rafmacalaba/datause-encoder-data huggingface dataset:
significant.

Importantly, the reduction in discrimination as a response is not explained by having more exposure in the labor market: as we show in Appendix Table A.2, variation in local informal employment is unrelated to the Venezuelan immigrants’ probability of being employed. While this may be a bit surprising, we have to take into account that 94% of Venezuelan immigrants are employed, so there is little margin for improvement. Additionally, for men we do not see any effects on wages, while we see that higher employment rates in the informal sector do have a positive effect on wages of Venezuelan women in Peru.

The fact that discrimination against men shows a stronger response to labor market conditions is potentially related to the types of interactions immigrants have with locals. In Table 3, we present the full results from the quadratic IV specification. For both men and women, reported discrimination increases with time spent in Peru, education and household socioeconomic status. On the other hand, it also increases with lower skilled occupations, particularly for women. This is consistent with reported discrimination reflecting a lack of progress in the labor market for high-skilled Venezuelans, whether this reflects being disappointed or actually being discriminated against is difficult to quantify.

To explore further the mechanisms underlying these effects, in Table 4 we exploit the fact that ENPOVE collects detailed information on where discrimination episodes took place. We show the OLS and IV results for our preferred specification, the one that includes all controls and fixed effects, and for the IV, the specification that uses the linear instrument. Discrimination at work seems to respond the least to local employment, with a coefficient that implies that a 10% increase in informal employment leads discrimination to decrease by 1.4%, although the relationship is not statistically significant. Interestingly, there is a clear gender split on whether discrimination in streets and public spaces. A 10% increase in informal employment causes a decrease in discrimination against men in streets and public spaces of about 3.7%, with no significant change in discrimination against women in these spaces. Finally, discrimination in public transit responds similar regardless of the gender, with an effect of about 2% for reductions in employment of 10%.

# **5.2 Immigration, Local’s Labor Market Outcomes and Perceptions**

In the previous section, we established that labor market conditions have a causal effect on the way Venezuelan immigrants perceive that are treated by locals: lower unemployment in the informal labor market leads to a decrease in discrimination. We now turn to study the flip-side, namely, the way in which the presence of Venezuelans affect Peruvians’ labor 16
classes in 2009 (80.5%). The share to premium class trips in FY12 is far lower for CO-based travel; reasonably, a similar relationship existed also for CY09.

# **Table A1.1: Breakdown of WBG Travel Data by Booked Seat Class for FY 2012.**

|TripOrigin|Class|Trips|Total Miles|AvgLength|% of Miles|
|---|---|---|---|---|---|
|CO|Economy|85,678|113,975,613|1,330.3|41.07%|
|CO|Business|55,683|107,057,207|1,922.6|38.58%|
|CO|First|118|202,132|1,713.0|0.07%|
|CO|Unknown|34,087|56,270,643|1,650.8|20.28%|
|CO Sub-Total||175,566|277,505,595|1,580.6|100.0%|
|HQ|Economy|31,477|63,421,648|2,014.9|17.39%|
|HQ|Business|91,089|238,565,602|2,619.0|65.43%|
|HQ|First|194|459,406|2,368.1|0.13%|
|HQ|Unknown|24,005|62,175,433|2,590.1|17.05%|
|HQSubtotal||146,765|364,622,089|2,484.4|100.0%|
|Combined|Economy|117,155|177,397,261|1,514.2|27.63%|
|Combined|Business|146,772|345,622,809|2,354.8|53.82%|
|Combined|First|312|661,538|2,120.3|0.10%|
|Combined|Unknown|58,092|118,446,076|2,038.9|18.45%|
|TOTAL||322,331|642,127,684|1,992.1|100.0%|

As the FY-12 data include neither flight number, airline, nor equipment flown, we simply assume the same basic flight distribution for FY12 as for CY09. Table A1.1 provides these distributions for the flights for which we have data (and, note, these data need to be scaled up by a factor of 1.055 to reach the correct overall total activity in FY12).

