rafmacalaba/datause-encoder-data huggingface dataset:
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# **5. Conclusions**

Absolute poverty is clearly a bigger problem in developing countries—where over fourfifths of the world’s population lives—than in developed ones. Virtually all of the one billion people subsisting on per capita incomes less than one dollar per day live in developing countries. Perhaps more surprisingly, inequality is also a bigger problem in developing countries. Looking at the world as a whole, there is a clear negative correlation between average levels of inequality and the level of development, and all countries with really high income inequality—a Gini index of (say) 0.50 or higher—are developing economies.

However, the evidence from the available cross-section of developing countries suggests that there is little aggregate tendency for these inequality levels to fall with economic growth. Although there are no developed countries today with inequality levels above a Gini index of 0.50, growth rates among developing countries are virtually uncorrelated with changes in inequality levels. This is our first stylized fact.

The absence of a robust cross-country correlation between changes in inequality and growth necessarily implies that there must be a strong negative correlation between growth and changes in poverty. This is confirmed empirically: on average, economies that grow faster reduce absolute poverty much more rapidly—our second stylized fact.

But this does not mean that policymakers in developing countries can ignore inequality. There are a number of reasons why persistently high inequality is a concern. Two primary reasons were not discussed here, namely the fact that higher inequality may be ethically objectionable in its own right, and the possibility that greater inequality may generate certain inefficiencies that could actually reduce the future rate of economic growth. World Bank (2005) contains summary discussions of both points; on the second also see Voitchovsky (2008). In this paper, we have focused on a third reason why persistent inequality may be undesirable in developing economies: the fact that, even for a given growth rate, inequality tends to reduce the growth elasticity of poverty reduction—our third stylized fact. Other things equal, one percentage point of growth leads to a smaller reduction in poverty in a very unequal country than in a less unequal one. And if inequality rises during the growth process, things are worse yet.

While these three stylized facts can be identified from a macro, cross-country perspective, an understanding of the economic factors behind changes in distribution (or behind the levels and incidence of growth) in developing countries requires a more microeconomic
Klette, Tor J. and Samuel Kortum (2004), “Innovating Firms and Aggregate Innovation,” Journal of Political Economy, 112(5), pp986-1018 Kortum, Samuel (1997), “Research, Patenting and Technological Change,” Econometrica, 65(6), pp 1389-1419.

Lentz, Rasmus and Dale Mortensen (2008), “An Empirical Model of Growth through Product Innovation,” Econometrical, 76(6), pp. 1317-73 Lileeva, Alla and Daniel Trefler (2007), “Improved Access to Foreign Markets Raises Plant Level Productivity... for Some Plants,” NBER Working Paper 13297.

Lopez, Ricardo A. (2005), “Trade and Growth: Reconciling the Macroeconomic and Microeconomic Evidence,” Journal of Economic Surveys, 19(4), pp 623-648.

Melitz, Marc .J (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity”, Econometrica, 71, 1695-1725 Muuls, Mirabelle and Mauro Pisu (2007), “Imports and Exports at the level of the firm: Evidence from Belgium,” CEP Discussion Paper No 801 Romer, Paul (1990), “Endogenous Technological Change,” Journal of Political Economy, 98:5, pp S71-102.

Rossi-Hansberg, Esteban and Mark L. J. Wright (2007), "Establishment Size Dynamics in the Aggregate Economy," American Economic Review, 97:5, pp. 1639-1666 Shaked, Avner and John Sutton (1987), “Product Differentiation and Industrial Structure,” Journal of Industrial Economics, 36, pp131-146.

Vogel, Alexander and Joachim Wagner (2008), “High Productivity in Importing German Manufacturing Firms: Self-Selection, Learning from Importing or Both?,” IZA Discussion Paper No.3854 33
difference between the incumbent’s vote share and that of the most successful challenger. If this difference is greater than zero, the incumbent won the election; if it is smaller than zero, the incumbent lost. In the absence of manipulation, the winner of a close election should have been, in effect, randomly assigned and therefore the probability of an incumbent victory should be equal to the probability of an incumbent loss. More precisely, the probability density function of the incumbent’s vote margin should be smooth across the threshold of zero, so that (in the limit) the incumbent was equally likely to win or lose close elections.

