rafmacalaba/datause-encoder-data huggingface dataset:
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.

# **2.1 Land ownership structure and institutional reforms in Ukraine**

After the fall of the Berlin wall, transition countries followed very heterogeneous trajectories that continue to influence how agricultural land markets operate (Swinnen _et al._ 2016). With more than 40 million hectares (ha), Ukraine has an agriculturally useable area that is larger than that of Germany, Poland, and France combined. Of this, approximately 20 million ha is farmed by large farms with average sizes of about 2,000 ha, many of which have links to foreign capital markets (Deininger et al. 2018); about 12 million ha is estimated to be cultivated by small and household farms; and some 9.2 million ha remained under communal and state ownership.<sup>2</sup>

> 2 An unknown share of this land was “privatized,” often in nontransparent ways that imposed significant losses to the public (Nivievskyi 2020).

5
directly (e.g., through the provision of geological surveys) or indirectly (e.g., through the allocation of property rights).

As a result of these economic features, out of the 15 disaggregated mining and quarrying sectors, 10 are classified as partially contestable (Table 5). Of the 10 contestable sectors, eight are deemed to be partially contestable due to sunk costs: mining of hard coal (510), mining of lignite (520), extraction of crude petroleum (610), extraction of natural gas (620), mining of iron ores (710), mining of uranium and thorium ores (721), mining of other non-ferrous metal ores (729), and mining of chemical and fertilizer minerals (891) (Table 6). Mining of uranium and thorium ores is also associated with negative externalities (Sorrentino 2020). The two remaining partially contestable sectors do not appear to have high entry barriers, but they are associated with possible market inefficiencies due to uncertainty; these include support activities for petroleum and natural gas extraction (910) and support activities for other mining and quarrying (990).

Five disaggregated sectors are considered competitive (Table 5). These sectors are quarrying of ornamental and building stone, limestone, gypsum, chalk, and slate (811), operation of gravel and sand pits and mining of clays and kaolin (812), extraction of peat (892), extraction of salt (893), and other mining and quarrying (899), which includes the mining and quarrying of different materials. The classification for each disaggregated sector in this industry is provided in Appendix 2, Table A.2.

_Table 5_ **_. SOE taxonomy in mining and quarrying_**

|_Dist_<br>**Competitive**|**Mining and Quarrying Indus**<br>_ribution of disaggregated secto_<br>**Natural Monopoly**|**try (B)**<br>_rs by category_<br>**Partially Contestable**|
|---|---|---|
|5|0|10|
|33%|0%|67%|

Source: Authors’ elaboration _Table 6_ **_. Non-competitive disaggregated sectors in mining and quarrying_**

|**Mining **<br>**NACE C**|**andQuarrying (B)**<br>**ode Classification**||**Market**|**Partially contest**<br>**(+)**|**able**<br>**(-) **|
|---|---|---|---|---|---|
|**Sector**|**Disaggregated sector**|**Code**|**power**|**externalities***|**externalities**|
|(5) Mining of coal and lignite|Mining of hard coal|510|X|||
||Mining of lignite|520|X|||
|(6) Extraction of crude petroleum<br>|Extraction of crude petroleum|610|X|||
|and natural gas|Extraction of natural gas|620|X|||
|(7) Mining of metal ores|Mining of iron ores|710|X|||
||Mining of uranium and thorium<br>ores|721|X||X|
||Mining of other non-ferrous<br>metal ores|729|X|||
|(8) Other mining and quarrying|Mining of chemical and<br>fertilizer minerals|891|X|||
|(9) Mining support service|Support activities for petroleum<br>and naturalgas extraction|910||X*||
|activities|Support activities for other<br>miningandquarrying|990||X*||

Note: *Other market failures include information frictions that may lead to under-provision. Source: Authors’ elaboration 18
these were converted into raster grids denoting distance from the water and sewerage networks. Two further types of data were assembled that reflected aspects of the quality of water and sewerage provision across these networks. First, data were obtained on recorded complaints made by Lusaka residents to LWSC about aspects of the sewer and piped water network. Complaints were aggregated by Township (the administrative level below the District Metering Area (DMA) used to administer water across the city, with 78 units across Lusaka District) and categorized as relating to water quality, water supply, or sewerage. Second, data were obtained from the Zambian office of the Centres for Disease Control ((CDC), 2018) who undertook water sampling at 290 randomly selected water source locations (taps, boreholes, tanks etc.) across the city and recorded whether each had evidence of contamination with _Escherichia coli_ bacteria, which served as a proxy for unsafe drinking water.

