rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10998

# **Abstract**

The shift from administrative to market-based valuation of assets is a key part of Ukraine’s transition from a planned to a market economy. This shift will improve the functioning of financial markets and the ability of local governments to obtain revenue effectively for local service provision. This - paper describes the rationale and evolution of Ukraine’s reg ulatory framework for market-based valuation. The analysis uses prices and publicly available parcel attributes for the nearly 200,000 agricultural land sale transactions during 2021–24 to estimate a land price model that is then used to predict prices for the roughly 20 million hectares of commercial agricultural land in the country. Mean predicted prices that are 25 to 33 percent above current valuations hide vast interregional differences. Given their proximity to active conflict, predicted prices in the East and South are close to or even below the normative value, but they are 80 percent above it in the West of the country. A transition to market-based valuation is thus a precondition for fair and equitable taxation and incentives for productive land use. By aligning with globally accepted standards for banking regulation and improving credit access in areas where land values have increased, the transition could also affect the speed and quality of reconstruction. The paper discusses legislative steps to move in this direction.

This paper is a product of the Development Research Group, Development Economics. . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org; dali1@worldbank.org; ebukin@worldbank.org, and agmartyn@gmail.com. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
level of inequality in the latter countries is often lower to begin with and their post-fiscal level of inequality is significantly and uniformly lower than other countries (Fuchs et al 2021).

Figure 2. Change in inequality (Gini index) from market income to final income Figure 3. Change in inequality from market to final income across countries (Gini index) <!-- Start of picture text --> 40.0<br>Tajikistan (2015)<br>Guatemala (2011)<br>35.0<br>Honduras (2011)<br>Indonesia (2012)<br>30.0<br>Burkina Faso (2014)<br>Peru (2011)<br>25.0<br>Moldova (2017)<br>Armenia (2011)<br>20.0<br>Nicaragua (2009)<br>Dominican Republic (200<br>15.0<br>Russia (2014)<br>Bolivia (2009)<br>10.0<br>El Salvador (2011)<br>Jordan (2017)<br>5.0<br>Belarus (2015)<br>Turkey (2016)<br>0.0<br>Bhutan (2017)<br>Market Disposable Consumable Final income<br>Ecuador (2011)<br>income income income<br>Tunisia (2010)<br>Mexico (2014)<br>Brazil (2008)<br>Colombia (2017)<br>Costa Rica (2010)<br>Argentina (2012)<br>-20.0 -15.0 -10.0 -5.0 0.0<br><!-- End of picture text --> _Source_ : World Bank staff calculation using BLSS 2017.

Source: World Bank staff calculation using BLSS 2017 for Bhutan. See references section for estimates for other countries. Pensions are treated as deferred income.

However, the net fiscal system leads to a small increase in poverty as of 2017. The $3.20 poverty rate (in 2011 purchasing power parity terms) is 8.2 percent using pre-fiscal (market) income. Once the burden of direct and indirect taxes and contributions is taken into account, the share of the population whose consumable income falls below the same poverty line increases to 9.3 percent. Most of this increase is due to the burden of indirect taxes, and to a lesser extent from contributions. The impact of direct taxes (i.e., personal income taxes) is very low, largely because of the high exemption threshold. This results in a fiscal system that is poverty-increasing in the absence of direct transfers or indirect subsidies. Increases in the poverty gap and poverty severity indices are also observed when moving from market income to consumable income (table 5). It is customary in the literature to not consider in-kind transfers when measuring the poverty effect because households do not observe the monetary benefits deriving from education and health spending. For this reason, Table 5 only reports poverty estimates for market income, disposable income and consumable income. The result that the fiscal system increases poverty in Bhutan is not unique—as shown in Figure 4, the fiscal system decreases poverty in some countries, while it increases it in others.

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# _Administrative Burdens on Start-Ups_

45. Romania’s policies in terms of facilitating the creation of new firms remain liberal relative to 2002, since administrative burdens are lower than among comparators for startups in general, and in specific service sectors, such as road transport and retail distribution (Figure 10). The indicator of administrative burdens for corporation measures the number of procedures, number of days, and the minimum capital required to start a limited liability company.

