rafmacalaba/data-use-mentions-extended huggingface dataset:
, and informal trade is likely to grow when such policies are in place. However, the study also finds suggestive evidence that the bans lead to higher prices and increased volatility in the imposing country, leading to increased instability in the domestic market.

Specifically, in the case of Malawi, Edelman, and Baluch (2016), in a descriptive study, note that export bans tend to limit access to food and artificially raise the cost of food for consumers. They are also likely to divert food from markets where there is high demand and send mixed signals to farmers as they make planting decisions ( _ibid._ ). It was further reported that export bans also tend to misdirect agricultural investments and encourage hoarding resulting in distorting the markets. Simulation models examining the economy-wide effects of Malawi’s long-term maize export ban and oilseed export levy from 2012-2017 found that these policies benefited the urban non-poor in the short-run, while poor farmers’ incomes and maize consumption levels declined in the longer run. The oilseed export levy also failed to achieve its longrun objectives (Aragie, Pauw and Pernechele, 2018).

The overall evidence from both CGE based studies as well price transmission analysis seems to suggest that export controls may not often be the right policy response to stabilize domestic food markets in Africa. Unpredictability in the announcement of such policies and imperfect enforcement may result in inefficient market outcomes in the short-run. Results from model-based studies also indicate that households engaged in the agricultural sector are likely to experience welfare losses in the aggregate. However, previous work in this area within the SSA context has usually ignored the potential spillover effects of trade policies within neighboring countries. This study builds upon and adds to previous work in several ways by using price transmission analysis methods in conjunction with spatially disaggregated price data from market locations across Malawi as well as within the neighboring countries.

7
# **Reforming Land Valuation and Taxation in Ukraine: A Path towards greater Sustainability Fairness, and Transparency**

## **1. Introduction**

The signals provided by market prices for land and property are important for many reasons, including for national accounts and consumer price indices to indicate macro-economic activity, fine-tune monetary policy, measure and regulate the financial sector’s risk exposure or stability, and inform individual decisionmaking (European Comission 2013). Accurate property valuations are a key precondition for investment and financial market operation, transparency in real estate markets, and taxation of capital gains or losses due to land price changes as well as recurrent property tax valuation as a basis for the social contract and provision of services that, in turn, tend to be capitalized into land values.

Ukraine has traditionally valued rural land based on a Soviet-style normative monetary value (NMV) that is based on the productive capacity of land indexed to inflation. Since independence in 1991, it has moved gradually towards building the institutional and legal infrastructure for a land market that could support land valuation methods that are more flexible in terms of reflecting actual conditions. Building on reforms enacted in 2020 data on agricultural land sales are available from July 1, 2021, when the country allowed agricultural land sales and put in place a sophisticated system for public monitoring of land relations. This system leverages central electronic information platforms, including the State Land Cadaster and the State Register of Real Property Rights, to increase transparency and effectively collect and manage data on all market transactions involving land plots and other real estate assets.

The paper uses data from this system to demonstrate how price data for more than 175,000 land transactions for commercial agricultural land, together with publicly available information on soil quality, land use and infrastructure access, allows to estimate land prices using a regression approach. As cross validation tests suggest the model consistently explains at least half of the variation in the data, we use it to predict land prices for the approximately 20 million hectares of commercial
**Table 1: Summary Statistics for transaction data from a trucking portal**

||Mean|Standard<br>Dev.|Minimum|Maximum|N|
|---|---|---|---|---|---|
|Freight rate (mt-km)|3.92|3.44|0.62|23.77|469974|
|Freight Cost/trip|43562|37311|10000|850000|469974|
|Average Distance/trip|924.25|700.34|7.05|3935|469974|
|Hours traveled/trip|17.25|12.75|0.14|79.21|469974|
|Truck capacity|18.18|8.35|1|35|469974|
|Share of light Trucks (10mt or less)|0.22|0.42|0|1|469974|
|Number of trips|286.43|425.21|1|2701|469974|

