rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- Ades, Alberto F. and Edward L. Glaeser (1995) “Trade and Circuses: Explaining Urban Giants,” _The Quarterly Journal of Economics_ , Vol. 110, No. 1, pp. 195–227.

- Anderson, James E. and Eric van Wincoop (2004) “Trade Costs,” _Journal of Economic Literature_ , Vol. 42, No. 3, pp. 691–751.

- Atack, Jeremy, Fred Bateman, Michael Haines, and Robert A. Margo (2010) “Did Railroads Induce or Follow Economic Growth?: Urbanization and Population Growth in the American Midwest, 1850-1860,” _Social Science History_ , Vol. 34, No. 2, pp. 171–197.

- Balk, Deborah, Francesca Pozzi, Gregory Yetman, Uwe Deichmann, and Andy Nelson (2004) “The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents,” working paper, CIESIN, Columbia University.

- Banerjee, Abhijit, Esther Duflo, and Nancy Qian (2009) “On the Road: Access to Transportation Infrastructure and Economic Growth in China,” mimeo, MIT.

- Barrios, Salvador, Luisito Bertinelli, and Eric Strobl (2006) “Climatic change and ruralurban migration: the case of sub-Saharan Africa,” _Journal of Urban Economics_ , Vol. 60, No. 3, pp. 357–371.

- Baum-Snow, Nathaniel (2007) “Did Highways Cause Suburbanization?” _The Quarterly Journal of Economics_ , Vol. 122, No. 2, pp. 775–805.

- Behrens, Kristian, Carl Gaign ́e, Gianmarco I.P. Ottaviano, and Jacques-Fran ̧cois Thisse (2006) “Is remoteness a locational disadvantage?” _Journal of Economic Geography_ , Vol. 6, No. 3, pp. 347–368.

- Burgess, Robin, R ́emi Jedwab, Edward Miguel, Ameet Morjaria, and Gerard Padr ́o i Miquel (2011) “Ethnic favoritism,” mimeo, Paris School of Economics.

- Buys, Piet, Uwe Deichmann, and David Wheeler (2010) “Road network upgrading and overland trade expansion in sub-Saharan Africa,” _Journal of African Economies_ , Vol. 19, No. 3, pp. 399–432.

- Chandra, Amitabh and Eric Thompson (2000) “Does public infrastructure affect economic activity?: Evidence from the rural interstate highway system,” _Regional Science and Urban Economics_ , Vol. 30, No. 4, pp. 457–490.

24
Figure 3: Percentage change in SNAP benefits per population due to the 2008 Farm Bill and the 2009 ARRA by state <!-- Start of picture text --> 25 MO<br>SD<br>WY<br>WI<br>MA<br>MN OK<br>KS<br>NEUT<br>MSNM DE<br>NDNC AL VA NH<br>20 IA MT GA NV AZMD FL ORWA<br>MI AR SCPA<br>KY WV NJ VT IDCTIL<br>TN<br>ME CO<br>IN<br>RICA DC NY<br>15 TX<br>5 10 15 20 25<br>Percentage change in benefits per population, Farm Bill<br>Fitted values Percentage change in benefits per population, ARRA<br><!-- End of picture text --> Notes: This figure plots out the percentage change in SNAP benefits per recipient for all states, excluding Ohio and Louisiana which were both outliers due to disaster relief events. The slope is negative and insignificant with a point estimate of -0.138 (0.098).

Figure 4: Variation across states during major changes in SNAP benefits per population and synthetic benefits per population, 2006-2015 <!-- Start of picture text --> (a) SNAP benefits per population (b) Synthetic benefits per population<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>10<br>20<br>5<br>10<br>2007 COLA 2007 COLA<br>2008 Farm Bill 2008 Farm Bill<br>2009 ARRA 2009 ARRA<br>0 2013 ARRA Expiration 0 2013 ARRA Expiration<br>-10 0 10 20 30 -10 0 10 20<br>% change in benefits per population % change in synthetic benefits per population<br>Frequency Frequency<br><!-- End of picture text --> Notes: This figure plots out the changes in SNAP benefits per population and synthetic benefits per population from 2006 to 2015 across four major events in which SNAP benefits changed. These four events are 2007 cost-of-living adjustments, 2008 Farm Bill, 2009 ARRA, and 2013 ARRA expiration. For SNAP benefits per population, the mean (standard deviation) of these 4 events are 0.072 (0.016), 0.16 (0.037), 0.20 (0.022), and -0.080 (0.010) respectively. For synthetic benefits per population, the means (standard deviations) of these four events are 0.073 (0.021), 0.14 (0.028), 0.18 (0.010), and -0.072 (0.0033), respectively. Several outliers due to disaster-relief events are dropped.