**Figure 10. Administrative Burdens on Startups a. Administrative burdens on businesses**

## **b. Sector specific administrative burden**

<!-- Start of picture text --> 5 5<br>4.3<br>4.1<br>4 4<br>3.0<br>3 3 2.6<br>2.3 2.2<br>2 1.4 1.8 1.9 2.0 2.0 2 1.5 1.6 1.9 1.9 2.0<br>1 0.8 1 0.8<br>0 0<br>Romania Bulgaria EU15 OECDSlovak Republic MICs HungaryCzech Rep. Poland Romania EU15 OECD BulgariaSlovak Republic HungaryCzech Rep. MICs Poland<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

46. The burden on business creation is substantially lower than in other countries even the EU15 and the OECD. The number and duration of procedures, as well as the cost involved in starting a company is lower in Romania than in other middle income countries and on a par with the best practice of top performers in the EU, such as Ireland or the UK.

47. This could, however, be partly driven by the fact that slightly different data and methods were used to calculate this indicator for Romania. A homogenization procedure was therefore necessary to make its score comparable to the other countries (see Table A2.8 for details). However, this excellent ranking is consistent with Doing Business’ data that rank Romania as the 7<sup>th</sup> best country (out of 175) in terms of ease of starting a business (it was the 6<sup>th</sup> in 2005).

48. Romania’s rankings and good performance vis-à-vis the ease of starting a business reflect the continued simplification and reduction (down to five) of the company registration procedures, especially after 2004. These took central stage in three consecutive Action Plans (2004, 2005, and 2006) for the reduction of administrative barriers to entry and operation of businesses, and were part of the PSAL/PAL programs. The establishment of one-stop shops for firm registration played an important role in this progress. The measures implemented through the Action Plans followed the recommendations of two World Bank/FIAS reports (2002 and 2004).

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percentage points. Also in this case, the result is largely due to beneficiaries lending money to other community members who then migrate, most of whom being minorities and facing credit constraints. However, the program reduces the probability of _direct_ beneficiaries to engage in migration by 0.34 percentage points. The positive overall effect described earlier at the community level is largely driven by eligible Dibao households lending to other households in the community who are credit-constrained and cannot finance the migration.

# **_Social assistance implicitly deterring migration_**

Imbert and Papp (2016) study the migration impact of another flagship Indian program, the employment guaranteed scheme NREGA. They use a gravity model to predict seasonal migration flows into each urban center and find that NREGA reduces short-term rural to urban migration by 9 percent and has no effect on long-term migration. This is largely attributed to the opportunity cost of urban migration. However, since the authors use village-level instead of household-level migration as their primary outcome indicator, differential impact on households of different socioeconomic status remained inconclusive.

In a more recent paper, the authors examine survey data from high out-migration areas at the border of three Indian states<sup>10</sup> (Imbert and Papp 2018). They find that NREGA reduces rural to urban mobility. Migrants who stay in the village to participate in NREGA choose to incur an income loss to avoid the utility cost of migration: “... structural estimates imply that the utility cost of one day away may be as high as 60 percent of migration earnings. Up to half of this cost can be explained by higher living costs and income risk. The other half likely reflects high nonmonetary costs from living and working in the city”.

In Brazil, Neto (2008) evaluates the impact of _Bolsa Familia_ , Brazil’s conditional cash transfer program, on differences in internal migration between beneficiary and non-beneficiary households. The paper finds that beneficiaries are 2.6 percentage points less likely to be migrants compared to non-beneficiaries. However, the study does not discuss any mechanism in support of this result.

In Mexico, Angelucci (2015) evaluates the impact of _PROGRESA_ , Mexico’s antecedent of the flagship _Prospera_ conditional cash transfer program. The author finds that, a few months after receiving the first transfers, labor migration to the United States from eligible individuals in treatment villages increases from 0.7 percent to 1.1 percent. She clarifies, however, that new 9
provided compares to the quality of home or informal care, leaving open the question of how children‟s development is affected by policies that encourage the use of more formal care.

# **5.2  Summary**

The studies of Latin American community day care programs described above generally find the programs to have substantial positive impacts on women‟s propensity to work, on their number of hours worked, and on the health, nutrition, and educational outcomes of children. Assessing the benefit-cost ratio of these programs requires an assessment of the monetary value of the child outcomes. One assessment for a community daycare program in Bolivia that is reported in Behrman, Cheng and Todd (2004) finds benefit-cost ratios of roughly 2:1 (for a discount rate of 5%), only taking into account the benefits accruing to the child participants and not taking into account any benefits for the mothers. Some additional support for the effectiveness of these programs comes from the evidence that the take-up for them is high.

