rafmacalaba/data-use-mentions-extended huggingface dataset:
- 48 -

- an updated municipal tax code;

- an updated tax map, incorporating recently urbanized areas;

- a new set of property records, containing updated information on the characteristics of each property in the jurisdiction;

- new unit cost tables, permitting the revised property characteristics data to be converted to estimates of value at current market prices;

- a new assessment roll, specifying the assessed value of each property and its current tax liability;

- a complete set of tax bills, incorporating the new assessments; and

- a ledger for recording payments against outstanding

liabilities.i7/

- 17/ CIATA also offers several computerizati **o** ptions. Prior to then widespread availability of microcomputers in Brazil, CIATA would maintain municipalities' property records at its regional headquarters, updating them on the basis of data supplied by the municipal assessor, and providing municipalities with a set of printed tax bills each year, (incorporating updated property data and any changes in tax rate). As the use of microcomputers has expanded, CIATA now supports decentralized records maintenance and billing, by assisting local governmen **t** o install systems on site. The use of microcomputers also permits collections to be recorded and monitored electronically (a process that was not possible under the previous centralized system, due to the difficulty of sending collections data to CIATA's regional headquarters.) The use of microcomputers supercedes the last three products of the standard CIATA package referred to in the tezt.
monetary policy, for instance, is potentially affected by the foreign bank penetration in the borrower country (c.f. De Haas and Van Lelyveld, 2006; Claessens and Van Horen, 2012; Ongena et al., 2015), the quality of bank supervision and regulation in borrower and lender countries (Ongena et al., 2013), the exchange rate system of the borrower country, and also the existence of restrictions on capital inflows into the borrower country (Rey, 2016).

In this paper, we investigate the role of foreign banks in the international transmission of monetary policy through the cross-border syndicated loan market using loan level data in 124 countries over the 1995-2015 period. Use of loan level data for multiple lender and borrower countries has two main advantages. First, including borrower*time fixed effects allows us to control for time-varying loan demand at the individual borrower level. Second, examining multiple countries allows us to investigate the impact of varying borrower and lender country policies and institutions on the transmission of lender-country monetary policy. Identification of an effect of monetary policy on loan supply volume and other loan terms is achieved by considering variation in the monetary policies relevant for banks in different countries that lend to the same firm in the same time period.

Our main finding is that the transmission of lender-country monetary policy through the cross-border syndicated loan market depends importantly on the existence of banking FDI in the borrowing country. Specifically, greater foreign banking presence reduces the sensitivity of the international loan supply to lender-country policy interest rates. This may to some extent reflect that an international bank with a local presence in the borrower country can substitute local funding for more expensive international funding if the lender-country monetary policy interest rate rises. Consistent with this, we find that the mitigating impact of banking FDI on the international transmission of monetary policy to loan volume is weaker if the borrower-country policy interest rate is higher, since this reduces the ability of a multinational bank to substitute borrower-country funding for lender-country funding. Our 3
a country. This exchange rate is available from the Penn World Tables from the series that measures the price level of GDP.<sup>14</sup> In columns [5] and [10], we use this measure of China’s real exchange rate.

> 13 Note that in this case, the exchange rate varies across time and the index varies across importer, exporter, and product so that the interaction term exploits the variation across all four dimensions.

> 14 A higher price level is associated with a higher price of non-tradables and hence signifies an appreciation of the real exchange rate (see Rogoff, 1996). This exchange rate variable, like the IMF’s nominal and effective exchange rate series, is a multilateral variable, and hence varies only by time and not across importers.

17
2018) demonstrate that the Rapid Consumption Methodology yields reliable estimates of poverty using an ex‐post assessment with household budget data from Hargeisa and mimicking the Rapid Consumption methodology by masking consumption of items that were not administered to households.<sup>11</sup>

# _Durable consumption flow_

The consumption aggregate includes the consumption flow of durables calculated based on the user‐cost approach. The consumption flow distributes the consumption value of the durable over multiple years.

9 Pape and Mistiaen (2018) test various other techniques for imputing total consumption, including OLS and tobit module‐wise regression and multiple imputation chained equations, concluding that multivariate normal regression is the preferred technique.

10 Negative imputed values are corrected by scaling all associated imputed values to an average of zero without affecting the variance.

11 Pape and Mistiaen (2018) compare the imputation results with consumption estimates from the full consumption modules of the 2013 Somaliland Household Survey. The authors present the performance of the estimation techniques in terms of the relative bias (mean of the error distribution) and the relative standard error. The methodology generally does not perform well at the household level (HH) but improves considerably already at the enumeration area level (EA) where the average of 12 households is estimated. At the national aggregation level, the Rapid Consumption methodology slightly over‐estimates consumption by 0.3 percent. Assessing the three standard poverty measures (Foster et al., 1984) including poverty headcount (FGT0), poverty depth (FGT1) and poverty severity (FGT2), the simulation results show that the Rapid Consumption methodology retrieves estimates within 1.5 percent of the reference measure (Figure A.5). Generally, the estimates are robust as suggested by the low standard errors (Figure A.6). Simulations were also run for the complete data set from the Somaliland 2012 household budget survey producing comparable results.