There are other possible reasons aside from fraud for why incumbents might have an advantage in close elections. An alternative explanation is that incumbents have a resource advantage over their challengers and, given accurate polling information, can expend precisely enough effort campaigning to secure a narrow victory. Consistent with this explanation, Grimmer et al. (2011) found that resource-advantaged candidates in races for the U.S. House of representatives were about 10 percent more likely to win elections decided by margins of between 0 and 5 percentage points. However, aside from elections to the U.S. House of Representatives, there is little evidence that incumbents (or other candidates with a resource advantage) can target campaigning effort in this fashion. Eggers et al. (2015) who analyzed over 40,000 tightly contested mayoral and legislative races in the United States and seven other countries, found no evidence that incumbents were more likely to win closely contested elections. Consistent with this result, this paper finds no evidence that narrow incumbent victories were associated with higher turnout, a plausible proxy for campaigning effort.<sup>13</sup> The analysis follows the method developed by McCrary (2008) to explore whether incumbents were more likely to win closely fought elections. Specifically, observations are grouped into bins of equal width and the following regression is estimated:

> 13Results available from authors on request.

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Policy Research Working Paper 7755

# **Abstract**

The paper compares the distribution of energy and food subsidies across households and the impact of subsidy reforms on household welfare in the Middle East and North Africa region. The analysis uses a unified model and harmonized household data. The results show that the distribution of subsidies and the welfare effects of subsidy reforms are quite diverse across countries and products. Energy subsidies tend to be pro-rich in terms of absolute amounts, but tend to be more important for the poor in terms of expenditure shares.

Instead, food subsidies are larger for the poor in absolute and relative terms. These findings do not apply everywhere, and the scale of these phenomena are different across countries and products. The welfare effect of a 30 percent reduction in subsidies can be important, especially considering the cumulated effect across products, but the cost of compensating the loss in welfare for the poor is generally low compared with the budget benefits of decreasing subsidies.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at Araar. Abdelkrim@ecn.ulaval.ca and pverme@worldbank.org.

_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
because there are different valid sales measures that firms could present when applying. Also, reported annual sales might be different from the administrative data we use in this paper. Despite these considerations, being larger than the eligibility cutoff significantly decreases the likelihood of a firm participating in the credit program by 14%. Panel B shows the effect of being at either side of the cutoff on leverage variation, measured as the change in (net) debt during 2020 relative to 2019 sales. Unreported RDD estimations show that crossing the size threshold and thus causally limiting the use of the credit guarantee program reduces the change in leverage by 4%, a result that is statistically different from zero.

Next, we decompose the change in indebtedness into the change in public guaranteed debt and non-guaranteed debt. By construction, public guaranteed debt needs to increase for firms participating in the program. We find that indebtedness coming from public guaranteed debt increases by 13.9 and 11.8 percentage points for banked and unbanked firms, respectively (Table 8, Columns 1 and 2). On the other hand, participating in the credit program could lead to higher or lower non-guaranteed debt. We find that indebtedness coming from nonguaranteed debt also increases, although the magnitude of the effect is significantly smaller (Columns 3 and 4). In other words, guaranteed and non-guaranteed debt are to some extent complementary to each other during the pandemic.

Having shown that the increase in debt occurs mostly through participating in the credit program, we next study how risk is related to the accumulation of this type of debt. We find that, within credit program users, riskier firms end up with more public guaranteed debt than safer firms, and this holds for both banked and unbanked firms (Table 9, Columns 1 and 2). The selection results from the previous section show that riskier firms are more likely to participate in the credit program (an expansion of the extensive margin). The results in this section show that, conditional on participating in the credit program, riskier firms end up with more guaranteed debt (an expansion of the intensive margin). In contrast, the relation between risk and non-guaranteed debt is negative, significant for banked firms and not significant for unbanked firms (Columns 3 and 4). That is, in the absence of a public guarantee, banks lend to safer firms. The results indicate that the existence of a public credit guarantee program changes the way banks allocate credit across the risk distribution of firms.