# **Carbon Footprint Calculations for FY12**

Table A1.2 provides calculations for the overall footprint of WBG air travel for FY-12, based on numbers in Table A1.1 scaled up with an expansion factor of 1.055 (to correct for the slightly greater flight volume in FY-12 compared to our sample period). The calculations are done for alternative assumptions about average load factors in premium classes for the flown aircraft (as these are particularly uncertain since they are not provided by airlines). Throughout we assume that the average load factor for coach class is 0.8, in accordance with reported industry averages.

26
|**TABLE A1. Tests for differences be**<br>Variables|**tween include**<br>(1)<br>Mean and te<br>between incl<br>|**d and excluded s**<br>(2)<br>sts for differences i<br>uded and excluded<br>|**ubsamples**<br>(3)<br>n means<br>samples<br>|
|---|---|---|---|
||Included|Excluded|_p_-value|
|_Panel A. Maternal characteristics_||||
|Age of the mother (years)|31.43|33.73|0.235|
|Indigenous ethnic group|0.16|0.16|0.995|
|Less than primary school|0.57|0.42|0.085|
|Literate|0.79|0.77|0.567|
|Attended antenatal care in 2002|0.94|0.92|0.734|
|_Panel B.Child characteristics_||||
|Child is a boy|0.50|0.51|0.847|
|Weight at birth|3.21|3.20|0.835|
|Long-term health problems|0.09|0.08|0.123|
|Age (in months)|63.5|63.42|0.568|
|Child is the eldest|0.16|0.17|0.723|
|Height for age Z-score|-1.42|-1.62|0.167|
|_Panel C.Household characteristics_||||
|Wealth index|0.49|0.49|0.934|
|Housing quality index|0.41|0.42|0.769|
|Consumption of durable goods index|0.37|0.35|0.582|
|Live in urban area|0.58|0.62|0.073|
|Household size|5.52|5.60|0.382|
|School aged children in the household (n)|1.33|1.32|0.923|
|_Panel D. Community characteristics_||||
|Violent crime in community|0.33|0.35|0.634|
|Social assistance (education) available|0.95|0.97|0.913|

Table A1 presents mean of the variables used in the analysis from the included sample (children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009) and excluded one (the rest of the sample).

33
_Matadi: Clinical services and community-based distribulon_ _15_ Table 3.

|Use of Contraceptive Methods Among M|arried Women|15-49 in M|atadi|
|---|---|---|---|
||1981|1983|1989|
|PRIOR USE (based on ever married women):|n=1318|n=1185|n=1014|
|Percent that have ever used:||||
|A modem method|10.0|45.8|63.9|
|A traditional method|79.4|86.0|85.8|
|CURRENT USE (based on currently married||||
|<br>women):|n= 1256|n= 1064|n=818|
|Percent that have ever used:||||
|A modem method|4.4|17.5|22.9|
|A traditional method|52.6|36.8|28.8|
|Any method|57.1|54.3|51.7|
|Method currently used:||||
|<br>Withdrawal|33.1|22.2|;6.0|
|Abstinence|14.0|13.1||
|Pill|1.8|7.7|10.6|
|Female sterilization|_1.5_|4.8|7.6|
|Vaginal tablets|0.0|2.1|0.9|
|Condom|0.6|1.1|2.0|
|JUD|0.0|_0.5_|0.0|
|Injectable|0.6|0.8|1.7|
|Rhythm|2.9|1.0|_5.5_|
|Foam, cream|0.0|0.4|0.1|
|Other|2.6|_0.5_|0.0|

# Crable 4).

These prevalence data reflect a gradual replacement of traditional methods with modern ones. Despite the marked increase in modem methods between 1981 and 1989, the single most widely used method in 1989 was still a traditional one: withdrawal (16 percent). In addition, 7 percent reported used of abstinence and 6 percent rhythm, bringing to 29 percent the proportion using a traditional method in 1989. The combined percentage using any method-modem or traditional-was 52 percent in 1989 (fable 3).