19
# **A Appendix Figures and Tables**

Figure A.1: Growth, poverty and inequality trends (2011-2019) <!-- Start of picture text --> Honduras<br>Honduras<br>Nicaragua<br>Nicaragua<br>El Salvador<br>LAC<br>Brazil<br>El Salvador<br>Brazil<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>Year Year<br>(a) GDP growth (b) Poverty ($6.85/day)<br>Brazil<br>Honduras<br>Nicaragua<br>El Salvador<br>2000 2005 2010 2015 2020<br>Year<br>(c) Gini coefficient<br>10 0.70<br>0.60<br>5 0.50<br>0.40<br>0.30<br>GDP growth (%) 0 0.20<br>Poverty headcount ($6.85/day)<br>0.10<br>-5 0.00<br>0.60<br>0.55<br>Gini index 0.50<br>0.45<br>0.40<br><!-- End of picture text --> _Note:_ These figures present trends for GDP growth, poverty rate (measured using the poverty line of $6.85/day) and inequality measured by the Gini coefficient (using household survey income) for Honduras and selected other Latin American countries. Data is from the World Bank Poverty and Inequality Platform (PIP).

40
**Figure 8. Income and Urban Apartment Tenure in Romania**

<!-- Start of picture text --> Romania<br>35<br>30<br>25<br>Ow ners<br>20<br>Public Rental<br>15<br>Private Rental<br>10<br>5<br>0<br>1 2 3 4 5<br><!-- End of picture text --> _Source :_ Household surveys (Table 1)

# **Demand Changes—Spatial and Social Mobility**

# **_Spatial Mobility and Labor Markets_**

# _Spatial Mobility / Migration_

Does the emerging rental sector in transition economies play the role in migration and labor market adjustment that it conceptually should play? Do the small rental sector sizes observed for some countries bar labor market adjustments and hamper economic growth? Mainstream migration research for transition countries continues to suffer from the weak linkage between the traditional labor market and the hypotheses formulated by the new spatial economics. Migration analysts invariably test for relative wages and unemployment factors but give little consideration to the effects of factors in the non-tradable goods sector. However, some of the analyses for transition countries hold indirect clues to the relevance of the housing sector.

Fidrmuc (2002), for example, finds for Poland, the Czech Republic, Slovakia, and Hungary that net migration between regions has been far less responsive (elastic) to inter-regional wage and unemployment differentials than expected under traditional labor market models. This suggests the presence of some threshold costs of migration that could include relative housing costs.

Kallai (2003) analyzes interregional migration patterns for Romania with explicit consideration of the costs of migration. She observes an unexpected positive correlation between migration and the regional income level for low-income groups. As in the Fidrmuc study, this suggests a cost threshold for migration that distorts labor market adjustment. The differences in income distribution in Romania are stark (Figure 9): the typical monthly rent of
<!-- Start of picture text --> s / OS<br>+ at xr o 5 10 km<br>ID 231 t |<br>~~ Pop density: x231 is<br>Opportunities: y231 a<br>= one ss os<br>—. ia GEER | tt 4<br>i p20 S0eee 6 oi<br>Sc S088 82 Seeeee i=]<br>a ge” EEEES BE 8 ES a<br>2 S5SS08 SERRE5RS 8 i<br>)| ee ie | | | a) ee<br>PTTi oT<br>i i i ma IDPop640 density: Xeao opulation density - Landscan 2012<br>_ Opportunities: yeso —_persons / km?)<br>- — wates00- Mend () 1101514885 -- 1488520634<br>' MB 0-1036 = 20634 - 27340<br>sci res heres ~ GHB 1036 - 2988 ty 27340 - 33256<br>’ GB 2988 - 5096 tay 33256 - 41426<br>GiB 5096 - 7532 gam 41426 - 48973<br>49 © 7532 - 11015 gay 45073 - 53793<br><!-- End of picture text -->

<!-- Start of picture text --> % 5 10 ken<br>ehntest 5 iv<br>ID 231 Ma<br>~ 'g Pop density: x231<br>. Opportunities: y231 an<br>x ay | | ba a Bee<br>es s 6 TToe | |<br>— ae 8 | Ef: San Ba |<br>| | | o.\ a @SH BEESEEne Hee8<br>‘<br>a He ae om<br>a B tia BE BESHE | E08BE58Baan a<br>Gea See ae | |<br>os oom | oo. oe<br>a || a 8 ID 640<br>2 6aeoe ‘<br
# **2.3 Poverty and welfare**

Poverty is often measured in monetary terms, such as the international poverty line of $1.90 a day. Monetary poverty captures a household’s ability to purchase goods and services, with the poverty line set to reflect a `minimum needs standard’ for existence. Monetary poverty is broadly used and has several advantages: it is straightforward to design and track, can easily be interpreted, can be compared across time and regions, and can be easily aggregated over numerous households. However, it only captures one dimension of poverty – a household’s consumption or income – and ignores other relevant development outcomes.