Berlinski and Galiani‟s (2007) study of a preschool building program in Argentina also finds full take-up of the new places for children created by the program. Simply expanding the availability of preschool led to an increase in women‟s labor force participation.

Formerly communist countries have undergone large changes in the pricing and availability of childcare. The modeling frameworks of Lokshin (1999) and Fong and Lokshin (2000) permit comparisons of the relative effectiveness of wage subsidy programs, childcare subsidy programs and income transfer programs in affecting female labor supply. That analysis finds that childcare subsidies are more effective than comparable wage subsidies or family allowance transfers in increasing the proportion of mothers working and the amount of time they spend at work. Unfortunately, not that much is known about whether and to what extent children in the context of these transition economies (Russia, Romania) benefit from being in formal daycare rather than other forms of care (e.g. home care or relative provided care). More evidence needs to be accumulated to better understand how these subsidy programs affect both mothers and their children.

46
generates social externalities, i.e. the program impact on the welfare of children and reduction of fertility and gender bias?

# _4.1 The Program's Impact on HouseholdIncomes_

In so far as the control and treatment localities were randomly selected, a simple comparison of mean incomes provides an unbiased estimate of the program impact on income. Table la presents such a comparison and Table lb summarises the definitions of variables used in the following analysis. Focusing on the total household income, the comparison across control and treatment villages indicates that the program increases total household income by nearly 27%, suggestive of a high economic return to the program. However, any deviations from random selection will mean that this is a biased estimate if both sets of villages differed systematically in terms of income-generating characteristics (e.g. landholdings, labourers, or access to income-earning opportunities outside of agriculture) prior to the program. Since the program is expected to generate benefits, it is possible that villages with higher average incomes or with more political influence tend to be better informed and have stronger bargaining power in securing such programs. Alternatively, officials may bias selection for program participation in favour of poorer villages.

Either way, neglecting the issue of endogenous program placement can have the consequence that interpretations of any empirical results can be misleading as to causal impacts. Therefore, testing for the endogeneity of village selection is an important part of our empirical investigation of the underlying direction of causality. Using OLS multivariate analysis, where we include a range of explanatory variables that could capture such systematic differences across control and treatment households, we can control for observed heterogeneity across households." However, we also need to allow for the possible selection of villages based on unobservable Of course, in the absence of baseline data for 1993 we are unable to control for unobserved differences.

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UNCTAD. 2019. _World Investment Report 2019._ New York and Geneva: UNCTAD.

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18 We rely on DOLLAR as our primary measure and use the others for "sensitivity" analysis, because it provided us with the largest number of consistent cross country observations for a fairly recent year, 1990. We could not use some of the other appealing measures of openness-like the one developed by Learner (1988)-for sensitivity analysis, because of the small number of observations for low- and middle-income countries.

Measuring labor market distortions and the degree of protection to "insiders" is also a difficult empirical task. Ideally, we would have liked to use the ratio of formal to informal or rural wages, adjusting it for different worker characteristics. Once again, however, the data is unavailable for a large number of countries. Hence, we used as the numerator the average wage of an unskilled worker in manufacturing (which we obtained from the ILO data base), assuming that unskilled workers will have characteristics that resemble those of informal workers. Finding a suitable denominator was even more problematic, because the informal sector wage rates are unavailable for many countries. Instead, we had to use non-manufacturing GDP per worker as proxy for the informal wage rate. Thus, our wage distortion index (IDx) is simply the ratio of the manufacturing wage rate to the non-manufacturing value added per worker. An increase of this index is taken to imply greater distortions and more privileges for insiders. Given the uncertainties surrounding the calculation of IDX, we decided, as discussed earlier, to use a second measure of labor market distortion: the proportion of workers in wage employment (PWE).