# **7 Aggregate Implications**

In this section, we analyze the aggregate implications of the credit guarantee program. We first study how indebtedness increases for different types of firms and how those firms, in 20
Table 5: Impact of Heat Exposure on Number of Negative Food Consumption Shocks

||Number of Ne<br>|gative Food Consu<br>|mption Shocks<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Lag T_>_33C|||_−_0_._006<br>(0_._006)|
|Lag T 30-33C|||0_._013<sup>_∗∗_</sup><br>(0_._006)|
|Lag T 27-30C|||_−_0_._004<br>(0_._004)|
|Lag T_>_33C: Growing Season||_−_0_._026<sup>_∗_</sup><br>(0_._015)||
|Lag T 30-33C: Growing Season||0_._013<br>(0_._009)||
|Lag T 27-30C: Growing Season||_−_0_._014<sup>_∗_</sup><br>(0_._008)||
|Lag T_>_33C: Major Season|0_._007<br>(0_._021)|||
|Lag T 30-33C: Major Season|0_._016<br>(0_._014)|||
|Lag T 27-30C: Major Season|0_._001<br>(0_._011)|||
|Lag T_>_33C: Non-Major Season|_−_0_._016<sup>_∗_</sup><br>(0_._009)|||
|Lag T 30-33C: Non-Major Season|0_._005<br>(0_._012)|||
|Lag T 27-30C: Non-Major Season|_−_0_._013<sup>_∗_</sup><br>(0_._007)|||
|Lag T_>_33C: Non-Growing Season||0_._010<br>(0_._008)||
|Lag T 30-33C: Non-Growing Season||0_._006<br>(0_._009)||
|Lag T 27-30C: Non-Growing Season||0_._003<br>(0_._007)||
|Outcome Mean|0.871|0.871|0.871|
|**Fixed Effects**||||
|Year|X|X|X|
|Enumeration Area|X|X|X|
|Observations|10,207|10,207|10,207|
|R<sup>2</sup>|0.331|0.332|0.328|

Notes. Estimates are from linear regressions using panel fixed effects. The dependent variable is the number of negative food consumption shocks experienced by the household in a given calendar year. The regressors represent the number of days in the temperature bins during the previous calendar year. All regressions control for precipitation bins from the previous calendar year. Errors are clustered at the enumeration area level. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

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provide a variety of insights. As Section 2 will demonstrate, the empirical studies that exploit data from investment climate surveys, while usually implicitly set in a macro context as the one described by our model, are examples of the added value provided by a disaggregated, microeconomic approach.

# **Section 2. Survey of Recent Enterprise-Level Business Climate Studies**

Firm-level enterprise and business climate data has proved a rich resource for research on the characteristics and constraints of firms in the developing and transitioning world. This section surveys the recent literature with a focus on empirical work that exploits the data to explain firm performance as a function of different aspects of the business climate. Sub-section 1 places the recent micro-level business climate literature into the context of the macro-studies that largely preceded it, thus highlighting the place of the firm-level literature as a whole and the added value it provides. Sub-section 2 discusses the available datasets. Sub-section 3 outlines the most serious econometric challenges encountered throughout the firm-level literature. Sub-section 4 then presents the key results of the literature for the four main sets of business climate variables that have been investigated: (1) Infrastructure, (2) Financial Constraints, (3) Corruption and Crime and (4) Competition and Regulation.

## _1. Firm Level Analyses in the Context of the Macro-Institutions Literature_

The microeconomic business climate literature surveyed here is framed by more macrooriented analyses. It is useful to briefly contrast the two literatures to put the firm-level studies into context and better understand their specific potential and advantages. The macro-literature has provided some interesting insight, but is characterized by a number of inherent limitations that microeconomic studies can help overcome.

