem of disentangling the effect of services liberalization reform from the effect of other reforms. We look at the episode of services liberalization in Ukraine in 2001-2007, which was isolated from other major deregulatory changes and was driven by political pressure imposed by trading partners as a precondition for the Ukrainian WTO accession.

We exploit rich data on Ukrainian manufacturing firms, which allows us to construct a firm-specific index of the services use intensity and interact it with sub-sector and timevarying indices of services liberalization provided by the European Bank for Reconstruction and Development (EBRD). We adopt the standard two-stage approach in the literature of estimating the effect of a policy change on productivity (Pavcnik, 2002; Javorcik, 2004; Amiti and Konings, 2007; Khandelwal and Topalova, 2011). At the first stage, we estimate the production function using the Olley-Pakes methodology (Olley and Pakes, 1996), controlling for demand shocks as suggested by De Loecker (2011), to extract total factor productivity (TFP) of manufacturing firms. At the second stage, we regress TFP on the firm-specific index of services liberalization, controlling for the
%). We allow the interest rate δ and the agricultural land rent _PA_ to vary with time. For δ, we use the annual values for South Africa in the World Development Indicator database (World Bank, 2016). We set the agricultural price at the city border _PA_ at 807 _Rands/m_<sup>2</sup> (annual) in 2011, which corresponds to the ninth decile in the sales data sets, when selecting only agricultural properties in rural areas. In the dynamic simulations, we assume that the agricultural land is constant in real terms and have its nominal value increase at the same rate as the average household nominal income.

Second, we calibrate wages, housing production function parameters, utility function parameters and amenities using partial relations from our model. Following Ahlfeldt et al. (2015) and Tsivanidis (2018), we recover the vector of wages ( _wic_ ) using data on job locations and residential locations. The scale parameter in the commuting formula, λ, is estimated using the distribution of residence-workplace distances in Cape Town. We identify the land elasticity of housing production ( _a_ ) and the scale parameter of housing production (κ) by regressing the log of equation (6) (see Appendix B for details) . We then consider equations (9) and (11), which relate utility levels to dwelling sizes, rents and amenities. The amenity term, _A_ ( **x** ) is expressed as a score for all locations, and specified as _A_ ( **x** ) = ∏ _i ai_ ( **x** )<sup>ν</sup><sup>_i_</sup> , where the _ai_ ( **x** ) are measures for each amenity type (see Appendix C.4), and ν _i_ are their marginal valuation, to be estimated. We simultaneously estimate the system of equations by maximum likelihood and recover parameters _{_ β _, q_ 0 _
241|1,298|1,548|580|304|129|6,135|
|fare|4|358|605|1,304|1,622|2,129|873|410|193|7,494|
|wel|5|326|621|1,604|2,127|3,192|1,451|768|309|10,398|
|ctive|6|108|210|639|898|1,393|767|450|215|4,680|
|Obje|7|51|95|271|420|736|484|337|182|2,576|
||8|26|50|105|182|353|230|159|142|1,247|
||Total|1,732|2,901|6,135|7,494|10,398|4,680|2,576|1,247|37,163|

_Source: LiTS II, 2010._ One hypothesis suggested by Ravallion and Lokshin (2002) is that responses to the economic welfare ladder question can be a function not only of household’s income, but also, of factors

> 13 In their paper, Ravallion and Lokshin (2002) find that the large differences between subjective and objective measures of welfare in Russia can only partly be explained by the differences in the weights ascribed to household demographic composition (in constructing equivalence scales) or cost-of-living differences. In this paper we are not able to test for this hypothesis, as the LiTS survey does not include local-level prices.

> 14 Here the objective rank is based on per capita household expenditures, and the table aggregates the observations for the entire sample.

> 15 Regressions show that only 10 percent or less of the variation in the welfare ladder is explained by household expenditures in any of the countries in the sample. The asset index exhibits a somewhat higher (albeit
MENA countries, using a data base collected from various sources throughout the region.

As international food prices increased significantly in recent years,<sup>3</sup> nearly all countries experienced an increase in their domestic food prices. Still, the question concerning the extent to which international food prices affect domestic food prices requires an empirical investigation. Domestic price levels can be affected not only by world food prices movements, but also by a number of country-specific factors, including food price policies, such as price controls and subsidies, trade and production policies, domestic supply chain issues, food stock management,

> 3 International food prices, measured by the World Bank food price index, increased by 76 percent from December 2006 to April 2011.