(iii) Household water and sanitation access and other characteristics. Data were compiled from two household surveys conducted in Lusaka: the World Bank Lusaka Sanitation Assessment survey (2015) and the Lusaka Sanitation Program (LSP) baseline survey undertaken by Vision RI (2016). Both were representative sample, questionnaire-based surveys in which selected households were asked broad ranging questions, including on their access to and use of improved water and sanitation facilities, and latitude and longitude coordinates of households were recorded via GPS. In order to use these detailed survey data as putative covariates of cholera risk across the city, two further steps were undertaken. First, questionnaire responses from each household were condensed into an index of risk for both water and sanitation, as detailed in Table 2. Second, the set of resulting risk level data at household locations was used in a Bayesian geostatistical model (P W Gething, Dasgupta, and Andres 2017; Peter W. Gething and Joseph 2017) to yield an interpolated raster grid (one each for sanitation and water risk). Three other geospatial covariates were also derived from the household survey data using the same approach: the percentage of households with soap in their toilet area (as a proxy for handwashing); the mean household size; and the percentage of households with unimproved sanitation. The latter variable was combined with an underlying population map to derive a population density of people without adequate sanitation across the city. Finally, geospatial data was obtained on the percentage of households classified as being in extreme poverty by municipal ward (de la Fuente, Murr, and Rascon 2015) and the location of graveyards across the city which is a potential risk factor for spread of cholera. Both were converted into raster grids.

To assess the association between each covariate and cholera risk, the Pearson's product moment correlation coefficient was calculated.
Two different approaches are found to be used in the literature to determine the causes of CO2, growth. Broadly, these approaches can be classified into two groups: identity approach and econometric approach. The first decomposes the independent variables to calculate their relative roles in driving the emission growth; it is also called index decomposition analysis, pioneered by Professor B.W. Ang of Singapore National University [3]. The second uses the standard econometric approach to check the causality between the dependent and independent variables.

Within the decomposition or identity approach, there are several specific techniques, of which the Logarithmic Mean Divisia Index (LMDI) method introduced by Ang and Zhang in 2000 [4] has been widely used. The popularity of this technique has increased due to its ability to handle cases with zero values without leaving residuals, consistency in aggregation, and path independency [5-9]. Since the 2000s, a growing body of studies has employed the LMDI approach to identify the factors impacting CO2 at the country and regional levels (see Table A1). For instance, at the regional level, González et al. [10] decompose CO2 in the European Union (EU) from 2000 to 2010 into five factors. Other scholars investigated CO2 at the country level (Hatzigeorgiou et al. [11] for Greece, Tunç et al. [12] for Turkey, Oh et al. [13] for the Republic of Korea, de Freitas and Kaneko [14] for Brazil, O’Mahony et al. [15] for Ireland, Zhang et al. [16] for China, Feng et al. [17] for the United States, and Mousavi et al. [18] for the Islamic Republic of Iran, among others).

On the econometric side, various methods are employed where the focus of these studies is usually the relation between CO2 and income and carried out at the national, regional and global levels. At the national level, Bento and Moutinho [19] explore the linkage between renewables energy adoption and CO2 emissions in Italy using TodaYamamoto causality tests. Similar studies have been conducted by Bélaïd and Youssef [20] for Algeria and Danish et al. [21] for Pakistan. At the regional level, Dong et al. [22, 23] use the VECM panel Granger causality method to estimate how expansion of renewable energy consumption would reduce CO2 emissions in the BRICS countries 3
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past couple of years, Bolivia remains the only country that has eliminated a large proportion of its debt obligations through a debt reduction program (in this case, through a buyback of debt at a deep discount).5 In other countries, of which Chile is the most notable, debt reduction has come in a piecemeal fashion, mainly as part of debt-equity swap programs, and indirect debt repurchase Agreements.6 Ironically, we will stress that debt-equity swaps represent a kind of debt reduction that is typically of significant harm to the debtor country, which helps to explain why most countries have ce'ncelled or very sharply limited their programs by this point. Moreover, much of the actual debt reduction that has taken place has involved private-sector debt, rather than the restructured public sector debt that is at the rore of the debt crisis. Since the main source of the debt crisis is a financially distressed public sector, private debt conversions might be helpful, but are unlikely to play more than an incidental role in resolving the debt crisis.