45
- 67 -

the expenditures of subnational governments traditionally supersede their own source revenues in practically all countries examined. In the larger countries (Argentina, Brazil, Colombia, Venezuela) the gap is of the order of 50 percent. (Table 4.20a Annex) In other words, the independent revenues of local authorities in these countries are barely sufficient to cover their basic administrative costs. Public services provision is being financed through other sources.

Table 4.20a: FISCAL GAP OF SUBNATIONAL GOVERNMENTS AS PERCENTAGE OF EXPENDITURES al

||1975|1976|<br>1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC AVERAGE SAMPLE|||||||||||
||-37.34|-33.56|-29.49|-30.06|-28.07|-21.76|-24.67|-21.22|-22.67|-15.61|

Table 4.20b: FISCAL GAP OF SUBNATIONAL GOVERNMENTS AS PERCENTAGE OF GROSS DOMESTIC PRODUCT a/

||1975|1976|1977|1973|1979|1980|1981|1982|1983|<br>1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC AVERAGE SAMPLE|||||||||||
||-1.13|-0.85|-0.57|-0.72|-0.65|-0.61|-0.82|-0.76|<br>-0.91|<br>-0.72|

---------------------------------------------------------------------

__------__----------------------------

**al** Fiscal **gap** - own revenues - expenditures and lending minus repayment.

Source: IMF, Government Finance Statistics Yearbook, various years.

# C. Current Strategies to Relieve Fiscal Stress

4.21 General. As we have seen above, the fiscal imbalance of many subnational governments is due to the fact that the expenditure demands arising from urbanization have not been matched by sufficient revenues. This may be due to the failure of local authorities to engage in proportionate efforts of revenue collection but also to the limited buoyancy of their revenue sources to resiond to the increasing needs and the fluctuations of the economy. As a result, almtst everywhere in Latin America the central governments are called upon to fill the
WPS3940

# **What Determines Protection of Property Rights? An Analysis of Direct and Indirect Effects**

## **Meghana Ayyagari Asli Demirgüç-Kunt Vojislav Maksimovic***

**Abstract:** Using cross-country data, this paper evaluates historical determinants of protection of property rights. We examine four historical theories that focus on conceptually distinct causal variables believed to shape institutions: legal origin, endowments, ethnic diversity and religion. There is only one realization of the data with relatively few observations, which have by now been well explored in the literature. Given the correlations between the explanatory variables, it is difficult to fashion empirical tests which are consistent in their treatment of the competing theories and to know which regressions to take seriously, giving rise to competing interpretations in the literature. We use Directed Acyclic Graph (DAG) methodology to identify which historical factors are direct determinants of property rights protection and which are not, and subject the outcomes to a battery of robustness tests. The empirical results support ethnic fractionalization as a robust determinant of property rights protection. Despite the attention it has received in the literature, the impact of legal origin on protection of property rights appears fragile and dependent on the inclusion of transition economies in the sample.

### **Keywords: Property Rights, Legal Origin, Endowments, Directed Acyclic Graphs JEL Classification: K4, D23.**

### World Bank Policy Research Working Paper 3940, June 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ **_<u>http://econ.worldbank.org</u>_** _<u>.</u>_

### **_____________________________**

*Ayyagari: School of Business, George Washington University; Demirgüç-Kunt: World Bank; Maksimovic: Robert H. Smith School of Business at the University of Maryland. We would like to thank Thorsten Beck, Stijn Claessens, Patrick Honohan, Aart Kraay, Ross Levine, L. Alan Winters, and seminar participants at the University of Maryland for helpful comments.
<!-- Start of picture text --> 12.00% 35%<br>30%<br>10.00%<br>e3<br>25% 2<br>xy<br>»v 8.00% band°<br>8éQ3 20% zSs“4 g<br>= 6.00% 8<br>a oy<br>a 15% §<br>Ba<br>8wa<br>S °<br>4.00% &<br>10% &3<br>g<br>é<br>2.00% 5%-<br>0.00% 0%<br><O0.0lha. 0.01-04ha. 04-1 ha 1-2 ha 2-4 ha. 4-10 ha. 10+ ha<br>Percentage of poor per land-holding size — Real income growth<br><!-- End of picture text -->
How Does the World Bank Influence the Development Policy Priorities of Low-Income and Lower-Middle Income Countries?