-3-
modestly larger θ values that are statistically significant. While the larger standard errors are evidence of non-trivial empirical strain from “doubling down” on the Anglo-Saxon exclusion idea, this robustness check is quite valuable for asserting that the measured effects do not display first-order evidence of reverse causality, recognizing the limits of empirical tests for ruling out these alternatives.

More deviation, however, is observed in Column 4 when using the technology-to-industry concordances that emphasize where manufacturing occurs, rather than where technologies are used. The main estimations focus on industry-of-use, affording a composite view of the technological opportunity developed for an industry. Keller (2002b) reports inter-industry R&D flows aid productivity growth significantly within OECD countries, equal to half or more of the ownindustry development. Estimations with manufacturing industries support the using-industry specifications in a weighted format, albeit with larger standard errors. Unweighted estimates have a zero-valued first stage that prevents further analysis. This difference emphasizes the importance of technology adoption behind the labor productivity results.

Columns 5-9 test variations on the construction of M %i,c,1980. Columns 5 and 6 show that very similar results are obtained when using first- or later-generation migrants for defining the spatial patterns of migrants. This comparability is not surprising given the persistence of ethnic enclaves in the United States and their attraction of new immigrants from the home country. This stability suggests that the results are not being influenced by endogenous migrant decisions about which cities will show particular strength in patenting growth for certain industries. Columns 7 and 8 show similar results when using bachelor’s educated workers from a country’s ancestry versus those without bachelor’s degrees, with the weighted estimations somewhat favoring the distribution of bachelor’s educated workers. Overall, these variations suggest a strong stability to this part of the IV’s construction. Finally, Column 9 shows the results strengthen slightly when using the 1990 city distribution of ancestry stocks for each country. The ancestry variable in the 1990 census is comparable to that in the 1980 census, albeit with slightly less
and prevalence of E. coli in drinking water sources were all input into the model.

**<u>Table 1- Data sources used to generate the covariates in the analysis</u>**

|Data Description|Data Type, Resolution, Dates, Obs,<br>samplingtechnique|Source|
|---|---|---|
|Cholera case data<br>Pre-existing geospatialgrids|De-identified GPS coordinates for<br>October 2017-May 2018 (5,444<br>cases). Sampling must have followed<br>the ZNPHI and GTFCC protocols for<br>identifyinga cholera outbreak.|Zambia National Public<br>Health Institute (ZNPHI)<br>http://znphi.co.zm/|
|Poverty rates|World Bank Group Small Area<br>Estimations – ward level|de la Fuente, A., Murr, A.<br>and Rascon, E.<br>(2015)_Mapping_<br>_Subnational Poverty_<br>_in Zambia_. Lusaka:<br>World Bank Group<br>and Zambia Central<br>Statistical Office.|
is left unchanged.

However, phone surveys can also substitute parts of in-person surveys. For instance, if agricultural labor data are collected via phone, these data may no longer need to be collected in-person, reducing the time and burden of in-person interviews. Especially if phone interviews can substitute an entire in-person visit, a substantial cost reduction could be achieved. The combination of in-person and phone modes will also necessitate a series of decisions to be taken with respect to the organizational machinery behind a survey. With mixed mode surveys there will be economies of scale in the creation of a call center for data collection if it can be used for several surveys, for example. Similarly, the organization of the interviewers’ training will have to adapt to the new structure and decisions will need to be made on whether to use the same interviewers for both modes or to use different interviewers for phone and in-person interviews.

As for which topics to include in the phone-based component of a mixed-mode survey, the discussion on survey length, response fatigue, data quality, and attrition indicates that phone interviews should not be too long, naturally limiting the number of topics that can be covered in any given phone call. Topical coverage could be increased by scheduling several phone survey rounds and spreading topics out across those rounds, a common strategy used in the World Bank’s L2A surveys, the LSMS-supported HFPS, and IPA’s RECOVR surveys (Etang and Himelein, 2020; Innovations for Poverty Action (IPA), 2020; Living Standards Measurement Study, 2022). Data items that require objective measures such as land area or crop cutting, or sensitive topics, are likely to be those where in-person remains the primary mode. Data that is more variable over time, less salient, or that can benefit from higher frequency data collection because they are more likely subject to recall error (agricultural labor inputs is one example) are on the contrary those where the promise for the use of phones appears to be higher.