The macroeconomic literature has generally attempted to use cross-country samples to explain GDP-based outcome variables<sup>4</sup> with broad, country-level indicators of institutional quality, the policy environment and infrastructure. The majority of such analyses have found significant effects of these variables on economic performance, even though recent studies have been more cautious in their interpretation of the evidence.

In a review paper for the European Investment Bank, Romp and de Haan (2005) find the consensus view in the macro-literature to be that ‘public capital furthers economic growth’.<sup>5</sup> With respect to institutions and the policy environment, Pande and Udry (2005) speak of ‘compelling evidence’ and a ‘persuasive case’ that ‘long run growth is faster in countries that have higher quality contracting institutions, better law enforcement, increased protection of private property rights, improved central government bureaucracy, smoother operating formal

> 4 Typically GDP per capita (e.g. in Acemoglu, Johnson & Robinson, 2001), GDP per worker (e.g. in Hall & Jones, 1999), or the growth rates of these two variables (e.g. in Knack & Keefer, 1995, or Mauro, 1995).

> 5 Romp and de Haan, 2005: 52

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# **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 agricultural land in Ukraine. Comparing results to the NMV shows not only that mean land prices are now between a quarter and a third above the NMV but that the size of the discrepancy varies systematically across regions: while mean predictions are slightly below the NMV in the East, they are well above it in the Center and the West. Use of the NMV to assess tax liabilities or value land posted as collateral would thus risk to constrain the ability of enterprises and local governments in regions characterized by an increase in relative land values to raise credit for investment or taxes for local service delivery in line with new opportunities or needs.

While Ukraine's experience offers valuable lessons for other transition countries, two aspects are relevant from a policy perspective. First, although this paper shows digital registry data can be leveraged to 2
25 Tharakan, P.K.M. (ed) (1991). Policy Implications of AntiduDping Measures, (Amsterdam: North Holland).

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Varangis, Panayotis, Carlos A. Prio Braga and Kenji Takeuchi (1993). *Tropical Timber Trade Policies: What Impact Will Eco-Labelling Have?," World Bank Policy Research **Working** Paper No. 1156, (Washington: World Bank. July).

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ceteris paribus. Surprisingly, the age of the firm is never statistically significant. In both manufacturing and service sectors, size is negatively correlated with productivity growth.<sup>39</sup> **Figure 3 FDI Flows into Europe, All Sectors, 1985–2009** <!-- Start of picture text --> Europe total 1,200<br>FDI inflow 1,000<br>($ billion) 800<br>600<br>400<br>200<br>0<br>100%<br>Inflow share EU15 South<br>among<br>subregions 80% EU15 North,<br>(percent) Continental,<br>60% European Free<br>Trade Area<br>40%<br>EU candidates/<br>eastern<br>20%<br>partnership<br>EU12<br>0%<br>1985 1990 1995 2000 2005 2009<br>Source: World Bank staff calculations based on United Nations Conference on Trade and Development (UNCTAD) data.<br><!-- End of picture text --> Table 2 presents the estimation results for Eq. (1) for the EU12. Columns (1) and (7) show the results of the complete model split into manufacturing and services industries, while the remaining columns present the results using the various sub-indices of business operations.

Productivity gains are correlated with increases in the availability of private credit, stock of inward FDI, workforce education, and business environment—especially trade and taxes. A one standard deviation increase in the overall business regulation index is conditionally correlated with a 6.35 percent increase in productivity growth for the average manufacturing firm and 7.93 percent for the average service firm. A one standard deviation improvement in the tax regulations index is correlated with 4.77 percent and 7.10 percent increases in labor productivity for manufacturing and service firms, respectively. A one standard deviation increase in the trade regulation index is associated with a 7.48 percent increase for the average service firm, but is not statistically significant in the manufacturing industry.

> 39 For a second method of evaluating the relative impact of firm-level variables, see Appendix XII.

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