In sum, when the banks advertise "more than $26 billion' of debt reduction,7 the vast bulk of this amount is private sector conversions (approximately $8.0 billion), and debt-equity swaps and local conversions (approximately $16 billion), versus only about $2 billion of debt buybacks and exit bonds for public sector 5See Sachs (1988b) for a detailed discussion of the Bolivian debt buyback.

6See Felipe Larrain (1988) for a detailed discussion of Chile's experience with debt reduction mechanisms.

7See the Institute of International Finance, OR-cit., p. 21.

3
_Are these results on public spending on health reliable?_ The results on public spending are not unexpected as the small difference in public sector health spending between the best and worst "outliers" in Table 3 strongly hinted that it was unlikely to have empirically large effects. Empirical studies that include health resource variables, such as physicians, nurses, or hospital beds per capita rarely find large and significant impacts (Kim and Moody, 1992). There have been very few previous estimates of the impact of public spending in developing countries, mostly because of the lack of data, but what there is Musgrove (1996) summarizes: "multivariate estimates of the determinants of child mortality give much the same answer: income is always significant, but the health share in GDP, the public share in health spending, and the share of public spending on health in GDP never are." 22 T-hree studies find a significant impact of public spending (these are Anand and Ravallion, 1993, Bidani and Ravallion, 1997, and Jarnison et al, 1996). Anand and Ravallion (1993) themselves warn that "[a] number of caveats should be noted about these results. 1) They are based only on the patterns observed in this sample of 22 countries." As we emphasize below the impact of public spending on health is not an immutable parameter, but is likely to vary widely from country to country and hence the results may be sensitive to the sample used.

Bidani and Ravallion (1997) use a particular functional form to desegregate the impact of various variables into its impact on the poor and non-poor. They consistently find an impact of public spending for the poor but not for the non-poor and their findings are consistent with a small or 22 There is a larger literature in developed countries, which also tends to find little impact of health spending, with some exceptions (Wolfe, 1986).

19
Health Scale 2”, is an adaptation of the widely-used General Health Questionnaire (GSQ-12).<sup>29</sup> It too includes 12 questions, with individual responses coded as “never” (coded as =1), “sometimes” (coded as =2) or “always” (coded as =3).<sup>30</sup> To estimate program effects on the responses to questions on this second scale, we first reversed the numerical codes for questions in which the response “always” would represent a worse outcome than the response “never”. In this way, higher numerical responses to any question always represent better outcomes.<sup>31</sup> We estimate the associations between mental health and education, and the effects of the CSP program on mental health both for responses to individual questions, and for averages across all 12 outcomes within a scale. These averages are estimated by running seemingly unrelated regressions (SUR) for all 12 outcomes, and using the estimated variance-covariance matrix of the estimates to calculate the standard error of the average (as in Kling et al. 2007; Duflo et al. 2007; Paxson and Schady 2009). They provide useful summary measures and have the advantage that they may be more precisely estimated than the individual effects.32 Table 6 shows that adolescents with higher education levels generally have better mental health as measured by the Mental Health Scale 1. On average, an additional year of schooling is associated with 0.079 more points on this scale (with a standard error of 0.021); however, there is no evidence that CSP recipients have better mental health than non-recipients, as measured by this scale. Table 6 also shows a positive association between schooling and mental health as measured by the Mental Health Scale 2. On average, one more year of schooling is associated with 0.053 more points on this scale (with a standard

> 29 For example, Schnritz, Kruse, and Tress (2009); Werneke, Goldberg, Yalcin, and Ustun (2000).

> 30 Both scales were carefully piloted in the field, and changes to the questions were made, as necessary, to ensure an adequate understanding by respondents.

> 31 In practice, very few respondents answered “never” to any question in which higher values indicated better

> outcomes (for example, less than 1% of respondents stated that they have “ _never_ been able to concentrate” in the last month), or “always” to any question in which higher values indicated better outcomes (for example, less than 4% of respondents stated that they have “ _always_ lost much sleep over worry” in the last month). We therefore turned responses to each question into a indicator variable, in which “sometimes” and “always” take on a value of one for questions in which higher values indicated better outcomes (for example, the question on ability to concentrate), and “sometimes” and “never” take on a value of one for questions in which higher values indicated worse outcomes (for example, the question on sleep loss).