Source: India Trucking Database

**Table 2: Determinants of Trucking Freight Rate (per mt-km)**

||Ln (Fr|eight Rate (mt-|km))|Ln(distance)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Ln (Av. Nightlights luminosity at destination)|-0.108***<br>(0.008)||||
|Ln (Av. Nightlights luminosity at origin)||0.031***<br>(0.010)||0.007***<br>(0.002)|
|Ln (Av. Nightlights luminosity at route level)|||-0.169***<br
with sector dummies. This exercise yields an insignificant result and hence we are not reporting it. This means that the effect is similar across sectors. Recall that we are working with four-digit sectors within manufacturing so this result is expected. If we worked with sectors like construction, services or agriculture, then the results might differ. Even when the bulk of foreign investment in Turkey is in services, we focus exclusively on the manufacturing sector, where the calculation of firm level productivity is more accurate, as it benefits from correct measurement of capital and materials, and the results are comparable across countries.

**Table 5: Foreign ownership and firm employment**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(L)|∆<sup>2</sup>ln(L)|∆<sup>3</sup>ln(L)|∆<sup>4</sup>ln(L)|∆<sup>4</sup>ln(L)|
|∆ln(FO)<br>∆<sup>2</sup>ln(FO)|0.000<br>(0.003)|0.353***<br>(0.003)||||
|∆<sup>3</sup>ln(FO)|||0.585*<br>(0.25)|||
|∆<sup>4</sup>ln(FO)||||-0.293***|-0.123***|
|||||(0.001)|(0.001)|
|Observations|34,128|32,231|28
# **Appendix A: Construction of the panel from the ASIF data**

**Construction of the panel from the ASIF data.** In the dataset, every firm is given a unique firm code. A small number of firms may have changed their firm codes within the sample period but remained in the sample. To address this issue, we follow Brandt et al. (2012) and Yang (2015) to obtain unique firm codes based on the firm’s name, zip code, telephone number, and founding year. We clean the data as follows. First, if the year _t_ observation of a firm cannot be matched to any firm’s observation in year _t+_ 1 based on the firm code, we try to find a firm with the same name in year _t+_ 1, and match them by giving the year _t+_ 1 observation the same firm code as the year _t_ observation. Second, for those firms that cannot be matched by the code or name, we rely on the combinations of the zip code, telephone number and the founding year to match them. We delete firms with missing key information, i.e. assets, fixed assets, sales and employment. Table A1 presents the frequency with which we can link the observations in different years for both SOEs and non-SOEs.

**Table A1. Evolution of the raw panel over time**

|Year|Total firms|Entrants|Incumb|ent,linked using|Exiting (in the|
|---|---|---|---|---|---|
||||NBS ID|Other information|nextyear)|
|1998|164,452||||28,709|
|1999|161,439|25,696|130,863|4,880|27,672|
|2000|162,350|28,583|130,538|3
# 3. Data and Summary Statistics

## Data construction

We constructed a panel data set for both publicly listed and private firms from 2003 to 2017 from the Orbis database provided by Bureau van Dijk. Our sample size is larger than in previously published research which covers only publicly traded firms included in the Compustat or Worldscope data bases. Following Armenter and Hnatkovska (2017), we define corporate savings as the corporate sector’s net financial position, that is, its financial assets net of liabilities. We restrict our sample to firms with more than 250 employees—consistent with the EU’s definition of large enterprises—and that have at least 100 observations each year and non-missing corporate savings observations for at least six of the 15 years in the sample period. After imposing these restrictions, the sample consists of more than 64,000 firms in 12 economies, with a total of 540,000 firm-year observations.