A specific question regarding the adoption
synergies between better data and methods for making single difference comparisons (on the one hand) and double-difference (on the other). Longitudinal observations can help reduce bias in single difference comparisons (eliminating the additive time-invariant component of selection bias). And successful efforts to clean out the heterogeneity in baseline data such as by PSM can reduce the bias in DD estimators.

**_What if baseline data are unavailable?_** Anti-poverty programs in developing countries often have to be set up quickly in response to a macroeconomic or agro-climatic crisis; it is not feasible to delay the operation to do a baseline survey.  (Needless-to-say, nor is randomization an option.)  Even so, under certain conditions, impacts can still be identified by observing participants’ outcomes in the absence of the program after the program rather than before it.  To see what is involved, recall that the key identifying assumption in all double-difference studies is

38
**Table 3: Exchange Rate Risk Metrics**

|**Before 2007**|**After 2007**|
|---|---|
|Roll-over ratio of foreign currency<br>borrowing|Currency composition of debt stock (local<br>vs. foreign)|
|Currency<br>composition<br>of<br>foreign<br>currency borrowing|Currency composition of FX-debt stock|

# **4. Technical Framework of TDSM**

Based on the conceptual framework described above, the TDSM was built on the Matlab platform interacting with MS Access, Excel, and Word for data input and output. The TDSM operates within three main modules. The first of these modules, ―Debt Stock Database,‖ operates in MS Access, and consists of detailed records of outstanding bond issues. The ―Scenario Generator‖ module produces scenarios for relevant financial variables in the Matlab environment. The ―Cash-Flow Engine‖ module in Matlab subsequently calculates the borrowing requirement based on the payment profile of debt stock, the financial scenarios, and the assumptions about the primary surplus and other cash-flows. The calculated financing need is met using user supplied issuance strategies. The ―Cash-Flow Engine‖ also calculates the overall cost and risk metrics for all strategies iteratively. The following sections elaborate on the technical aspects of TDSM.

# **4.1 Debt Stock Database**

Information on the instruments of the current debt portfolio is held in this database. Each strategy simulation starts with the actual bond portfolio reading from the database, which contains detailed records of the bonds and bills issued by the Treasury in local and international markets.

The configuration of the database reflects the structure of Turkey’s central government gross debt stock that has changed considerably over the last ten years. At the end of 2002, local currency debt represented only about 42% of total debt stock and consisted mainly of shortterm zero coupon instruments, including treasury bills and floating rate notes. The foreign currency-denominated/
**

|**_Field of science_**|**Total**<br>**researchers**|**%**|**PhDs**|**%**|**Candidates of**<br>**Science**|**%**|
|---|---|---|---|---|---|---|
|**Total**|**392,849**||**25,213**||**78,512**||
|Natural|94,668|24|11,479|46|33,359|42|
|Technical|244,475|62|4,809|19|23,552|30|
|Medical|16,734|4|3,934|16|7,540|10|
|Agricultural|13,743|3|1,478|6|5,100|6|
|Public|13,740|3|1,632|6|5,008|6|
|Humanities|9,489|2|1,881|7|3,953|5|

Source: Indicators of Science (2009) – MoES, ROSSTAT and the Higher School of Economics.

The public sector is not only the main funding source, but is also the main performer of R&D. The business sector apparently “performs” 64 percent of total R&D (Table 3). However, the following categories are classified as business entities in the business sector of official Russian statistics: state owned-companies and branches of research institutes that form part of the RAS. These institutions – conduct many of the publicly financed innovation activities (MoES 2009). Gokhberg and Kuztnetzova (2010) assess using Rosstat data that the number of publicly owned R&D performing units in
.<sup>29</sup>

## – Table 8 about here –

All in all, the results in Tables 7 and 8 confirm the intuition that visa provisions in RTAs are beneficial for international migration subject to more binding administrative regulations (i.e. unskilled workers). To strengthen this intuition, in Table A4 we approximate the restrictiveness of administrative regulation on emigration with a dummy for the existence of exit permit requirement to emigrate from origin country _i_ , and interact it with our visa provision dummy. In line with our reasoning, visa provision is more effective in origin countries where an exit permit is needed to emigrate. In particular, for origins that require ana exit permit to emigrate, the presence of visa provision in RTA increases bilateral migration by 22%.

