rafmacalaba/datause-encoder-data huggingface dataset:
The paper is organized as follows: The next section reviews stylized facts associated with external imbalances in the Eurozone and discusses the analytical interpretations of the imbalances. The third section presents the empirical model and the results of the panel VAR estimation for the full sample of Eurozone countries and for the Eurozone core and periphery groups. The fourth section discusses some policy implications of the findings and concludes.

# **2. Stylized facts and interpretations of external imbalances in the Eurozone**

The divergence between the external balances in Eurozone periphery and core countries surfaced in the mid-1990s, prior to the advent of the euro, with deficits in the periphery building up gradually and reaching, on average, 10 percent of GDP prior to the global financial crisis (Figure 1). In contrast, core Eurozone countries maintained a current account surplus throughout the 1990s, which grew somewhat larger in proportion to GDP after the introduction of the euro. External imbalances in the periphery have started unwinding since 2009, as a result of the recession triggered by the 2008-09 crisis.

**Figure 1: External current account imbalances in the Eurozone core and periphery: stylized facts** (in % of GDP 1990-2011)

<!-- Start of picture text --> 5<br>0<br>-5<br>-10<br>Average Core Average Periphery<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: BPS database.

Explanations of Eurozone external imbalances emphasizing the role of financial integration and domestic demand growth were initially proposed by Blanchard and Giavazzi (2002) who demonstrated that foreign borrowing is optimal for a less advanced country in a monetary union which expects to catch up with richer countries. External borrowing—and hence the current account deficit that mirrors the excess of investment over domestic savings—would be higher the greater the expected income growth as a result of convergence and the lower the interest rate. Such “good imbalances” would be an opportunity for less advanced Eurozone countries to capitalize on financial integration through the common currency, by boosting investment and productivity growth while smoothing consumption during the convergence process. Empirical studies have confirmed the tendency for capital to flow from high-income to less advanced 5
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educatlao in Indonesia, and 27 percent of thLose for lower secondary schools in Papua New Guinea. Primary education by tradition i5 free in Bangladesh, Peru and Brazil so recovery of costs should be very a: st or zero at this level; however, as was true with Indonesia and Papua hew Guinea, no information was reported for these countries on the proportion of the entire recurrent expenses for education that was recouped via user charges. Revenues from user charges as a percent of expenditues by education levels are summarized below based on the foregoing countries with available data:

|LELELOF EDUCATION|RANGE|-<br>NOREP|ORTING|
|---|---|---|---|
||(%)|||
|Primary|0|6||
|Secondary & Higher Education|0- 27|4; 0 in|2 cases|
|Overall|0 - 20|4; 0 in|2 cases|

7. Non-user fees were important to the financing of public education in a few of the sample countries. In Brazil, fiscal resources spent for education were raised partly through the so-called "education salary tax"; that **is, a 2.5** percent ax applied on the wage bill of firms employing more than 100 workers. In Togo, Mali **and** Sudan foreign **aid** became an important source of financing recurrent expenses, versus the usual practice of using such funds largely for investment expenditures. Salaries of expatriate teachers and/or fellowships granted to students at the post-primary levels in Togo and Mali were financed partly through foreign aid, while in Sudan grants from abroad augmented fiscal resources spent for universities and technical institutes.

8. Pricing policies for public education vary markedly in the sample countries. Policies whereby tuition fees are charged among students at the primary, secondary and higher education were observed in Lesotho and Haiti. Djibouti and Sudan by contrast have free tuition at all levels of education. In between these two extremes were the cases of Bangladesh, Indonesia, Peru and Brazil with practices of free tuition for primary education only; and Papua New Guinea where fees are levied only among students in the primary and lower secondary schools. Books are supplied free of charge to all students in Djibouti and Sudan, and to all primary pupils in Lesotho, Bangladesh and Nepal. Such practices do not exist in Burkina, Togo, Peru, Brazil and Haiti where all students have to purchase their own textbooks. In Sudan and Papua New Guinea free boarding is provided among students in upper secondary schools and universities, on top of free tuition and books. Cash allowances are paid to students in Sudan. Provisions for free boarding are also found in Oman on account of inadequate transportation in that country. The distribution of the sample according to the above-mentioned policies is as follows:

