rafmacalaba/data-use-mentions-extended huggingface dataset:
yes|
|Full set of controls included|yes|yes|yes|yes|yes|yes|yes|yes|yes|

Dependent variable is the probability of export survival of product _k_ from industrial sector (industry) _i_ exported by country _c_ to the USA. Export survival probability is measured _l_ years after the beginning of export spell, with _l_ = 1 in columns (1), (4) and (7), _l_ = 5 in columns (2), (5) and (8), and _l_ = 10 in columns (3), (6) and (9). The regressions are estimated by OLS and contain a full set of fixed effects at the product level and the (exporting country)*time level, with time referring to the beginning of a given export spell. Stock market turnover is measured as the ratio between the value of stock market transactions and the stock market capitalization. Other variables are described in Table 2, Table 3, and Table 4. The full set of controls also includes initial export, total export, number of suppliers, multiple spell, and interaction terms between physical and human capital endowments of country _c_ and the corresponding capital intensities at the industry level. All time-varying explanatory variables are measured at the beginning of the export spell. Robust standard errors are clustered at the (exporting country)*time level, with time referring to the beginning of a given export spell. _a_ , _b_ , _c_ denote statistical significance at the 1%, 5%, and 10% level, respectively.

stock market turnover is insignificant across all time dimensions. This contrasts with the previous results for the size of stock markets that did promote export survival of these products at the longer term horizons of five and ten years (second and third columns of Table 5). While active stock markets do affect the real economy in other contexts (e.g., Levine and Zervos 1998, Manova 2008), our results suggest that they
|1.079|
||(0.101)|(0.164)|(0.443)|(0.523)|(0.577)|(0.662)|(0.750)|(0.791)|
|Constant|0.279***|0.286|-1.942***|-2.562***|-1.966***|-1.427***|-3.278***|-3.284***|
||(0.097)|(0.232)|(0.535)|(0.459)|(0.357)|(0.354)|(0.479)|(0.500)|
|Number of observations|619|469|630|473|679|522|554|427|
|r2_w|0.215|0.334|||||||
|r2_b|0.111|0.079|||||||
|r2_o|0.097|0.077|||||||
|rho|0.620|0.677|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

47
JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. The 50 most-cited publications for each of the listed institutions were then assigned either one of the 12 topics listed in the appendix or a ―topic not assigned‖ code. The latter included publications that were too general (some were actually textbooks) some had no economic content despite being in EconLit, and some had no health content despite the JEL code.
spending shocks.<sup>10</sup> The methodology has the advantage of overcoming the issue of fiscal foresight, whereby anticipated fiscal policy changes may be incorporated into current economic decisions (Forni and Gambetti 2010; Leeper et al. 2012, 2013). However, this approach relies on the availability and quality of public spending forecast data. Additional caveats relate to the nature of fiscal projections: first, they may not be fully orthogonal to past macroeconomic trends; and second, they rely on subjective, heterogeneous assumptions about future macroeconomic developments.

# **2.4. Estimates of public investment multipliers**

While the empirical work on total public spending multipliers across a broad range of countries is voluminous, the subset of the literature that distinguishes public investment multipliers is limited and primarily focuses on advanced economies. In general, there is an emerging consensus that public investment tends to have a positive growth impact in the medium term (Auerbach and Gorodnichenko 2013; Eden and Kraay 2014; Furceri and Li, 2017; Ilzetzki et al. 2013; Izquierdo et al. 2019; Leduc and Wilson 2012). However, there remains considerable variation in the estimates of the output elasticity to public investment; in a meta-analysis of 68 studies, Bom and Ligthart (2014b), report that the range is -1.7 to 2.0. In reviews of the literature, Gechert and Rannenberg (2018) and Vagliasindi and Gorgulu (2021) also report that public investment multiplier estimates range widely with an average of about 1.5. Appendix table B1 provides a comprehensive review of public investment multipliers in the literature over the past two decades.

Empirical work focusing on EMDEs has been especially scarce, given data constraints and methodological challenges in identifying spending shocks. Nevertheless, limited research on EMDEs also documents important growth impacts of public investment. For instance, Miyamoto et al. (2020) report an increase in output by 0.4 percent over 4 years for a sample of 39 EMDEs. Ilzetzki et al
2016) and Hanasaki et al. (2013).

The socioeconomic pathway SSP1 provides an alternative dietary preference scenario which moderates overconsumption and aims to reduce of the share of red meat in the diets compared to the diet preferences in the baseline (SSP2). Two additional diet scenarios are also modeled– healthy diet and healthy sustainable diet. The Healthy Diet scenario modifies current diets to set them strictly in line with the World Health Organization dietary recommendations and the Healthy Sustainable Diet scenario considers the land and water resource requirements of food items.