The benefit of defining corporate savings as financial net worth are twofold. First, changes in aggregated financial net worth from firms’ financial statements correspond conceptually to, and are statistically closely associated with, the corporate sector’s net borrowing or lending in the System of National Accounts (SNA), thus allowing us to derive macroeconomic implications on national and global payments imbalances. By contrast, changes in such variables as cash holdings, the values of fixed and financial assets, debt, and retained earnings, neither correspond conceptually nor are closely related statistically to SNA-based net lending/borrowing in the 10
average of father and mother‘s education as an indicator of parental education and re-estimate all of the regressions. The results in Table 10 show that if anything, the magnitudes of estimates are slightly larger in this new formulation. Thus our estimates of intergenerational correlations presented in the previous tables can be taken as conservative estimates of the effects of family background. The gender and geographic patterns and trends also remain unchanged with this alternative definition of parental education.

Our main empirical results indicate that sibling and intergenerational correlations in education in India remained largely unchanged over a period of almost a decade and a half (19932006) after the economic liberalization in 1991. The only group that experienced significant decline in the sibling and intergenerational correlations are women in urban areas. These results seem to contradict the evidence presented by Jalan and Murgai (2008) who find substantial improvements in educational mobility in India over time. They use 1998-99 NFHS data and find that the magnitude of the _intergenerational regression coefficient_ declines substantially for younger age cohorts. We discussed earlier the pitfalls in relying on cohort based analysis when data consists of only the coresident children. In fact, Jalan and Murgai (2008) are well aware of the limitations of the cohort analysis and discuss many of the same points we raised earlier. Our results thus can differ on two grounds: (i) intergenerational correlations take into account the declining variance in education in children‘s generation, and (ii) we compare the same age cohorts (16-27 years) across two surveys, instead of relying on the different age cohorts in a single survey round. It is, however, important to check if we get estimates similar to those of Jalan and Murgai (2008) from a cohort based analysis. We replicate the analysis in Jalan and Murgai (2008) and report the estimated intergenerational regression coefficients for both rural and urban areas for three age cohorts [15-19, 20-24 and 25-29 year] in Table 11. It is interesting that the estimates show a declining effect of parental education for the younger
379,241|727,087|846,080|917,584|2,321,841|
|**Domestic credit**<br>**cards**|||||||||
|Processing costs|5,250|2.9%|1.5%|1.4%|0.7%|0.6%|0.6%|0.2%|
|Monthly fee|11,925|6.7%|3.3%|3.1%|1.6%|1.4%|1.3%|0.5%|
|Other monthly fees|1,137|0.6%|0.3%|0.3%|0.2%|0.1%|0.1%|0.0%|
|**TOTAL**|**18,312**|**10.2%**|**5.1%**|**4.8%**|**2.5%**|**2.2%**|**2.0%**|**0.8%**|

_Source: Superintendencia Bancaria; Encuesta Calidad de Vida 2003_ 16. **More bank services are available for higher income clients.** Less than 10 out of the 26 banks surveyed by the Superintendencia Bancaria provide savings services for the lowest population quintile, by income level. In addition, none of the banks offer credit card services to this segment. This supply side finding confirms the demand side finding that income is a key factor in determining access to financial services (see Chapter 3).

38
81%<br>81%|ally a function of logistical feasibility (e.g. number of available staf)<br>staf more experienced, more could be done over time. The percentag<br>oth).|
|ble F.1: Valid|dation<br>Validated|18<br>12<br>6<br>8<br>24<br>17<br>25<br>38<br>31<br>61|240|ound was princip<br>re familiar and <br>cash, CBT, or b|
|Ta|Vali<br>Selected|0<br>24<br>24<br>10<br>10<br>26<br>27<br>28<br>44<br>0<br>0<br>35<br>69|297|ed in each r<br> became mo<br> treatment (|
|||||t<br>es<br>y|
||ys<br>Target #|100<br>100<br>100<br>100<br>100<br>398<br>398<br>398<br>398<br>501<br>501<br>501<br>501|4096|portion selec<br>As procedur<br> includes an|
||Surve<br>Round|3-week<br>5-month<br>7-month<br>12-month<br>13-month<br>3-week<br>5-week<br>12-month<br>13-month<br>3-week<br>5-week<br>12-month<br>13-month||_Notes:_ The pro<br> were selected. <br>eatment group|
||Phase|1<br>2<br>3|All|none <br>the tr|
payment was made was 6 months. As a result of this nonpayment, property owners owed US$ 24 on average for past water use and behind on US$ 38 of their infrastructure loan repayments.