# _4. Source of Contrmceptives_

With regard to the source of contraceptive method, the most frequent responses among 1989 users of modem contraceptives were hospital (34 percent), clinic (23 percent), pharmacy (19 percent), and distributor (11 percent). However, based on these data alone, it is difficult to determine precisely the relative contribution of each of the four service delivery mechanisms
confirms the hypothesis that bank branches are underrepresented in low income neighborhoods in Bogotá, while the hypothesis is not valid for financial credit cooperatives. When taking financial credit cooperatives into account, Medina’s analysis finds that lower income areas in Bogotá are significantly better served in terms of branch presence.

25. **However, within an urban area, bank branch presence is driven by other factors in addition to current income levels** . Medina’s analysis finds that the number of bank branches in Bogotá is positively correlated with the level of permanent household income and welfare. In addition, positive correlations are found between the number of bank branches and the security of the neighborhood, the health of its infrastructure and the presence of religious and cultural institutions. On the other hand, the presence of bank branches is found to be negatively correlated with population density, an area’s unemployment rate and the percentage of an area occupied by industrial activity. The particular importance of security in access to finance is highlighted by analysis which shows that the cost of handling cash is a key factor in bank branch profitability<sup>39</sup> .

26. Focus groups also revealed that remote bank locations are a key factor in financial exclusion for people in income strata 1, while people in income strata 2 and 3 did not mention location as a key reason for lack of financial access<sup>40</sup> . For Bogotá, the demand survey confirms that 62% of the unbanked are over 10 blocks away from the next bank branch (see Chapter 3). Greater distance to bank branches increases transaction costs for low income clients and the unbanked.

27. **Alternative access mechanisms are limited.** ATMs provide a costeffective alternative of physical access to formal financial services, such as payment services, check deposits or savings deposits. While the number of ATMs in Colombia has remained broadly stable over the past few years, the number of ATM transactions increased, pointing to increasing acceptance of ATMs among clients. No further data is available regarding the location of ATMs and their proximity to low income neighborhoods.

# **2.4 CULTURAL BARRIERS**

28. In addition to regulatory, cost and location barriers, other subtle barriers exist between the providers of formal financial services and the unbanked. Banks are not used to dealing with a different client profile of low income clients. Given their smaller transaction size, potential irregular income, limited asset base and collateral, and on average lower education (see Chapter 3) different business models are required for this

> 39 Arango, 1999.  Banks with comprehensive national coverage in Colombia, such as _Banco Agrario_ , frequently have to resort to transporting cash by helicopter due to the security situation. 40 Marulanda, 2004.

43
as firm-level fixed effects to control for all time-unvarying characteristics of a firm. Finally, we cluster the standard errors at the firm level.<sup>34</sup>

# **5. Results**

We now discuss our findings. We first summarize the effect that the FD Law had on aggregate state outcomes, including public fiscal balance, economic activity, and poverty rates. We then discuss our loan-level evidence on the unwinding of crowding out in the credit markets after the introduction of the FD Law. Next, we describe our firm-level results of the impact of the crowding-out unwinding on firms’ real outcomes. Finally, we discuss the extent to which the impact of the FD Law depends on the ex-ante composition of states’ fiscal spending.

# **5.1. Impact of the FD Law on Fiscal Balance, Economic Activity and Bank Lending of States**

In this section, we first discuss the impact that the debt ceilings introduced in the FD Law had on states’ fiscal balances. We then summarize how aggregate economic activity of states as well as their poverty rates and income inequality measures were affected by the Law.

# _5.1.1. Fiscal Balance of States_

Table 2A reports our estimates of equation 1 on the fiscal components of states. In Panel A, we show the results for the yearly fiscal expenditure indicators of states. The first indicator, _Total Expendituress,y_ , corresponds to the ratio of total public spending over a state’s GDP. Consistent with the fiscal austerity that the new debt ceilings impose, we find evidence that more ex-ante indebted state governments contract their fiscal expenditures after the introduction of the FD Law (columns 1 and 2). A one-standard-deviation increase in the ex-ante public indebtedness of a state resulted in a 4.4 percent contraction in public spending after the Law (with the state’s ratio of public expenditure to GDP dropping by -0.012*0.7=0.7 p.p.). The second indicator, _Infrastructures,y_ , is the ratio of public spending on infrastructure to the state’s GDP. This