|SERVICES|Free at All<br> <br>Levyls<br>-------------|Free at Selected<br>Levels Only<br>-----------<br>(No. of Cas|Not Free at<br>Al Levels<br>es)-----------|Not<br>Reorting<br>-------------|
|---|---|---|---|---|
|Tuition|2|6|2|10|
|Books|2|3|5|10|
|Boarding|<br>0|3|4<br>(no boarding<br>provision)|13|
data are available but only for the third quarter of<sup>2001or later,butnot forthefirst quarterof</sup> 2002, the score assigned is "2". If the quarterly<sup>data are onlyavailable for the second quarter of</sup> 2001 or are even older; the score is a "3". If<sup>thedataarereportedat alowerfrequency,for</sup> example, they are annual, then in order to<sup>get a"4",thedata needto beavailablefor atleast up</sup> to the year 2001. Otherwise it will be assigned<sup>a</sup><sup>_"5"._Again,"6"will beassignedif none of the</sup> sources checked have the data.

For annual data, such as government revenue or<sup>expenditure,if thedataareavailableup</sup> to the year 2001, it is assigned a "1"; "2" is assigned<sup>if the most recentdata arefor the year 2000.</sup> Otherwise the score is "3". Using this methodology,<sup>the UnitedStatesis assigneda valueofI</sup> for the consumer price index because the<sup>CPI forSeptember2002 isavailable intheIFS</sup> November 2002 edition. Uruguay is assigned<sup>avalueof2becausethemostrecentCPIis for</sup> June 2002. And Zambia is assigned a<sup>valueof 5 becausethemostrecentCPIwasreportedfor</sup> 1997.' For a couple of countries the coding was not<sup>followedexactly.Two countriesgot a better</sup> score for having higher than "desirable" reporting<sup>frequency thoughtheirscoreswouldhave</sup> been lower since the lag in data was longer<sup>than theoptimumordesiredlag.Armeniahas GDP</sup> figures up to October 2001 and Luxembourg has<sup>FDI data upto April 2001;both areof monthly</sup> frequency. The former could only score a "2" and<sup>thelattera "3",byconsideringthelags.But</sup> they receive "1" and "2", respectively, since the<sup>dataareavailable ata higherthan"desired"</sup> frequency.<sup>8</sup>

> 7 Note that for some of the countries the index was prepared looking at end-June publications. The index was then broadened to cover 40 more countries, but the<sup>end-date for theseis November.Thisdiscrepancyhas not made much</sup> of a difference since countries that tend not to report<sup>on a timelybasiswould havethesametendencywhetherone</sup> looks at their numbers in June or in November.

> 8 For these two countries the cut-off point was June;<sup>they werein the first groupinvestigated.</sup>

9
the 2002 data (Fox et al, 2008) and was also seen in the 2005 rural household income data (Cunguara, 2011).

The status of HEs as primary or secondary employment is defined by households themselves, and not determined by any objective measure. The reporting therefore very likely depended on identity and status considerations, as well as the success of the enterprise. Hence, the impact on welfare from an HE being reported as primary or secondary employment is very likely endogenous. Of interest from a policy point of view is if there are systematic differences between HEs reported by the owner as primary or secondary employment. The IOF 2008/09 shows that owners of primary HEs have a higher education level than those owners reporting their HE as secondary employment (figure 5). As would be expected, HE activity is lower among owners reporting their HE as secondary, though they are in fact both very active. But urban and rural primary HEs are on average open about one month more a year, 7 days more a month and 42 minutes more a day than secondary employment HEs (table A5).