Why did property owners not pay their loans or consumption bills? Figure 2 presents property owner self-reported reasons for (i) not making a payment in the last 2 months and (ii) never making a payment based on self-reported data collected from a 2018 survey of 5,091 property owners in six settlements. Service quality was the most cited reason for nonpayment, reported by about half of respondents, lack of liquidity was the second most cited reason, and third was not knowing how to make payments. The payment system and billing infrastructure were not a perceived constraint.

Next, we look at correlates with payment behavior within a regression framework, using the 2018 survey data merged with the utility administrative billing data. The sample consists of compounds that (i) have a water connection and (ii) have tenants. The dependent variables include whether the property owner ever made a payment; (ii) the proportion of bills paid; (iii) the outstanding proportion of water connection loan; and (iv) whether the loan has been fully paid.

9
622|3,836,397|
|**plo**<br>**(5)**|Average|9,201,794|13,746|2,097,909|
|**Em**|Total Employer|46,008,972|68,733|10,489,545|
||% of Market|5%|3%|5%|
|**t**|Min|1,523,589|2,840|320,274|
|**den**<br>|Max|34,102,743|106,596|8,348,993|
|**pen**<br>**(3)**|Average|22,295,590|63,303|5,593,792|
|**nde**|Total Independent|66,886,770|189,908|16,781,376|
|**I**|% of Market|8%|8%|8%|

Source: HFSA raw data.

13. The MPFs sponsored by financial institutions typically outsource asset management and administration to other specialized companies within the same financial group. The other MPFs usually outsource these services as well, sometimes to the same companies, although there are MPFs sponsored by employers that manage their assets internally. When asset management is outsourced, the asset management company typically charges much lower fees to MPFs outside the financial group. There is a marked segmentation in terms of fees that has not translated into increased switching by members from high fee funds to low fee funds. As noted below, this is due in part to the lack of readily comparable information on returns and fees until recently.

14. Finally, arrangements for revenue collection have remained deficient. Individual MPFs deal with many employers, including thousands of small and medium enterprises. As a result, the industry has been considering the
Clearly, the main difficulty encountered are the data themselves (available or not). This lead to several kinds of situation: <u>The data do exist but are dispersed or too plentiful: this is typically the case of the large river basins,</u> international or national ones. As regards the large transboundary basins (for example the Danube, the Rhine, etc.), the data exist at the level of the countries.

RBOs may also cover very large national river basins, or several river basins, and in that case the data may be available in a database, or several databases: this means making choices between data widely different in each point of the basin or basins.

An example can be given: it concerns the water quality parameters. The same parameters may be used in the whole basin, with perhaps some additional ones in very specific sampling points, but the results may be very different from a measurement station to another. This will depend of the station location. In the case of the Rhone basin for instance, a station located in Lyons (a very large town with an industrial complex on the main watercourse) will provide results very different (as pollution is high) from a station located on a tributary in the mountains (although some nitrates may be found in agricultural areas). Therefore, giving some exact figures characterizing the whole basin is impossible, even if all these data do exist.

<u>The river basin is small but the data do not exist or are not gathered in a database. Giving figures</u> (exact or estimated), if at all, may be difficult in that case. This might be better in the future, as river basin databases are now created when the RBOs are being set up (although this is not true everywhere).

# **Outcome**

In spite of these difficulties, many countries have responded. The following is a tally of the responses by regions:

# _Africa_

The RBOs
Njong, A. M. and Dschang, P. N. (2008) Characterising Weights in the Measurement of Multidimensional Poverty: An Application of data-driven approaches to Cameroonian data. OPHI Working Paper No. 21, wired at _www.ophi.org.uk_ 1 Ravallion, M. (2011) On Multidimensional Indices of Poverty,” _Journal of Economic Inequality,_ 9(2), 235-248.