As a response, other metrics of poverty have been developed, such as the global MDP developed by the Oxford Poverty and Human Development Initiative (OPHI) (Alkire and Foster, 2011). It was developed to capture a broader picture of poverty based on widely available survey data, coinciding with wider recognition that poverty is a complex concept which cannot be captured using income or consumption alone (Stiglitz et al., 2009). The MDP considers individuals to face poverty if they cannot meet the basic needs deemed necessary for their existence, for example access to sufficient food and clean drinking water, access to education and other basic services, and social empowerment. In other words, the MDP intends to directly capture the households’ ability to meet these basic needs, and is thus outcome focused. This is more in line with Amartya Sen’s idea of capabilities, which reflect how individuals can convert income into achievements that improve human wellbeing (Sen, 1989). Moreover, the MDP is closely aligned with several of the UN’s Sustainable Development Goals. The other benefit of any MDP measure is that it directly captures deprivations households face and allows policies to prioritize specific deprivations. However, it is more difficult to interpret and compare across space as multiple deprivations are aggregated into one number, and the choice of what measures to include and their
nearly all respondents who left the region managed to escape poverty, while poverty reduction among non-migrants was more modest. These descriptive statistics reinforce the results reported in Beegle, De Weerdt, and Dercon (2011): individuals who moved did considerably better than those who decided to stay.

# _[Table 3 here]_

After moving, migrants remain linked to extended family members at home: 90 percent of the migrants report that they communicated with a non-migrant network member in the 12 months preceding the survey. Migrants who maintained some form of communication experienced an average consumption growth of 110 percent, while those who did not grew by 88 percent.<sup>16</sup> This difference is statistically significant at the 1 percent level. The severing of the most basic links does not seem to be associated with higher consumption growth; if anything, the reverse is true.

We use data from shock modules administered in 2004 and 2010. During both of these rounds, the panel respondents were asked to consider each year between the survey rounds and indicate whether a particular year was, in economic terms, 'Very good', 'Good', 'Normal', 'Bad', 'Very bad'. For each 'Very bad' response, the respondents were asked to provide the main reason for the hardship. We consider each 'Very bad' response as an economic shock. More than 60 percent of the panel respondents reported experiencing at least one such shock between 1994 and 2009.

Table 4 provides an overview of the shocks experienced. Most frequently reported economic shocks were death of a family member, serious illness and poor harvest due to bad weather.

# _[Table 4 here]_

The shock data were collected at the individual level – in particular for each person on the 2010 roster who also appears on the original 1991/94 rosters. Since our focus is to examine the role of shocks on household consumption, the data had to be

> 16 The mean consumption growth among those who
6

## **2.2. Second generation reforms: governance and FDI**

While macroeconomic performance has been impressive since the introduction of the stabilizationcum-transformation program in 1997, the progress in implementing the second-stage institutional reforms has been less spectacular, albeit not disappointing. With some delays privatization took off and a number of measures improving business climate have been implemented.<sup>2</sup> Since 1998, Bulgaria has reformed the regulatory and institutional framework of the banking sector and privatized private banks to strategic investors. As a result, the financial sector has fully recovered from huge financial losses and damage to its credibility suffered in the aftermath of the financial meltdown in 1996-97.

This section examines Bulgaria’s position in terms of the progress in structural reforms as captured by corruption indices, World Bank’s indicators of governance, and EBRD indicators of economic reforms. It assesses progress both over time and vis-à-vis its regional peers. Since the test of pudding is eating, we also look at links between achieved progress and FDI inflows.

# _A. Perception of corruption, governance and structural reforms_

How does Bulgaria compare to other countries in the region in terms of progress achieved in structural reforms? In order to answer this question, we use three different indicators: Corruption Perception Index (CPI), World Bank-based aggregate index of governance quality, and EBRD-based index of progress in second generation reforms. Admittedly, each of them is very subjective based on experts’ opinions rather than ‘hard’ numbers. Yet, taken together they provide basis for assessment of changes over time and Bulgaria’s position vis-à-vis its regional peers. Taken together, they point to very significant progress in improving business climate achieved especially over the last three years.