**Figure 4 Household livelihoods, 2009** _(percent of households with income from source)_

<!-- Start of picture text --> Wage-public<br>Wage-private<br>Wage-Agriculture<br>HE<br>Family farm<br>0.0 20.0 40.0 60.0 80.0 100.0<br><!-- End of picture text --> _Source:_ Authors calculations, IOF 2008 /09

**Figure 5 Education level among primary and secondary HEs**

<!-- Start of picture text --> 100<br>Complete upper<br>secondary or above<br>80<br>Complete lower<br>60 secondary<br>Complete upper<br>40<br>primary<br>20 Complete lower<br>primary<br>0<br>Incomplete primary<br>HE primary employment HE secondary employment<br><!-- End of picture text --> _Source:_ Authors calculations, IOF 2008 /09 _HE as income source_ 9 | P a g e
Policy Research Working Paper 9655

# **Abstract**

This paper shows expansionary fiscal austerity via reallocation of credit supply, but with a raise in poverty. For identification, the paper exploits the introduction of a Mexican law limiting the debt of subnational governments along with matched credit register, firm, bank, and state datasets. After the law, states with higher ex ante public debt grow substantially faster, despite larger fiscal consolidation (higher taxes and lower public expenditure). Banks operating in more indebted states reallocate credit supply away from local governments into private firms, with stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. Effects only happen after the law, not before, and there are strong firm-level real effects associated. The reduction of crowding out is stronger for financially constrained firms and for firms operating in states with higher ex ante public spending on social services over infrastructure projects. In states more affected by the law, despite better economic effects, extreme poverty increases––especially in states with higher ex ante public spending on social services over infrastructure––consistent with a strong reduction for social services during the fiscal consolidation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cruizortega@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
than 100,000. West Bengal departs from the norm with a higher proportion of CTs, which are located close to a larger city, especially Kolkata. However, a closer look at the location of the new CTs and the analysis that has already been conducted in West Bengal indicate a shift in the spatial distribution of urban settlements in the state (Chatterjee, 2014). We also classify CTs by contiguity of built-up area, which we call settlement agglomerations (SA) (Denis and Marius-Gnanou, 2011). The results, as shown in Table 3, differ slightly between the two approaches.

**Table 3: Census Towns close to Large Urban Areas by Number and Population**

||Bihar|Jharkhand|Odisha|West Bengal|All India|
|---|---|---|---|---|---|
|**Proximity meas**|**ured by distance**|**buffer**||||
|Number|38.3|34.6|11.2|48.7|41.0|
|Population|37.6|44.6|13.8|52.5|49.0|
|**Proximity meas**|**ured by contiguit**|**y of built up area**||||
|Number|31.7|13.3|3.4|38.1|37.3|
|Population|29.6|32.7|6.2|42.0|51.4|

**Note** : Large Urban Areas are those with more than 100,000 in population

Source: Authors’ analysis As indicated in the discussion below on specific case studies, the study includes CTs that are part of an agglomeration, close to larger towns, as well as CTs that are stand-alone and those that are part of agglomerations with other CTs or villages.

In addition to proximity to large urban areas, the size of the settlement was a criterion for selection. Figures 1 to 3 present the distribution of settlement size separately for CTs and statutory towns (STs) for the selected states. As can be seen, their median sizes vary considerably, given that STs are much older and the larger settlements are more likely to acquire statutory status. While the distribution of CTs is relatively similar across the four states, there is visible difference across states with respect to STs. West Bengal has a distinctive distribution, skewed towards larger towns. Given that the criterion for becoming a statutory town is more stringent in West Bengal; this is to be expected.

# **_Selected Settlements_**

In the case of Odisha, Erein (pop: 7,890), which is about 65km away from Baleshwar Municipality (pop: 144,373) and 6km away from Bhadrak Municipality (pop: 121,338) was selected. Erein is very close to Bhadrak railway station and is part of a panchayat with a number of other villages. It is also on NH-5, part of the four-lane national highway Golden Quadrilateral. The new rail line that connects Dhamra port to Bhadrak passes through lands of Erein and substantial land acquisition has taken place, which is a possible source of capital. According to the port’s website, a 125 meter wide corridor from Dhamra to Bhadrak has been acquired to accommodate two rail tracks and a four lane road. In Phase-I, the 62 km rail connectivity from Dhamra to Bhadrak has 7
# **5.1  Model Specification**

We specify our estimation model as follows.