> 28 Available here: `https://www.oecd.org/els/mig/dioc.htm` . OECD DIOC data are available only for the period 2000-2015.

> 29It should be noted that point estimates in Tables 7 and 8 cannot be directly compared to baseline results because the OECD DIOC database covers only a sub-set of countries while our baseline regressions use a wider set of country-pairs.

13
|
|Public * Male|−0.184|0.095|*|−0.229|0.039|***|
|Public * Vocational training|0.260|0.203||−0.226|0.052|***|
|Constant|−0.321|0.107|***|0.771|0.045|***|
|Observations||856|||3893||
|R-squared||0.129|||0.264||
|Mean squared error||0.62|||0.55||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _Our model_

We now turn to the estimation of our model. The lack of observations leads us to adopt a simple estimation method, the nonlinear least squares (minimum distance estimation) method, which has better small-sample properties than the Generalized Method of Moments used by Lemieux (1998). The model estimates are shown in table 5.

Very different patterns emerge for the 1993–1998 and 2002–2006 periods. In the 1993–1998 panel, the returns to the workers' comparative advantage in the public as compared with the private sector (α) are significantly inferior to one. In the 2002–2006 panel, they are not significantly different from one, and the point estimate is greater than one. This change suggests that the public sector, which initially offered lower returns on workers' comparative advantage than the private sector, has now adopted compensation patterns that reward workers at least as much. Interestingly, the point estimate of ηt is also rising but is never significantly different from zero. Therefore, the higher pay in the 20
show that these unexpected shocks tend to be uncorrelated with initial firm attributes, and account for firm fixed effects throughout the estimation.

The empirical analysis draws on an unusually rich collection of data sets for the population of private sector firms operating in Portugal during 2006-2018. We combine a yearly firm census with information on export transactions and employer-employee panel data. We also use an auxiliary data set from a management survey for 2016, covering a subset of these firms, in order to establish a correlation between managerial skill and observable management practices. The employer-employee data allow us to distinguish between effects on each of the various components of worker compensation, including base wages, overtime pay, and other regular and

> 1Recent contributions to this literature, reviewed in more detail below, include Card, Cardoso, Heining, and Kline (2018), Kline, Petkova, Williams, and Zidar (2019), Aghion, Akcigit, Hyytinen, and Toivanen (2018), Grigsby, Hurst, and Yildirmaz (2021), and Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024).

1
seeking, elite capture, and clientelism; and of the design to respond to the governance and political economy constraints.

Among an overall sample of 180 operations drawn from 344 newly approved operations in fiscal year 2008, the report included 17 public sector governance (PSG) operations (5 DPLs and 12 ILs). These were found to be somewhat more frequently responsive to GPE dimensions than the sampled Bank operations overall (see Table 4).

**Table 4: Operations included in the QAG GAC review and responsiveness on the GPE dimension**

||Responsive t<br>all reviewe<br>(N=<br>#|o GPE factors<br>d operations<br>180)<br>%|PSG<br>(N<br>#|total<br>=17)<br>%|PSG<br>DPLs<br>(N=6)<br>#|PSG<br>ILs<br>(N=11)<br>#|
|---|---|---|---|---|---|---|
|Very responsive (1 &2)|32|_18_|6|_35_|_4_|_2_|
|Somewhat responsive (3)|50|_27_|4|_24_|_1_|_3_|
|Limited effort (4)|56|_32_|6|_35_|_1_|_5_|
|Unresponsive (5 & 6)|42|_23_|1|_6_|_0_|_1_|

This paper compares the operations‘ level of GPE responsiveness at entry with their most recent status or outcome rating to explore any relation between the two.

Table 5 summarizes the relationship between the QAG panel GPE ratings and status or outcome ratings. It shows that operations seen as ̳somewhat responsive‘ or better to GPE issues outperformed those with limited effort or being unresponsive to GPE issues.

**Table*