> 34 To validate our difference-in-difference strategy in the firm-year-level data, we again conduct several checks. First, we compare firm outcomes in the pre-reform period for firms located in municipalities with local public debt exposure above and below the median (Table A3C). The data confirms that both groups of firms are statistically indistinguishable on our outcomes of interest prior to the Law. Second, we check if the firm-level outcomes followed parallel trends in the pre-reform period. To conduct this test, we run regressions outlined on equation 2 on our firmyear data, restricting the sample to the period prior to the introduction of the FD Law. Results summarized in Table A2F, confirm that in the pre-reform period, the outcomes of interest follow parallel trends across states of varying indebtedness.

22
policy measures include each harmonization directive and MRA concluded between the countries in the set. We concord the policy measures, which often pertain to a speci...c attribute (e.g. safety) of a variety of products, with trade data at the SITC (revision 2) 3-digit industry level. We then estimate the signi...cance of the impacts of these measures on bilateral trade across countries and over time, controlling for other in‡uences.

Our evidence broadly con...rms the conclusions drawn from the model. Regional harmonization signi...cantly increases intra-regional trade in a¤ected industries. Exports to the region of excluded developed countries also increase, but exports of excluded developing countries decline. These asymmetric e¤ects may arise because developing country ...rms are hurt more by an increase in the stringency of standards and bene...t less from economies of scale in integrated markets. Mutual Recognition Agreements (MRAs) promote trade both within the region and with the rest of the world. But when they contain restrictive rules of origin, then intra-regional trade increases at the expense of trade with other, especially developing, countries.

To place our contribution in the context of the existing literature,<sup>9</sup> the analytical section builds on the work of Baldwin (2000) and Ganslandt and Markusen (2001). In particular, Baldwin (2000) anticipated some of the results of this paper on MRAs, but assumed identical countries with identical ...xed costs of complying with standards. So the implications of harmonization and asymmetric e¤ects on excluded countries were beyond the scope of that model. As far as we know, only two previous studies have empirically explored the impact of shared standards on trade. Swann et al (1996) regressed British net exports, exports and imports over the period 1985-1991 on counts of voluntary national ("idiosyncratic") and international standards recognized by the United Kingdom and Germany. They discovered that international standards to which Britain was a party had little impact on imports but a signi...cantly positive e¤ect on exports while British national standards tended to raise both imports and exports. Moenius (1999) regressed bilateral trade volumes in 4-digit SITC sectors on counts of shared standards in a sample of 14 countries over 16 years, and found a positive and highly signi...cant relationship. Our paper di¤ers from these empirical contributions in a number of aspects. First, instead

> 9 See Maskus and Wilson (2001) for a review of the literature.

6
mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts.

# **4. Implications for public policy**

## **Hazard management is a task both for the public sector and for private households and firms.**

For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16.3 percent of total hazard related mortality and 51.3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. _Individual_ dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated. Households in the risk zone have limited options for protecting against these hazards individually. But some form of large, _collective_ risk mitigation is sometimes feasible, such as levees or slope stabilization measures. The following paragraphs discuss the role of public policies in supporting individual versus collective hazard risk reduction.

## **_Good urban management goes a long way towards reducing urban hazard risk, especially the small-scale, under-the-radar-screen events._**