- (15)  �� � �� ��� ���� � ���� ����� ����� ������ ��a� ���h� �ε�

- Expected signs:  β1, β4, β5, β7 <0 ; β3< β2<0; β6 >0

- where

- ci    =  Cumulative percent cleared in 2016, cell i

- di = Distance of cell i from nearest road segment

- qk = Quality dummy variable for nearest road segment

- [q2: secondary road; q3: tertiary road] (q1 excluded (effect absorbed in the

- regression constant) to prevent total collinearity)

- pi = Legal protection status of cell i [1 if protected; 0 otherwise]

- ti = Transport time from cell i to the nearest urban market center

- ai = Agricultural opportunity value of land in cell i

- hi = Elevation of cell i

- Ɛi = Random error term

A priori, we expect the impact on forest clearing to be negative for distance from the nearest road (β1< 0); decreasing as road quality declines (β3< β2<0); negative for legally‐protected areas (β4< 0); negative for transport distance to the nearest urban center (β5 < 0), positive for the agricultural opportunity value of the land (β6>0); and negative for more elevated terrain (β7 < 0).

# **5.2  Variable Measures**

Our data and sources are as follows.

**Cumulative percent cleared, 2016** :  this incorporates two Hansen measures: percent cleared prior to 2001 and annual clearing from 2001 to 2016 (Hansen et al. (2013)).  We produce estimates at .005-degree resolution by resampling Hansen 30 m raster values.

**Distance from road segment** :  distance from the centroid of each cell to the nearest road segment, calculated in ArcGIS 10.

15
**Annex 1 Measurement and Data Issues**

and national poverty lines, where used by the Bank. In the case of Russia, the survey used was the Russia Longitudinal Monitoring Survey (round X), as access to the official HBS was not possible at the time the analysis was carried out. In the case of Hungary and Poland, the welfare aggregates developed by the state statistical bodies were used. This step also involved extensive liaison with Bank staff in sectoral and research units to obtain the datasets and documentation necessary to make full use of the data.

Datasets for Czech, Estonia, Latvia, Slovakia, Slovenia were not obtained because they were not easily available to the study team and pursuing these data was considered a lower priority given that Central Europe and the Baltics were already represented in the datasets and the countries current or expected near term development prospects were reasonably good. Datasets of sufficient quality were not available for Croatia and Macedonia at the time the data were being assembled. The final set of surveys was used to provide the data used in this report is listed below.

Table 1

# ECA INFRASTRUCTURE AND URBAN POVERTY STUDY HOUSEHOLD SURVEYS INCLUDED IN THE ANALYSIS

||Country|Date|Survey|Urban - R<br>Adjustmen|ural Price<br>t|
|---|---|---|---|---|---|
|||||Price level<br>only|<br>Price and<br>consumption<br>basket|
|1|Albania|2002|LivingStandards Measurement Study|Y|N|
|2|Armenia|2001|Integrated LivingConditions Survey|Y|N|
|3|Azerbaijan|2001|Household Budget Survey(new design)|Y|N|
|4|Belarus|2001|Income and Expend. Survey(newer design)|Y|N|
|5|Bosnia & Herzegovina|2001|LivingStandards Measurement Study|Y|N|
|6|Bulgaria|2001|Integrated Household Survey|Y|N|
|7|Georgia|2001|Surveyof Georgian Households|N|N|
|8|Hungary|2000|Household Budget Survey|N|N|
|9|Kazakhstan|2001|Household Budget Survey|N|N|
|10|Kosovo|2000|LivingStandards Measurement Survey|Y|N|
|11|Kyrgyz Republic|2001|Household Budget Survey|Y|N|
|12|Lithuania|2000|Household Budget Survey|N|N|
|13|Moldova|2001|Household Budget Survey|Y|N|
|14|Poland|2001|Household Budget Survey|N|N|
|15|Romania|2002|FamilyBudget Survey|Y|N|
|16|Russia|2001|Russia Longitudinal Monitoring Study (RLMS),<br>RoundX|<br>N|N*|
|17|Serbia|2002|PovertyHousehold Survey( SMMRI, Dutch TF)|Y|N|
|18|Tajikistan|1999|LivingStandards Measurement Survey|N|N|
|19|Turkmenistan|1998|LivingStandards Measurement Survey|N|N|
|20|Uzbekistan|2000|Household Budget Survey|Y|N|