We evaluate the impacts of climate change using spatially explicit impacts on crop yields, irrigation water requirements which were calculated by the crop growth model EPIC (Balkovič et al., 2013) under the representative concentration pathway 2.6 and 8.5 for five global climate models (GCMs) and coupled to GLOBIOM. Additionally, the water available for irrigation is determined by the globally gridded monthly surface water availability and environmental flows is modeled by LPJmL (Palazzo et al., 2018, 2017, Pastor et al., 2016, 2014).

Over the time period, our baseline assumption is that the efficiency of irrigation systems (water application efficiency) moderately increases. We model two alternative specifications: a high water application efficiency for irrigation systems scenario with rapidly increasing irrigation water application efficiency through substitution of the inefficient systems by sprinkler and drip, and a low water application efficiency scenario with stagnating water use efficiency.

Finally, we analyze the effects of more integrated international markets (open trade) and more regionalized markets (restricted trade) and their role in irrigation investment needs.

Additionally, we combined various parameters and assumptions of the uncertainty scenarios described above to form more extreme scenarios. The goal of these “extreme scenarios” is to identify the smallest and largest expansion of irrigated areas as well as the least and greatest irrigation 11
<br>‐10 0<br>Repo Rate WPI inf<br>mln $<br>% , or % change<br>% or % Change<br>2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08 2008‐09 2009‐10 2010‐11 2011‐12 2012‐13 2013‐14<br><!-- End of picture text --> Source: RBI Handbook of Statistics on the Indian Economy Second, the policy response to surges and stops consists of reserve management, liquidity management, and capital flow measures. The “domestic orientation” in monetary

> 24Mohan and Kapur caution that in analyzing the growth in reserve money supply, one needs to adjust for changes in the CRR. An increase in CRR, even as it impounds excess liquidity from the banking system, ends up showing a larger expansion in reserve money in the Reserve Bank’s balance sheet.

> 25 During the 1990s bank rate as well as money supply tracked domestic inflation closely.

22
- Central Statistics Agency of Ethiopia (CSA) (2017). Ethiopia Socioeconomic Survey 20152016, Wave 3. Public Use Dataset. Ref: ETH ~~2~~ 015 ~~E~~ SS ~~v~~ 02 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/2783` on 6 September 2019.

- Central Statistics Agency of Ethiopia (CSA) (2020). Ethiopia Socioeconomic Survey 20182019. Public Use Dataset. Ref: ETH ~~2~~ 018 ~~E~~ SS ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/3823` on 9 February 2024.

- Central Statistics Agency of Ethiopia (CSA) (2024). Ethiopia Socio-Economic Panel Survey 2021-2022. Public Use Dataset. Ref: ETH ~~2~~ 021 ~~E~~ SPS-W5 ~~v~~ 03 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/6161` on 15 January 2024.

- Chen, J. J., V. Mueller, Y. Jia, and S. K.-H. Tseng (2017). Validating migration responses to flooding using satellite and vital registration data. _American Economic Review 107_ (5), 441–45.

- Chen, M., W. Shi, P. Xie, V. B. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak (2008). Assessing objective techniques for gauge-based analyses of global daily precipitation. _Journal of Geophysical Research 113_ , D04110.

- Corno, L., N. Hildebrandt, and A. Voena (2020). Age of marriage, weather shocks, and the direction of marriage payments. _Econometrica 88_ (3), 879–915
4.70 (11.68)|
|Skilled workers (%)|71.16 (30.72)|
|Unskilled workers (%)|28.84 (30.72)|
|Firm age (year)|18.76 (15.72)|
|Exporter status||
|Non-exporter (%)<br>|85|
|Exporter (%)|15|
|Road infrastructure (within 25km radius)||
|Highways (km)|66.18 (114.58)|
|Primary roads (km)|291.52 (365.83)|
|Secondary roads (km)|325.94 (372.01)|
|Tertiary roads (km)|426.39 (446.90)|
|Population density|5,248.32 (6,889.85)|
|PM2.5: dif between 1998 and 2019|-2.72 (13.15)|
|Broad sector||
|Manufacturing (%)|55|
|Services (%)|45|
|Region||
|Africa (%)|20|
|East Asia and Pacifc (%)|11|
|Europe and Central Asia (%)|31|
|Latin America and the Caribbean (%)|20|
|Middle East and North Africa (%)|10.0|
|South Asia (%)|9.2|
|Number of observations = 141,815||

(Sabater, 2019).<sup>10</sup> The C3S provides a comprehensive reanalysis dataset of var-

> 10Historical temperatures and relative humidity can be obtained from the European Union’s C3S Climate Data Store. We downloaded the data on November 25, 2022.
and Wang (2020) find an investment effect of QE also in the United States, consistent with improved prospects for firms stemming from QE.