Sen, A.K.(1976) Poverty: an ordinal approach to measurement, Econometrica 44, 219–231.

20
The most common output-oriented measure of technical efficiency is the ratio of observed output to the corresponding stochastic frontier output: It measures the output of the i-th firm relative to the output that could be produced by a fully-efficient firm, using the same input vector. According to Coelli et al.(2005), panel data often allows us: i) to relax some of the strong distributional assumptions that were necessary to disentangle the separate effects of inefficiency and noise, ii) to obtain consistent predictions of technical efficiencies, and iii) to investigate changes in technical efficiencies over time. Panel data versions (Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977)) can be written in the general form: The expression  6 is identical to the model  4 except we have added a subscript “t” to represent time. The expectation is that inefficient firms improve their efficiency levels over time, with managers learning from experience, and for their technical efficiency levels to change systematically over time. One model (Battese and Coelli 1993) allows for time-varying technical inefficiency and takes the form: _uit_  _f_  _t_  _ut_ , where _f_  . is a function that determines how technical inefficiency varies time: In  7 ,<sup></sup> is unknown parameter to be estimated. The Battese and Coelli (1993) function has the properties _f_  _t_  0 and _f_  _T_  1, and is either nonincreasing or non-decreasing depending on the sign of<sup></sup> . However, it is convex for all values of<sup></sup> . The Battese and Coelli (1993) model can be estimated under the assumption that<sup>_u_</sup> _i_ has a truncated normal distribution:<sup>_u_</sup> _i_ ~ _iddN_  ,  _u_ 2  .

3.2 . The Efficiency Distribution Dynamic 8
# - <u>3. Global inequality over the long run: From proletarians to migrants</u>

I will now look at global inequality over the long sweep of history. It is here that we can establish an important finding, which goes, I think, into some core issues of political philosophy and economics.

Let us try to do for the entire period since the Industrial Revolution the same type of global inequality calculations which we have just shown for the last 20 years. We ask, “what was global inequality then—say, around the mid-19<sup>th</sup> century?” It is a question impossible to answer with any precision, because we do not have household surveys or any other reliable sources of income data for these times. Nonetheless, some important attempts to estimate it have been made before, notably by François Bourguignon and Christian Morrisson (2002), who were the first to take such a long-run view. They used income levels (GDP per capita) from Angus Maddison’s database (2004; 2007), and some of their own (necessarily often shaky) estimates of income distributions for different parts of the world, to create global income distributions for eleven benchmark years spanning the period 1820-1992. They did the best one could do with the available data —and their results have been corroborated, to the extent that it is possible to corroborate something as tentative, by several later authors (Van Zanden, Baten, Foldvari and van Leeuwen 2010; Milanovic, 2011). The basic story that emerges from these calculations of income inequality in far-away times is that since the Industrial Revolution, which launched a score of European countries and their overseas off-shoots onto a path of faster growth, global inequality kept on rising until the mid-20<sup>th</sup> century. There was a period of more than a century of steady increase in global inequality, followed by perhaps fifty years (between the end of the Second World War and the turn
,690|1,354|
|GNI per capita/operational cutoff|764|1.75|0.80|7.59|1.09|
|Years over cutoff|764|5.86|0|26|5.73|
|Creditworthiness|742|29.0|3.6|80.2|14.3|
|Poverty headcount|880|0.21|0.00|0.96|0.22|
|Log(population)|1001|16.3|13.8|21.0|1.5|
|Life expectancy|999|64.2|41.4|78.5|8.7|
|Urban population share|970|0.42|0.11|0.69|0.14|
|ICRG rating|690|0.40|0.04|0.69|0.13|
|Resource rents/GDP|830|0.09|0.00|0.60|0.11|
|OECD real GNI per capita|1,010|33,412|24,991|39,416|4,526|