> 32 Like other estimates in the paper, these averages also correct for clustering at the level of the primary feeder school.

24
selection of immigrants out of their origin countries, described by Borjas in his 1987 study. More specifically, of those who come from countries having originally sent positively selected migrants to US, it is the “worst of the best” that leave while the out-migrants originating from countries characterized by negative selection vis-à-vis the US are the “best of the worst.” Using the longitudinal component of the Current Population Survey (CPS)<sup>18</sup> and a methodology described in Kim (2009b), Kim (2009a) finds that the extent of return migration is small and its impact on assimilation not significant.

# **_OCCUPATIONAL PLACEMENT AS A PROXY FOR PERFORMANCE_**

While the majority of US migrant assimilation studies focus on earnings, there have been attempts in the literature to account for patterns in occupational assimilation and mobility. Chiswick (1977) brings one of the first contributions to this area. Jasso and Rosenzweig (1986, 1988) use occupational indicators in addition to earnings to assess immigrants’ performance. Green (1999) employs 1981, 1986 and 1991 censuses to show that after a period of adjustment lasting for about three years since migration, Canadian immigrants are more likely to be found in professional and more skilled manufacturing occupations compared to natives, an advantage due to the nature of the Canadian immigration policy, but found to decline across successive cohorts. Canadian immigrants are more occupationally mobile even long after arrival, compared to those who are not assessed based on their skills at entry into Canada or who lack initial language fluency. Using 2000 US Census data, Mattoo et al. (2008) analyze to what extent skilled migrants from various countries are employed in unskilled jobs on the US labor market. The study finds evidence of significant variation in immigrants’ occupational placement by countries of origin which persists even after accounting for education and experience accumulated in the US. However, a large part of the variation is explained by country of origin attributes affecting the quality of human capital on one hand, and by US immigration policy affecting selection of migrants on the other. Therefore, the authors conclude that the “underplaced” immigrants suffer primarily from low (or poorly transferable) skill levels rather than skill underutilization. Chiswick et al. (2005) design and test on Australian longitudinal data a model of immigrants’ occupational mobility. The model predicts that occupational mobility from the last job in the country of origin to the first and then the subsequent ones in the destination countries follows a U shape, the depth of which depends on the similarity between origin and destination countries, but also on migrants’ skill levels. The decline in occupational status from the last job in the country of origin to the first job at destination mirrors lack or poorly transferable skills. As these skills

> 18 The CPS Merged Outgoing Rotation Group

11
higher public-private capital ratio and a lower consumption-capital ratio. In both cases the adjustment path corresponds to the sequence ABA′.

The important implication of the foregoing analysis is thus that, if public infrastructure is sufficiently “productive” in the health production technology (in the sense that the elasticity of output of health services with respect to public capital is sufficiently high), the positive effect of an increase in infrastructure spending may outweigh the negative effect of lower public spending _per se_ on health services on consumption and growth. Put differently, the best strategy for increasing the supply and consumption of health services in the long run and stimulate growth may not be to increase direct government spending on health, but rather to increase spending on other “production” inputs, in this particular case infrastructure<sup>23</sup> . This is an important policy message, to which we will return in the next section.

# **3.  Growth-Maximizing Allocation Rule**

Setting dγ/dυI = 0 in equation (11), it can readily be established that the growth-maximizing share of spending on infrastructure, υI*, is given by<sup>24</sup> so that, from (9), υH* = 1- υI*. Formula (12), first established in Agénor (2005 _f_ ), has the following properties. If μ = 0, that is, in the “standard” case where health services are produced only with government spending on health, υI* = α/(α+β). This essentially indicates that the share of spending on infrastructure must be equal to the elasticity of goods output with respect to public capital in infrastructure, divided by the sum of the elasticities with respect to public capital and effective labor (α and

> 23Our results may also help to understand why several empirical studies (such as Filmer, Hammer, and Prichett (2000)) found no significant correlation between public health spending and health outcomes ; this may be because infrastructure may have been a binding constraint.

> 24See Agénor (2005 _f_ ) and Agénor and Neanidis (2006 _a_ ) for a derivation of the welfaremaximizing allocation in related models, as well as a comparison with the growth-maximizing solution.

32