This paper’s focus on the impact of the PEPP on equity prices and CDS spreads is warranted, as it provides new insights into QE as a monetary tool to improve financial conditions for firms at a time of economic crisis. A main finding is that the PEPP raises share prices and reduces CDS spreads for firms with an investment grade rating, as the ECB is restricted to purchasing investment grade debt. Importantly, however, the benefits of the PEPP are shown to extend towards non-investment grade firms, and firms that are not incorporated or headquartered in the euro area, as evidence of policy spillovers. Going beyond prior papers, this paper examines the division of the gains from the PEPP between shareholders and debtholders, finding that debtholders gain relatively more if the firm is more leveraged, consistent with the existence of debt overhang. The paper has two main findings directly related to the COVID-19 crisis. First, firms that are more heavily impacted by the pandemic benefit relatively little from the PEPP, suggesting that monetary policy has only a limited potential to offset a large negative shock to a firm’s earnings capacity. Second, the PEPP is a complement to national fiscal responses to the pandemic in the sense that a strong pre-PEPP fiscal response enhances the potential for the PEPP to raise a firm’s equity and debt valuations. In the remainder, section 2 discusses the data and the methodology. Section 3 presents the empirical results, and section 4 concludes.

# **2. Data and methodology**

# **2.1 Data**

We obtain data on publicly traded firms headquartered in Europe from Thomson Reuters. We exclude financial firms, utilities and not-for-profit and governmental firms (firms with primary 8
# **6. Data availability and gaps for economic impact assessments**

This section includes a non-exhaustive list of some of the key data sets that are available for use in the different models described in sections 4 and 5.

- UNCTAD Comrade – includes values and weight of international trade between countries; not mode-specific.

- OECD Transport cost database – an estimate of the transport costs for flows of trade between countries.

- EUROSTAT –value, volume, and mode choice for trade flows between European countries and between Europe and the rest of the world.

- Customs data – both trade value and volume information highly disaggregated to individual shipments. The data are not available for every country.

- ECLAC trade database– a trade data set that includes trade value, volume, and mode choice between Latin American and Caribbean countries and the rest of the world.

- UNCTAD LSCI database – a database describing the Liner Shipping Connectivity Index for each country worldwide from 2006 to 2017.

- UNCTAD Container port throughput data – a data set containing the amount of container traffic handled by each country worldwide from 2004 to 2018. Information is aggregated at country level; traffic at the port level is not available.

- MDS Trans modal database on the schedule of liner shipping companies – a data set that contains the sequence of port calls and their schedule for the Liner Shipping Companies worldwide. The data are available for a fee.

One key issue is that many of these data sources are incomplete or not publicly available. This might restrict the full potential that they offer both in terms of geographical limits (e.g. some trade data for specific countries may be missing) and time limits (e.g. OECD transport cost data ends in 2007 and has not been updated since). In particular, there is an absence in some of these data sources for information on SIDS and LDCs, who may not have national statistics or reporting mechanisms. This is of particular concern, as the IMO sees a special need to consider the
farms, up to the limit of about 1,000 ha in our sample, pay significantly higher lease prices than smaller ones, pointing towards unutilized scope for growth in what, in Ukraine, are considered mid-size farms. The fact that, beyond size, tenants value complete control of a field, points to non-negligible cost of contract negotiation and enforcement.

While differences in coefficients from equivalent regressions for land sales as well as buyer attributes suggest factor market imperfections may make it easier to exercise market power in sales markets, drawing strong conclusions from the limited sample for which data were available to us may be premature. Exploring differences between lease and sale markets based on better access to registry data will be an important area for follow-up research that could also help identify options for the future trajectory of Ukraine’s agriculture sector and ways in which these would be affected by different policies.

The rest of the paper is structured as follows. Section 2 provides country context by describing the evolution of Ukraine’s rural property rights structure and variable construction, recent land market reforms, and the evolution of transaction volume in land sales and rental markets. Section 3 discusses descriptive data for rental and sales markets respectively and identifies the econometric approach as well as expected results. Section 4 explains results for lease prices separately for commercial (with and without registry data) or personal use land as well as land sales. Section 5 concludes with policy and research implications.

# **2. Country context**

To set the scene and justify the models applied, this section discusses key aspects of land tenure and land markets in Ukraine, focusing on the types of land and the land ownership structure created by land right privatization in the early 2000s; the sources of informality; and the nature and implementation timeline of land policy reforms in 2019 and 2020. We discuss evidence from the literature and our data on some 900,000 and 300,000 lease and sales transactions, respectively, that form the basis for subsequent analysis.