`*` In Russia (RLMS 2001) the price and consumption basket is adjusted on regional level not urban/rural.

64
and firm growth are positively and statistically related to the use of capital market financing.<sup>15</sup> The main difference between firms issuing equity and bonds is that the marginal effect of size on the probability of capital raising activity is much higher for bond issuers than for equity issuers. For example, a one standard deviation increase in the log of assets raises the likelihood of an issuance by about 3 percentage points for equity issuances and by about 30 percentage points for bond issuances.

In sum, we find that throughout our sample, while primary and secondary capital market activity has increased markedly, (1) only a few firms issued securities in the median country, and indeed in the vast majority of countries, (2) of the few firms that issued securities, only a handful of those accounted for the bulk of the funds raised by listed firms in capital markets, and (3) the ones that did issue securities in domestic and foreign markets tended to be large and fast growing.

# **5. How Do Assets, Sales, and Employment Evolve for Issuing and Non-issuing Firms?**

This section assesses (1) whether issuers grow faster than non-issuers, (2) how the growth of issuers and non-issuers differs across the entire distribution of firm size, (3) whether there is a bump in growth immediately after a firm issues securities, and (4) whether the growth of issuing firms is associated with the amount of capital raised relative to firm size.

We begin by estimating four probability density functions that capture the FSD: two for 2003 (one for issuers of either equity or bonds and one for non-issuers) and two analogous ones for 2010. Due to data availability on firm-level balance sheets in Orbis, we focus the rest of the paper on the 2003-2010 sample. That is, issuing firms that raised capital through equity or bonds only in 2011 are excluded from the analysis henceforth. However, the results are qualitatively similar if we use the full sample available for 2003-2011.

As illustrated in Figure 3, three key findings about the FSD emerge when using assets, sales,

> 15 Issuance activity is also related to firms with a longer liability maturity structure and with lower profitability.

15
reasoning is measured using a set of 12 Raven’s Progressive Matrices (Carpenter et al. 1990; Raven 2000). This test was also administered at baseline. When administered to the same caregiver again 11 months later, non-verbal reasoning scores showed a correlation of ��� � 0.46.

To account for the multidimensionality of parent-child interaction (Bornstein, 2012), we developed a questionnaire that covers multiple aspects of parenting and that fits well the Cambodian context. Caregivers answered 25 items related to regular activities, parent-child bonding, punitive behavior and different child-rearing methods. Possible responses ranged from 1 (never or almost never) to 5 (every day or almost every day). The factor structure of these parenting questions is reported in the Results section.

**_Child survey and assessment_ .** We control for child age, measured as the number of days between birth and follow-up, divided by 365.25. Stunted growth is defined as having a heightfor-age below two standard deviations of the growth standards by the World Health Organization (2006) ���.380, ��� �.706�. We use cutoff points other than -2 SD in the robustness analysis below.

The child assessment includes different tests for the domains of language development, early numeracy and executive function. Most of the child tests are based on the Measuring Early Learning Quality and Outcomes (MELQO) toolkit (see UNESCO, 2017 for a description of the measures development process).

We implemented multiple pretests to obtain reliable measures. Some tests from MELQO were removed during the adaptation process. For example, we removed a backward-digit span test and a mental transformation tasks because very few children were able to understand them.

10