We begin with CPI providing information on perception of international investors of corruption in a country in comparative context. International investors’ perception of the Bulgarian investment climate has substantially improved since 1998. The verdicts given by participants of Transparency International annual surveys have been increasingly favorable for Bulgaria. While in
the modell, and the columns indicate interaction terms. Column 1 presents coefficient estimates on interactions between the row variables and a treatment indicator of being assigned to both the _Boys_ and _Goal_ treatment, column 2 presents estimates of coefficients on interactions of the row variables with being assigned to the _Boys_ treatment, column 3 presents estimates of coefficients on interactions of the row variables with being assigned to the _Goal_ treatment, and column 4 presents estimates of coefficients on level controls of the variables at the start of the rows. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Male baseline data.

55
characteristics might matter either at a more macro level, with sufficiently large variation in poverty across larger jurisdictions; or, alternately, at a more micro-level, such as targeting the poorest households within a village (as in the arguments of Stokes, 2007). The main purpose here, however, is not to contribute to the existing literature on the targeting of vote buy offers across households; rather, to contribute the first evidence of the correlation between jurisdictionlevel variation in the prevalence of vote buying with jurisdiction-level measures of service delivery.

Other socio-economic variables are also explored—the degree to which vote buying practices might be correlated with the level of social capital or local collective action in communities (using survey measures of a particular institution for local collective action and mutual assistance in the Philippines, described in Labonne and Chase, 2011—participation in _bayanihan_ ); whether the mayor is able to claim political affiliation with a larger number of politicians, thereby having access to wider networks to enable vote buying to be an effective political strategy; and, location and population of villages. None of these variables exhibit a robustly significant correlation with vote buying. This is shown in column (5)-(7) of Table 1.

Column (6) adds an indicator variable for whether the incumbent mayor is in her 3<sup>rd</sup> term in office, and therefore ineligible for stand for re-election because of a term limit. In elections with lame-duck mayors, it may be that more challengers emerge and use vote buying strategies to compete. However, the data show no significant evidence of this, although the coefficient sign is indeed positive.

Column (7) adds a measure of municipal dependency on fiscal transfers from the national government. Recent work, with data from Brazil, has suggested that greater fiscal dependency can act like a “political resource curse”, reducing the quality of local politicians (Brollo et al, 2013). In municipalities where national fiscal transfers constitute the vast bulk of local revenues political challengers
shortcoming of including very (small) gradual changes that are only poorly related to significant regime changes in democratic transitions.<sup>14</sup> Applying these criteria to our 74-country data set, we obtain 67 regime changes, of which 42 are transitions into democracy and 25 are transitions into autocracy (see table S.1). The distribution of these reforms is uniform over time (53 percent before 1985) but not across continents: approximately 50 percent of the reforms are in Africa, 28 percent are in Asia, and 18 percent are in Latin America.

To avoid the use of very brief reform episodes, we introduce the criterion that the dependent variable must be observed for at least four years before and after each regime transition. Under this rule, the effective number of reform episodes decreases to approximately 40. As a robustness check, we relax this criterion to only two years of observable outcomes; this period includes nearly all of the reform episodes reported in table S.1.

To check the robustness of our results, we use a distinct definition of regime transitions. Specifically, we use the recently developed data set by Papaioannou and Siourounis (2008) to define regime changes. These data are based on a more complex procedure than that applied above. Specifically, to identify the precise timing of each regime change, Papaioannou and Siourounis (2008) rely not only on the Polity2 index and the Freedom House democracy index but also historical evidence derived from numerous political archives and election databases. Using this procedure, the authors identify “full” or “partial” democratization episodes. However, because their analysis focuses solely on permanent democratization, the use of their coding applies to a lower number of transition episodes (23) in our data (see table S.1).

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(0.174)|(0.178)|(0.202)|(0.213)|
|Polity||0.136***||0.083|
|||(0.041)||(0.053)|
|Trade|||0.017***<br>(0.006)|0.018***<br>(0.006)|
|Constant|6.855***|6.487***|7.341***|7.015***|
||(1.426)|(1.322)|(1.407)|(1.368)|
|Prob > chi2|0.000|0.000|0.000|0.000|
|Observations|219|216|199|196|
|Groups<br>Robust standard<br>*** p<0.01, ** p|40<br>errors in parenth<br><0.05, * p<0.1|39<br>esis|40|39|

In conclusion, this analysis has unveiled that transition from a centrally planned economy to a market economy exhibits a positive and significant effect on the returns to education in postsocialist countries. Although post-socialist countries continued to increase their average years of schooling after 1992, schooling kept exerting a positive and significant effect on the returns of these countries. This might be explained by commercial openness, which also showed a positive and significant effect on the returns of post-socialist countries suggesting that trade might have driven additional demand for more educated human capital.

The transition induced by the fall of the Soviet Union was not the only major economic and political change undergone by post-socialist economies. Thirteen years later, a few of them joined the EU, for which they needed to follow a series of economic, political, and institutional