Stephen Knack<sup>_∗_</sup> Bradley C. Parks<sup>_†_</sup> Ani Harutyunyan<sup>_‡_</sup> Matthew DiLorenzo<sup>_§_</sup> _Keywords:_ foreign aid, reform, conditionality, policy evaluation, survey methods _JEL Classification:_ C81, C83, D04, F34, F35, F55, O19, O20

> _∗_ Development Research Group, World Bank

> _†_ AidData, Global Research Institute, The College of William and Mary; and Center for Global Development; email: bparks@aiddata.wm.edu.

> _‡_ Sunwater Institute; email: aharutyunyan@sunwater.org.

> _§_ Department of Political Science and Geography, Old Dominion University; email: mdiloren@odu.edu.
that the top 20% richest households would gain proportionately more than the bottom 20%. Yet, the poor would benefit more than the rich in 25 countries.

We also demonstrate the importance of having very disaggregated data by showing that using more aggregated data yields biased estimates of the gains from trade. The mean absolute difference in average gains estimated using disaggregated data versus aggregated data is 0.75 percentage points, or 30% of the average gains from trade across countries.

Granularity and heterogeneity are among the key features of the HIT data set, which can be downloaded from **http://www.worldbank.org/en/research/brief/hit** . The website also contains an online tariff reform simulation tool. The online data appendix describes in more detail how the data were harmonized, and how the tariff data can be updated.

The remainder of the paper is organized as follows. Section 2 presents the data and harmonization procedures, as well as some descriptive statistics both on the structure of protection as well as households’ income and consumption portfolios. Section 3 presents a simple framework for assessing the first-order impacts of trade reform on household welfare. Section 4 presents the results from agricultural tariff cuts. A final section concludes.

# **2 Harmonizing Household Survey and Trade Data**

## **2.1 Harmonizing household surveys**

Household surveys are the predominant instrument for analyzing poverty and income inequality and are thus a natural starting point for evaluating the distributional impacts of trade policy. In this paper we introduce a harmonized household-level information data set, which is designed to assess trade policy, that covers 54 low- and middle-income countries (see Table 1 for a list of all surveys included in the HIT data set).<sup>1</sup> These data fill an important gap in the toolbox of policy makers and researchers, because these types of household-survey-based data are not usually readily amenable to analyzing the impacts of trade-reforms and can be hard to access. The list of countries, household surveys, year of data collection and sample sizes are reported in Table 1.

> 1Household surveys are typically collected by national statistical agencies.

2
39

# **IMPROVING LOCAL GOVERNANCE AND STAKEHOLDER INVOLVEMENT**

Central governments aside, schools and local educational authorities have also paid little attention to time loss and its costs. As the data from Pernambuco showed, many school and class registers had scarcely been filled, a sign that local authorities may not search for the evidence of instruction that registries could provide. One problem is that schools and teachers often get little supervision from principals and supervisors, and that time use is not a variable that is used frequently. Hopefully the data from this and other studies will have some effect as findings are disseminated.

Weak incentives for performance, corruption, imperfect monitoring, and administrative logjams are frequent culprits. Some countries have tried to address the problem by involving the stakeholders in service delivery. Sometimes results have been impressing. Giving parents a voice over their children’s education, patients a say over hospital management, making agency budgets transparent-all contribute to improving outcomes in human development.<sup>124</sup> However, low-income parents often do not know how to recognize the features of effective schools. It is possible to raise consciousness among parents improve local governance by disseminating information on the visual hallmarks of effective schools: teacher attendance, textbook provision, time use in interactive instruction. A potential outcome measure to be used is that all students should become fluent readers by the end of grade 2.<sup>125</sup> Parents and village elders (even those who are illiterate) can perceive and monitor reading fluency, because it resembles human speech. Similarly, simple goals may be established for math. These characteristics can be taught through videos that contrast acceptable and unacceptable features in schools as well as examples of children who can read fluently.

# **TRAINING AND CONSCIOUSNESS RAISING ON THE CONSEQUENCES OF TIME LOSS**

Good time use in many societies is not a priority, and this concept may be novel, particularly for educators who have spent a lifetime moving at a certain pace and watching their own teachers do so. Suitable and “memorable” training is needed to show the effects of time loss at all levels of the educational chain and to promote culturally viable strategies for doing so.