# **2
If the intensity of climate change events, such as extreme temperatures, precipitation, and floods, declined from 2016 to 2023, we might expect a corresponding reduction in public demand for climate actions and willingness to support such policies. That support would also change if climatic events affected people differently in the periods covered by the two survey rounds.

To test the personal experience hypothesis, we use administrative data on several environmental risks at the primary sampling unit (PSU) level.<sup>11</sup> The top panel of Table 5 shows changes in the means of these risk factors between 2016 and 2023. The levels of precipitation remained relatively constant over that period. The temperature, however, increased across all three dimensions. The level of air pollution and flood severity index were higher in 2016 than in 2023. The bottom panel of <u>Table 5</u> shows the coefficients on the risk factors when we add them to the specification in <u>Table 4. None of the factors was significantly different from zero in both years. Only the flood</u> severity index significantly but negatively affects the willingness to pay in 2016. Our results fail to confirm the personal experience hypothesis, in terms of both differences in levels and changes in the effects of risk factors on willingness to pay for climate change actions.

# **_Extended hypothesis tests_**

Does the Oaxaca-Blinder decomposition of the changes in willingness to pay higher taxes to combat climate change confirm our hypothesis results? <u>Table 6</u> shows the decomposition in which we extended our baseline specification in Table 2 by adding indexes of right-wing populism and economic conservatism and the set of the PSU-level variables representing the environmental risks. The results are consistent with our findings. The right-wing populism index is the only factor that significantly affects the difference in willingness to pay between the third and fourth rounds of the LiTS (Specification 2). The effect of this variable comes from the changes in “endowment,” or
estimated to cover safe travel to a government school with secondary grades, which in rural areas was typically located some distance from the girl’s village. While the program benefit is small in absolute terms compared with those in samples of CCT programs in developing countries that have been evaluated (Garcia and Saavedra 2017), the level of the benefit as a percentage of household consumption spending is within the range of benefit sizes in these samples (Baird et al. 2014; Benhassine et al. 2015).

> 9 Based on statistics from the 2004/05 Pakistan Household Income and Expenditure Survey. We are unable to estimate the program benefit level as a percentage of average household consumption spending per adult equivalent in program districts because available household sample surveys that gather data on income and consumption spending are not representative at the district level.

11
|14.7|13.3|
|**Sindh**|24.1|26.0|21.9|18.4|24.0|22.1|20.0|
|**KPK**|29.5|32.3|29.3|25.4|31.5|30.3|27.8|
|**Balochistan**|42.7|46.7|40.8|36.7|45.9|43.4|39.7|
|**Urban**|10.9|13.4|11.1|8.1|12.6|12.5|11.3|
|**Rural**|28.2|31.0|26.4|22.2|27.8|25.5|23.2|

Source: Authors’ calculation from HIES 2018/19 data. Note: Preferred specification uses household-specific inflation as described in the methodological section until FY23. Due to the lack of disaggregated inflation projections, past FY23 projections have been deflated using headline inflation.

An advantage of a non-distributionally neutral model is that it provides information on which individuals will be most affected during a crisis. Given the modeling process, this is intrinsically linked to the evolution of sectoral growth and the initial distribution of households of different welfare levels across sectors. For reference, we present the changes in welfare levels across the expenditure distribution between FY22 andFY23. Figure 5 shows that while there is a reduction in expenditure for households across all quintiles, the highest losses occur for those at the bottom 40 percent.

13
**Figure 8. Share of population from bottom 40 percent across regions and type of welfare aggregate, Kazakhstan 2010**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>nominal deflated<br>Akmola Aktobe ***Almaty ***Atyrau West_Kaz **Jambyl Karaganda **Kostanay ***Kyzylorda ***Mangystau ***South_Kaz Pavlodar North_kaz **East_Kaz ***Astana_city ***Almaty_city<br><!-- End of picture text --> Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations. Notes: * significant difference in proportions test at 10 percent, ** at 5 percent, *** at 1 percent level.

In addition to changes in the distribution of the population in the bottom 40 percent, the choice of welfare aggregate can also affect the individual profile of the population in the bottom 40. However, as shown in table 4, the difference is small using the example of individual characteristics in Tajikistan. The impact may be stronger if people are very different across the fourth, fifth and sixth deciles where the largest reshuffling takes place.

**Table 4. Individual characteristics of bottom 40 percent in Tajikistan across type of welfare aggregate**

||b40,deflated welfare aggregate|b40,nominal welfare aggregate|
|---|---|---|
||**Education level, 15+**||
|Basic education|22.2|21.9|
|General secondary education|54.4|55.2|
|Special secondary education|8.7|8.6|
|Tertiary education|6.5|6.3|
|None/less than primary education|8