Figure 2 shows the mean values of the two measures of labor market distortions for different groups of low- and middle-income countries. As expected, the two measures are negatively correlated-a high wage distortion, measured by IDX, is associated with a smaller formal sector and thus a lower PWE. West and East Africa, followed by South Asia, have the
Figure 20. Financial Result Compared before and after the Retirement Age Raise. Years 2018-2080 <!-- Start of picture text --> 2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8%<br>-10%<br>-12%<br>Current Rise in retirement age<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text -->

# Source: author elaborations based on administrative data, IESS

The results obtained are the ones expected. From a financial perspective, the alternative scenario would imply a one-year delay in system deficit, though, considering that there would be gradual changes in retirement age, that allows approximate savings for one percentage point of the GDP during a whole deficit term, while savings were 2 percentage points in the last years.

A complementary idea that usually arises in public policy discussions relates to the inclusion of a funding pillar. In Ecuador's particular case, the option under analysis involves including an individual funding scheme targeted to managing workers' social security savings from the top income quintiles. The reason for this relates to a potential inequity resulting from a higher subsidy (given the 40% transfer of state expenditures) received by these workers as beneficiaries.

Thus, the impact on financial results from the traditional pay-as-you-go scheme managed by the IESS that would lead to an individual funding scheme has been simulated. Therefore, it is assumed that the total active population between 15 and 50 years of age, working and earning wages 2.5 times higher than the unified basic wages,<sup>9</sup> choose the funding scheme and contribute up to that cap on the payas-you-scheme and above it to the funding scheme. This accounts for approximately 15% of total contributors to the IESS (Figure 21).

> 9 In 2018, the Unified Basic Wages were equal to USD386 monthly. In 2019, this value climbed up to USD394.

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Table 8 <u>OLS Forest Level Models of Total Carbon per Hectare and Total Number of Trees per Hectare</u>

||Carbo|nper hectare (|kg)|Numbe|roftrees per|hectare|
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model3|Model 1|Model 2|Model3|
|CFdummy|-0.674||-0.338|0.084||0.078|
||(2.20)**||(1.04)|(0.53)||(0.56)|
|CanIDFUGyear|0.793|0.438||-0.014|0.028||
||(3.72)***|(1.60)||(0.08)|(0.18)||
|NDVI_1990|3.891|3.993|3.826|1.448|1.445|1.449|
||(3.12)***|(2.95)***|(2.96)***|(2.00)**|(1.99)**|(2.00)**|
|Totalforestarea|0.002|0.002|0.002|0.000|0.000|0.000|
||(3.24)***|(3.44)***|(3.73)***|(1.02)|(1.15)|(1.03)|
|Hills|0.033|-0.051|0.031|0.287|0.297|0.287|
||(0.12)|(0.18)|(0.11)|(1.51)|(1.57)|(1.52)|
|Altitude|0.000|0.000|0.000|0.000|0.000|0.000|
||(1.05)|(0.74)|(0.94)|(0.62)|(0.69)|(0.62)|
|Slope|-0.001|-0.008|-0.004|0.016|0.017|0.017|
||(0.10)|(0.72)|(0.28)|(2.00)**|(2.12)**|(2.01)**|
|Fire|0.595|0.489|0.347|0.781|0.791|0.785|
||(1.90)*|(1.40)|(1.15)|(4.75)***|(4.83)***|(5.05)***|
|Foresttype|-0.360|-0.621|-0.498|-0.414|-0.380|-0.412|
||(0.70)|(1.42)|(0.87)|(1.57)|(1.49)|(1.56)|
|Forestperhh|-0.011|-0.016|-0.017|0.040|0.041|0.040|
||(0.15)|(0.28)|(0.26)|(0.89)|(0.88)|(0.89)|
|HHsinfug|0.000|0.000|0.000|0.000|0.000|0.000|
||(1.06)|(0.76)|(1.37)|(2.03)**|(2.18)**|(2.06)**|
|Migrationrate|2.043|1.618|2.825|1.124|1.173|1.110|
||(1.27)|(0.94)|(1.66)*|(0.86)|(0.91)|(0.87)|
|Constant|8.568|9.077|9.218|4.958|4.890|4.948|
||(8.31)***|(9.68)***|(8.08)***|(9.20)***|(9.68)***|(9.16)***|
|_R_<sup>2</sup>|0.34|0.29|0.29|0.34|0.34|0.34|
|_N_|130|130|130|129|129|129|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; Dependent Variables in Logs, Robust t Statistics in Parentheses

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