- High-level officials may be engaged partly through WBI (and the Joint Africa Institute).  Decision makers may be asked to think about the points in their systems where intervention may increase instructional time and discuss measures that are likely to be politically acceptable. (See exercise in Annex C.);

- The media may be used to raise awareness the public and to discuss the budgetary and learning implications of time use. Audiovisual means (presented during meetings or through TV programs) can show what occupied and idle classes look like as well as how children should read at various ages when classroom time is used well; and

124. World Development Report 2004.

125. Abadzi 2006
large developing countries. The multilateral price indexes and Engel curve methods are set out in section III. Estimation results and comparisons between the various deflators are in section IV, and the impacts of different deflators on poverty and real inequality are described in section V. A limited cost-benefit evaluation is in section VI, while the conclusions are in section VII.

# II. CONTEXT AND DATA DESCRIPTION

Over the past two decades Vietnam has conducted eight household living standards surveys that have been widely used to monitor progress in poverty reduction. In the first of these, the 1992/93 Vietnam Living Standards Survey (VLSS), prices in local markets were surveyed to provide one source of information on regional differences in the cost-of-living. The poverty lines calculated in 1993 suggested urban prices were 20% above rural prices, while the highest cost region (of seven then demarcated) had costs of living about 35% above the lowest cost region. The next VLSS, in 1998, fielded a price survey just in rural areas, with poverty line updating in urban areas relying on prices already collected for the CPI. The next four surveys (the Vietnam Household Living Standards Surveys [VHLSS] of 2002, 2004, 2006, and 2008) relied solely on already-collected CPI prices to update rural and urban poverty lines and spatial deflators.

There were several concerns with using temporal index prices to calculate a spatial index. Vietnam’s CPI is ostensibly national in scope but the prices used to form the spatial deflators were from just forty of Vietnam’s sixty provinces. Also, the outlet sample for the CPI is not spatially representative since outlets need to be easily accessible (some item prices are observed every ten days) and in areas of dense demand so that target specifications are always in stock. Moreover, the CPI changed in 2006 to let provinces pick item specifications that suited the peculiarities of local demand, rather than using nationally-consistent specifications, so reported spatial price differences 9
The test requires information on μoc, which is not directly observed. But notice that the ongoing duration of a tenancy, doc, is a (noisy) indicator of the underlying insecurity of tenure. In Jovanovic’s (1979) job-matching model, for example, the hazard rate of job separation in the presence of speci fi c investment is a function of elapsed job duration and of match quality. If workers (tenants) have different costs of search and fi rms (landlords) have different retention policies, then the separation hazard also depends on these additional exogenous sources of turnover. Since the hazard rate (which is one minus the contract renewal probability) uniquely de fi nes the distribution of tenancy duration, we may write log(doc) = E [log(doc)|μoc]+ξoc, where ξoc is random ‘luck’. Taking a linear approximation to the conditional expectation delivers

which is exact if elapsed duration is distributed as a Weibull. Equation 7 suggests putting (demeaned) log duration interacted with the dummy variable Lci into equation 6 as a proxy for Lci × μoc. It also implies that doing so leads to an errors-in-variables problem, because of ξoc. Since the resulting estimate of γ is biased toward zero, we will tend to fi nd weaker evidence against the null (that γ = 0) than if we observed μoc directly. Of course, this is a moot point if we actually end up rejecting the null.<sup>30</sup> Tenancy duration may also be endogenous in equation 6 for reasons other than measurement error, as we discuss in detail in subsection 4.3.

# 4.2 Evidence from landlords

For our test of the tenure security effect to have power, there must be a reasonable amount of variation in tenancy duration. But variation in doc does not necessarily imply variation in μoc, or in θ for that matter. To see why, take the case where θ, the subjective probability of retention, does not vary across tenants. Since actual contract renewal is a stochastic process, we would still observe a nondegenerate distribution of elapsed tenancy durations, but no relationship between duration and investment. It is thus important to assess the extent to which variation in tenancy duration re fl ects heterogeneity in the objective factors underlying μoc, like tenant mobility and landlord behavior.<sup>31</sup> To do so, we use data collected from the landlords in our sample. For each plot that

> 30 Note that, since E [μocLci] = 0, the estimate of α is robust to measurement error in μoc.