Natural disasters are the man-made consequences of geo-physical hazard events. This applies for large as much as for small-scale hazards. But smaller disasters can be more easily avoided. Good routine urban management already reduces disaster risk 23
**Table 3: Municipality Characteristics Economic Census**

||Early adopters<br>average|Late adopters<br>average|Difference in<br>averages|Coefficient on<br>quarter of<br>implementation|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Panel A: Levels 1999|||||
|Log GDP per capita (value added)|9.546|9.145|0.401*|-0.1046**|
||(0.578)|(0.707)|(0.221)|(0.039)|
|Log establishments per capita|3.509|3.438|0.071|-0.013|
||(0.436)|(0.239)|(0.121)|(0.017)|
|Log fixed assets per capita|9.637|9.376|0.261|-0.0966**|
||(0.615)|(0.924)|(0.269)|(0.046)|
|Log investment per capita|6.833|6.733|0.100|-0.080|
||(0.725)|(1.042)|(0.308)|(0.049)|
|Panel B: Changes 1994 - 1999|||||
|Log GDP per capita (value added)|1.112|1.106|0.006|0.001|
||(0.232)|(0.286)|(0.089)|(0.016)|
|Log establishments per capita|0.112|0.145|-0.033|0.010|
||(0.103)|(0.094)|(0.034)|(0.006)|
|Log fixed assets per capita|0.783|1.053|-0.270|0.020|
||(0.669)|(0.343)|(0.182)|(0.021)|
|Log investment per capita|0.827|1.123|-0.296|0.059|
||(0.754)|(0.963)|(0.297)|(0.069)|
|Observations|17|17|34|34|

_Note:_ Standard errors in parentheses. I define early adopters as the municipalities which implemented the reform between May 2002 and March 2004. Late adopters are municipalities which adopted the reform between April 2004 and December 2004, when my dataset ends. Economic data come from the 1994 and 1999 Economic Census and are converted to per capita levels using data from the 1995 Population Count and the 2000 Demographic Census. Establishments are measured per 1000 inhabitants. Changes are defined as [Y(1999)-Y(1994)]. Column 4 presents the coefficients of a separate regression for each variable or change on quarter of implementation. Significance levels: * 10%, ** 5%, *** 1% 36
Table 2: RDD estimates of gaps in teacher characteristics associated with teachers hired pre- and post-FPE (bandwidth of <u>7 years).</u>

||Pooled|Kenya|Madagascar|Mozambique|Tanzania|Togo|Uganda|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Urban|-0.019|-0.064|-0.186*|0.097|-0.036|0.058|0.037|
||(0.036)|(0.079)|(0.107)|(0.148)|(0.071)|(0.154)|(0.074)|
|Age|2.03***|2.99***|3.08*|0.87|2.76***|-1.15|0.84|
||(0.48)|(1.12)|(1.60)|(1.76)|(0.70)|(2.28)|(0.85)|
|Male|0.054|-0.082|0.132|0.244*|0.045|0.133|0.120|
||(0.041)|(0.082)|(0.117)|(0.146)|(0.082)|(0.127)|(0.091)|
|Secondary or more|0.017|0.029|-0.073|0.057|-0.009|0.144|0.021|
||(0.022)|(0.019)|(0.102)|(0.062)|(0.031)|(0.177)|(0.020)|
|Diploma or more|0.016|0.112|-0.061|-0.140|-0.064|0.007|0.031|
||(0.033)|(0.080)|(0.105)|(0.097)|(0.060)|(0.128)|(0.041)|
|Bachelors or more|-0.011|0.033|-0.065|-0.119|-0.034|-0.043|0.017|
||(0.021)|(0.041)|(0.077)|(0.093)|(0.045)|(0.027)|(0.022)|
|Teaching certification|-0.050*|0.082|-0.040|-0.233|0.003|-0.030|0.021|
||(0.029)|(0.060)|(0.086)|(0.150)|(0.009)|(0.163)|(0.035)|
|Born in district|0.082**|-0.003|0.151|0.081|0.053|0.003|0.193***|
||(0.039)|(0.082)|(0.108)|(0.154)|(0.068)|(0.166)|(0.091)|
|“Permanent” contract status|-0.025|0.012|-0.008|0.114|0.002|0.133|-0.143|
||(0.030)|(0.070)|(0.089)|(0.130)|(0.013)|(0.081)|(0.092)|

Notes: Standard errors in parenthesis. *, **, *** indicates statistically significant at the 10, 5, and 1 percent levels respectively. See text for definition of variables. Each estimate and associated standard error are the result of a separate regression model. Models use a bandwidth of 7 years.

23