> 31 Match quality, which by de fi nition is not speci fi c to the landlord or to the tenant, cannot be distinguished from the error term ξoc.

17
4 two-person society. Based on the Edgeworth Box analysis, J’s welfare is: JF(0)+JR(0) and I’s welfare is [P-JF(0)]+[O-JR(0)]. Now, suppose the government wants to take the economy towards the frontier OP and improve, thereby, both the total welfare and its distribution. For this, it considers two programs, which are expected, _a priori_ , to achieve such economic and social objectives, i.e., a ‘dashed’ (dashed line) program (D) and a ‘solid’ (solid line) program (S). As can be seen from Figure 1, the ‘dashed’ program moves the economy from ( _i_ , _j_ ) 0 to ( _i_ , _j_ ) _D_ and the ‘solid’ program moves the economy to ( _i_ , _j_ ) _S_ . Both policies are Pareto optimal in the sense that they satisfy the condition of utility maximization for both individuals/groups. But, the ‘dashed’ program is less efficient as it falls short of the production possibilities frontier (OP) and ends with an inner frontier, O’P’<OP. However, from a political economy perspective, the ‘dashed’ program can be the second best option and it may very well be the final choice of the policy maker. This is because I (rich) is less likely to oppose this program with a lower loss in allocation as compared to the ‘solid’ program.

In the discussion so far, the focus is only on the welfare and distribution implications of two alternative programs. In this case, an _ex-ante_ assessment of the development path and its economic implications is usually conducted with actual and expected information before the policy choice is made. But, such assessments do not account for the economic externalities from institutional facilitations and impact synergies from related programs. Using Figure 1, we can demonstrate how the additional welfare gains are missed when impact synergies and institutional roles are ignored. Let us assume that the economy is, again, in status quo at ( _i_ , _j_ ) 0 . Suppose that a development program, say, an irrigation project, is implemented, leading to a development path represented by the dashed line. Now, the economy attains a new equilibrium at ( _i_ , _j_ ) _D_ on the production frontier O’P’. Clearly, the new equilibrium, though increasing the welfare with more food and recreation, falls short of the optimum. Suppose, there is another development program, say, crop intensification that is also implemented either along with or subsequent to the irrigation project. Since crop intensification enhances the welfare impacts of the irrigation-based program, the latter can get
**Table 3. Alternative poverty thresholds by household size in 2017 (in rubles per month)**

|**Household Type**|**Estimated with**<br>|**absolute line**<br>|**Estimated with**<br>|**relative line**<br>|**Official**|
|---|---|---|---|---|---|
||**Pooled OL **|**BUC**|**Pooled OL **|**BUC**||
||_Househo_|_lds without chi_|_ldren_|||
|One adult, no children|9,607|9,607|10,800|10,800|9,607|
|Two adults, no children|14,891|12,777|13,913|16,306|19,214|
|Three adults, no children|19,310|14,987|15,931|20,488|28,821|
|Four adults, no children|23,153|16,908|17,520|24,066|38,428|
|Five adults, no children|26,707|18,542|17,397|25,150|48,035|
||_House_|_hold with child_|_ren_|||
|One adult, one child|14,122|12,297|12,226|14,035|19,532|
|Two adults, one child|17,773|14,218|14,973|18,632|29,139|
|Two adults, two children|18,734|14,795|15,422|19,493|39,064|
|Three adults, one child|20,847|15,755|15,225|20,062|38,746|
|Three adults, two children|20,847|15,852|18,885|24,788|48,671|
|Four adults, one child|23,537|17,100|20,098|27,685|48,353|
|Four adults, two children|22,673|16,812|20,311|27,494|58,278|
|Five adults, one child|26,131|18,253|20,201|28,855|57,960|

**Note:** Population weights are applied. Standard errors for poverty rates are adjusted for complex survey design. Poverty line for reference “one adult” is defined as an average of minimum subsistence levels for working-age individual and pensioners in 2017. The level of absolute poverty line is 10899 rubles per month for working-age individual, is 8315 rubles per month for pensioner and is 9925 rubles per month for child in 2017. Relative poverty line is set on 60% of household size-weighted median equivalized income for each household type using RLMS data in 2017. Poverty lines of reference adult are adjusted with weights in Table 2 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048) and using Pooled Ordered Logit model (where baseline elasticity equals 0.636 and every child has a weight 0.078).

29